DATASET	Abbreviation	Sites	SUBJECT	SCANS	MALES	FEMALES	MINAGE	MAXAGE	MEANAGE	STD	Median	Q25	Q75	CDR0_0	CDR0_5	CDR1_0	CDR2_0	CDR3_0	Race	Ethnicity	Pathology	LINK	Description	Population	Data	Design	Publication	Research_Article_1_Link	Research_Article_1_Title	Research_Article_1_Description	Research_Article_2_Link	Research_Article_2_Title	Research_Article_2_Description	Platform	Tip1	Tip2	anat	rs-fMRI	t-fMRI	dwi	eeg	pet	cognitive	behavioural	genetics	fluid_biomarkers	Others	Drawback	Goal	LaunchYear	Register	Script
ABIDE-1	Autism Brain Imaging Data Exchange	20	1112	1112	948	164	6.5	64	17	8	14.7	11.7	20.1	0	0	0	0	0			Autism	https://fcon_1000.projects.nitrc.org/indi/abide/	A large multi-site resting-state fMRI dataset focused on autism spectrum disorders, designed to facilitate biomarker discovery and reproducible research. 	USA	neuroimaging (MRI), phenotypic	Cross-sectional	https://doi.org/10.1038/mp.2013.78	https://doi.org/10.1038/s41598-022-09821-6	rs‑fMRI and machine learning for ASD diagnosis: a systematic review and meta‑analysis	Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria through a lengthy and time-consuming process. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis. In particular, using Machine Learning classifiers based on resting-state fMRI (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy and reliability. Therefore, we conducted a systematic review and meta-analysis to summarize the available evidence in the literature so far. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies that offered sufficient information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. SVM stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for ANN classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seems promising, achieving higher sensitivities when compared to rs-fMRI data alone (84.7% versus 72.8%). Finally, our analysis showed AUC values between acceptable and excellent. Still, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of those classification algorithms to clinical settings.	https://doi.org/10.1192/bjp.2022.13	Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism.	Background Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences. Aims To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity. Method We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups. Results stDNN achieved consistently high classification accuracy in distinguishing between females and males with ASD. Notably, stDNN trained to distinguish between females and males with ASD could not distinguish between neurotypical females and males, suggesting that there are gender differences in the functional brain organisation in ASD that differ from normative gender differences. Brain features associated with motor, language and visuospatial attentional systems reliably distinguished between females and males with ASD. Crucially, these results were observed in a large multisite cohort and replicated in a fully independent cohort. Furthermore, brain features associated with the motor network's primary motor cortex node predicted the severity of restricted/repetitive behaviours in females but not in males with ASD. Conclusions Our replicable findings reveal that the brains of females and males with ASD are functionally organised differently, contributing to their clinical symptoms in distinct ways. They inform the development of gender-specific diagnoses and treatment strategies for ASD, and ultimately advance precision psychiatry.	indi	ABIDE-1 and ABIDE-2 are disjoint multi-site datasets with different subject IDs.	Use the AWS fcp-indi over IDA to download the MRI images.	1	1	0	0	0	0	0	0	0	0	WISC_IV, VINELAND, SRS, FIQ, VIQ, and PIQ scores, age-matched	Choose between old ABIDE-1 and new ABIDE-2	To accelerate the pace of discovery setting the stage for the next generation of Autism spectrum disorder studies.	2013	https://fcon_1000.projects.nitrc.org/indi/req_access.html	
AIBL	Australian Imaging, Biomarker and Lifestyle	2	691	1274	305	385	55	96	73.7	6.8	73	68	79	922	289	52	9	2			AD	https://aibl.org.au/	A prominent longitudinal research initiative launched in 2006 to investigate the development and progression of Alzheimer's disease (AD). 	Australia	neuroimaging (MRI, PET), cognitive, lifestyle, behvioural, fluid biomarkers	Longitudinal	https://doi.org/10.1017/s1041610209009405	https://doi.org/10.3233/adr-210005	Fifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer’s Disease	Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer's disease dementia (AD)) as an 'Inception cohort' who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an 'Enrichment cohort' (as of 10 April 2019). Objective: Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation. Methods: Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations. Results: AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression. Conclusion: This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.	https://doi.org/10.1002/alz.13556	Physical activity and brain amyloid beta: A longitudinal analysis of cognitively unimpaired older adults	INTRODUCTION: The current study evaluated the relationship between habitual physical activity (PA) levels and brain amyloid beta (Aβ) over 15 years in a cohort of cognitively unimpaired older adults. METHODS: PA and Aβ measures were collected over multiple timepoints from 731 cognitively unimpaired older adults participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Aging. Regression modeling examined cross-sectional and longitudinal relationships between PA and brain Aβ. Moderation analyses examined apolipoprotein E (APOE) ε4 carriage impact on the PA-Aβ relationship. RESULTS: PA was not associated with brain Aβ at baseline (β = –0.001, p = 0.72) or over time (β = –0.26, p = 0.24). APOE ε4 status did not moderate the PA-Aβ relationship over time (β = 0.12, p = 0.73). Brain Aβ levels did not predict PA trajectory (β = –54.26, p = 0.59). DISCUSSION: Our study did not identify a relationship between habitual PA and brain Aβ levels. Highlights: Physical activity levels did not predict brain amyloid beta (Aβ) levels over time in cognitively unimpaired older adults (≥60 years of age). Apolipoprotein E (APOE) ε4 carrier status did not moderate the physical activity–brain Aβ relationship over time. Physical activity trajectories were not impacted by brain Aβ levels.	IDA LONI	ABIL serves as a suitable test set for models trained on ADNI.	Correlation analysis of lifestyle factors with dementia is recommended.	1	0	0	0	0	1	1	1	1	1	MMSE, CVLT-II, Logical Memory I and II (WMS; Story 1 only), D-KEFS verbal fluency, 30-item Boston Naming Test (BNT), Wechsler Test of Adult Reading (WTAR), Digit Span and Digit Symbol-Coding subtests of the Wechsler Adult Intelligence Scale – Third edition (WAIS–III), the Stroop task (Victoria version), and the Rey Complex Figure Test (RCFT)	NA	To identify and validate neuroimaging and fluid biomarkers, understand risk and protective factors (including lifestyle), and improve early prediction of AD	2016	https://ida.loni.usc.edu/collaboration/access/appLicense.jsp	
ADNI	Alzheimer’s Disease Neuroimaging Initiative	61	738	3996	414	324	55	96	76.6	6.8	77	72	81	2307	2489	0	0	0	mostly white		AD	https://adni.loni.usc.edu/	A longitudinal study collecting MRI, PET, and clinical data to track Alzheimer's disease progression.	USA	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1002/jmri.21049	https://doi.org/10.1038/s41467-022-31037-5	Multimodal deep learning for Alzheimer’s disease dementia assessment	Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer’s disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.	https://arxiv.org/pdf/2408.15647	Leveraging Persistent Homology for Differential Diagnosis of Mild Cognitive Impairment	Mild cognitive impairment (MCI) is characterized by subtle changes in cognitive functions, often associated with disruptions in brain connectivity. The present study introduces a novel fine-grained analysis to examine topological alterations in neurodegeneration pertaining to six different brain networks of MCI subjects (Early/Late MCI). To achieve this, fMRI time series from two distinct populations are investigated: (i) the publicly accessible ADNI dataset and (ii) our in-house dataset. The study utilizes sliding window embedding to convert each fMRI time series into a sequence of 3-dimensional vectors, facilitating the assessment of changes in regional brain topology. Distinct persistence diagrams are computed for Betti descriptors of dimension-0, 1, and 2. Wasserstein distance metric is used to quantify differences in topological characteristics. We have examined both (i) ROI-specific inter-subject interactions and (ii) subject-specific inter-ROI interactions. Further, a new deep learning model is proposed for classification, achieving a maximum classification accuracy of 95% for the ADNI dataset and 85% for the in-house dataset. This methodology is further adapted for the differential diagnosis of MCI sub-types, resulting in a peak accuracy of 76.5%, 91.1% and 80% in classifying HC Vs. EMCI, HC Vs. LMCI and EMCI Vs. LMCI, respectively. We showed that the proposed approach surpasses current state-of-the-art techniques designed for classifying MCI and its sub-types using fMRI.	IDA LONI	The ADNIMERGE file (ADNIMERGE - Key ADNI tables merged into one table [ADNI1, GO, 2, 3]) may be of interest as it combines various variables into one file.	Downloading MRI images from IDA can be difficult due to the large number of files per subject. Use Advanced Search with filters like anat T1w MRI, generate an image collection, download the CSV, inspect series descriptions (e.g., MPRAGE), and refine filters accordingly. Use the IDA Downloader for final retrieval.	1	1	0	1	0	1	1	1	1	1	CDR; MMSE, MoCA, RAVLT, ADAS-Cog, Digit Symbol Substitution, Boston Naming Test	NA	Develop and validate biomarkers for AD progression and trials.	2004	https://ida.loni.usc.edu/collaboration/access/appLicense.jsp;jsessionid=0C05DA8D53C8EC0B87570354F0074F8D	
Age-ility	Age-ility	1	131	131	66	65	15	35	21.7	5.2	20	18	24	0	0	0	0	0			HC	https://www.nitrc.org/projects/age-ility/	A multimodal neuroimaging dataset designed to investigate the neural mechanisms underlying cognitive control across the adult lifespan. It provides high-quality, open-access data suitable for studying brain structure, function, and electrophysiology in healthy young adults.	Australia	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1016/j.neuroimage.2015.04.047	https://doi.org/10.1371/journal.pone.0236418	Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps	Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.				Nitrc	Download using the provided script instead of downloading each TAR file individually.	Click on "See all files", select all, scroll to the bottom, and download.	1	1	0	1	1	0	0	0	0	0	Resting state EEG	Data from neuropsychological, psychometric and cognitive tasks are not currently being made available	To understand the complex interplay between structural and functional organisation of brain network using multi-modal analyses approaches.	2014	https://www.nitrc.org/projects/age-ility/	wget -qO- http://www.nitrc.org/frs/?group_id=911 | grep download.php | sed 's/.*href="//;s/".*//' | while read subject; do echo "$subject"; wget https://www.nitrc.org"$subject"; done
AOMIC_ID1000	Amsterdam Open MRI Collection	1	928	928	445	483	19	26	22.8	1.7	22.8	21.2	24.2	0	0	0	0	0			HC	https://nilab-uva.github.io/AOMIC.github.io/	Part of the Amsterdam Open MRI Collection, this dataset includes multimodal MRI data from 1,000 participants, focusing on individual differences.	Netherlands	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/s41597-021-00870-6	https://proceedings.neurips.cc/paper_files/paper/2022/file/8600a9df1a087a9a66900cc8c948c3f0-Paper-Conference.pdf	Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data	Self-supervised learning techniques are celebrating immense success in natural language processing (NLP) by enabling models to learn from broad language data at unprecedented scales. Here, we aim to leverage the success of these techniques for mental state decoding, where researchers aim to identify specific mental states (e.g., the experience of anger or joy) from brain activity. To this end, we devise a set of novel self-supervised learning frameworks for neuroimaging data inspired by prominent learning frameworks in NLP. At their core, these frameworks learn the dynamics of brain activity by modeling sequences of activity akin to how sequences of text are modeled in NLP. We evaluate the frameworks by pre-training models on a broad neuroimaging dataset spanning functional Magnetic Resonance Imaging data from 11, 980 experimental runs of 1, 726 individuals across 34 datasets, and subsequently adapting the pre-trained models to benchmark mental state decoding datasets. The pre-trained models transfer well, generally outperforming baseline models trained from scratch, while models trained in a learning framework based on causal language modeling clearly outperform the others.	https://doi.org/10.1016/j.isci.2024.110532	Is political ideology correlated with brain structure? A preregistered replication	We revisit the hypotheses that conservatism positively correlates with amygdala and negatively with anterior cingulate cortex (ACC) gray matter volume.Using diverse measures of ideology and a large and representative sample (Amsterdam Open MRI Collection [n = 928]), we replicate a small positive relationship between amygdala volume and conservatism. However, we fail to find consistent evidence in support of the ideology-ACC volume link. Using a split-sample strategy,we conducted exploratory whole-brain analyses on half the data, preregistered the findings, and then conducted subsequent confirmatory tests that additionally highlight weak, positive associations between the right fusiform gyri and conservatism. This is the largest preregistered replication study in the context of political neuroscience. By using Dutch as opposed to British or American data, we also extend the amygdala-conservatism link to a multiparty, multidimensional political context. We discuss the implications for future investigations of the neural substrates of ideology.	openNeuro	Do not combine all three AOMIC datasets blindly.	General population data with slightly higher age range and diversity.	1	0	1	1	0	0	0	0	0	0	NEO-FFI, religious_importance	less variablilty	Create a large representative dataset of the general population	2021		aws s3 sync --no-sign-request s3://openneuro.org/ds003097 . --exclude "*" --include "sub-*/anat/*T1w*"
AOMIC_PIOP1	Amsterdam Open MRI Collection	1	216	216	89	120	18.2	26.2	22.2	1.8	22.2	20.8	23.2	0	0	0	0	0			HC	https://nilab-uva.github.io/AOMIC.github.io/	Datasets from the Amsterdam Open MRI Collection, containing task-based and resting-state fMRI data to study cognitive and emotional processes.​	Netherlands	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/s41597-021-00870-6	https://doi.org/10.1093/cercor/bhad396	Brain asymmetry is globally different in males and females: exploring cortical volume, area, thickness, and mean curvature	Brain asymmetry is a cornerstone in the development of higher-level cognition, but it is unclear whether and how it differs in males and females. Asymmetry has been investigated using the laterality index, which compares homologous regions as pairwise weighted differences between the left and the right hemisphere. However, if asymmetry differences between males and females are global instead of pairwise, involving proportions between multiple brain areas, novel methodological tools are needed to evaluate them. Here, we used the Amsterdam Open MRI collection to investigate sexual dimorphism in brain asymmetry by comparing laterality index with the distance index, which is a global measure of differences within and across hemispheres, and with the subtraction index, which compares pairwise raw values in the left and right hemisphere. Machine learning models, robustness tests, and group analyses of cortical volume, area, thickness, and mean curvature revealed that, of the three indices, distance index was the most successful biomarker of sexual dimorphism. These findings suggest that left–right asymmetry in males and females involves global coherence rather than pairwise contrasts. Further studies are needed to investigate the biological basis of local and global asymmetry based on growth patterns under genetic, hormonal, and environmental factors.	https://doi.org/10.1016/j.isci.2024.110532	Is political ideology correlated with brain structure? A preregistered replication	We revisit the hypotheses that conservatism positively correlates with amygdala and negatively with anterior cingulate cortex (ACC) gray matter volume.Using diverse measures of ideology and a large and representative sample (Amsterdam Open MRI Collection [n = 928]), we replicate a small positive relationship between amygdala volume and conservatism. However, we fail to find consistent evidence in support of the ideology-ACC volume link. Using a split-sample strategy,we conducted exploratory whole-brain analyses on half the data, preregistered the findings, and then conducted subsequent confirmatory tests that additionally highlight weak, positive associations between the right fusiform gyri and conservatism. This is the largest preregistered replication study in the context of political neuroscience. By using Dutch as opposed to British or American data, we also extend the amygdala-conservatism link to a multiparty, multidimensional political context. We discuss the implications for future investigations of the neural substrates of ideology.	openNeuro	Do not combine all three AOMIC datasets blindly.	University students encompass the cohort.	1	0	1	1	0	0	0	0	0	0	physiology	less variablilty	Large representative dataset of university students	2021		aws s3 sync --no-sign-request s3://openneuro.org/ds002790 . --exclude "*" --include "sub-*/anat/*T1w*"
