Xi-Nian ZuopersName, Jeffrey S. AndersonpersName, Pierre BellecpersName, Rasmus M. BirnpersName, Bharat B. BiswalpersName, Janusch BlautzikpersName, John C.S. BreitnerpersName, Randy L. BucknerpersName, Vince D CalhounpersName, F. Xavier CastellanospersName, Antao ChenpersName, Bing ChenpersName, Jiangtao ChenpersName, Xu ChenpersName, Stanley J. ColcombepersName, William CourtneypersName, R. Cameron CraddockpersName, Adriana Di MartinopersName, Hao-Ming DongpersName, Xiaolan FupersName, Qiyong GongpersName, Krzysztof J. GorgolewskipersName, Ying HanpersName, Ye HepersName, Yong HepersName, Erica J. HopersName, Avram J. HolmespersName, Xiao‐Hui HoupersName, Jeremy F. HuckinspersName, T. JiangpersName, Yi JiangpersName, William J. KelleypersName, Clare KellypersName, Margaret KingpersName, Stephen M. LaContepersName, Janet E. LainhartpersName, Lei XupersName, Hui‐Jie LipersName, Kaiming LipersName, Kuncheng LipersName, Qixiang LinpersName, Dongqiang LiupersName, Jia LiupersName, Xun LiupersName, Yijun LiupersName, Guangming LupersName, Jie LupersName, Beatríz LunapersName, Jing LuopersName, Daniel J. LuriepersName, Ying MaopersName, Daniel S. MarguliespersName, Andrew R. MayerpersName, Thomas MeindlpersName, M. Elizabeth MeyerandpersName, Weizhi NanpersName, Jared A. NielsenpersName, David O’ConnorpersName, David J. PaulsenpersName, Vivek PrabhakaranpersName, Zhigang QipersName, Jiang QiupersName, Chunhong ShaopersName, Zarrar ShehzadpersName, Weijun TangpersName, Arno VillringerpersName, Huiling WangpersName, Kai WangpersName, Dongtao WeipersName, Gao‐Xia WeipersName, Xu-Chu WengpersName, Xuehai WupersName, Ting XupersName, Ning YangpersName, Zhi YangpersName, Yu‐Feng ZangpersName, Lei ZhangpersName, Qinglin ZhangpersName, Zhe ZhangpersName, Zhiqiang ZhangpersName, Ke ZhaopersName, Zonglei ZhenpersName, Yuan ZhoupersName, Xing-Ting ZhupersName, Michael P. MilhampersName (2014date). “An open science resource for establishing reliability and reproducibility in functional connectomics.” Scientific Datatitle·venue.
Object of study — A TEST-RETEST RESOURCE BUILT SO THAT RELIABILITY ITSELF CAN BE MEASURED - 40 repeat-scan datasets from 36 laboratories at 18 institutions, published as the instrument for checking every other instrument in functional connectomics
Research question — how reliable and reproducible the many rfMRI-derived connectome measures actually are, and what data would be needed to find out
Contribution — the CoRR repository, its standardised metadata, a quality-assurance battery run over every dataset, and a minimal set of derivatives computed identically across sites
phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | reliability treated as a measurable property of a measure rather than an assumption about it — “Unfortunately, the field lacks a data platform by which researchers can rigorously explore the reliability of the many indices that continue to emerge. Such a platform is crucial for the refinement and evaluation of novel methods, as well as those that have gained widespread usage without sufficient consideration of reliability.” | |
| claim-type | method — “It is our hope that among its many possible uses, the CoRR repository will facilitate the: (1) Establishment of test-retest reliability and reproducibility for commonly used MR-based connectome metrics, (2) Determination of the range of variation in the reliability and reproducibility of these metrics across imaging sites and retest study designs, (3) Creation of a standard/benchmark test-retest dataset for the evaluation of novel metrics.” | |
| method — evidence | — “Consistent with the established FCP/INDI policy, all data contributed to CoRR was made available to users regardless of data quality. Justifications for this decision include the lack of consensus within the functional imaging community on criteria for quality assurance, and the utility of ‘lower quality’ datasets for facilitating the development of artifact correction techniques.” | |
| object of study — evidence | — “It is our hope that among its many possible uses, the CoRR repository will facilitate the: (1) Establishment of test-retest reliability and reproducibility for commonly used MR-based connectome metrics, (2) Determination of the range of variation in the reliability and reproducibility of these metrics across imaging sites and retest study designs, (3) Creation of a standard/benchmark test-retest dataset for the evaluation of novel metrics.” | |
| research question — evidence | — “Unfortunately, the field lacks a data platform by which researchers can rigorously explore the reliability of the many indices that continue to emerge. Such a platform is crucial for the refinement and evaluation of novel methods, as well as those that have gained widespread usage without sufficient consideration of reliability.” | |
| contribution — evidence | — “It is our hope that among its many possible uses, the CoRR repository will facilitate the: (1) Establishment of test-retest reliability and reproducibility for commonly used MR-based connectome metrics, (2) Determination of the range of variation in the reliability and reproducibility of these metrics across imaging sites and retest study designs, (3) Creation of a standard/benchmark test-retest dataset for the evaluation of novel metrics.” | |
| mechanism | THE MEASURES WERE IN WIDE USE BEFORE ANYONE COULD CHECK THEM: `the field lacks a data platform by which researchers can rigorously explore the reliability of the many indices that continue to emerge. Such a platform is crucial for the refinement and evaluation of novel methods, as well as those that have gained widespread usage without sufficient consideration of reliability.` The resource is the instrument for checking the instruments — “Unfortunately, the field lacks a data platform by which researchers can rigorously explore the reliability of the many indices that continue to emerge. Such a platform is crucial for the refinement and evaluation of novel methods, as well as those that have gained widespread usage without sufficient consideration of reliability.” | mechanisms[0] |
| mechanism | AND WHERE A DEFINITION DOES NOT EXIST THE PAPER SAYS SO AND GIVES THE CONVENTION INSTEAD: `There is no exact definition of the minimum time for detecting developmental effects across scans, though designs typically span at least 3-6 months`. A boundary that everyone uses and nobody has fixed — “Scan repeated at a distant time-point not believed to be developmentally equivalent. There is no exact definition of the minimum time for detecting developmental effects across scans, though designs typically span at least 3–6 months” | mechanisms[1] |
| mechanism | AND WHERE THE COMMUNITY HAS NO CRITERIA, THE POLICY IS TO PUBLISH EVERYTHING: `all data contributed to CoRR was made available to users regardless of data quality. Justifications for this decision include the lack of consensus within the functional imaging community on criteria for quality assurance, and the utility of `LOWER QUALITY` DATASETS for facilitating the development of artifact correction techniques.` The exact opposite policy to a published exclusion list, taken for the same reason - there is no agreed criterion — “Consistent with the established FCP/INDI policy, all data contributed to CoRR was made available to users regardless of data quality. Justifications for this decision include the lack of consensus within the functional imaging community on criteria for quality assurance, and the utility of ‘lower quality’ datasets for facilitating the development of artifact correction techniques.” | mechanisms[2] |