AOMIC_PIOP2	Amsterdam Open MRI Collection	1	226	226	96	129	18.2	25.8	22	1.8	22	20.5	23.2	0	0	0	0	0			HC	https://nilab-uva.github.io/AOMIC.github.io/	Datasets from the Amsterdam Open MRI Collection, containing task-based and resting-state fMRI data to study cognitive and emotional processes.​	Netherlands	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/s41597-021-00870-6	https://proceedings.neurips.cc/paper_files/paper/2022/file/8600a9df1a087a9a66900cc8c948c3f0-Paper-Conference.pdf	Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data	Self-supervised learning techniques are celebrating immense success in natural language processing (NLP) by enabling models to learn from broad language data at unprecedented scales. Here, we aim to leverage the success of these techniques for mental state decoding, where researchers aim to identify specific mental states (e.g., the experience of anger or joy) from brain activity. To this end, we devise a set of novel self-supervised learning frameworks for neuroimaging data inspired by prominent learning frameworks in NLP. At their core, these frameworks learn the dynamics of brain activity by modeling sequences of activity akin to how sequences of text are modeled in NLP. We evaluate the frameworks by pre-training models on a broad neuroimaging dataset spanning functional Magnetic Resonance Imaging data from 11,980 experimental runs of 1,726 individuals across 34 datasets, and subsequently adapting the pre-trained models to benchmark mental state decoding datasets. The pre-trained models transfer well, generally outperforming baseline models trained from scratch, while models trained in a learning framework based on causal language modeling clearly outperform the others.	https://doi.org/10.1016/j.isci.2024.110532	Is political ideology correlated with brain structure? A preregistered replication	We revisit the hypotheses that conservatism positively correlates with amygdala and negatively with anterior cingulate cortex (ACC) gray matter volume.Using diverse measures of ideology and a large and representative sample (Amsterdam Open MRI Collection [n = 928]), we replicate a small positive relationship between amygdala volume and conservatism. However, we fail to find consistent evidence in support of the ideology-ACC volume link. Using a split-sample strategy,we conducted exploratory whole-brain analyses on half the data, preregistered the findings, and then conducted subsequent confirmatory tests that additionally highlight weak, positive associations between the right fusiform gyri and conservatism. This is the largest preregistered replication study in the context of political neuroscience. By using Dutch as opposed to British or American data, we also extend the amygdala-conservatism link to a multiparty, multidimensional political context. We discuss the implications for future investigations of the neural substrates of ideology.	openNeuro	Do not combine all three AOMIC datasets blindly.	Large representative dataset of university students.	1	0	1	1	0	0	0	0	0	0	physiology	less variablilty	Large representative dataset of university students	2021		aws s3 sync --no-sign-request s3://openneuro.org/ds002785 . --exclude "*" --include "sub-*/anat/*T1w*"
BGSP	Brain Genomics Superstruct Project	5	1570	1570	665	905	19	35	21.5	2.9	21	19	23	0	0	0	0	0	W_NOT_HL	W_NOT_HL	HC	https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/25833	A large-scale neuroimaging dataset developed by Harvard and Mass General Hospital, the BGSP includes structural and functional MRI data from over 1,500 healthy young adults, along with behavioral, cognitive, and genetic information.	USA	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/sdata.2015.31	https://doi.org/10.1002/hbm.24213	Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems	The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).	https://doi.org/10.1093/cercor/bhx179	Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI	A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/ tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).	IDA LONI	Apply for access at both Harvard Dataverse and IDA; IDA provides detailed metadata for analysis.	Use your institutional email; otherwise, the dataset team may reject your request/application.	1	1	0	0	0	0	1	1	1	0	Flanker; cognitive, behavioural and genetics data is avaialable upon request	Slightly hard to access	Enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation	2014	https://fcon_1000.projects.nitrc.org/indi/req_access.html	aws s3 sync --no-sign-request s3://fcp-indi/data/Projects/BGSP/orig_bids/ . --exclude "*" --include "sub-*/*/anat/*"
BHRC	Brazilian High Risk Cohort Study	2	610	907	358	252	5.8	14.3	9.8	1.8	9.7	8.5	11.1	0	0	0	0	0	White	Hispanic or Latino	HC	https://osf.io/ktz5h/	A longitudinal study based in Brazil aiming to understand the development of mental disorders in children and adolescents.	Brazil	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1002/mpr.1459	https://doi.org/10.1001/jamapediatrics.2023.3221	Estimated Prevalence of Depressive Disorders in Children From 2004 to 2019 A Systematic Review and Meta-Analysis	Importance: Depression during childhood (ie, age <13 years) poses a major health burden. Recent changes in environmental and lifestyle factors may increase children's risk of mental health problems. This has been reported for anxiety disorders, but it is unclear whether this occurs for depressive disorders. Objective: To provide prevalence estimates for the depressive disorders (ie, major depressive disorder [MDD], dysthymia, disruptive mood dysregulation disorder [DMDD], and overall) in children, and whether they have changed over time. Data Sources: The MEDLINE, PsycINFO, Embase, Scopus, and Web of Science databases were searched using terms related to depressive disorders, children, and prevalence. This was supplemented by a systematic gray literature search. Study Selection: Studies were required to provide population prevalence estimates of depressive disorder diagnoses (according to an established taxonomy and standardized interviews) for children younger than 13 years, information about participants' year of birth, and be published in English. Data Extraction and Synthesis: Data extraction was compliant with the Meta-Analysis of Observational Studies in Epidemiology guidelines. A total of 12 985 nonduplicate records were retrieved, and 154 full texts were reviewed. Data were analyzed from 2004 (the upper limit of a previous review) to May 27, 2023. Multiple proportional random-effects meta-analytic and mixed-effects meta-regression models were fit. Main Outcomes and Measures: Pooled prevalence rates of depressive disorders, prevalence rate differences between males vs females and high-income countries (HICs) vs low-and middle-income countries (LMICs), and moderating effects of time or birth cohort. Results: A total of 41 studies were found to meet the inclusion criteria. Pooled prevalence estimates were obtained for 1.07% (95% CI, 0.62%-1.63%) for depressive disorders overall, 0.71% (95% CI, 0.48%-0.99%) for MDD, 0.30% (95% CI, 0.08%-0.62%) for dysthymia, and 1.60% (95% CI, 0.28%-3.90%) for DMDD. The meta-regressions found no significant evidence of an association with birth cohort, and prevalence rates did not differ significantly between males and females or between HICs and LMICs. There was a low risk of bias overall, except for DMDD, which was hindered by a lack of studies. Conclusions and Relevance: In this systematic review and meta-analysis, depression in children was uncommon and did not increase substantially between 2004 and 2019. Future epidemiologic studies using standardized interviews will be necessary to determine whether this trend will continue into and beyond the COVID-19 pandemic..	https://doi.org/10.1176/appi.ajp.2021.21090944	Early Adversity and Development: Parsing Heterogeneity and Identifying Pathways of Risk and Resilience	Adversity early in life is common and is a major risk factor for the onset of psychopathology. Delineating the neurodevelopmental pathways by which early adversity affects mental health is critical for early risk identification and targeted treatment approaches. A rapidly growing cross-species literature has facilitated advances in identifying the mechanisms linking adversity with psychopathology, specific dimensions of adversity and timing-related factors that differentially relate to outcomes, and protective factors that buffer against the effects of adversity. Yet, vast complexity and heterogeneity in early environments and neurodevelopmental trajectories contribute to the challenges of understanding risk and resilience in the context of early adversity. In this overview, the author highlights progress in four major areas—mechanisms, heterogeneity, developmental timing, and protective factors; synthesizes key challenges; and provides recommendations for future research that can facilitate progress in the field. Translation across species and ongoing refinement of conceptual models have strong potential to inform prevention and intervention strategies that can reduce the immense burden of psychopathology associated with early adversity.	RBC	The phenotypic and metadata is not easily accessible; you must sign in, request data access, and fill out a DUA.	Reproducible Brain Charts hosts this data, and it is easy to download.	1	1	0	1	0	0	0	0	0	0	1.5T scans only	Slightly hard to access the metadata	Understanding developmental trajectories of psychopathology and mental disorders in High Risk Cohort Study for the Development of Childhood Psychiatric Disorders.	2013		git clone https://github.com/ReproBrainChart/BHRC_BIDS.git && cd BHRC_BIDS && datalad clone https://github.com/ReproBrainChart/BHRC_BIDS.git -b complete-pass-0.1 . && datalad get *
BrainLat	BrainLat	5	761	761	322	439	21	98	68.5	10.9	70	62	76	0	0	0	0	0			AD	https://www.synapse.org/Synapse:syn51549340/files/	The BrainLat Project is a comprehensive, open-access neuroimaging dataset developed by the Latin American Brain Health Institute to advance research on neurodegenerative diseases within underrepresented populations. It encompasses multimodal data from 780 participants across five Latin American countries: Argentina, Chile, Colombia, Mexico, and Peru.​	Argentina, Chile, Colombia, Mexico, Peru	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/s41597-023-02806-8	https://doi.org/10.1038/s41591-024-03209-x	Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations	Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.	https://doi.org/10.1016/j.ebiom.2025.105614	Altered spatiotemporal brain dynamics of interoception in behavioural-variant frontotemporal dementia	Background Dysfunctional allostatic-interoception, altered processing of bodily signals in response to environmental demands, occurs in behavioural-variant frontotemporal dementia (bvFTD) patients. Previous research has not investigated the dynamic nature of interoception using methods like intrinsic neural timescales. We hypothesised that longer intrinsic neural timescales of interoception would occur in bvFTD patients, evidencing dysfunctional allostatic-interoception.	Synapse	Data requests should be submitted through Synapse.	Use the five diagnoses provided.	1	1	0	1	1	0	1	1	0	0	MOCA, IFS, Alzheimer’s disease, AD, behavioral variant frontotemporal dementia, bvFTD, Parkinson’s disease, PD, multiple sclerosis, MS, healthy controls, HC	Does not include data from entire of South america; including brazil	Facilitate the identifcation of new biomarkers, ultimately contributing to advancements in understanding and treating neurodegenerative diseases in Latin American populations which display extensive heterogeneity triggered by the unique combination of genetic and environmental diferences. 	2023	https://www.synapse.org/Synapse:syn51549340/files/	
CAMCAN	Cambridge Centre for Ageing Neuroscience	1	2681	2681	1173	1508	18.5	102.2	60.6	20.9	63.4	41.7	79.7	0	0	0	0	0			HC	https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/index.php	A lifespan dataset with multimodal imaging and cognitive assessments from adults aged 18–88.	UK	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1016/j.neuroimage.2015.09.018	https://doi.org/10.1038/s41593-023-01299-3	Functional brain networks reflect spatial and temporal autocorrelation	High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.	https://doi.org/10.1073/pnas.2214634120	Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment	The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer's disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. brain age | cognitive impairment | Alzheimer's disease | deep learning	sftp	Apply for data access through the dataset's official website.	Use FileZilla to understand the file structure and download required files.	1	1	1	1	0	0	1	1	0	0	MMSE, MEG, CArdiovascular measures, Benton, Cardiovascular measures, Cattell, Emotional expression recogn., Emotional memory, Emotion regulation, Famous faces, Force matching, Hotel task, Motor learning, Picture priming, Proverbs, RT choice, RT simple, Synsem, TOT, VSTM colour	Takes a couple of days to get the approval and stlughty hard to download the data; not comparible with linux machines or servers; even though there is a way	Understand how age-related changes to neural structure and function interact to support cognitive abilities across the lifespan.	2014	https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/index.php	
CC359	Calgary-Campinas-359	3	359	359	176	183	29	80	53.5	7.8	55	49	58.5	0	0	0	0	0			HC	https://www.ccdataset.com/download	A open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset comprising 359 T1-weighted MRI scans for evaluating brain segmentation algorithms.	Canada	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1016/j.neuroimage.2017.08.021	https://openaccess.thecvf.com/content/CVPR2022/papers/Yiasemis_Recurrent_Variational_Network_A_Deep_Learning_Inverse_Problem_Solver_Applied_CVPR_2022_paper.pdf	Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction	Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours. However, MRI suffers from very long acquisition times that make it susceptible to patient motion artifacts and limit its potential to deliver dynamic treatments. Conventional approaches such as Parallel Imaging and Compressed Sensing allow for an increase in MRI acquisition speed by reconstructing MR images from subsampled MRI data acquired using multiple receiver coils. Recent advancements in Deep Learning combined with Parallel Imaging and Compressed Sensing techniques have the potential to produce high-fidelity reconstructions from highly accelerated MRI data. In this work we present a novel Deep Learning-based Inverse Problem solver applied to the task of Accelerated MRI Reconstruction, called the Recurrent Variational Network (RecurrentVarNet), by exploiting the properties of Convolutional Recurrent Neural Networks and unrolled algorithms for solving Inverse Problems. The RecurrentVarNet consists of multiple recurrent blocks, each responsible for one iteration of the unrolled variational optimization scheme for solving the inverse problem of multi-coil Accelerated MRI Reconstruction. Contrary to traditional approaches, the optimization steps are performed in the observation domain (k-space) instead of the image domain. Each block of the RecurrentVarNet refines the observed k-space and comprises a data consistency term and a recurrent unit which takes as input a learned hidden state and the prediction of the previous block. Our proposed method achieves new state of the art qualitative and quantitative reconstruction results on 5-fold and 10-fold accelerated data from a public multi-coil brain dataset, outperforming previous conventional and deep learning-based approaches. Our code is publicly available at https://github.com/NKI-AI/direct.	https://doi.org/10.1109/JPROC.2022.3141367	AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis	Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence, deep neural networks and CS algorithms are being integrated to redefine the state of the art of fast MRI. The past several years have witnessed substantial growth in the complexity, diversity, and performance of deep-learning-based CS techniques that are dedicated to fast MRI. In this meta-analysis, we systematically review the deep-learning-based CS techniques for fast MRI, describe key model designs, highlight breakthroughs, and discuss promising directions. We have also introduced a comprehensive analysis framework and a classification system to assess the pivotal role of deep learning in CS-based acceleration for MRI.	CONP	The NIfTI images are in processed_original.zip.	You can treat the three vendors as three different sites.	1	0	0	0	0	0	0	0	0	0	1.5T, 3T	Too much raw data was given i really found it difficult to find the original nifit images	Allows investigation of the influences of both vendor and magnetic field strength on quantitative analysis of brain MR, parameter optimization for automatic segmentation methods and potentially machine learning classifiers with big data 	2016		
CCNP	Developmental Chinese Color Nest Project	1	195	195	93	102	6.5	17.9	11.9	3.1	11.4	9.3	14.4	0	0	0	0	0	Asian	not Hispanic or Latino	HC	https://www.scidb.cn/en/detail?dataSetId=c81f0e90a51b4cfca348ce4da6ca734e	A developmental neuroimaging dataset focusing on brain maturation in children and adolescents.	china	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1016/j.dcn.2021.101020	https://doi.org/10.1038/s41593-022-01218-y	Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data	Across the brain sciences, institutions and individuals have begun to actively acknowledge and address the presence of racism, bias, and associated barriers to inclusivity within our community. However, even with these recent calls to action, limited attention has been directed to inequities in the research methods and analytic approaches we use. The very process of science, including how we recruit, the methodologies we utilize and the analyses we conduct, can have marked downstream effects on the equity and generalizability of scientific discoveries across the global population. Despite our best intentions, the use of field-standard approaches can inadvertently exclude participants from engaging in research and yield biased brain–behavior relationships. To address these pressing issues, we discuss actionable ways and important questions to move the fields of neuroscience and psychology forward in designing better studies to address the history of exclusionary practices in human brain mapping.	https://doi.org/10.1038/s44220-023-00057-5	Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology	Our capacity to measure diverse aspects of human biology has developed rapidly in the past decades, but the rate at which these techniques have generated insights into the biological correlates of psychopathology has lagged far behind. The slow progress is partly due to the poor sensitivity, specificity and replicability of many findings in the literature, which have in turn been attributed to small effect sizes, small sample sizes and inadequate statistical power. A commonly proposed solution is to focus on large, consortia-sized samples. Yet it is abundantly clear that increasing sample sizes will have a limited impact unless a more fundamental issue is addressed: the precision with which target behavioral phenotypes are measured. Here, we discuss challenges, outline several ways forward and provide worked examples to demonstrate key problems and potential solutions. A precision phenotyping approach can enhance the discovery and replicability of associations between biology and psychopathology.	RBC	Use the RBC portal to get neuroimaging data and ScienceDB to request access to cognitive measures.	Use `datalad export-archive` to convert the dataset into a `.tar.gz` file and remove file pointers.	1	1	0	0	0	0	1	1	0	0	psychological assessments of behavior, cognition, and emotion	metadata is not easily available	Create normative charts for brain structure and function across the human lifespan, and link age-related changes in brain imaging measures to psychological assessments of behavior, cognition, and emotion using an accelerated longitudinal design.	2021		git clone https://github.com/ReproBrainChart/CCNP_BIDS.git && cd CCNP_BIDS && datalad clone https://github.com/ReproBrainChart/CCNP_BIDS.git -b complete-pass-0.1 . && datalad get *