| mechanism | and the release carries a disclaimer about what it does NOT mean: `the absence of screening for data quality in the data release does not mean that the inclusion of poor quality datasets in imaging analyses is routine practice for the contributing sites.` An empty state stated as a claim rather than left to be inferred — “However, the absence of screening for data quality in the data release does not mean that the inclusion of poor quality datasets in imaging analyses is routine practice for the contributing sites.” | mechanisms[3] |
| mechanism | AND THE ARTEFACT IS ITSELF RELIABLE, WHICH IS WORSE THAN NOISE: `motion itself has moderate test-retest reliability, and appears to contribute to reliability when low, though it compromises reliability when high`. A subject who moves the same way twice makes the measurement look reproducible - the confound and the signal have the same statistical signature — “Specifically, Yan and colleagues found that motion itself has moderate test-retest reliability, and appears to contribute to reliability when low, though it compromises reliability when high” | mechanisms[4] |
| mechanism | AND AN ACQUISITION PARAMETER MOVES A DERIVED MEASURE, WITH THE MECHANISM NAMED: `In the case of fALFF, site-specific differences can be directly related to the temporal sampling rate (that is, TR), as lower TR datasets include A broader range of frequencies in the denominator - thereby REDUCING THE RESULTING fALFF SCORES ... rfMRI estimates can be highly sensitive to acquisition parameters` — “In the case of fALFF, site-specific differences can be directly related to the temporal sampling rate (that is, TR; see Figure 3 ), as lower TR datasets include a broader range of frequencies in the denominator—thereby reducing the resulting fALFF scores (differences in aliasing are likely to be present as well). This note of caution about fALFF raises the general issue that rfMRI estimates can be highly sensitive to acquisition parameters” | mechanisms[5] |
| extends | the 1000 Functional Connectomes Project and its International Neuroimaging Data-sharing Initiative, through which these test-retest datasets are shared — “It is our hope that among its many possible uses, the CoRR repository will facilitate the: (1) Establishment of test-retest reliability and reproducibility for commonly used MR-based connectome metrics, (2) Determination of the range of variation in the reliability and reproducibility of these metrics across imaging sites and retest study designs, (3) Creation of a standard/benchmark test-retest dataset for the evaluation of novel metrics.” | claim_relations[0] |
| supports | the finding that head motion has moderate test-retest reliability of its own - verified here in the CoRR datasets and driven by the lower-motion subsets — “Although a comprehensive examination of this issue is beyond the scope of the present work, we did verify that motion does have moderate test-retest reliability in the CoRR datasets (see Figure 2 ) as previously suggested. Interestingly, this relationship appeared to be driven by the lower motion datasets (mean FD<0.2mm).” | claim_relations[1] |
| sense | `developmentally equivalent` - UNDEFINED BY THE FIELD AND FILLED BY CONVENTION: `There is no exact definition of the minimum time for detecting developmental effects across scans, though designs typically span at least 3-6 months` — “Scan repeated at a distant time-point not believed to be developmentally equivalent. There is no exact definition of the minimum time for detecting developmental effects across scans, though designs typically span at least 3–6 months” | senses[0] |
| sense | `data quality` - HAS NO AGREED CRITERION IN THIS FIELD, which is the stated reason nothing is screened out: `the lack of consensus within the functional imaging community on criteria for quality assurance` — “Consistent with the established FCP/INDI policy, all data contributed to CoRR was made available to users regardless of data quality. Justifications for this decision include the lack of consensus within the functional imaging community on criteria for quality assurance, and the utility of ‘lower quality’ datasets for facilitating the development of artifact correction techniques.” | senses[1] |
| sense | `Regional Homogeneity (ReHo)` - `The synchronicity of a voxel's time series and that of its nearest neighbors based on Kendall's coefficient of concordance`. A neighbourhood, a statistic, and a name — “Regional Homogeneity (ReHo) 38–40 . The synchronicity of a voxel’s time series and that of its nearest neighbors based on Kendall’s coefficient of concordance to measure the local brain functional homogeneity.” | senses[2] |
| sense | `intrinsic functional connectivity` - operationalised down to a coordinate and a diameter: `the mean time series from a spherical region of interest (diameter=8 mm) centered in PCC` and `Pearson's correlation ... Fisher r-to-z transformed` — “Intrinsic Functional Connectivity (iFC) of Posterior Cingulate Cortex (PCC) 41 . Using the mean time series from a spherical region of interest (diameter=8 mm) centered in PCC (x=−8, y=−56, z=26) 42 , functional connectivity with PCC is calculated for each voxel in the brain using Pearson’s correlation (results are Fisher r-to-z transformed).” | senses[3] |
| sense | `head motion` - operationalised as framewise displacement, itself defined by a geometric convention: rotations are converted to distances `based on the changes across the surface of a 50 mm radius sphere` — “Mean framewise displacement (FD) 29 . A measure of subject head motion, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.” | senses[4] |
| control | a published quality-metric battery - SNR, FBER, EFC, smoothness, ghost-to-signal, framewise displacement, DVARS, global correlation - computed identically over every contributed dataset — held — “As can be seen in Supplementary Figure 4 , for all the derivatives, the mean value or coefficient of variation obtained for a site was highly reliable.” — rules out site differences being attributed to biology when they are properties of the scanner, and it bound: 'the battery of quality metrics revealed notable variations in image properties across sites' — “Mean framewise displacement (FD) 29 . A measure of subject head motion, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.” | controls[0] |
| control | screening poor-quality datasets out of the release — absent-declared-unreported — “For CoRR, the inclusion of datasets with significant artifacts related to factors such as motion are particularly valuable, as it enables the determination of the impact of such real-world confounds on reliability and reproducibility” — rules out nothing, and deliberately so: 'made available to users regardless of data quality' because of 'the lack of consensus ... on criteria for quality assurance' and because the artefacts are what artefact-correction research needs — “Consistent with the established FCP/INDI policy, all data contributed to CoRR was made available to users regardless of data quality. Justifications for this decision include the lack of consensus within the functional imaging community on criteria for quality assurance, and the utility of ‘lower quality’ datasets for facilitating the development of artifact correction techniques.” | controls[1] |
| control | standardisation of derivative values across sites (mean-regression, mean centering, variance normalisation) — absent-declared-unreported — “As can be seen in Supplementary Figure 4 , for all the derivatives, the mean value or coefficient of variation obtained for a site was highly reliable.” — rules out nothing here, refused on purpose: 'We opted to not use increasingly popular standardization approaches ... as the test-retest framework provides users a unique opportunity to consider the reliability of site-related differences' — “We opted to not use increasingly popular standardization approaches (for example, mean-regression, mean centering +/− variance normalization) in the calculation of derivative values, as the test-retest framework provides users a unique opportunity to consider the reliability of site-related differences.” | controls[2] |
| control | a formal test-retest reliability analysis of the derivatives — absent-declared-unreported — “Additional questions may include: How reliable are image quality metrics? How does reliability and reproducibility impact prediction accuracy? How do imaging modalities (for example, rfMRI, dMRI, sMRI) differ with respect to reproducibility and reliability? And within modality, are some derivatives more reliable than others? Can reliability and reproducibility be used to optimize imaging analyses? How can such optimizations avoid being driven by artifacts such as motion? How much information regarding inter-individual variation is shared and distinct among imaging metrics? Which features best differentiate one individual from another?” — rules out nothing in this paper - stated as out of scope: 'formal test-retest reliability or reproducibility analyses are beyond the scope of the present data description' — “While formal test-retest reliability or reproducibility analyses are beyond the scope of the present data description, we illustrate the broad range of potential questions that can be answered for rfMRI, dMRI and sMRI using the resource.” | controls[3] |
| weight | the 1000 Functional Connectomes Project and its International Neuroimaging Data-sharing Initiative, through which these test-retest datasets are shared — 0.85 — “It is our hope that among its many possible uses, the CoRR repository will facilitate the: (1) Establishment of test-retest reliability and reproducibility for commonly used MR-based connectome metrics, (2) Determination of the range of variation in the reliability and reproducibility of these metrics across imaging sites and retest study designs, (3) Creation of a standard/benchmark test-retest dataset for the evaluation of novel metrics.” | claim_relations[0] |
| weight | the finding that head motion has moderate test-retest reliability of its own - verified here in the CoRR datasets and driven by the lower-motion subsets — 0.8 — “Although a comprehensive examination of this issue is beyond the scope of the present work, we did verify that motion does have moderate test-retest reliability in the CoRR datasets (see Figure 2 ) as previously suggested. Interestingly, this relationship appeared to be driven by the lower motion datasets (mean FD<0.2mm).” | claim_relations[1] |
| note | subject head motion —operationalised-as→ Mean framewise displacement — 'Mean framewise displacement (FD). A measure of subject head motion, which compares the motion between the current and previous volumes ... The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.' A construct fixed to a number by a geometric convention - the sphere's radius is a choice, and it sets how much a rotation counts — “Mean framewise displacement (FD) 29 . A measure of subject head motion, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.” | concept_edges[0] |
| weight | subject head motion —operationalised-as→ Mean framewise displacement — 0.95 — “Mean framewise displacement (FD) 29 . A measure of subject head motion, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.” | concept_edges[0] |
| note | temporal sampling rate —causes→ fALFF — 'site-specific differences can be directly related to the temporal sampling rate ... as lower TR datasets include A broader range of frequencies in the denominator - thereby REDUCING THE RESULTING fALFF SCORES'. The mechanism is stated, not inferred: the denominator widens and the ratio falls. Weight 0.9 because the authors say 'can be directly related to' rather than asserting it for every site — “In the case of fALFF, site-specific differences can be directly related to the temporal sampling rate (that is, TR; see Figure 3 ), as lower TR datasets include a broader range of frequencies in the denominator—thereby reducing the resulting fALFF scores (differences in aliasing are likely to be present as well). This note of caution about fALFF raises the general issue that rfMRI estimates can be highly sensitive to acquisition parameters” | concept_edges[1] |
| weight | temporal sampling rate —causes→ fALFF — 0.9 — “In the case of fALFF, site-specific differences can be directly related to the temporal sampling rate (that is, TR; see Figure 3 ), as lower TR datasets include a broader range of frequencies in the denominator—thereby reducing the resulting fALFF scores (differences in aliasing are likely to be present as well). This note of caution about fALFF raises the general issue that rfMRI estimates can be highly sensitive to acquisition parameters” | concept_edges[1] |
| outcome | control — held — “Mean framewise displacement (FD) 29 . A measure of subject head motion, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.” — “As can be seen in Supplementary Figure 4 , for all the derivatives, the mean value or coefficient of variation obtained for a site was highly reliable.” | controls[0] |
| outcome | control — absent-declared-unreported — “Consistent with the established FCP/INDI policy, all data contributed to CoRR was made available to users regardless of data quality. Justifications for this decision include the lack of consensus within the functional imaging community on criteria for quality assurance, and the utility of ‘lower quality’ datasets for facilitating the development of artifact correction techniques.” — “For CoRR, the inclusion of datasets with significant artifacts related to factors such as motion are particularly valuable, as it enables the determination of the impact of such real-world confounds on reliability and reproducibility” | controls[1] |
| outcome | control — absent-declared-unreported — “We opted to not use increasingly popular standardization approaches (for example, mean-regression, mean centering +/− variance normalization) in the calculation of derivative values, as the test-retest framework provides users a unique opportunity to consider the reliability of site-related differences.” — “As can be seen in Supplementary Figure 4 , for all the derivatives, the mean value or coefficient of variation obtained for a site was highly reliable.” | controls[2] |
| outcome | control — absent-declared-unreported — “While formal test-retest reliability or reproducibility analyses are beyond the scope of the present data description, we illustrate the broad range of potential questions that can be answered for rfMRI, dMRI and sMRI using the resource.” — “Additional questions may include: How reliable are image quality metrics? How does reliability and reproducibility impact prediction accuracy? How do imaging modalities (for example, rfMRI, dMRI, sMRI) differ with respect to reproducibility and reliability? And within modality, are some derivatives more reliable than others? Can reliability and reproducibility be used to optimize imaging analyses? How can such optimizations avoid being driven by artifacts such as motion? How much information regarding inter-individual variation is shared and distinct among imaging metrics? Which features best differentiate one individual from another?” | controls[3] |