CHBMP	Cuban Human Brain Mapping Project 	1	282	282	195	87	18	68	32	9.3	31	25	37	0	0	0	0	0		Cuban	HC	https://chbmp-open.loris.ca/	A population-based initiative focused on creating a normative brain dataset for the Cuban population. It includes high-resolution MRI, EEG, and neuropsychological assessments from healthy individuals, aiming to understand brain variability and support brain research and clinical neuroscience in Latin America.	cuba	neuroimaging (MRI)	Cross-sectional	http://nature.com/articles/s41597-021-00829-7	https://doi.org/10.1016/j.neuroimage.2022.119521	A reusable benchmark of brain-age prediction from M/EEG resting-state signals	Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R2 scores between 0.60-0.74. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.	https://doi.org/10.1016/j.neuroimage.2024.120636	Brain health in diverse settings: How age, demographics and cognition shape brain function	Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.	LORIS	Application approval is generally quick.	Underrepresented population data.	1	0	0	0	1	0	0	0	0	0	EEG		Development of tools and health applications based on multimodal neuroimaging	2004	https://chbmp-open.loris.ca/	
CHCP	Chinese Human Connectome Project	1	366	366	167	199	18	79	34	18.1	24	21	53.8	0	0	0	0	0	Asian	Chinese	HC	https://doi.org/10.11922/sciencedb.01374	A large-scale neuroimaging project modeled after the U.S. HCP, designed to map the structural and functional connectivity of the Chinese brain.	china	neuroimaging (MRI)	Cross-sectional	https://www.nature.com/articles/s41593-022-01215-1	https://doi.org/10.1016/j.scib.2024.03.006	The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration	Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.	https://doi.org/10.1002/hbm.70166	The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study	The shape of the brain's white matter connections is relatively unexplored in diffusion magnetic resonance imaging (dMRI) tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement two machine learning models (1D-CNN and Least Absolute Shrinkage and Selection Operator [LASSO]) to predict individual cognitive performance scores. We study a large-scale database from the Human Connectome Project Young Adult study (n = 1065). We apply an atlas-based fiber cluster parcellation (953 fiber clusters) to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models (using fivefold cross-validation) to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHapley Additive exPlanations (SHAP), to assess the importance of each fiber cluster for prediction. Our results demonstrate that fiber cluster shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are generally as effective for prediction as traditional microstructure and connectivity measures. The 1D-CNN model generally outperforms the LASSO method for prediction. Further interpretation and analysis using SHAP values from the 1D-CNN suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.	ScienceDB	This dataset is the Chinese version of the Human Connectome Project.	Use the provided URLs and `aria2c` to download the files.	1	1	1	1	0	0	1	0	0	0	Emotional fMRI, Gambling fMRI		To address the bias in large-scale multimodal neuroimaging datasets which have been obtained primarily from people living in Western countries, omitting the crucial contrast with populations living in other regions.	2022		
COBRE	The Center for Biomedical Research Excellence	1	148	148	109	37	18	65	37	12.8	35	26	48	0	0	0	0	0			Shiz	https://fcon_1000.projects.nitrc.org/indi/retro/cobre.html	A dataset including structural and resting-state fMRI data from individuals with schizophrenia and healthy controls.​	USA	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1002/hbm.22065	https://doi.org/10.1002/hbm.22065	Functional imaging of the hemodynamic sensory gating response in schizophrenia	The cortical (auditory and prefrontal) and/or subcortical (thalamic and hippocampal) generators of abnormal electrophysiological responses during sensory gating remain actively debated in the schizophrenia literature. Functional magnetic resonance imaging has the spatial resolution for disambiguating deep or simultaneous sources but has been relatively under-utilized to investigate generators of the gating response. Thirty patients with chronic schizophrenia (SP) and 30 matched controls participated in the current experiment. Hemodynamic response functions (HRFs) for single (S1) and pairs (S1 + S2) of identical ("gating-out" redundant information) or nonidentical ("gating-in" novel information) tones were generated through deconvolution. Increased or prolonged activation for patients in conjunction with deactivation for controls was observed within auditory cortex, prefrontal cortex, and thalamus in response to single tones during the late hemodynamic response, and these group differences were not associated with clinical or cognitive symptomatology. Although patient hyperactivation to paired-tones conditions was present in several regions of interest, the effects were not statistically significant for either the gating-out or gating-in conditions. Finally, abnormalities in the postundershoot of the auditory HRF were also observed for both single and paired-tones conditions in patients. In conclusion, the amalgamation of the entire electrophysiological response to both S1 and S2 stimuli may limit hemodynamic sensitivity to paired tones during sensory gating, which may be more readily overcome by paradigms that use multiple stimuli rather than pairs. Patient hyperactivation following single tones is suggestive of deficits in basic inhibition, neurovascular abnormalities, or a combination of both factors. © 2012 Wiley Periodicals, Inc.	https://doi.org/10.1016/j.neuroimage.2013.06.038	Using joint ICA to link function and structure using MEG and DTI in schizophrenia	In this study we employed joint independent component analysis (jICA) to perform a novel multivariate integration of magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data to investigate the link between function and structure. This model-free approach allows one to identify covariation across modalities with different temporal and spatial scales [temporal variation in MEG and spatial variation in fractional anisotropy (FA) maps]. Healthy controls (HC) and patients with schizophrenia (SP) participated in an auditory/visual multisensory integration paradigm to probe cortical connectivity in schizophrenia. To allow direct comparisons across participants and groups, the MEG data were registered to an average head position and regional waveforms were obtained by calculating the local field power of the planar gradiometers. Diffusion tensor images obtained in the same individuals were preprocessed to provide FA maps for each participant. The MEG/FA data were then integrated using the jICA software (http://mialab.mrn.org/software/fit). We identified MEG/FA components that demonstrated significantly different (p<. 0.05) covariation in MEG/FA data between diagnostic groups (SP vs. HC) and three components that captured the predominant sensory responses in the MEG data. Lower FA values in bilateral posterior parietal regions, which include anterior/posterior association tracts, were associated with reduced MEG amplitude (120-170. ms) of the visual response in occipital sensors in SP relative to HC. Additionally, increased FA in a right medial frontal region was linked with larger amplitude late MEG activity (300-400. ms) in bilateral central channels for SP relative to HC. Step-wise linear regression provided evidence that right temporal, occipital and late central components were significant predictors of reaction time and cognitive performance based on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) cognitive assessment battery. These results point to dysfunction in a posterior visual processing network in schizophrenia, with reduced MEG amplitude, reduced FA and poorer overall performance on the MATRICS. Interestingly, the spatial location of the MEG activity and the associated FA regions are spatially consistent with white matter regions that subserve these brain areas. This novel approach provides evidence for significant pairing between function (neurophysiology) and structure (white matter integrity) and demonstrates that this multivariate, multimodal integration technique is sensitive to group differences in function and structure. © 2013 Elsevier Inc.	indi	Schizophrenia data is available.	Use AWS to download the data.	1	1	0	0	0	0	0	0	0	0	MEG	DWI data is not shared	To create a schizophrenia dataset	2013	https://fcon_1000.projects.nitrc.org/indi/req_access.html	
CORR	Consortium for Reliability and Reproducibility	34	1534	4285	764	768	6	88	25.7	13.9	22	19	27	0	0	0	0	0			HC	https://fcon_1000.projects.nitrc.org/indi/CoRR/html/index.html	A multi-site dataset aimed at assessing the reliability of functional connectivity measures.	earth	neuroimaging (MRI)	Test-Retest	https://doi.org/10.1038/sdata.2014.49	https://doi.org/10.1016/j.neuroimage.2019.116157	A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis	Background: Once considered mere noise, fMRI-based functional connectivity has become a major neuroscience tool in part due to early studies demonstrating its reliability. These fundamental studies revealed only the tip of the iceberg; over the past decade, many test-retest reliability studies have continued to add nuance to our understanding of this complex topic. A summary of these diverse and at times contradictory perspectives is needed. Objectives: We aimed to summarize the existing knowledge regarding test-retest reliability of functional connectivity at the most basic unit of analysis: the individual edge level. This entailed (1) a meta-analytic estimate of reliability and (2) a review of factors influencing reliability. Methods: A search of Scopus was conducted to identify studies that estimated edge-level test-retest reliability. To facilitate comparisons across studies, eligibility was restricted to studies measuring reliability via the intraclass correlation coefficient (ICC). The meta-analysis included a random effects pooled estimate of mean edge-level ICC, with studies nested within datasets. The review included a narrative summary of factors influencing edge-level ICC. Results: From an initial pool of 212 studies, 44 studies were identified for the qualitative review and 25 studies for quantitative meta-analysis. On average, individual edges exhibited a “poor” ICC of 0.29 (95% CI = 0.23 to 0.36). The most reliable measurements tended to involve: (1) stronger, within-network, cortical edges, (2) eyes open, awake, and active recordings, (3) more within-subject data, (4) shorter test-retest intervals, (5) no artifact correction (likely due in part to reliable artifact), and (6) full correlation-based connectivity with shrinkage. Conclusion: This study represents the first meta-analysis and systematic review investigating test-retest reliability of edge-level functional connectivity. Key findings suggest there is room for improvement, but care should be taken to avoid promoting reliability at the expense of validity. By pooling existing knowledge regarding this key facet of accuracy, this study supports broader efforts to improve inferences in the field.	http://dx.doi.org/10.1038/nrn.2016.167	Scanning the horizon: towards transparent and reproducible neuroimaging research 	Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.	indi	MRI data harmonization is recommended, as this is a multi-site dataset.	Phenotypic information and headers are inconsistent across sites.	1	1	0	1	0	0	0	0	0	0	ASL, CBF		Adress the issues of test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results and to establish test-retest reliability as a minimum standard for methods development in functional connectomics.	2014	https://fcon_1000.projects.nitrc.org/indi/req_access.html	aws s3 sync --no-sign-request s3://fcp-indi/data/Projects/CORR/RawDataBIDS/ /CORR --exclude * --include */sub-*/ses-*/anat/* 
DENSE_F	28andMe	1	1	60	0	1	23	23	23	0	23	23	23	0	0	0	0	0	white	white	HC	https://openneuro.org/datasets/ds002674/versions/1.0.6	28andMe is a dense-sampling, multimodal neuroimaging study conducted by the Emily Jacobs Lab at UC Santa Barbara. It aimed to investigate how endogenous fluctuations in sex hormones across a complete menstrual cycle influence brain function and structure.	USA	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1016/j.neuroimage.2020.117091	https://doi.org/10.1016/j.neubiorev.2023.105259	Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry	A main goal in translational neuroscience is to identify neural correlates of psychopathology (“biomarkers”) that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.	https://doi.org/10.1162/netn_a_00169	Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle	Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization.	openNeuro	As this is densely sampled dataset, this dataset can be used to validate deep learning briain age predicition models.	Test your brain age prediction models on this dataset to evaluate performance.	1	1	0	0	0	0	0	0	0	0	Estrogen levels, menturation cycle tracking; participant_id  session_id  HormoneStatus  CycleNotes  Cycle / Pill Pack  Estradiol (LCMS; pg/mL)  Progesterone (LCMS; ng/mL)  LH (mIU/ml)  FSH (mIU/ml)  poms_tension  poms_depression  poms_anger  poms_vigor  poms_fatigue  poms_confusion  psqi  state_anxiety  perceived_stress  total_calorie_intak	Single subject	Understand the extent to which steroid hormone receptors and signaling molecules influence the large-scale functional architecture in males	2020		aws s3 sync --no-sign-request s3://openneuro.org/ds002674 . --exclude "*" --include "sub-*/anat/*T1w*"
DENSE_M	28andHe	1	1	40	1	0	26	26	26	0	26	26	26	0	0	0	0	0	White	white	HC	https://openneuro.org/datasets/ds005115/versions/1.2.0	28andHe is a complementary study to 28andMe, focusing on a male participant to explore diurnal hormone fluctuations and their impact on brain function. Conducted by the same research team, this study provides insights into how daily variations in hormones like testosterone affect neural dynamics.	USA	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1523/JNEUROSCI.1856-23.2024	https://doi.org/10.1162/imag_a_00474	Exploring neuroendocrine influences on the sensorimotor-association axis in a female and a male individual	Human neuroimaging studies consistently show multimodal patterns of variability along a key principle of macroscale cortical organization—the sensorimotor-association (S-A) axis. However, little is known about day-to-day fluctuations in functional activity along this axis within an individual, including sex-specific neuroendocrine factors contributing to such transient changes. We leveraged data from two densely sampled healthy young adults, one female and one male, to investigate intra-individual daily variability along the S-A axis, which we computed as our measure of functional cortical organization by reducing the dimensionality of functional connectivity matrices. Daily variability was greatest in temporal limbic and ventral prefrontal regions in both participants, and was more strongly pronounced in the male subject. Next, we probed local- and system-level effects of steroid hormones and self-reported perceived stress on functional organization. Beyond shared patterns of effects, our findings revealed subtle and unique associations between neuroendocrine fluctuations and intra-individual variability along the S-A axis in the female and male participants. In sum, our study points to neuroendocrine factors as possible modulators of intra-individual variability in functional brain organization, highlighting the need for further research in larger samples to assess the sex specificity of these effects.	https://doi.org/10.1523/JNEUROSCI.0573-24.2024	Circadian Rhythms Tied to Changes in Brain Morphology in a Densely Sampled Male	Circadian, infradian, and seasonal changes in steroid hormone secretion have been tied to changes in brain volume in several mammalian species. However, the relationship between circadian changes in steroid hormone production and rhythmic changes in brain morphology in humans is largely unknown. Here, we examined the relationship between diurnal fluctuations in steroid hormones and multiscale brain morphology in a precision imaging study of a male who completed forty MRI and serological assessments at 7 A.M. and 8 P.M. over the course of a month, targeting hormone concentrations at their peak and nadir. Diurnal fluctuations in steroid hormones were tied to pronounced changes in global and regional brain morphology. From morning to evening, total brain volume, gray matter volume, and cortical thickness decreased, coincident with decreases in steroid hormone concentrations (testosterone, estradiol, and cortisol). In parallel, cerebrospinal fluid and ventricle size increased from A.M. to P.M. Global changes were driven by decreases within the occipital and parietal cortices. These findings highlight natural rhythms in brain morphology that keep time with the diurnal ebb and flow of steroid hormones. Significance Statement Though rhythmic changes in steroid hormone secretion have been tied to changes in brain volume in several mammalian species, this relationship has not been well-characterized in humans. In this precision neuroimaging study, we found that global and regional brain morphology and steroid hormone levels exhibit tandem circadian rhythms. These findings provide high-resolution insight into the anatomical signature of diurnal changes in brain morphology and steroid hormone production in a human male and reveal the metronomic regularity of these rhythms over time.	openNeuro	As this is densely sampled dataset, this dataset can be used to validate deep learning briain age predicition models.	Test your brain age prediction models on this dataset to evaluate performance.	1	1	0	0	0	0	0	0	0	0	Testosterone levels; participant_id         session_id         time_of_day         cortisol_serum         cortisol_saliva         estradiol_serum         free_testosterone_serum         total_testosterone_serum         total_testosterone_saliva         perceived_stress         aggression         psqi         stai         poms_total         poms_tension         poms_depression         poms_anger         poms_vigor         poms_fatigue         poms_confusion         calories_burned         steps		Understand the extent to which steroid hormone receptors and signaling molecules influence the large-scale functional architecture in females	2024		aws s3 sync --no-sign-request s3://openneuro.org/ds005115 . --exclude "*" --include "sub-*/anat/*T1w*"
DLBS	Dallas Lifespan Brain Study	1	315	315	117	198	20.6	89.1	54.6	20.1	54.3	36.3	71.1	0	0	0	0	0	White/Caucasian	Non-hispanic	AD	https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html	A dataset focusing on cognitive aging, including MRI and behavioral data across the adult lifespan.​	USA	neuroimaging (MRI)	Longitudinal	https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html	https://doi.org/10.3389/fnagi.2016.00098	The Busier the Better: Greater Busyness Is Associated with Better Cognition	Sustained engagement in mentally challenging activities has been shown to improve memory in older adults. We hypothesized that a busy schedule would be a proxy for an engaged lifestyle and would facilitate cognition. Here, we examined the relationship between busyness and cognition in adults aged 50-89. Participants (N = 330) from the Dallas Lifespan Brain Study (DLBS) completed a cognitive battery and the Martin and Park Environmental Demands Questionnaire (MPED), an assessment of busyness. Results revealed that greater busyness was associated with better processing speed, working memory, episodic memory, reasoning, and crystallized knowledge. Hierarchical regressions also showed that, after controlling for age and education, busyness accounted for significant additional variance in all cognitive constructs-especially episodic memory. Finally, an interaction between age and busyness was not present while predicting cognitive performance, suggesting that busyness was similarly beneficial in adults aged 50-89. Although correlational, these data demonstrate that living a busy lifestyle is associated with better cognition.	https://doi.org/10.1002/alz.12062	Age-dependent amyloid deposition is associated with white matter alterations in cognitively normal adults during the adult life span	Introduction: Both beta-amyloid (Ab) deposition and decline in white matter integrity, are brain alterations observed in Alzheimer's disease (AD) and start to occur by the fourth and fifth decades. However, the association between both brain alterations in asymptomatic subjects is unclear. Methods: Amyloid positron emission tomography (PET) and diffusion tensor imaging (DTI) were obtained in 282 cognitively normal subjects (age 30-89 years). We assessed the interaction of age by abnormal amyloid PET status (Florbetapir F-18 PET >1.2 standard uptake value ratio [SUVR]) on regional mean diffusivity (MD) and global white matter hyperintensity (WMH) volume, controlled for sex, education, and hypertension. Results: Subjects with abnormal amyloid PET (n = 87) showed stronger age-related increase in global WMH and regional MD, particularly within the posterior parietal regions of the white matter. Discussion: Sporadic Aβ deposition is associated with white matter alterations in AD predilection areas in an age-dependent manner in cognitively normal individuals.	indi	To access DLBS imaging data, remove `dlbs.html` from the URL to view the file explorer.	This dataset includes genetics, imaging, and cognitive data, making it suitable for Alzheimer's detection studies.	1	0	0	0	0	1	1	0	1	0	MMSE		Understand the antecedents of preservation and decline of cognitive function at different stages of the adult lifespan, with a particular interest in the early stages of a healthy brain’s march towards Alzheimer Disease	2008	https://fcon_1000.projects.nitrc.org/indi/req_access.html	wget https://fcon_1000.projects.nitrc.org/indi/retro/dlbs_content/dlbs_imaging.tar.gz
FCON1000	1000 Functional Connectomes Project	33	1277	1277	526	614	7.9	85	28.6	13.5	23	21	30	0	0	0	0	0			HC	http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html	An open-access collection of resting-state fMRI datasets from multiple sites to facilitate connectome studies.	earth	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1073/pnas.0911855107	https://doi.org/10.1371/journal.pone.0068910	BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics	The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/). © 2013 Xia et al.	https://doi.org/10.1152/jn.00338.2011	The Organization of the Human Cerebral Cortex Estimated by Functional Connectivity	Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surfacebased alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition. © 2011 the American Physiological Society.	indi	The dataset is old and phenotypic information is missing for many subjects.	Using AWS, the data can be downloaded easily.	1	1	0	0	0	0	0	0	0	0	NA		To initiate discovery science of brain function	2009	https://fcon_1000.projects.nitrc.org/indi/req_access.html	aws s3 sync --no-sign-request s3://fcp-indi/data/Projects/FCON1000/ . --exclude "*" --include "*/anat/*"
HBN	Healthy Brain Network	3	3563	3563	2264	1226	5	22	10.2	3.4	9.5	7.6	12.1	0	0	0	0	0			HC	https://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/MRI_EEG.html	A large-scale dataset collecting imaging and behavioral data from children and adolescents to study mental health and learning disorders.	USA	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/sdata.2017.181	https://doi.org/10.1038/s41467-024-46150-w	Data leakage inflates prediction performance in connectome-based machine learning models	Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage–involving feature selection, covariate correction, and dependence between subjects–on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.	https://doi.org/10.1038/s41380-018-0321-0	Big data approaches to decomposing heterogeneity across the autism spectrum	Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as ‘spectrum’ or ‘autisms’ reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case–control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being ‘feature-rich’, big data should be both ‘broad’ (i.e., large sample size) and ‘deep’ (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model’s utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with ‘supervised’ models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.	indi, RBC	Access to cognitive data is time-consuming and can take several months.	The DUA must be signed by both the PI and the head of the institution.	1	1	1	1	1	0	0	0	0	0	NA		A large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology	2017		aws s3 sync --no-sign-request s3://fcp-indi/data/Projects/HBN/MRI/ . --exclude "*" --include "*/sub-*/anat/*"
HCP_1200	Human Connectome Project	1	1206	1206	550	656	23.5	33	28.8	3.5	28	28	33	0	0	0	0	0			HC	https://www.humanconnectome.org/study/hcp-young-adult	A comprehensive dataset with high-resolution MRI data from 1,200 healthy adults, aiming to map human brain connectivity.​	USA	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1016/j.neuroimage.2013.05.041	https://doi.org/10.1038/s41583-021-00474-4	The default mode network in cognition: a topographical perspective	The default mode network (DMN) is a set of widely distributed brain regions in the parietal, temporal and frontal cortex. These regions often show reductions in activity during attention-demanding tasks but increase their activity across multiple forms of complex cognition, many of which are linked to memory or abstract thought. Within the cortex, the DMN has been shown to be located in regions furthest away from those contributing to sensory and motor systems. Here, we consider how our knowledge of the topographic characteristics of the DMN can be leveraged to better understand how this network contributes to cognition and behaviour.	https://doi.org/10.1038/s41593-021-00962-x	A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence	Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence.	connectomeDB	Highly recommended to use AWS to download the data—enable AWS in ConnectomeDB, get credentials, and set up your account on the server.	There are multiple data releases; be careful to choose the correct folder.	1	1	1	1	0	0	1	1	0	0	7T scans, Emotional fMRI, gampbling fMRI, https://brain.labsolver.org/hbn.html		Characterize human brain connectivity and function in a population of 1200 healthy adults and to enable detailed comparisons between brain circuits, behavior, and genetics at the level of individual subjects	2017	https://www.humanconnectome.org/study/hcp-young-adult	aws s3 sync s3://hcp-openaccess/HCP_1200/HCP_1200 --exclude "*" --include "*/T1w/T1w_acpc_dc_restore_brain.nii.gz"
ICMB	International Consortium for Brain Mapping 	3	189	189	85	104	19	80	41.7	15.4	41	29	54	0	0	0	0	0	White	Not Hispanic or Latino	HC	https://ida.loni.usc.edu/collaboration/access/appLicense.jsp	A global collaborative effort aimed at developing a probabilistic, voxel-based atlas of the human brain. it provides standardized brain templates and tools to facilitate the comparison of functional and structural brain data across studies.	earth	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1098/rstb.2001.0915	https://doi.org/10.1371/journal.pcbi.1005350	Mindboggling morphometry of human brains	Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.	https://doi.org/10.1126/science.abb4588	Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture	Cytoarchitecture is a basic principle of microstructural brain parcellation. We introduce Julich-Brain, a three-dimensional atlas containing cytoarchitectonic maps of cortical areas and subcortical nuclei. The atlas is probabilistic, which enables it to account for variations between individual brains. Building such an atlas was highly data- and labor-intensive and required the development of nested, interdependent workflows for detecting borders between brain areas, data processing, provenance tracking, and flexible execution of processing chains to handle large amounts of data at different spatial scales. Full cortical coverage was achieved by the inclusion of gap maps to complement cortical maps. The atlas is dynamic and will be adapted as mapping progresses; it is openly available to support neuroimaging studies as well as modeling and simulation; and it is interoperable, enabling connection to other atlases and resources.	IDA LONI	IDA (LONI) requires that the download starts from the same IP as used in the browser session.	If on VPN, use the terminal command `firefox` to open a browser and log in to IDA through it to download the data.	1	1	0	1	0	1	1	0	0	0	All healthy		To create standardized, population-based brain templates and atlases for accurate spatial normalization and anatomical labeling in neuroimaging research.	1998	https://ida.loni.usc.edu/collaboration/access/appApply.jsp?project=ICBM}	
ISYB	Imaging Chinese Young Brains	1	215	215	59	156	18	30	22.6	2.7	23	20	24	0	0	0	0	0	Chinese	Chinese Han	HC	https://doi.org/10.11922/sciencedb.00740	A publicly available neuroimaging resource comprising multimodal MRI data from 215 healthy, right-handed Chinese adults aged 18–30. 	earth	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/s41597-022-01413-3	https://doi.org/10.1016/j.dcn.2023.101244	Brief mock-scan training reduces head motion during real scanning for children: A growth curve study	Pediatric neuroimaging datasets are rapidly increasing in scales. Despite strict protocols in data collection and preprocessing focused on improving data quality, the presence of head motion still impedes our understanding of neurodevelopmental mechanisms. Large head motion can lead to severe noise and artifacts in magnetic resonance imaging (MRI) studies, inflating correlations between adjacent brain areas and decreasing correlations between spatial distant territories, especially in children and adolescents. Here, by leveraging mock-scans of 123 Chinese children and adolescents, we demonstrated the presence of increased head motion in younger participants. Critically, a 5.5-minute training session in an MRI mock scanner was found to effectively suppress the head motion in the children and adolescents. Therefore, we suggest that mock scanner training should be part of the quality assurance routine prior to formal MRI data collection, particularly in large-scale population-level neuroimaging initiatives for pediatrics.	https://doi.org/10.1016/j.dcn.2024.101443	Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics	Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics. In past genetic research, robust findings were historically undermined by oversimplified biological hypotheses, mistaken assumptions about expectable effect sizes, replication problems, confounding by population structure, and shared biological patterns across disorders. Overcoming these challenges has contributed to current successes in the field. Drawing parallels across psychiatric genetics and neuroimaging, we identify key shared challenges as well as pinpoint relevant insights that could be gained in psychiatric neuroimaging from the transition that occurred from the candidate gene to (post) genome-wide “eras” of psychiatric genetics. Finally, we discuss the prominent developmental component of psychiatric neuroimaging and how that might be informed by epidemiological and omics approaches. The evolution of psychiatric genetic research offers valuable insights that may expedite the resolution of key challenges in psychiatric neuroimaging, thus potentially moving our understanding of psychiatric pathophysiology forward.	ScienceDB	The data has 3 versions; the latest requires a DUA. Download version 2 if DUA is not feasible.	Version 1 does not have phenotypic information.	1	0	0	1	0	0	1	0	0	0	CBF		To support cross-ethnic and cross-cultural brain research by providing open-access, high-quality multi-modal MRI data from healthy young Chinese adults.	2021		
IXI	Information eXtraction from Images	3	593	593	267	326	20	86.3	49.4	16.7	50.6	34	63.4	0	0	0	0	0		White	HC	https://brain-development.org/ixi-dataset/	A dataset comprising MRI scans from healthy subjects, used for developing and testing image processing algorithms.​	UK	neuroimaging (MRI)	Cross-sectional	https://brain-development.org/publication-list/	https://openaccess.thecvf.com/content/WACV2024/papers/Kim_Adaptive_Latent_Diffusion_Model_for_3D_Medical_Image_to_Image_WACV_2024_paper.pdf	Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study	Multi-modal images play a crucial role in comprehensive evaluations in medical image analysis providing complementary information for identifying clinically important biomarkers. However, in clinical practice, acquiring multiple modalities can be challenging due to reasons such as scan cost, limited scan time, and safety considerations. In this paper, we propose a model based on the latent diffusion model (LDM) that leverages switchable blocks for image-to-image translation in 3D medical images without patch cropping. The 3D LDM combined with conditioning using the target modality allows generating high-quality target modality in 3D overcoming the shortcoming of the missing out-of-slice information in 2D generation methods. The switchable block, noted as multiple switchable spatially adaptive normalization (MS-SPADE), dynamically transforms source latents to the desired style of the target latents to help with the diffusion process. The MS-SPADE block allows us to have one single model to tackle many translation tasks of one source modality to various targets removing the need for many translation models for different scenarios. Our model exhibited successful image synthesis across different source-target modality scenarios and surpassed other models in quantitative evaluations tested on multi-modal brain magnetic resonance imaging datasets of four different modalities and an independent IXI dataset. Our model demonstrated successful image synthesis across various modalities even allowing for one-to-many modality translations. Furthermore, it outperformed other oneto-one translation models in quantitative evaluations. Our code is available at https:// github.com/ jongdory/ ALDM/	https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Brain_Image_Synthesis_With_Unsupervised_Multivariate_Canonical_CSCl4Net_CVPR_2021_paper.pdf	Brain Image Synthesis with Unsupervised Multivariate Canonical CSCℓ4Net	Recent advances in neuroscience have highlighted the effectiveness of multi-modal medical data for investigating certain pathologies and understanding human cognition. However, obtaining full sets of different modalities is limited by various factors, such as long acquisition times, high examination costs and artifact suppression. In addition, the complexity, high dimensionality and heterogeneity of neuroimaging data remains another key challenge in leveraging existing randomized scans effectively, as data of the same modality is often measured differently by different machines. There is a clear need to go beyond the traditional imaging-dependent process and synthesize anatomically specific target-modality data from a source input. In this paper, we propose to learn dedicated features that cross both intre- and intra-modal variations using a novel CSCℓ4Net. Through an initial unification of intramodal data in the feature maps and multivariate canonical adaptation, CSCℓ4Net facilitates feature-level mutual transformation. The positive definite Riemannian manifoldpenalized data fidelity term further enables CSCℓ4Net to reconstruct missing measurements according to transformed features. Finally, the maximization ℓ4-norm boils down to a computationally efficient optimization problem. Extensive experiments validate the ability and robustness of our CSCℓ4Net compared to the state-of-the-art methods on multiple datasets.	NA	Data was acquired at 3 sites (2 with 1.5T scanners and 1 with a 3T scanner).	Demographic codes are provided in different sheets of the Excel file.	1	0	0	1	0	0	1	0	0	0			Data set of nearly 600 MR images from normal, healthy subjects, along with demographic characteristics, collected as part of the Information eXtraction from Images (IXI) project	2016		aria2c -x 10 -j 10 -s 10 http://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI-T1.tar