| limitation | THE SITE IS A SIGNAL: `for all the derivatives, the mean value or coefficient of variation obtained for a site was highly reliable`. Whatever else the measures capture, they reliably capture WHERE THE SCAN WAS TAKEN — “As can be seen in Supplementary Figure 4 , for all the derivatives, the mean value or coefficient of variation obtained for a site was highly reliable.” | limitations[0] |
| limitation | and the authors REFUSED TO NORMALISE THAT AWAY, on purpose: `We opted to not use increasingly popular standardization approaches ... in the calculation of derivative values, as the test-retest framework provides users a unique opportunity to consider the reliability of site-related differences.` The variation is left visible because it is the subject — “We opted to not use increasingly popular standardization approaches (for example, mean-regression, mean centering +/− variance normalization) in the calculation of derivative values, as the test-retest framework provides users a unique opportunity to consider the reliability of site-related differences.” | limitations[1] |
| limitation | AND THE PAPER DOES NOT DO THE ANALYSIS ITS TITLE PROMISES, AND SAYS SO: `While formal test-retest reliability or reproducibility analyses are beyond the scope of the present data description, we illustrate the broad range of potential questions that can be answered`. It is a resource for establishing reliability, not an establishment of it — “While formal test-retest reliability or reproducibility analyses are beyond the scope of the present data description, we illustrate the broad range of potential questions that can be answered for rfMRI, dMRI and sMRI using the resource.” | limitations[2] |
| limitation | and the open questions are printed as questions rather than as gaps to be inferred: `How reliable are image quality metrics? ... Can reliability and reproducibility be used to optimize imaging analyses? How can such optimizations avoid being driven by artifacts such as motion? ... Which features best differentiate one individual from another?` — “Additional questions may include: How reliable are image quality metrics? How does reliability and reproducibility impact prediction accuracy? How do imaging modalities (for example, rfMRI, dMRI, sMRI) differ with respect to reproducibility and reliability? And within modality, are some derivatives more reliable than others? Can reliability and reproducibility be used to optimize imaging analyses? How can such optimizations avoid being driven by artifacts such as motion? How much information regarding inter-individual variation is shared and distinct among imaging metrics? Which features best differentiate one individual from another?” | limitations[3] |
| limitation | AND THE POOR-QUALITY DATA ARE KEPT BECAUSE THEY ARE THE POINT: `the inclusion of datasets with significant artifacts related to factors such as motion are particularly valuable, as it enables the determination of the impact of such real-world confounds on reliability and reproducibility` — “For CoRR, the inclusion of datasets with significant artifacts related to factors such as motion are particularly valuable, as it enables the determination of the impact of such real-world confounds on reliability and reproducibility” | limitations[4] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | — “Unfortunately, the field lacks a data platform by which researchers can rigorously explore the reliability of the many indices that continue to emerge. Such a platform is crucial for the refinement and evaluation of novel methods, as well as those that have gained widespread usage without sufficient consideration of reliability.” | content_tags.findings[0] |
| finding | — “Scan repeated at a distant time-point not believed to be developmentally equivalent. There is no exact definition of the minimum time for detecting developmental effects across scans, though designs typically span at least 3–6 months” | content_tags.findings[1] |
| finding | — “Consistent with the established FCP/INDI policy, all data contributed to CoRR was made available to users regardless of data quality. Justifications for this decision include the lack of consensus within the functional imaging community on criteria for quality assurance, and the utility of ‘lower quality’ datasets for facilitating the development of artifact correction techniques.” | content_tags.findings[2] |
| finding | — “However, the absence of screening for data quality in the data release does not mean that the inclusion of poor quality datasets in imaging analyses is routine practice for the contributing sites.” | content_tags.findings[3] |
| finding | — “Specifically, Yan and colleagues found that motion itself has moderate test-retest reliability, and appears to contribute to reliability when low, though it compromises reliability when high” | content_tags.findings[4] |
| finding | — “In the case of fALFF, site-specific differences can be directly related to the temporal sampling rate (that is, TR; see Figure 3 ), as lower TR datasets include a broader range of frequencies in the denominator—thereby reducing the resulting fALFF scores (differences in aliasing are likely to be present as well). This note of caution about fALFF raises the general issue that rfMRI estimates can be highly sensitive to acquisition parameters” | content_tags.findings[5] |
| finding | — “As can be seen in Supplementary Figure 4 , for all the derivatives, the mean value or coefficient of variation obtained for a site was highly reliable.” | content_tags.findings[6] |
| finding | — “We opted to not use increasingly popular standardization approaches (for example, mean-regression, mean centering +/− variance normalization) in the calculation of derivative values, as the test-retest framework provides users a unique opportunity to consider the reliability of site-related differences.” | content_tags.findings[7] |
| finding | — “While formal test-retest reliability or reproducibility analyses are beyond the scope of the present data description, we illustrate the broad range of potential questions that can be answered for rfMRI, dMRI and sMRI using the resource.” | content_tags.findings[8] |
| quantity | 3-6 months — the conventional span of a longitudinal developmental design, standing in for a minimum time nobody has defined — “Scan repeated at a distant time-point not believed to be developmentally equivalent. There is no exact definition of the minimum time for detecting developmental effects across scans, though designs typically span at least 3–6 months” | content_tags.quantities[0] |
| quantity | 50 mm radius sphere — the geometric convention converting head rotations into distances inside framewise displacement — “Mean framewise displacement (FD) 29 . A measure of subject head motion, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.” | content_tags.quantities[1] |
| quantity | 8 mm diameter — the size of the posterior cingulate seed region that defines intrinsic functional connectivity here — “Intrinsic Functional Connectivity (iFC) of Posterior Cingulate Cortex (PCC) 41 . Using the mean time series from a spherical region of interest (diameter=8 mm) centered in PCC (x=−8, y=−56, z=26) 42 , functional connectivity with PCC is calculated for each voxel in the brain using Pearson’s correlation (results are Fisher r-to-z transformed).” | content_tags.quantities[2] |