LASI	Longitudinal Aging Study in India Diagnostic Assessment of Dementia	1	148	149	80	62	0	85	66.3	11.3	67	63	72	78	129	4	4	0	Indian	Indian	AD	https://lasi-dad.org/	A study collecting health and social data from older adults in India; imaging data availability is limited.	India	neuroimaging (MRI)	Longitudinal	http://dx. doi.org/10.1136/bmjopen- 2019-030300	https://doi.org/10.2196/27113	Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study	Background: The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (n=4096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents. Objective: This study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status. Methods: Clinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A 2-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and kappa statistic. The ultimate model was selected based on overall agreement as measured by kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status. Results: Among the 2528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability, as indicated by AUROC >.90, and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and kappa (.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%. Conclusions: The selected machine learning model has outstanding discriminative ability and substantial agreement with a clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision support tool or virtual rater to assist diagnosis of dementia.	https://doi.org/10.1101/2024.01.04.24300840	Effect of APOE ε4 and its modification by sociodemographic characteristics on cognitive measures in South Asians from LASI-DAD	BACKGROUND We investigated effects of APOE ε4 and its interactions with sociodemographic characteristics on cognitive measures in 2,563 South Asians from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD). METHODS Linear regression models were used to assess the association between APOE ε4 and global- and domain-specific cognitive function. Effect modification by age, sex, and education were explored using cross-product interaction terms and subgroup analyses. RESULTS APOE ε4 was inversely associated with most cognitive measures. This association was stronger in the older age group (age >68) for general cognitive function, orientation, and language/fluency, as well as in females for memory and language/fluency. Interaction between APOE ε4 and education was mostly nonsignificant. DISCUSSION APOE ε4 is associated with lower cognitive function in South Asians from India, with a more pronounced impact observed in females and older individuals.	IDA LONI	LASI is hosted on IDA, but access must be requested. The data managers are responsive.	SubjectID and ImageID follow different patterns; the Research Group variable on IDA LONI is not the actual diagnosis.	1	1	0	0	0	0	1	1	1	0			Estimate the prevalence of dementia and mild cognitive impairment and to contribute to a better understanding of the determinants of late-life cognition, cognitive aging, and dementia in india	2017	https://ida.loni.usc.edu/project_info.jsp	
Lexical	A longitudinal neuroimaging dataset on multisensory lexical processing in school-aged children	1	188	376	99	89	7.4	16.4	11.2	1.9	11	9.7	12.6	0	0	0	0	0	White	Not Hispanic or Latino	HC	https://openneuro.org/datasets/ds001894/versions/1.4.2	Children dataset is an open-access neuroimaging resource designed to investigate how children process spoken and written language over time. It combines multimodal neuroimaging with behavioral assessments to explore the neural mechanisms underlying reading development in school-aged children.​	USA	neuroimaging (MRI)	Cross-sectional	https://www.nature.com/articles/s41597-019-0338-5	https://doi.org/10.1038/s41539-023-00201-x	Arithmetic skills are associated with left fronto-temporal gray matter volume in 536 children and adolescents	There are large individual differences in arithmetic skills. Although a number of brain-wide association studies have attempted to identify the neural correlates of these individual differences, studies have focused on relatively small sample sizes and have yielded inconsistent results. In the current voxel-based morphometry study, we merged six structural imaging datasets of children and adolescents (from 7.5 to 15 years) whose levels of arithmetic skills were assessed, leading to a combined sample of n = 536. Controlling for individual differences in age, gender, as well as language, and intelligence, we found a unique positive relation between arithmetic skill and gray matter volume in the left inferior frontal gyrus (IFG) and middle temporal gyrus (MTG). Our results suggest that individual differences in arithmetic skills are associated with structural differences in left fronto-temporal areas, rather than in regions of the parietal cortex and hippocampus that are often associated with arithmetic processing.	https://doi.org/10.1162/netn_a_00414	Patterns of the left thalamus embedding into the connectome associated with reading skills in children with reading disabilities	We examined how thalamocortical connectivity structure reflects children's reading performance. Diffusion-weighted MRI at 3 T and a series of reading measures were collected from 64 children (33 girls) ages 8-14 years with and without dyslexia. The topological properties of the left and right thalamus were computed based on the whole-brain white matter network and a hub-attached reading network, and were correlated with scores on several tests of children's reading and reading-related abilities. Significant correlations between topological metrics of the left thalamus and reading scores were observed only in the hub-attached reading network. Local efficiency was negatively correlated with rapid automatized naming. Transmission cost was positively correlated with phonemic decoding, and this correlation was independent of network efficiency scores; follow-up analyses further demonstrated that this effect was specific to the pulvinar and mediodorsal nuclei of the left thalamus. We validated these results using an independent dataset and demonstrated that that the relationship between thalamic connectivity and phonemic decoding was specifically robust. Overall, the results highlight the role of the left thalamus and thalamocortical network in understanding the neurocognitive bases of skilled reading and dyslexia in children.	openNeuro	OpenNeuro data is very easy to access.	Read the associated research publication for context.	1	1	1	0	0	0	0	0	0	0	Race, Ethnicity		Understand the underlying mechanisms of reading	2022		aws s3 sync --no-sign-request s3://openneuro.org/ds001894 . --exclude "*" --include "*T1w.nii.gz"
MCSA	The Mayo Clinic Study of Aging	7	1802	3090	968	834	49	89.9	72.8	9.8	74.4	65.4	80.5	2606	402	34	2	1	White	Not Hispanic	AD	https://www.mayo.edu/research/centers-programs/alzheimers-disease-research-center/research-activities/mayo-clinic-study-aging/overview	A population-based study including imaging and clinical data to investigate aging and Alzheimer's disease.​	USA	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1159/000115751	https://doi.org/10.1126/scitranslmed.ado8076	Flortaucipir PET uncovers relationships between tau and amyloid-β in primary age–related tauopathy and Alzheimer’s disease	[ 18 F]-Flortaucipir positron emission tomography (PET) is considered a good biomarker of Alzheimer's disease. However, it is unknown how flortaucipir is associated with the distribution of tau across brain regions and how these associations are influenced by amyloid-β. It is also unclear whether flortaucipir can detect tau in definite primary age-related tauopathy (PART). We identified 248 individuals at Mayo Clinic who had undergone [ 18 F]-flortaucipir PET during life, had died, and had undergone an autopsy, 239 cases of which also had amyloid-β PET. We assessed nonlinear relationships between flortaucipir uptake in nine medial temporal and cortical regions, Braak tau stage, and Thal amyloid-β phase using generalized additive models. We found that flortaucipir uptake was greater with increasing tau stage in all regions. Increased uptake at low tau stages in medial temporal regions was only observed in cases with a high amyloid-β phase. Flortaucipir uptake linearly increased with the amyloid-β phase in medial temporal and cortical regions. The highest flortaucipir uptake occurred with high Alzheimer's disease neuropathologic change (ADNC) scores, followed by low-intermediate ADNC scores, then PART, with the entorhinal cortex providing the best differentiation between groups. Flortaucipir PET had limited ability to detect PART, and imaging-defined PART did not correspond with pathologically defined PART. In summary, spatial patterns of flortaucipir mirrored the histopathological tau distribution, were influenced by the amyloid-β phase, and were useful for distinguishing different ADNC scores and PART.	https://doi.org/10.1038/s41591-022-02049-x	Amyloid and tau PET-positive cognitively unimpaired individuals are at high risk for future cognitive decline	A major unanswered question in the dementia field is whether cognitively unimpaired individuals who harbor both Alzheimer’s disease neuropathological hallmarks (that is, amyloid-β plaques and tau neurofibrillary tangles) can preserve their cognition over time or are destined to decline. In this large multicenter amyloid and tau positron emission tomography (PET) study (n = 1,325), we examined the risk for future progression to mild cognitive impairment and the rate of cognitive decline over time among cognitively unimpaired individuals who were amyloid PET-positive (A+) and tau PET-positive (T+) in the medial temporal lobe (A+TMTL+) and/or in the temporal neocortex (A+TNEO-T+) and compared them with A+T− and A−T− groups. Cox proportional-hazards models showed a substantially increased risk for progression to mild cognitive impairment in the A+TNEO-T+ (hazard ratio (HR) = 19.2, 95% confidence interval (CI) = 10.9–33.7), A+TMTL+ (HR = 14.6, 95% CI = 8.1–26.4) and A+T− (HR = 2.4, 95% CI = 1.4–4.3) groups versus the A−T− (reference) group. Both A+TMTL+ (HR = 6.0, 95% CI = 3.4–10.6) and A+TNEO-T+ (HR = 7.9, 95% CI = 4.7–13.5) groups also showed faster clinical progression to mild cognitive impairment than the A+T− group. Linear mixed-effect models indicated that the A+TNEO-T+ (β = −0.056 ± 0.005, T = −11.55, P < 0.001), A+TMTL+ (β = −0.024 ± 0.005, T = −4.72, P < 0.001) and A+T− (β = −0.008 ± 0.002, T = −3.46, P < 0.001) groups showed significantly faster longitudinal global cognitive decline compared to the A−T− (reference) group (all P < 0.001). Both A+TNEO-T+ (P < 0.001) and A+TMTL+ (P = 0.002) groups also progressed faster than the A+T− group. In summary, evidence of advanced Alzheimer’s disease pathological changes provided by a combination of abnormal amyloid and tau PET examinations is strongly associated with short-term (that is, 3–5 years) cognitive decline in cognitively unimpaired individuals and is therefore of high clinical relevance.	IDA LONI	Probably the best alternative to ADNI; with PET’s increasing popularity and decreasing cost, this is an immensely valuable resource.	MRI data is acquired at a single location but with multiple scanners configured identically. This can be treated as single-site data.	1	0	0	0	0	1	1	0	0	0			Establish a prospective population-based cohort to investigate the prevalence, incidence and risk factors for mild cognitive impairment (MCI) and dementia	2008	https://ida.loni.usc.edu/collaboration/access/appApply.jsp?project=MCSA	
MIRIAD	Minimal Interval Resonance Imaging in Alzheimer's Disease	1	69	708	31	38								0	0	0	0	0			AD	https://www.ucl.ac.uk/drc/research-clinical-trials/minimal-interval-resonance-imaging-alzheimers-disease-miriad	A dataset with longitudinal MRI scans of Alzheimer's patients and controls, used for evaluating brain atrophy.	UK	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1016/j.neuroimage.2012.12.044	https://doi.org/10.1016/j.asoc.2024.111749	Information fusion-based Bayesian optimized heterogeneous deep ensemble model based on longitudinal neuroimaging data	The fusion of multimodal longitudinal data is difficult but crucial for enhancing the accuracy of deep learning models for disease identification and helps provide tailored and patient-centric decisions. This study explores the fusion of multimodal data to detect the progression of Alzheimer's disease (AD) using ensemble learning. We propose a heterogeneous ensemble framework of Bayesian-optimized time-series deep learning models to identify progressive deterioration of brain damage. Experimental results show that the heterogeneous ensemble of three models with patient's temporal data outperforms all other variants of ensemble models by achieving an average performance of 95% for accuracy. We also propose a novel explainability approach, which enables domain experts and practitioners to better comprehend the model's final decision. The visual explainability of infected brain regions and the model's robustness is evaluated by our two medical domain experts showing its promising use in real medical environment. To evaluate the model's generalizability and robustness, our optimized model is tested on a dataset with different distribution. The experiments demonstrate that the proposed model, which was trained on ADNI data, exhibits reliable generalization to NACC data with an average precision of 90%, recall of 91%, F1-score of 89%, AUC of 88%, and accuracy of 88%.	https://openaccess.thecvf.com/content/ACCV2024/papers/Chen_MedBLIP_Bootstrapping_Language-Image_Pre-training_from_3D_Medical_Images_and_Texts_ACCV_2024_paper.pdf	MedBLIP: Bootstrapping Language-Image Pretraining from 3D Medical Images and Texts	Vision language pretraining (VLP) models have proven effective in numerous computer vision applications. In this paper, we focus on developing a VLP model for the medical domain to facilitate computer-aided diagnoses (CAD) based on image scans and text descriptions from electronic health records. To achieve this, we introduce MedBLIP, a lightweight CAD system that bootstraps VLP from offthe-shelf frozen pre-trained image encoders and large language models. We incorporate a MedQFormer module to bridge the gap between 3D medical images and 2D pre-trained image encoders and language models. To evaluate the effectiveness of our MedBLIP, we have collected over 30,000 image volumes from five public Alzheimer’s disease (AD) datasets: ADNI, NACC, OASIS, AIBL, and MIRIAD. On this largescale AD collection, our model demonstrates impressive performance in zero-shot classification of healthy, mild cognitive impairment (MCI), and AD subjects, and also shows its capability in medical visual question answering (VQA) on the M3D-VQA-AD dataset. The code and pre trained models are available at https://github.com/Qybc/MedBLIP.	NA	Log into XANT and use the “Move Columns” button to get additional information in the CSV file.	Age labels are in the "sessions" tab.	1	0	0	0	0	0	1	0	0	0					http://miriad.drc.ion.ucl.ac.uk/atrophychallenge	
MPI	MPI-Leipzig Mind-Brain-Body	1	318	318	189	129	27.5	67.5	57.5	17.3	67.5	57.5	67.5	0	0	0	0	0			HC	https://openneuro.org/datasets/ds000221/versions/00002	This dataset aims to explore the interplay between brain structure, function, and emotional processing across the adult lifespan. It provides valuable insights into how these factors contribute to individual differences in emotional regulation and cognitive performance. 	Germany	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/sdata.2018.308	https://doi.org/10.1073/pnas.2310012121	Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization	Sex plays a crucial role in human brain development, aging, and the manifestation of psychiatric and neurological disorders. However, our understanding of sex differences in human functional brain organization and their behavioral consequences has been hindered by inconsistent findings and a lack of replication. Here, we address these challenges using a spatiotemporal deep neural network (stDNN) model to uncover latent functional brain dynamics that distinguish male and female brains. Our stDNN model accurately differentiated male and female brains, demonstrating consistently high cross-validation accuracy (>90%), replicability, and generalizability across multisession data from the same individuals and three independent cohorts (N ~ 1,500 young adults aged 20 to 35). Explainable AI (XAI) analysis revealed that brain features associated with the default mode network, striatum, and limbic network consistently exhibited significant sex differences (effect sizes > 1.5) across sessions and independent cohorts. Furthermore, XAI-derived brain features accurately predicted sex-specific cognitive profiles, a finding that was also independently replicated. Our results demonstrate that sex differences in functional brain dynamics are not only highly replicable and generalizable but also behaviorally relevant, challenging the notion of a continuum in male-female brain organization. Our findings underscore the crucial role of sex as a biological determinant in human brain organization, have significant implications for developing personalized sex-specific biomarkers in psychiatric and neurological disorders, and provide innovative AI-based computational tools for future research.	https://doi.org/10.1038/s41598-024-66049-2	Aperiodic component of EEG power spectrum and cognitive performance are modulated by education in aging	Recent studies have shown a growing interest in the so-called “aperiodic” component of the EEG power spectrum, which describes the overall trend of the whole spectrum with a linear or exponential function. In the field of brain aging, this aperiodic component is associated both with age-related changes and performance on cognitive tasks. This study aims to elucidate the potential role of education in moderating the relationship between resting-state EEG features (including aperiodic component) and cognitive performance in aging. N = 179 healthy participants of the “Leipzig Study for Mind–Body-Emotion Interactions” (LEMON) dataset were divided into three groups based on age and education. Older adults exhibited lower exponent, offset (i.e. measures of aperiodic component), and Individual Alpha Peak Frequency (IAPF) as compared to younger adults. Moreover, visual attention and working memory were differently associated with the aperiodic component depending on education: in older adults with high education, higher exponent predicted slower processing speed and less working memory capacity, while an opposite trend was found in those with low education. While further investigation is needed, this study shows the potential modulatory role of education in the relationship between the aperiodic component of the EEG power spectrum and aging cognition.	openNeuro	MPI-LEMON is a subset of the MPI Mind and Body dataset, with additional tests conducted.	A subset of this dataset is deeply phenotyped and serves well as a disjoint test dataset.	1	1	0	0	1	0	1	1	0	0					https://fcon_1000.projects.nitrc.org/indi/req_access.html	aws s3 sync --no-sign-request s3://openneuro.org/ds000221/ . --exclude * --include "**/*T1w.nii.gz"
NACC	Standardized Centralized Alzheimer’s & Related Dementias Neuroimaging	56	3179	3726	1272	1907	21	98	72.4	8.7	73	67	78	1938	981	185	51	6	White	Not Hispanic	AD	https://scan.naccdata.org/	A database aggregating clinical and imaging data from Alzheimer's Disease Centers across the U.S.​	USA	neuroimaging (MRI)	Longitudinal	https://scan.naccdata.org/	https://doi.org/10.1002/alz.14378	Positron emission tomography harmonization in the Alzheimer’s Disease Neuroimaging Initiative: A scalable and rigorous approach to multisite amyloid and tau quantification	INTRODUCTION: A key goal of the Alzheimer's Disease NeuroImaging Initiative (ADNI) positron emission tomography (PET) Core is to harmonize quantification of β-amyloid (Aβ) and tau PET image data across multiple scanners and tracers. METHODS: We developed an analysis pipeline (Berkeley PET Imaging Pipeline, B-PIP) for ADNI Aβ and tau PET images and applied it to PET data from other multisite studies. Steps include image pre-processing, refacing, magnetic resonance imaging (MRI)/PET co-registration, visual quality control (QC), quantification of tracer uptake, and standardization of Aβ and tau standardized uptake value ratios (SUVrs) across tracers. RESULTS: Measurements from 10,105 cross-sectional and longitudinal Aβ and tau PET scans acquired in several studies between 2010 and 2024 can be processed, harmonized, and directly merged across tracers and cohorts. DISCUSSION: The B-PIP developed in ADNI is a scalable image harmonization approach used in several observational studies and clinical trials that facilitates rigorous Aβ and tau PET quantification and data sharing. Highlights: Quantitative results from ADNI Aβ and tau PET data are generated using a rigorous, scalable image processing pipeline This pipeline has been applied to PET data from several other large, multisite studies and trials Quantitative outcomes are harmonizable across studies and are shared with the scientific community.			NA	IDA LONI	It is difficult to match MRI data with UDS visits.	To get subject age in IDA LONI, refer to `investigator_scan_mri_nacc66` for birthdate and extract scan date from DICOM headers.	1	1	0	0	0	1	1	1	0	0					https://nacc.redcap.rit.uw.edu/surveys/?s=KHNPKLJW8TKAD4DA	
narratives		1	315	315	132	183	18	53	22	4.8	21	19	23	0	0	0	0	0			HC	https://snastase.github.io/datasets/ds002345	functional MRI data collected over seven years (2011–2018) while participants listened to naturalistic spoken stories. This dataset is well-suited for evaluating models of naturalistic language comprehension and serves as a benchmark for understanding how the brain processes complex auditory narratives. 	earth	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/s41597-021-01033-3	https://doi.org/10.1016/j.bandl.2025.105569	Revealing human brain syntactic processing: insights from voxel-wise models and network representation	Syntax serves as the framework that organizes words and is crucial for the human brain to comprehend language. However, the details of how the brain processes syntax remain poorly understood. Here, using functional magnetic resonance imaging (fMRI) data obtained from subjects listening to narratives, we developed voxel-wise models of syntax at the word-pair level from a data-driven perspective. Our findings suggest that the intensity of activation varies across brain regions when processing the same syntactic structure. In addition, using syntactic structures, we constructed syntactic networks for each voxel. The syntactic network provides a knowledge representation of syntax in the brain and further validates the differences in various brain regions during syntactic processing. Accordingly, our study highlights the intricate nature of the syntactic processing system of the brain and provides new insights into how the brain processes logical structures in language.	https://doi.org/10.1038/s41467-025-56162-9	Incremental accumulation of linguistic context in artificial and biological neural networks	Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we show how the brain, unlike LLMs that process large text windows in parallel, integrates short-term and long-term contextual information through an incremental mechanism. Using fMRI data from 219 participants listening to spoken narratives, we first demonstrate that LLMs predict brain activity effectively only when using short contextual windows of up to a few dozen words. Next, we introduce an alternative LLM-based incremental-context model that combines incoming short-term context with an aggregated, dynamically updated summary of prior context. This model significantly enhances the prediction of neural activity in higher-order regions involved in long-timescale processing. Our findings reveal how the brain’s hierarchical temporal processing mechanisms enable the flexible integration of information over time, providing valuable insights for both cognitive neuroscience and AI development.	openNeuro	A very good dataset for modeling brain function and cognition.	Using fMRI and corresponding content, research can focus on generating or retrieving images from fMRI signals ("Reading the Brain").	1	0	0	1	0	0	0	0	0	0						aws s3 sync --no-sign-request s3://openneuro.org/ds000221 ds000221-download/ . --exclude * --include "**/*T1w.nii.gz"
NIFD	The Frontotemporal Lobar Degeneration Neuroimaging Initiative 	11	330	3730	180	150	36	90	65	8.1	65	59	71	849	846	634	446	43	White		AD	https://memory.ucsf.edu/research-trials/research/allftd	A dataset focusing on imaging biomarkers in frontotemporal dementia.	earth	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1002/alz.12033	https://doi.org/10.1038/s41591-022-02075-9	CSF tau microtubule-binding region identifies pathological changes in primary tauopathies	Despite recent advances in fluid biomarker research in Alzheimer’s disease (AD), there are no fluid biomarkers or imaging tracers with utility for diagnosis and/or theragnosis available for other tauopathies. Using immunoprecipitation and mass spectrometry, we show that 4 repeat (4R) isoform-specific tau species from microtubule-binding region (MTBR-tau275 and MTBR-tau282) increase in the brains of corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), frontotemporal lobar degeneration (FTLD)-MAPT and AD but decrease inversely in the cerebrospinal fluid (CSF) of CBD, FTLD-MAPT and AD compared to control and other FTLD-tau (for example, Pick’s disease). CSF MTBR-tau measures are reproducible in repeated lumbar punctures and can be used to distinguish CBD from control (receiver operating characteristic area under the curve (AUC) = 0.889) and other FTLD-tau, such as PSP (AUC = 0.886). CSF MTBR-tau275 and MTBR-tau282 may represent the first affirmative biomarkers to aid in the diagnosis of primary tauopathies and facilitate clinical trial designs.	https://doi.org/10.1038/s41591-022-01942-9	Temporal order of clinical and biomarker changes in familial frontotemporal dementia	Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.	IDA LONI	There can be multiple scans for the same subject on the same day—handle with care.	Fewer subjects but many scans per subject.	1	1	0	1	0	1	1	0	0	0	CDR, MMSE, California verbal learning testing, MoCA, Neuropsycaritic invesntiry				https://ida.loni.usc.edu/collaboration/access/appApply.jsp?project=NIFD	
NKI	Enhanced Nathan Kline Institute - Rockland Sample	1	1340	2345	532	808	6	85	36.2	22.7	36	14	57	0	0	0	0	0	White	Not Hispanic or Latino or Spanish	HC	https://fcon_1000.projects.nitrc.org/indi/enhanced/	Offers several datasets, including the Enhanced NKI-Rockland Sample, with multimodal imaging and behavioral data across the lifespan.​	USA	neuroimaging (MRI)	Longitudinal	https://doi.org/10.3389/fnins.2012.00152	http://dx.doi.org/10.1016/j.neuron.2016.09.018	The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance	Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions; however, it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of fMRI data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.	https://doi.org/10.1038/s41593-024-01679-3	A shifting role of thalamocortical connectivity in the emergence of cortical functional organization	The cortical patterning principle has been a long-standing question in neuroscience, yet how this translates to macroscale functional specialization in the human brain remains largely unknown. Here we examine age-dependent differences in resting-state thalamocortical connectivity to investigate its role in the emergence of large-scale functional networks during early life, using a primarily cross-sectional but also longitudinal approach. We show that thalamocortical connectivity during infancy reflects an early differentiation of sensorimotor networks and genetically influenced axonal projection. This pattern changes in childhood, when connectivity is established with the salience network, while decoupling externally and internally oriented functional systems. A developmental simulation using generative network models corroborated these findings, demonstrating that thalamic connectivity contributes to developing key features of the mature brain, such as functional segregation and the sensory-association axis, especially across 12–18 years of age. Our study suggests that the thalamus plays an important role in functional specialization during development, with potential implications for studying conditions with compromised internal and external processing.	indi	This is a deeply phenotyped, multimodal dataset; data collection is ongoing.	Access to 1000+ scans is available through fcp-indi.	1	1	0	1	0	0	1	1	1	0	WASI‑II (Wechsler Abbreviated Scale of Intelligence), RAVLT (Rey Auditory Verbal Learning Test), TMT A & B (Trail Making Test), Digit Span and Coding subtests from the WAIS, CPT (Continuous Performance Test), CANTAB tasks (e.g., Spatial Working Memory, Reaction Time)			2020	https://fcon_1000.projects.nitrc.org/indi/req_access.html	aws s3 sync --no-sign-request s3://fcp-indi/data/Projects/RocklandSample/RawDataBIDSLatest/ NKI_fMRI/ --exclude "*" --include "sub-*/ses-BAS*/*"
NPC	Neuroimaging predictors of creativity in healthy adults	1	66	66	29	36	20	35	26.6	4.3	25	23	30	0	0	0	0	0				https://openneuro.org/datasets/ds002330/versions/1.0.0/file-display/dir-pa_epi.json	This dataset investigates the neural correlates of creativity in healthy adults by assessing structural and functional brain features alongside performance on various creativity tasks.	Canada	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1016/j.neuroimage.2019.116292	https://doi.org/10.3389/fnagi.2021.607988	Healthy Aging Alters the Functional Connectivity of Creative Cognition in the Default Mode Network and Cerebellar Network	Creativity is a higher-order neurocognitive process that produces unusual and unique thoughts. Behavioral and neuroimaging studies of younger adults have revealed that creative performance is the product of dynamic and spontaneous processes involving multiple cognitive functions and interactions between large-scale brain networks, including the default mode network (DMN), fronto-parietal executive control network (ECN), and salience network (SN). In this resting-state functional magnetic resonance imaging (rs-fMRI) study, group independent component analysis (group-ICA) and resting state functional connectivity (RSFC) measures were applied to examine whether and how various functional connected networks of the creative brain, particularly the default-executive and cerebro-cerebellar networks, are altered with advancing age. The group-ICA approach identified 11 major brain networks across age groups that reflected age-invariant resting-state networks. Compared with older adults, younger adults exhibited more specific and widespread dorsal network and sensorimotor network connectivity within and between the DMN, fronto-parietal ECN, and visual, auditory, and cerebellar networks associated with creativity. This outcome suggests age-specific changes in the functional connected network, particularly in the default-executive and cerebro-cerebellar networks. Our connectivity data further elucidate the critical roles of the cerebellum and cerebro-cerebellar connectivity in creativity in older adults. Furthermore, our findings provide evidence supporting the default-executive coupling hypothesis of aging and novel insights into the interactions of cerebro-cerebellar networks with creative cognition in older adults, which suggest alterations in the cognitive processes of the creative aging brain.	https://doi.org/10.1038/s41598-020-80293-2	Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition	Creative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.	openNeuro	OpenNeuro data is very easy to access.	Easy to download.	1	0	1	1	0	0	0	1	0	0	T1 and T2, fieldmaps; Abbreviated Torrance Test for Adults (ATTA); Creative Achievement Questionnaire (CAQ); Creative Behaviour Inventory (CBI); Wechler Adult Intelligence Scale (WAIS-IV)		To contribute to a convergent cognitive neuroscience of creativity			aws s3 sync --no-sign-request s3://openneuro.org/ds002330 ds002330-download/ . --exclude "*/dwi/*" --exclude "*/func/*" --exclude "*/fmap/*"
OASIS1	Open Access Series of Imaging Studies 1	1	416	416	160	256	18	96	52.7	25.1	56	24	75	316	70	28	2	0			AD	https://sites.wustl.edu/oasisbrains/home/oasis-1/	Open Access Series of Imaging Studies providing MRI data across the adult lifespan, including individuals with Alzheimer's disease.	USA	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1162/jocn.2007.19.9.1498	http://openaccess.thecvf.com/content/ICCV2023/papers/Butoi_UniverSeg_Universal_Medical_Image_Segmentation_ICCV_2023_paper.pdf	Universeg: Universal medical image segmentation	While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models. This is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and an example set of image-label pairs that define a new segmentation task, UniverSeg employs a new CrossBlock mechanism to produce accurate segmentation maps without additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that Uni-verSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.edu	https://doi.org/10.1073/pnas.2216399120	Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets	Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyze such scans could transform neuroimaging research. Yet, their potential remains untapped since no automated algorithm is robust enough to cope with the high variability in clinical acquisitions (MR contrasts, resolutions, orientations, artifacts, and subject populations). Here, we present SynthSeg + , an AI segmentation suite that enables robust analysis of heterogeneous clinical datasets. In addition to whole-brain segmentation, SynthSeg + also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations (mainly caused by scans of very low quality). We demonstrate SynthSeg + in seven experiments, including an aging study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality. SynthSeg + is publicly released as a ready-to-use tool to unlock the potential of quantitative morphometry. clinical brain MRI | segmentation | deep learning | domain-agnostic	oasis	The orientation information is incorrect; you must manually correct or delete it.	OASIS-1 subjects with missing CDR values can be treated as healthy.	1	0	0	0	0	0	1	0	0	0	4 Raw images and 1 Processed image using the 4					for i in {1..12}; do aria2c -x 10 -j 10 -s 10 https://download.nrg.wustl.edu/data/oasis_cross-sectional_disc$i.tar.gz; done
OASIS2	Open Access Series of Imaging Studies 2	1	150	373	62	88	60	98	77	7.6	77	71	82	206	123	41	3	0			AD	https://sites.wustl.edu/oasisbrains/home/oasis-2/	Open Access Series of Imaging Studies providing MRI data across the adult lifespan, including individuals with Alzheimer's disease.	USA	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1162/jocn.2009.21407	https://doi.org/10.1152/jn.00339.2011	The organization of the human cerebellum estimated by intrinsic functional connectivity	The cerebral cortex communicates with the cerebellum via polysynaptic circuits. Separate regions of the cerebellum are connected to distinct cerebral areas, forming a complex topography. In this study we explored the organization of cerebrocerebellar circuits in the human using restingstate functional connectivity MRI (fcMRI). Data from 1,000 subjects were registered using nonlinear deformation of the cerebellum in combination with surface-based alignment of the cerebral cortex. The foot, hand, and tongue representations were localized in subjects performing movements. fcMRI maps derived from seed regions placed in different parts of the motor body representation yielded the expected inverted map of somatomotor topography in the anterior lobe and the upright map in the posterior lobe. Next, we mapped the complete topography of the cerebellum by estimating the principal cerebral target for each point in the cerebellum in a discovery sample of 500 subjects and replicated the topography in 500 independent subjects. The majority of the human cerebellum maps to association areas. Quantitative analysis of 17 distinct cerebral networks revealed that the extent of the cerebellum dedicated to each network is proportional to the network's extent in the cerebrum with a few exceptions, including primary visual cortex, which is not represented in the cerebellum. Like somatomotor representations, cerebellar regions linked to association cortex have separate anterior and posterior representations that are oriented as mirror images of one another. The orderly topography of the representations suggests that the cerebellum possesses at least two large, homotopic maps of the full cerebrum and possibly a smaller third map. © 2011 the American Physiological Society.	https://doi.org/10.1038/s42256-023-00670-0	The importance of resource awareness in artificial intelligence for healthcare	Artificial intelligence and machine learning (AI/ML) models have been adopted in a wide range of healthcare applications, from medical image computing and analysis to continuous health monitoring and management. Recent data have demonstrated a clear trend that AI/ML model sizes, as well as their computational complexity, memory consumption and the scale of the required training data and costs, are experiencing an exponential increase. The developments in current computing hardware platforms, storage infrastructure, networking and domain expertise cannot keep up with this exponential growth in resources demanded by the AI/ML models. Here, we first analyse this recent trend and highlight that there are resource sustainability issues in AI/ML for healthcare. We then present various algorithm/system innovations that will help address these issues. We finally outline future directions to proactively and prospectively tackle these resource sustainability issues.	oasis	The orientation information is incorrect; you must manually correct or delete it.	NA	1	0	0	0	0	0	1	0	0	0						aria2c -x 10 -j 10 -s 10 https://download.nrg.wustl.edu/data/OAS2_RAW_PART1.tar.gz && aria2c -x 10 -j 10 -s 10 https://download.nrg.wustl.edu/data/OAS2_RAW_PART2.tar.gz