| variable | fALFF — “In the case of fALFF, site-specific differences can be directly related to the temporal sampling rate (that is, TR; see Figure 3 ), as lower TR datasets include a broader range of frequencies in the denominator—thereby reducing the resulting fALFF scores (differences in aliasing are likely to be present as well). This note of caution about fALFF raises the general issue that rfMRI estimates can be highly sensitive to acquisition parameters” | content_tags.variables[0] |
| definition | Regional Homogeneity — “Regional Homogeneity (ReHo) 38–40 . The synchronicity of a voxel’s time series and that of its nearest neighbors based on Kendall’s coefficient of concordance to measure the local brain functional homogeneity.” | content_tags.definitions[0] |
| definition | Mean framewise displacement — “Mean framewise displacement (FD) 29 . A measure of subject head motion, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.” | content_tags.definitions[1] |
| concept | subject head motion — not defined in this paper's own words; the confound that is itself reliably reproduced across sessions — “Mean framewise displacement (FD) 29 . A measure of subject head motion, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.” | concepts[0] |
| concept | Mean framewise displacement — not defined in this paper's own words; the operationalisation of head motion - summed absolute displacement and rotation between consecutive volumes, rotations converted on a 50 mm sphere — “Mean framewise displacement (FD) 29 . A measure of subject head motion, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.” | concepts[1] |
| concept | temporal sampling rate — not defined in this paper's own words; the acquisition parameter that changes the value of a derived measure — “In the case of fALFF, site-specific differences can be directly related to the temporal sampling rate (that is, TR; see Figure 3 ), as lower TR datasets include a broader range of frequencies in the denominator—thereby reducing the resulting fALFF scores (differences in aliasing are likely to be present as well). This note of caution about fALFF raises the general issue that rfMRI estimates can be highly sensitive to acquisition parameters” | concepts[2] |
| concept | fALFF — not defined in this paper's own words; fractional amplitude of low frequency fluctuations - the derivative whose site differences trace directly to the sampling rate — “In the case of fALFF, site-specific differences can be directly related to the temporal sampling rate (that is, TR; see Figure 3 ), as lower TR datasets include a broader range of frequencies in the denominator—thereby reducing the resulting fALFF scores (differences in aliasing are likely to be present as well). This note of caution about fALFF raises the general issue that rfMRI estimates can be highly sensitive to acquisition parameters” | concepts[3] |
| concept | Regional Homogeneity — not defined in this paper's own words; synchronicity of a voxel's time series with its nearest neighbours, by Kendall's coefficient of concordance — “Regional Homogeneity (ReHo) 38–40 . The synchronicity of a voxel’s time series and that of its nearest neighbors based on Kendall’s coefficient of concordance to measure the local brain functional homogeneity.” | concepts[4] |
| concept | data quality — not defined in this paper's own words; the property the field has no agreed criteria for, and therefore does not screen on — “Consistent with the established FCP/INDI policy, all data contributed to CoRR was made available to users regardless of data quality. Justifications for this decision include the lack of consensus within the functional imaging community on criteria for quality assurance, and the utility of ‘lower quality’ datasets for facilitating the development of artifact correction techniques.” | concepts[5] |
| claim type | method — a second reading reaching the same value; recorded: method — “Such a platform is crucial for the refinement and evaluation of novel methods, as well as those that have gained widespread usage without sufficient consideration of reliability.” | alternatives.claim_type |
| method | data-resource-aggregation — a second reading DIFFERING from the recorded value; recorded: system-or-tool-building — “To address these challenges, the Consortium for Reliability and Reproducibility (CoRR) has aggregated previously collected test-retest imaging datasets from more than 36 laboratories around the world and shared them via the 1000 Functional Connectomes Project (FCP) 5 , 17 and its International Neuroimaging Data-sharing Initiative (INDI) 18 .” | alternatives.method |
| phenomenon | test-retest reliability and reproducibility of resting-state functional connectomics measures — a second reading DIFFERING from the recorded value; recorded: reliability treated as a measurable property of a measure rather than an assumption about it — “Unfortunately, the field lacks a data platform by which researchers can rigorously explore the reliability of the many indices that continue to emerge.” | alternatives.phenomenon |
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Sci Data Sci Data 2628 sdata Scientific Data 2052-4463 Nature Publishing Group PMC4421932 PMC4421932.1 4421932 4421932 25977800 10.1038/sdata.2014.49 sdata201449 1 Data Descriptor An open science resource for establishing reliability and reproducibility in functional connectomics ZuopersName Xi-Nian a 1 2 AndersonpersName Jeffrey S 3 BellecpersName Pierre 4 BirnpersName Rasmus M 5 BiswalpersName Bharat B 6 BlautzikpersName Janusch 7 BreitnerpersName John C.S 8 BucknerpersName Randy L 9 CalhounpersName Vince D 10 CastellanospersName F. Xavier 11 12 ChenpersName Antao 2 ChenpersName Bing 13 ChenpersName Jiangtao 2 ChenpersName Xu 2 ColcombepersName Stanley J 11 CourtneypersName William 10 CraddockpersName R Cameron 11 14 Di MartinopersName Adriana 12 DongpersName Hao-Ming 1 15 Fu XiaolanpersName 1 16 GongpersName Qiyong 17 GorgolewskipersName Krzysztof J 18 HanpersName Ying 19 He Ye 1 15 He YongpersName 20 Ho EricapersName 11 14 HolmespersName Avram 21 HoupersName Xiao-Hui 1 15 HuckinspersName Jeremy 22 JiangpersName Tianzi 23 JiangpersName Yi 1 KelleypersName William 22 KellypersName Clare 12 KingpersName Margaret 10 LaContepersName Stephen M 24 LainhartpersName Janet E 5 LeipersName Xu 2 Li Hui-JiepersName 1 Li KaimingpersName 17 Li KunchengpersName 25 LinpersName Qixiang 20 LiupersName Dongqiang 13 LiupersName Jia 20 LiupersName Xun 1 LiupersName Yijun 2 Lu GuangmingpersName 26 Lu JiepersName 25 LunapersName Beatriz 27 LuopersName Jing 28 LuriepersName Daniel 11 14 MaopersName Ying 29 MarguliespersName Daniel S 18 MayerpersName Andrew R 10 MeindlpersName Thomas 7 MeyerandpersName Mary E 30 NanpersName Weizhi 1 15 NielsenpersName Jared A 3 O’ConnorpersName David 11 14 PaulsenpersName David 27 PrabhakaranpersName Vivek 31 Qi ZhigangpersName 25 QiupersName JiangpersName 2 ShaopersName Chunhong 32 ShehzadpersName Zarrar 11 14 TangpersName Weijun 33 VillringerpersName Arno 34 WangpersName Huiling 35 WangpersName Kai 1 15 WeipersName Dongtao 2 WeipersName Gao-Xia 1 WengpersName Xu-Chu 13 Wu XuehaipersName 29 Xu TingpersName 1 11 14 YangpersName Ning 1 15 YangpersName Zhi 1 ZangpersName Yu-Feng 13 ZhangpersName LeipersName 1 15 