OASIS3	Open Access Series of Imaging Studies 3	1	1063	1088	478	585	43	97	70.4	8.7	71	66	76	813	275	0	0	0	White	Non-Hispanic	AD	https://sites.wustl.edu/oasisbrains/home/oasis-3/	Open Access Series of Imaging Studies providing MRI data across the adult lifespan, including individuals with Alzheimer's disease.	USA	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1101/2019.12.13.19014902	https://doi.org/10.1038/s41591-024-02931-w	APOE4 homozygosity represents a distinct genetic form of Alzheimer’s disease	This study aimed to evaluate the impact of APOE4 homozygosity on Alzheimer’s disease (AD) by examining its clinical, pathological and biomarker changes to see whether APOE4 homozygotes constitute a distinct, genetically determined form of AD. Data from the National Alzheimer’s Coordinating Center and five large cohorts with AD biomarkers were analyzed. The analysis included 3,297 individuals for the pathological study and 10,039 for the clinical study. Findings revealed that almost all APOE4 homozygotes exhibited AD pathology and had significantly higher levels of AD biomarkers from age 55 compared to APOE3 homozygotes. By age 65, nearly all had abnormal amyloid levels in cerebrospinal fluid, and 75% had positive amyloid scans, with the prevalence of these markers increasing with age, indicating near-full penetrance of AD biology in APOE4 homozygotes. The age of symptom onset was earlier in APOE4 homozygotes at 65.1, with a narrower 95% prediction interval than APOE3 homozygotes. The predictability of symptom onset and the sequence of biomarker changes in APOE4 homozygotes mirrored those in autosomal dominant AD and Down syndrome. However, in the dementia stage, there were no differences in amyloid or tau positron emission tomography across haplotypes, despite earlier clinical and biomarker changes. The study concludes that APOE4 homozygotes represent a genetic form of AD, suggesting the need for individualized prevention strategies, clinical trials and treatments.	https://doi.org/10.1038/s41591-024-03144-x	Brain aging patterns in a large and diverse cohort of 49,482 individuals	Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.	oasis	The UDS visit and the MRI session are not on the same day.	Use the diagnosis from the nearest UDS visit, preferably within six months before the scan.	1	1	0	1	0	0	1	1	1	0	Anat, fMRI, pcASL and deep phenotyped dataset					
Pixar	MRI data of 3-12 year old children and adults during viewing of a short animated film	1	155	155	71	84	3.5	39	10.6	8.1	7.7	5.3	11	0	0	0	0	0			HC	https://openneuro.org/datasets/ds000228/versions/1.1.1	MRI data from children (ages 3–12) and adults while viewing a silent version of Disney Pixar's animated short film "Partly Cloudy." The study aimed to investigate the development of the social brain by examining how different age groups process social and emotional cues during naturalistic movie viewing. This dataset provides insights into the neural mechanisms underlying social cognition and its development across the lifespan.	USA	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/s41467-018-03399-2	https://doi.org/10.1073/pnas.2405460121	Evaluating large language models in theory of mind tasks	Eleven large language models (LLMs) were assessed using 40 bespoke false-belief tasks, considered a gold standard in testing theory of mind (ToM) in humans. Each task included a false-belief scenario, three closely matched true-belief control scenarios, and the reversed versions of all four. An LLM had to solve all eight scenarios to solve a single task. Older models solved no tasks; Generative Pre-trained Transformer (GPT)-3-davinci-003 (from November 2022) and ChatGPT-3.5-turbo (from March 2023) solved 20% of the tasks; ChatGPT-4 (from June 2023) solved 75% of the tasks, matching the performance of 6-y-old children observed in past studies. We explore the potential interpretation of these results, including the intriguing possibility that ToM-like ability, previously considered unique to humans, may have emerged as an unintended by-product of LLMs' improving language skills. Regardless of how we interpret these outcomes, they signify the advent of more powerful and socially skilled AI-with profound positive and negative implications.	https://doi.org/10.1016/j.neuroimage.2020.117445	Movies and narratives as naturalistic stimuli in neuroimaging	Using movies and narratives as naturalistic stimuli in human neuroimaging studies has yielded significant advances in understanding of cognitive and emotional functions. The relevant literature was reviewed, with emphasis on how the use of naturalistic stimuli has helped advance scientific understanding of human memory, attention, language, emotions, and social cognition in ways that would have been difficult otherwise. These advances include discovering a cortical hierarchy of temporal receptive windows, which supports processing of dynamic information that accumulates over several time scales, such as immediate reactions vs. slowly emerging patterns in social interactions. Naturalistic stimuli have also helped elucidate how the hippocampus supports segmentation and memorization of events in day-to-day life and have afforded insights into attentional brain mechanisms underlying our ability to adopt specific perspectives during natural viewing. Further, neuroimaging studies with naturalistic stimuli have revealed the role of the default-mode network in narrative-processing and in social cognition. Finally, by robustly eliciting genuine emotions, these stimuli have helped elucidate the brain basis of both basic and social emotions apparently manifested as highly overlapping yet distinguishable patterns of brain activity.	openNeuro	OpenNeuro data is very easy to access.	NA	1	0	1	0	0	0	0	0	0	0			To measure developmental change in the pain matrix and theory of mind network	2017		aws s3 sync --no-sign-request s3://openneuro.org/ds000228 . --exclude "*" --include "sub-*/anat/*T1w*"
PNC	Philadelphia neurodevelopmental cohort 	1	1601	1601	764	837	8.1	23.1	14.9	3.7	15.2	11.8	18	0	0	0	0	0		not Hispanic or Latino	HC	https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html	A dataset with imaging and cognitive data from youths aged 8–21 to study brain development.	USA	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1016/j.neuroimage.2015.03.056	https://doi.org/10.1038/s41467-018-04920-3	Task-induced brain state manipulation improves prediction of individual traits	Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses.	https://doi.org/10.1038/s41593-021-00908-3	Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction	Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates.	RBC	Hard to get access to.	Using `datalad` and RBC, you can download the data.	1	1	0	0	0	0	0	0	0	0	NA					git clone https://github.com/ReproBrainChart/PNC_BIDS.git && cd PNC_BIDS && datalad clone https://github.com/ReproBrainChart/PNC_BIDS.git -b complete-pass-0.1 . && datalad get *
PPMI	Parkinson's progression markers initiative	1	33	33	17	16	34	79	66.7	9.5	69	62	74	0	0	0	0	0			Parkinsons	https://www.ppmi-info.org/	A longitudinal study collecting imaging and clinical data to identify biomarkers of Parkinson's disease progression.	earth	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1002/acn3.644	https://doi.org/10.3389/fnagi.2021.633752	Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature	Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.	https://doi.org/10.1093/brain/awx118	Clinical criteria for subtyping Parkinson’s disease: biomarkers and longitudinal progression	Parkinson's disease varies widely in clinical manifestations, course of progression and biomarker profiles from person to person. Identification of distinct Parkinson's disease subtypes is of great priority to illuminate underlying pathophysiology, predict progression and develop more efficient personalized care approaches. There is currently no clear way to define and divide subtypes in Parkinson's disease. Using data from the Parkinson's Progression Markers Initiative, we aimed to identify distinct subgroups via cluster analysis of a comprehensive dataset at baseline (i.e. cross-sectionally) consisting of clinical characteristics, neuroimaging, biospecimen and genetic information, then to develop criteria to assign patients to a Parkinson's disease subtype. Four hundred and twenty-one individuals with de novo early Parkinson's disease were included from this prospective longitudinal multicentre cohort. Hierarchical cluster analysis was performed using data on demographic and genetic information, motor symptoms and signs, neuropsychological testing and other non-motor manifestations. The key classifiers in cluster analysis were a motor summary score and three non-motor features (cognitive impairment, rapid eye movement sleep behaviour disorder and dysautonomia). We then defined three distinct subtypes of Parkinson's disease patients: 223 patients were classified as 'mild motor-predominant' (defined as composite motor and all three non-motor scores below the 75th percentile), 52 as 'diffuse malignant' (composite motor score plus either ≥1/3 non-motor score >75th percentile, or all three non-motor scores >75th percentile) and 146 as 'intermediate'. On biomarkers, people with diffuse malignant Parkinson's disease had the lowest level of cerebrospinal fluid amyloid-β (329.0 ± 96.7 pg/ml, P = 0.006) and amyloid-β/total-tau ratio (8.2 ± 3.0, P = 0.032). Data from deformation-based magnetic resonance imaging morphometry demonstrated a Parkinson's disease-specific brain network had more atrophy in the diffuse malignant subtype, with the mild motor-predominant subtype having the least atrophy. Although disease duration at initial visit and follow-up time were similar between subtypes, patients with diffuse malignant Parkinson's disease progressed faster in overall prognosis (global composite outcome), with greater decline in cognition and in dopamine functional neuroimaging after an average of 2.7 years. In conclusion, we introduce new clinical criteria for subtyping Parkinson's disease based on a comprehensive list of clinical manifestations and biomarkers. This clinical subtyping can now be applied to individual patients for use in clinical practice using baseline clinical information. Even though all participants had a recent diagnosis of Parkinson's disease, patients with the diffuse malignant subtype already demonstrated a more profound dopaminergic deficit, increased atrophy in Parkinson's disease brain networks, a more Alzheimer's disease-like cerebrospinal fluid profile and faster progression of motor and cognitive deficits.	IDA LONI	Data is hosted at IDA (LONI).	Requires domain knowledge to diagnose.	1	1	0	1	0	0	1	1	1	1	CT, SPECT, Genetic, Motor Assessments, Neurobehavioral/Neuropsychiatric Testing, Autonomic, Olfaction, Sleep Testing, DaTSCAN, MRI, and other imaging sub-studies, DNA, RNA, Whole Blood, Serum, Plasma, Urine, CSF, iPSC, Skin biopsy, Post-mortem tissue					
preventAD	PResymptomatic EValuation of Experimental or Novel Treatments for Alzheimer's Disease	1	308	1375	0	0	55	88.2	64.6	5.4	63.6	61.1	67.5	0	0	0	0	0			AD	NA	A longitudinal study focusing on cognitively healthy individuals over 55 with a family history of Alzheimer's disease. It collects multimodal MRI, cerebrospinal fluid biomarkers, genetic data, and cognitive assessments to identify early indicators of Alzheimer's disease progression. ​	earth	neuroimaging (MRI)	Longitudinal	https://doi.org/10.1016/j.nicl.2021.102733	https://doi.org/10.1002/ana.26308	Plasma p-tau231, p-tau181, PET Biomarkers, and Cognitive Change in Older Adult	Objective: The objective of this study was to evaluate novel plasma p-tau231 and p-tau181, as well as Aβ40 and Aβ42 assays as indicators of tau and Aβ pathologies measured with positron emission tomography (PET), and their association with cognitive change, in cognitively unimpaired older adults. Methods: In a cohort of 244 older adults at risk of Alzheimer's disease (AD) owing to a family history of AD dementia, we measured single molecule array (Simoa)-based plasma tau biomarkers (p-tau231 and p-tau181), Aβ40 and Aβ42 with immunoprecipitation mass spectrometry, and Simoa neurofilament light (NfL). A subset of 129 participants underwent amyloid-β (18F-NAV4694) and tau (18F-flortaucipir) PET assessments. We investigated plasma biomarker associations with Aβ and tau PET at the global and voxel level and tested plasma biomarker combinations for improved detection of Aβ-PET positivity. We also investigated associations with 8-year cognitive change. Results: Plasma p-tau biomarkers correlated with flortaucipir binding in medial temporal, parietal, and inferior temporal regions. P-tau231 showed further associations in lateral parietal and occipital cortices. Plasma Aβ42/40 explained more variance in global Aβ-PET binding than Aβ42 alone. P-tau231 also showed strong and widespread associations with cortical Aβ-PET binding. Combining Aβ42/40 with p-tau231 or p-tau181 allowed for good distinction between Aβ-negative and -positive participants (area under the receiver operating characteristic curve [AUC] range = 0.81–0.86). Individuals with low plasma Aβ42/40 and high p-tau experienced faster cognitive decline. Interpretation: Plasma p-tau231 showed more robust associations with PET biomarkers than p-tau181 in presymptomatic individuals. The combination of p-tau and Aβ42/40 biomarkers detected early AD pathology and cognitive decline. Such markers could be used as prescreening tools to reduce the cost of prevention trials. ANN NEUROL 2022;91:548–560.	https://doi.org/10.1002/alz.13318	Longitudinal blood biomarker trajectories in preclinical Alzheimer's disease	INTRODUCTION: Plasma biomarkers are altered years prior to Alzheimer's disease (AD) clinical onset. METHODS: We measured longitudinal changes in plasma amyloid-beta (Aβ)42/40 ratio, pTau181, pTau231, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) in a cohort of older adults at risk of AD (n = 373 total, n = 229 with Aβ and tau positron emission tomography [PET] scans) considering genetic and demographic factors as possible modifiers of these markers’ progression. RESULTS: Aβ42/40 ratio concentrations decreased, while NfL and GFAP values increased over the 4-year follow-up. Apolipoprotein E (APOE) ε4 carriers showed faster increase in plasma pTau181 than non-carriers. Older individuals showed a faster increase in plasma NfL, and females showed a faster increase in plasma GFAP values. In the PET subsample, individuals both Aβ-PET and tau-PET positive showed faster plasma pTau181 and GFAP increase compared to PET-negative individuals. DISCUSSION: Plasma markers can track biological change over time, with plasma pTau181 and GFAP markers showing longitudinal change in individuals with preclinical AD. Highlights: Longitudinal increase of plasma pTau181 and glial fibrillary acidic protein (GFAP) can be measured in the preclinical phase of AD. Apolipoprotein E ε4 carriers experience faster increase in plasma pTau181 over time than non-carriers. Female sex showed accelerated increase in plasma GFAP over time compared to males. Aβ42/40 and pTau231 values are already abnormal at baseline in individuals with both amyloid and tau PET burden.	COINS	Neuroimaging data can be downloaded using `datalad` via the CONP dataset portal.	For cognitive data access, apply through CONIS.	1	1	1	1	0	1	1	0	1	1	fluid biomarkers, genetics, neurosensory, and cognitive assessments, prevention cohort, asl, bold, dwi65, qT2star, fieldmap, FLAIR, MP2RAGE, t1w, T2star, t2w, task-encoding-bold, task-retrieval-bold					
RBPL1	The Reading Brain Project L1 Adults	1	52	52	25	27	18	40	22.8	4.7	22	19	24.2	0	0	0	0	0			HC	https://blclab.org/reading-brain	This dataset includes neuroimaging and behavioral data from 52 native English-speaking adults. Participants underwent two sessions involving reading tasks, aiming to explore the neural mechanisms underlying reading in first-language (L1) adults.	USA	neuroimaging (MRI)	Cross-sectional	NA	https://doi.org/10.1126/sciadv.adn7744	Predicting the next sentence (not word) in large language models: What model-brain alignment tells us about discourse comprehension	Current large language models (LLMs) rely on word prediction as their backbone pretraining task. Although word prediction is an important mechanism underlying language processing, human language comprehension occurs at multiple levels, involving the integration of words and sentences to achieve a full understanding of discourse. This study models language comprehension by using the next sentence prediction (NSP) task to investigate mechanisms of discourse-level comprehension. We show that NSP pretraining enhanced a model's alignment with brain data especially in the right hemisphere and in the multiple demand network, highlighting the contributions of nonclassical language regions to high-level language understanding. Our results also suggest that NSP can enable the model to better capture human comprehension performance and to better encode contextual information. Our study demonstrates that the inclusion of diverse learning objectives in a model leads to more human-like representations, and investigating the neurocognitive plausibility of pretraining tasks in LLMs can shed light on outstanding questions in language neuroscience.	https://doi.org/10.3758/s13428-022-01842-3	From eye movements to scanpath networks: A method for studying individual differences in expository text reading	Eye movements have been examined as an index of attention and comprehension during reading in the literature for over 30 years. Although eye-movement measurements are acknowledged as reliable indicators of readers’ comprehension skill, few studies have analyzed eye-movement patterns using network science. In this study, we offer a new approach to analyze eye-movement data. Specifically, we recorded visual scanpaths when participants were reading expository science text, and used these to construct scanpath networks that reflect readers’ processing of the text. Results showed that low ability and high ability readers’ scanpath networks exhibited distinctive properties, which are reflected in different network metrics including density, centrality, small-worldness, transitivity, and global efficiency. Such patterns provide a new way to show how skilled readers, as compared with less skilled readers, process information more efficiently. Implications of our analyses are discussed in light of current theories of reading comprehension.	openNeuro	OpenNeuro data is very easy to access.	NA	1	1	0	1	0	0	0	1	0	0	fMRI, eye tracking and behaviouaral data		Understand the critical variables, including the interaction between cognitive and linguistic abilities of the reader and knowledge structure of the text, that impact individual differences in reading comprehension. 			aws s3 sync --no-sign-request s3://openneuro.org/ds003974 . --exclude "*/dwi/*" --exclude "*/func/*" --exclude "*/fmap/*"