ZhangpersName Qinglin 2 ZhangpersName Zhe 1 15 ZhangpersName Zhiqiang 26 ZhaopersName Ke 1 ZhenpersName Zonglei 20 ZhoupersName Yuan 1 ZhupersName Xing-Ting 1 15 MilhampersName Michael P b 11 14 1 Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China 2 Faculty of Psychology, Southwest University , Beibei, Chongqing 400715, China 3 Division of Neuroradiology, University of Utah , Salt Lake City, Utah 84132, USA 4 Unité de neuroimagerie fonctionnelle, Centre de recherche de l’institut universitaire de gériatrie de Montréal, Université de Montréal , Montreal, Quebec, Canada H3W 1W5 5 Department of Psychiatry, University of Wisconsin—Madison , Madison, Wisconsin 53719, USA 6 Department of Biomedical Engineering, New Jersey Institute of Technology , Newark, New Jersey 07102, USA 7 Institute of Clinical Radiology, Ludwig-Maximilians-University , 80336 Munich, Germany 8 Centre for Studies on Prevention of Alzheimer’s Disease, Department of Psychiatry, Douglas Institute, McGill University Faculty of Medicine , Montreal, Quebec, Canada H4H 1R3 9 Department of Psychology, Harvard University , Cambridge, Massachussetts 02138, USA 10 Mind Research Network , Albuquerque, New Mexico 87106, USA 11 Nathan S. Kline Institute for Psychiatric Research , Orangeburg, New York 10962, USA 12 Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, the Child Study Center at NYU Langone Medical Center , New York, New York 10016, USA 13 Center for Cognition and Brain Disorders, Hangzhou Normal University, Gongshu , Hangzhou, Zhejiang 311121, China 14 Center for the Developing Brain, Child Mind Institute , New York, New York 10022, USA 15 University of Chinese Academy of Sciences , Shijingshan, Beijing 100049, China 16 State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences , Chaoyang, Beijing 100101, China 17 Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Wuhou , Chengdu, Sichuan 610041, China 18 Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany 19 Department of Neurology, Xuanwu Hospital, Capital Medical University , Xicheng, Beijing 100053, China 20 State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University , Haidian, Beijing 100875, China 21 Department of Psychology, Yale University , New Haven, Connecticut 06511, USA 22 Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College , Hanover, New Hampshire 03755, USA 23 Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences , Haidian, Beijing 100190, China 24 Virginia Tech Carilion Research Institute , Roanoke, Virginia 24016, USA 25 Department of Radiology, Xuanwu Hospital, Capital Medical University , Xicheng, Beijing 100053, China 26 Department of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Xuanwu , Nanjing, Jiangsu 210002, China 27 Department of Psychiatry, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania 15213, USA 28 Beijing Key Laboratory of Learning and Cognition, Department of Psychology, Capital Normal University , Haidian, Beijing 100048, China 29 Department of Neurosurgery, Huashan Hospital, Fudan University , Jingan, Shanghai 200040, China 30 Department of Biomedical Engineering, University of Wisconsin—Madison , Madison, Wisconsin 53705, USA 31 Department of Radiology, University of Wisconsin—Madison , Madison, Wisconsin 53705, USA 32 Department of Psychiatry, Huashan Hospital, Fudan University , Jingan, Shanghai 200021, China 33 Department of Radiology, Huashan Hospital, Fudan University , Jingan, Shanghai 200040, China 34 Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig 04103, Germany 35 Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei 430060, China a X.-N.Z. (email: zuoxn@psych.ac.cn ) b M.P.M. (email: michael.milhampersName@childmind.org ) Consortium Leadership : Michael P. MilhampersName, Xi-Nian ZuopersName. COINS Database Organization : Vince CalhounpersName, William CourtneypersName, Margaret KingpersName. Data Organization : Hao-Ming DongpersName, Ye He, EricapersName Ho, Xiao-Hui HoupersName, Dan LuriepersName, David O’ConnorpersName, TingpersName Xu, Ning YangpersName, LeipersName ZhangpersName, Zhe ZhangpersName, Xing-Ting ZhupersName. Quality Control Protocol Development : R. Cameron CraddockpersName, Zarrar ShehzadpersName. Data Analysis : TingpersName Xu, Dan LuriepersName, Zarrar ShehzadpersName, Michael P. MilhampersName, Xi-Nian ZuopersName. Data Contribution : Jeffrey S. AndersonpersName, Pierre BellecpersName, Rasmus M. BirnpersName, Bharat BiswalpersName, Janusch BlautzikpersName, John C.S. BreitnerpersName, Randy L. BucknerpersName, Vince D. CalhounpersName, F. Xavier CastellanospersName, Antao ChenpersName, Bing ChenpersName, Jiangtao ChenpersName, Xu ChenpersName, Stanley J. ColcombepersName, R. Cameron CraddockpersName, Adriana Di MartinopersName, XiaolanpersName Fu, Qiyong GongpersName, Krzyszotof J. GorgolewskipersName, Ying HanpersName, YongpersName He, Avram HolmespersName, Jeremy HuckinspersName, Tianzi JiangpersName, Yi JiangpersName, William KelleypersName, Clare KellypersName, Stephen M. LaContepersName, Janet E. LainhartpersName, Xu LeipersName, Hui-JiepersName Li, KaimingpersName Li, KunchengpersName Li, Qixiang LinpersName, Dongqiang LiupersName, Jia LiupersName, Xun LiupersName, Yijun LiupersName, GuangmingpersName Lu, JiepersName Lu, Beatriz LunapersName, Jing LuopersName, Ying MaopersName, Daniel S. MarguliespersName, Andrew R. MayerpersName, Thomas MeindlpersName, Mary E. MeyerandpersName, Michael P. MilhampersName, Weizhi NanpersName, Jared A. NielsenpersName, David PaulsenpersName, Vivek PrabhakaranpersName, ZhigangpersName Qi, JiangpersName QiupersName, Chunhong ShaopersName, Weijun TangpersName, Arno VillringerpersName, Huiling WangpersName, Kai WangpersName, Dongtao WeipersName, Gao-Xia WeipersName, Xu-Chu WengpersName, XuehaipersName Wu, Zhi YangpersName, Yu-Feng ZangpersName, Qinglin ZhangpersName, Zhiqiang ZhangpersName, Ke ZhaopersName, Zonglei ZhenpersName, Yuan ZhoupersName, Xi-Nian ZuopersName.
09 12 2014 2014 1 249156 140049 19 06 2014 14 10 2014 09 12 2014 14 05 2015 06 02 2020 Copyright © 2014, Macmillan Publishers Limited 2014 Macmillan Publishers Limited https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse. Test-retest measurements and digital validation for in vivo neuroscience 2 20 1 2015 140057 Scientific Data PMC4412151 25977807 Test-retest resting-state fMRI in healthy elderly persons with a family history of Alzheimer’s disease 2 13 10 2015 150043 Scientific Data PMC4603392 26504522 A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity 3 15 3 2016 160016 Scientific Data PMC4792176 26978040 Longitudinal test-retest neuroimaging data from healthy young adults in southwest China 4 14 2 2017 170017 Scientific Data PMC5308199 28195583 Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.
pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement yes pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Background & Summary Functional connectomics is a rapidly expanding area of human brain mapping 1–4 . Focused on the study of functional interactions among nodes in brain networks, functional connectomics is emerging as a mainstream tool to delineate variations in brain architecture among both individuals and populations 5–8 . Findings that established network features and well-known patterns of brain activity elicited via task performance are recapitulated in spontaneous brain activity patterns captured by resting-state fMRI (rfMRI) 3–6 , 9–12 , have been critical to the wide-spread acceptance of functional connectomics applications.
A growing literature has highlighted the possibility that functional network properties may explain individual differences in behavior and cognition 4 , 7 , 8 —the potential utility of which is supported by studies that suggest reliability for commonly used rfMRI measures 13 . Unfortunately, the field lacks a data platform by which researchers can rigorously explore the reliability of the many indices that continue to emerge. Such a platform is crucial for the refinement and evaluation of novel methods, as well as those that have gained widespread usage without sufficient consideration of reliability. Equally important is the notion that quantifying the reliability and reproducibility of the myriad connectomics-based measures can inform expectations regarding the potential of such approaches for biomarker identification 13–16 .
To address these challenges, the Consortium for Reliability and Reproducibility (CoRR) has aggregated previously collected test-retest imaging datasets from more than 36 laboratories around the world and shared them via the 1000 Functional Connectomes Project (FCP) 5 , 17 and its International Neuroimaging Data-sharing Initiative (INDI) 18 . Although primarily focused on rfMRI, this initiative has worked to promote the sharing of diffusion imaging data as well. It is our hope that among its many possible uses, the CoRR repository will facilitate the: (1) Establishment of test-retest reliability and reproducibility for commonly used MR-based connectome metrics, (2) Determination of the range of variation in the reliability and reproducibility of these metrics across imaging sites and retest study designs, (3) Creation of a standard/benchmark test-retest dataset for the evaluation of novel metrics.
Here, we provide an overview of all the datasets currently aggregated by CoRR, and describe the standardized metadata and technical validation associated with these datasets, thereby facilitating immediate access to these data by the wider scientific community. Additional datasets, and richer descriptions of some of the studies producing these datasets, will be published separately (for example, A high resolution 7-Tesla rfMRI test-retest dataset with cognitive and physiological measures 19 ). A list of all papers describing these individual studies will be maintained and periodically updated at the CoRR website ( http://fcon_1000.projects.nitrc.org/indi/CoRR/html/data_citation.html ).
Methods Experimental design At the time of submission, CoRR has received 40 distinct test-retest datasets that were independently collected by 36 imaging groups at 18 institutions. All CoRR contributions were based on studies approved by a local ethics committee; each contributor’s respective ethics committee approved submission of de-identified data. Data were fully deidentified by removing all 18 HIPAA (Health Insurance Portability and Accountability)-protected health information identifiers, and face information from structural images prior to contribution. All data distributed were visually inspected before release. While all samples include at least one baseline scan and one retest scan, the specific designs and target populations employed across samples vary given the aggregation strategy used to build the resource. Since many individual (uniformly collected) datasets have reasonably large sample sizes allowing stable test-retest estimates, this variability across datasets provides an opportunity to generalize reliability estimates across scanning platforms, acquisition approaches, and target populations. The range of designs included is captured by the following classifications: Within-Session Repeat.
o Scan repeated on same day o Behavioral condition may or may not vary across scans depending on sample Between-Session Repeat.
o Scan repeated one or more days later o In most cases less than one week Between-Session Repeat (Serial).
o Scan is repeated for 3 or more sessions in a short time-frame that is believed to be developmentally stable Between-Session Repeat (Longitudinal developmental).
o Scan repeated at a distant time-point not believed to be developmentally equivalent. There is no exact definition of the minimum time for detecting developmental effects across scans, though designs typically span at least 3–6 months Hybrid Design.
o Scans repeated one or more times on same day, as well as across one or more sessions Table 1 presents an overview of the specific samples included in CoRR (Data Citations 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, , 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31). The vast majority included a single retest scan (48% within-session, 52% between-session). Three samples employed serial scanning designs, and one sample had a longitudinal developmental component. Most samples included presumed neurotypical adults; exceptions include the pediatric samples from Institute of Psychology at Chinese Academy of Sciences (IPCAS 2/7), University of Pittsburgh School of Medicine (UPSM) and New York University (NYU) and the lifespan samples from Nathan Kline Institute (NKI 1).
Data Records Data privacy Prior to contribution, each investigator confirmed that the data in their contribution was collected with the approval of their local ethical committee or institutional review board, and that sharing via CoRR was in accord with their policies. In accord with prior FCP/INDI policies, face information was removed from anatomical images (FullAnonymize.sh V1.0b; http://www.nitrc.org/frs/shownotes.php?release_id=1902 ) and Neuroimaging Informatics Technology Initiative (NIFTI) headers replaced prior to open sharing to minimize the risk of re-identification.
Distribution for use CoRR data sets can be accessed through either the COllaborative Informatics and Neuroimaging Suite (COINS) Data Exchange ( http://coins.mrn.org/dx ) 20 , or the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC; http://fcon_1000.projects.nitrc.org/indi/CoRR/html/index.html ). CoRR datasets at the NITRC site are stored in .tar files sorted by site, each containing the necessary imaging data and phenotypic information. The COINS Data Exchange offers an enhanced graphical query tool, which enables users to target and download files in accord with specific search criteria. For each sharing venue, a user login must be established prior to downloading files. There are several groups of samples which were not included in the data analysis as they were in the data contribution/upload, preparation or correction stage at the time of analysis: Intrinsic Brain Activity, Test-Retest Dataset (IBATRT), Dartmouth College (DC 1), IPCAS 4, Hangzhou Normal University (HNU 2), Fudan University (FU 1), FU 2, Chengdu Huaxi Hospital (CHH 1), Max Planck Institute (MPG 1) 19 , Brain Genomics Superstruct Project (GSP) and New Jersey Institute of Technology (NJIT 1) (see more details on these sites at the CoRR website). Table 1 provides a static representation of the samples included in CoRR at the time of submission.
Imaging data Consistent with its popularity in the imaging community and prior usage in FCP/INDI efforts, the NIFTI file format was selected for storage of CoRR imaging datasets, independent of modalities such as rfMRI, structural MRI (sMRI) and dMRI. Tables 2 , 3 , 4 (available online only) provide descriptions of the MRI sequences used for the various modalities for each of the imaging data file types.
Phenotypic information All phenotypic data are stored in comma separated value (.csv) files. Basic information such as age and gender has been collected for each site to facilitate aggregation with minimal demographic variables. Table 5 (available online only) depicts the data legend provided to CoRR contributors.