RBPL2	The Reading Brain Project L2 Adults	1	52	52	25	27	18	40	22.8	4.7	22	19	24.2	0	0	0	0	0			HC	https://blclab.org/reading-brain	This dataset comprises data from 56 bilingual adults who are second-language (L2) English readers. It investigates how bilingualism and second-language acquisition influence the neural processes of reading.	USA	neuroimaging (MRI)	Cross-sectional	NA	https://doi.org/10.1080/02702711.2021.1912867	Predicting Expository Text Processing: Causal Content Density as a Critical Expository Text Metric	In this investigation, we examine the contribution of intrinsic content density (ICD) to measures of expository text processing. In Studies 1 and 2, the factor structure of select text density metrics was examined and refined using two text samples (Ns = 150) randomly selected from an expository text corpus. Scores on the ICD measure based on the entire text sample (N = 300) explained unique variance in readability and text easability. In Study 3, ICD predicted adults’ text ratings of interest and ease of comprehension above and beyond established easability measures. Participants’ text familiarity moderated the relation between ICD and ease of comprehension, revealing a density-facilitative effect for participants more familiar with the text content. Finally, in Study 4, measures of text difficulty, processing, and comprehension were obtained from adult readers using 10 researcher-constructed science texts; evidence of descriptive density effects on each measure was obtained. Implications for future research are discussed.	https://doi.org/10.1016/j.bandc.2020.105655	Effects of socioeconomic status in predicting reading outcomes for children: The mediation of spoken language network	The present longitudinal study investigated the effects of early childhood socioeconomic status on language-related resting-state functional connectivity and reading outcome in adolescence. Seventy-nine children participated in this study. Socioeconomic status was measured via parent questionnaire measuring parental education and family income at 1 month. At age 14, resting-state fMRI data and reading-related behavioral data of the children were collected. Resting-state functional connectivity (RSFC) analysis was performed based on four regions of interest, including the left inferior frontal gyrus (L.IFG), left anterior superior temporal gyrus (L.aSTG), left posterior superior temporal gyrus (L.pSTG) and right anterior superior temporal gyrus (R.aSTG). Significant associations were found between parental education and the language-related RSFC, including the RSFC of L.IFG-L.aSTG and the RSFC of L.aSTG-L.pSTG, while no association was found between family income and language-related RSFC. Furthermore, the parental education-associated functional connectivity patterns (i.e., L.IFG-L.aSTG and L.aSTG-L.pSTG) were found to be positively correlated with children's reading skills (word list reading and sentence reading fluency). Finally, path analyses indicated that the intrinsic brain connectivity between L.aSTG and L.pSTG influenced the relationship between parental education and children's reading outcomes.	openNeuro	OpenNeuro data is very easy to access.	NA	1	1	0	1	0	0	0	1	0	0	fMRI, eye tracking and behaviouaral data		Understand the critical variables, including the interaction between cognitive and linguistic abilities of the reader and knowledge structure of the text, that impact individual differences in reading comprehension. 			aws s3 sync --no-sign-request s3://openneuro.org/ds003872 . --exclude "*/dwi/*" --exclude "*/func/*" --exclude "*/fmap/*"
SALD	Southwest University Adult Lifespan Dataset	1	494	494	185	307	19	80	45.2	17.4	48	25	59	0	0	0	0	0	Chinese	Chinese	HC	https://fcon_1000.projects.nitrc.org/indi/retro/sald.html	A cross-sectional dataset from Southwest University in China, encompassing structural and resting-state fMRI data from 494 healthy adults aged 19 to 80. It serves as a resource for studying brain aging and lifespan-related neural changes. ​	China	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/sdata.2018.134	https://doi.org/10.1038/s42003-022-03880-1	Choice of Voxel-based Morphometry processing pipeline drives variability in the location of neuroanatomical brain markers	Fundamental and clinical neuroscience has benefited tremendously from the development of automated computational analyses. In excess of 600 human neuroimaging papers using Voxel-based Morphometry (VBM) are now published every year and a number of different automated processing pipelines are used, although it remains to be systematically assessed whether they come up with the same answers. Here we examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity and between-pipeline reproducibility of the processed gray matter brain maps were generally low between pipelines. Examination of sex-differences and age-related changes revealed considerable differences between the pipelines in terms of the specific regions identified. Machine learning-based multivariate analyses allowed accurate predictions of sex and age, however accuracy differed between pipelines. Our findings suggest that the choice of pipeline alone leads to considerable variability in brain structural markers which poses a serious challenge for reproducibility and interpretation.	https://doi.org/10.1111/psyp.14159	The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging	The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.	indi	Easy to download with AWS.	NA	1	1	0	0	0	0	0	1	0	0	Test-retest and reliability		To uncover the developmental trajectory of the human brain and to understand the changes that occur as a function of ageing		https://fcon_1000.projects.nitrc.org/indi/req_access.html	
SLIM	Southwest University Longitudinal Imaging Multimodal	1	594	1048	264	330	17	28.4	20.7	1.4	20.6	20	21.5	0	0	0	0	0			HC	https://fcon_1000.projects.nitrc.org/indi/retro/southwestuni_qiu_index.html	A longitudinal dataset featuring multimodal MRI (structural, diffusion, and resting-state fMRI) and behavioral assessments collected over three and a half years. It includes repeated measures from healthy young adults, facilitating research on brain development and test-retest reliability.	China	neuroimaging (MRI)	Test-Retest	https://doi.org/10.1038/sdata.2017.17	https://doi.org/10.1001/jamaneurol.2019.1708	Progressive Cortical Thinning in Patients With Focal Epilepsy	Importance: It is controversial whether epilepsy is a static or progressive disease. Evidence of progressive gray matter loss in epilepsy would support early diagnosis, rapid treatment, and early referral for surgical interventions. Objective: To demonstrate progressive cortical thinning in patients with focal epilepsy distinct from cortical thinning associated with normal aging. Design, Setting, and Participants: A case-control neuroimaging study was conducted from August 3, 2004, to January 26, 2016, among 190 patients with focal epilepsy at a tertiary epilepsy referral center (epilepsy data) and 3 independent comparison cohorts matched for age and sex (healthy volunteer data; n = 141). Exposures: Two or more high-resolution T1-weighted magnetic resonance imaging scans at least 6 months apart (mean [SD] interval, 2.5 [1.6] years). Main Outcomes and Measures: Global and vertexwise rate of progressive cortical thinning. Results: A total of 190 people with focal epilepsy (99 women and 91 men; mean [SD] age, 36 [11] years; 396 magnetic resonance imaging scans) were compared with 141 healthy volunteers (76 women and 65 men; mean [SD] age, 35 [17] years; 282 magnetic resonance imaging scans). Widespread highly significant progressive cortical thinning exceeding normal aging effects, mainly involving the bilateral temporal lobes, medial parietal and occipital cortices, pericentral gyri, and opercula, was seen in 146 individuals with epilepsy (76.8%; 95% CI, 58%-95%). The mean (SD) annualized rate of global cortical thinning in patients with epilepsy was twice the rate of age-associated thinning observed in healthy volunteers (0.024 [0.061] vs 0.011 [0.029] mm/y; P =.01). Progression was most pronounced in adults older than 55 years and during the first 5 years after the onset of seizures. Areas of accelerated cortical thinning were detected in patients with early onset of epilepsy and in patients with hippocampal sclerosis. Accelerated thinning was not associated with seizure frequency, history of generalized seizures, or antiepileptic drug load and did not differ between patients with or without ongoing seizures. Progressive atrophy in temporal (n = 101) and frontal (n = 28) lobe epilepsy was most pronounced ipsilaterally to the epileptic focus but also affected a widespread area extending beyond the focus and commonly affected the contralateral hemisphere. For patients with temporal lobe epilepsy, accelerated cortical thinning was observed within areas structurally connected with the ipsilateral hippocampus. Conclusions and Relevance: Widespread progressive cortical thinning exceeding that seen with normal aging may occur in patients with focal epilepsy. These findings appear to highlight the need to develop epilepsy disease-modifying treatments to disrupt or slow ongoing atrophy. Longitudinal cortical thickness measurements may have the potential to serve as biomarkers for such studies.	https://doi.org/10.1038/s41398-020-0829-3	A neuromarker of individual general fluid intelligence from the white-matter functional connectome	Neuroimaging studies have uncovered the neural roots of individual differences in human general fluid intelligence (Gf). Gf is characterized by the function of specific neural circuits in brain gray-matter; however, the association between Gf and neural function in brain white-matter (WM) remains unclear. Given reliable detection of blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) signals in WM, we used a functional, rather than an anatomical, neuromarker in WM to identify individual Gf. We collected longitudinal BOLD-fMRI data (in total three times, ~11 months between time 1 and time 2, and ~29 months between time 1 and time 3) in normal volunteers at rest, and identified WM functional connectomes that predicted the individual Gf at time 1 (n = 326). From internal validation analyses, we demonstrated that the constructed predictive model at time 1 predicted an individual’s Gf from WM functional connectomes at time 2 (time 1 ∩ time 2: n = 105) and further at time 3 (time 1 ∩ time 3: n = 83). From external validation analyses, we demonstrated that the predictive model from time 1 was generalized to unseen individuals from another center (n = 53). From anatomical aspects, WM functional connectivity showing high predictive power predominantly included the superior longitudinal fasciculus system, deep frontal WM, and ventral frontoparietal tracts. These results thus demonstrated that WM functional connectomes offer a novel applicable neuromarker of Gf and supplement the gray-matter connectomes to explore brain–behavior relationships.	indi	Easy to download with AWS.	NA	1	1	0	1	0	0	0	1	0	0	3 time points within 3 years, beviourally and pheotyping				https://fcon_1000.projects.nitrc.org/indi/req_access.html	aws s3 sync --no-sign-request s3://fcp-indi/data/Projects/INDI/SLIM/RawData/ ./data/ --exclude "*" --include "sub-*/ses-*/anat/*"
SRPBS	Japanese Strategic Research Program for the Promotion of Brain Science	11	1410	1410	808	602	16	80	38.4	13.6	37	27	47	0	0	0	0	0	Asian	Asian	Multiple	https://bicr-resource.atr.jp/srpbsopen/	A Japanese nationwide initiative providing a multi-site, multi-disorder MRI dataset. It includes resting-state fMRI and structural MRI data from 993 patients across various psychiatric disorders and 1,421 healthy controls, supporting research into brain network markers and cross-site harmonization. ​	Japan	neuroimaging (MRI)	Cross-sectional	https://doi.org/10.1038/s41597-021-01004-8	https://doi.org/10.1016/j.eclinm.2023.102276	A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study	Background Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems.	https://doi.org/10.1093/schbul/sbad022	Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets	Background and Hypothesis: Dynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders. Study Design: We applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network. Study Results: DCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively. Conclusions: DCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.	NA	One of the 14 sites does not provide phenotypic information. A DUA must be signed and submitted to access this.	NA	1	1	0	0	0	0	1	0	0	0	multisite and multi-disorder		To support the development of machine learning classifiers and harmonization methods for psychiatric and neurological disorders using large-scale, multi-site, multi-disorder neuroimaging data.			
YALE_HighRes	Yale High-Resolution Controls	1	120	120	68	52	18	58	28.8	8.5	27	23	32	0	0	0	0	0			HC	https://fcon_1000.projects.nitrc.org/indi/retro/yale_hires.html	A dataset comprising high-resolution structural MRI scans from healthy control participants. It is utilized for studies requiring detailed anatomical brain imaging and serves as a control group in various neuroimaging analyses. ​	USA	neuroimaging (MRI)	Cross-sectional	https://fcon_1000.projects.nitrc.org/indi/retro/yale_hires.html	https://doi.org/10.1038/nn.4135	Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity	Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.	https://doi.org/10.1016/j.neuroimage.2013.05.081	Groupwise whole-brain parcellation from resting-state fMRI data for network node identification	In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages. © 2013 Elsevier Inc.	indi	Easy to download with AWS.	NA	1	1	0	0	0	0	0	0	0	0	multiple fMRI runs		To characterize the stability and organization of intrinsic functional brain networks using high-resolution, multi-session resting-state fMRI.		https://fcon_1000.projects.nitrc.org/indi/req_access.html	aws s3 sync --no-sign-request s3://fcp-indi/data/Projects/INDI/YALE/folder/highres/ . --exclude "*" --include "sub-*/ses-*/anat/*MPR_T1w.nii.gz"
YALE_LowRes	Yale Low-Resolution Controls	1	100	100	50	50	18	66	34.3	11.3	32	25	42	0	0	0	0	0			HC	https://fcon_1000.projects.nitrc.org/indi/retro/yale_lowres.html	This dataset includes low-resolution resting-state fMRI data from 100 healthy individuals. It is designed for assessing the intrinsic organization of the human brain at rest and is suitable for studies focusing on functional connectivity.	USA	neuroimaging (MRI)	Cross-sectional	https://fcon_1000.projects.nitrc.org/indi/retro/yale_lowres.html#:~:text=The%20Yale%20Low%2DResolution%20Controls,the%20human%20brain%20at%20rest.	https://doi.org/10.1016/j.neuroimage.2009.12.119	Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data	Resting-state fMRI provides a method to examine the functional network of the brain under spontaneous fluctuations. A number of studies have proposed using resting-state BOLD data to parcellate the brain into functional subunits. In this work, we present two state-of-the-art graph-based partitioning approaches, and investigate their application to the problem of brain network segmentation using resting-state fMRI. The two approaches, the normalized cut (Ncut) and the modularity detection algorithm, are also compared to the Gaussian mixture model (GMM) approach. We show that the Ncut approach performs consistently better than the modularity detection approach, and it also outperforms the GMM approach for in vivo fMRI data. Resting-state fMRI data were acquired from 43 healthy subjects, and the Ncut algorithm was used to parcellate several different cortical regions of interest. The group-wise delineation of the functional subunits based on resting-state fMRI was highly consistent with the parcellation results from two task-based fMRI studies (one with 18 subjects and the other with 20 subjects). The findings suggest that whole-brain parcellation of the cortex using resting-state fMRI is feasible, and that the Ncut algorithm provides the appropriate technique for this task. © 2010 Elsevier Inc. All rights reserved.	https://doi.org/10.1016/j.jneumeth.2011.04.020	Social network theory applied to resting-state fMRI connectivity data in the identification of epilepsy networks with iterative feature selection	Epilepsy is a brain disorder usually associated with abnormal cortical and/or subcortical functional networks. Exploration of the abnormal network properties and localization of the brain regions involved in human epilepsy networks are critical for both the understanding of the epilepsy networks and planning therapeutic strategies. Currently, most localization of seizure networks come from ictal EEG observations. Functional MRI provides high spatial resolution together with more complete anatomical coverage compared with EEG and may have advantages if it can be used to identify the network(s) associated with seizure onset and propagation. Epilepsy networks are believed to be present with detectable abnormal signatures even during the interictal state. In this study, epilepsy networks were investigated using resting-state fMRI acquired with the subjects in the interictal state. We tested the hypothesis that social network theory applied to resting-state fMRI data could reveal abnormal network properties at the group level. Using network data as input to a classification algorithm allowed separation of medial temporal lobe epilepsy (MTLE) patients from normal control subjects indicating the potential value of such network analyses in epilepsy. Five local network properties obtained from 36 anatomically defined ROIs were input as features to the classifier. An iterative feature selection strategy based on the classification efficiency that can avoid 'over-fitting' is proposed to further improve the classification accuracy. An average sensitivity of 77.2% and specificity of 83.86% were achieved via 'leave one out' cross validation. This finding of significantly abnormal network properties in group level data confirmed our initial hypothesis and provides motivation for further investigation of the epilepsy process at the network level. © 2011 Elsevier B.V.	indi	Easy to download with AWS.	NA	1	1	0	0	0	0	0	0	0	0	multiple fMRI runs		To establish a reference dataset for studying the impact of spatial resolution on functional connectivity measurements and provide a conventional-resolution counterpart to high-resolution acquisitions.		https://fcon_1000.projects.nitrc.org/indi/req_access.html	aria2c -x 10 -s 10 -j 10 https://fcp-indi.s3.amazonaws.com/data/Projects/INDI/YALE/lowres.tar.gz