Technical Validation Consistent with the established FCP/INDI policy, all data contributed to CoRR was made available to users regardless of data qualityterm. Justifications for this decision include the lack of consensus within the functional imaging community on criteria for quality assurance, and the utility of ‘lower quality’ datasets for facilitating the development of artifact correction techniques. For CoRR, the inclusion of datasets with significant artifacts related to factors such as motion are particularly valuable, as it enables the determination of the impact of such real-world confounds on reliability and reproducibility 21 , 22 . However, the absence of screening for data qualityterm in the data release does not mean that the inclusion of poor quality datasets in imaging analyses is routine practice for the contributing sites. Figure 1 provides a summary map describing the anatomical coverage for rfMRI scans included in the CoRR dataset.
To facilitate quality assessment of the contributed samples and selection of datasets for analyses by individual users 23 , we made use of the Preprocessed Connectome Project quality assurance protocol (http://preprocessed-connectomes-project.github.io), which includes a broad range of quantitative metrics commonly used in the imaging literature for assessing data qualityterm, as follows. They are itemized below: Spatial Metrics (sMRI, rfMRI) o Signal-to-Noise Ratio (SNR) 24 . The mean within gray matter values divided by the standard deviation of the air values.
o Foreground to Background Energy Ratio (FBER) o Entropy Focus Criteria (EFC) 25 . Shannon’s entropy is used to summarize the principal directions distribution.
o Smoothness of Voxels 26 . The full-width half maximum (FWHM) of the spatial distribution of image intensity values.
o Ghost to Signal Ratio (GSR) (only rfMRI) 27 . A measure of the mean signal in the ‘ghost’ image (signal present outside the brain due to acquisition in the phase encoding direction) relative to mean signal within the brain.
o Artifact Detection (only sMRI) 28 . The proportion of voxels with intensity corrupted by artifacts normalized by the number of voxels in the background.
o Contrast-to-Noise Ratio (CNR) (only sMRI) 24 . Calculated as the mean of the gray matter values minus the mean of the white matter values, divided by the standard deviation of the air values.
Temporal Metrics (rfMRI) o Head Motion ▪ Mean framewise displacementterm (FD) 29 . A measure of subject head motionterm, which compares the motion between the current and previous volumes. This is calculated by summing the absolute value of displacement changes in the x, y and z directions and rotational changes about those three axes. The rotational changes are given distance values based on the changes across the surface of a 50 mm radius sphere.
▪ Percent of volumes with FD greater than 0.2 mm ▪ Standardized DVARS. The spatial standard deviation of the temporal derivative of the data (D referring to temporal derivative of time series, VARS referring to root-mean-square variance over voxels) 29 , normalized by the temporal standard deviation and temporal autocorrelation ( http://blogs.warwick.ac.uk/nichols/entry/standardizing_dvars ).
o General ▪ Outlier Detection. The mean fraction of outliers found in each volume using 3dTout command in the software package for Analysis of Functional NeuroImages (AFNI: http://afni.nimh.nih.gov/afni ).
▪ Median Distance Index. The mean distance (1-spearman’s rho) between each time-point’s volume and the median volume using AFNI’s 3dTqual command.
▪ Global Correlation (GCOR) 30 . The average of the entire brain correlation matrix, which is computed as the brain-wide average time series correlation over all possible combinations of voxels.
Imaging data preprocessing was carried out with the Configurable Pipeline for the Analysis of Connectomes (C-PAC: http://www.nitrc.org/projects/cpac ). Results for the sMRI images (spatial metrics) are depicted in Supplementary Figure 1 , for the rfMRI scans in Supplementary Figure 2 (general spatial and temporal metrics) and Supplementary Figure 3 (head motion). For both sMRI and rfMRI, the battery of quality metrics revealed notable variations in image properties across sites. It is our hope that users will explore the impact of such variations in quality on the reliability of data derivatives, as well as potential relationships with acquisition parameters. Recent work examining the impact of head motion on reliability suggests the merits of such linespersName of questioning. Specifically, Yan and colleagues found that motion itself has moderate test-retest reliability, and appears to contribute to reliability when low, though it compromises reliability when high 31–33 . Although a comprehensive examination of this issue is beyond the scope of the present work, we did verify that motion does have moderate test-retest reliability in the CoRR datasets (see Figure 2 ) as previously suggested. Interestingly, this relationship appeared to be driven by the lower motion datasets (mean FD<0.2mm). Future work will undoubtedly benefit from further exploration of this phenomena and its impact of findings.
Beyond the above quality control metrics, a minimal set of rfMRI derivatives for the datasets were calculated for the datasets included in CoRR to further facilitate comparison of images across sites: o Fractional Amplitude of Low Frequency Fluctuations (fALFFterm) 34 , 35 . The total power in the low frequency range (0.01–0.1 Hz) of an fMRI image, normalized by the total power across all frequencies measured in that same image.
o Voxel-Mirrored Homotopic Connectivity (VMHC) 36 , 37 . The functional connectivity between a pair of geometrically symmetric, inter-hemispheric voxels.
o Regional Homogeneityterm (ReHo) 38–40 . The synchronicity of a voxel’s time series and that of its nearest neighbors based on Kendall’s coefficient of concordance to measure the local brain functional homogeneity.
o Intrinsic Functional Connectivity (iFC) of Posterior Cingulate Cortex (PCC) 41 . Using the mean time series from a spherical region of interest (diameter=8 mm) centered in PCC (x=−8, y=−56, z=26) 42 , functional connectivity with PCC is calculated for each voxel in the brain using Pearson’s correlation (results are Fisher r-to-z transformed).
To enable rapid comparison of derivatives, we: (1) calculated the 50th, 75th, and 90th percentile scores for each participant, and then (2) calculated site means and standard deviations for each of these scores (see Table 6 (available online only)). We opted to not use increasingly popular standardization approaches (for example, mean-regression, mean centering +/− variance normalization) in the calculation of derivative values, as the test-retest framework provides users a unique opportunity to consider the reliability of site-related differences. As can be seen in Supplementary Figure 4 , for all the derivatives, the mean value or coefficient of variation obtained for a site was highly reliable. In the case of fALFFterm, site-specific differences can be directly related to the temporal sampling rateterm (that is, TR; see Figure 3 ), as lower TR datasets include a broader range of frequencies in the denominator—thereby reducing the resulting fALFFterm scores (differences in aliasing are likely to be present as well). This note of caution about fALFFterm raises the general issue that rfMRI estimates can be highly sensitive to acquisition parameters 7 , 13 . Specific factors contributing to differences in the other derivatives are less obvious (it is important to note that the correlation-based derivatives have some degree of standardization inherent to them). Interestingly, the coefficient of variation across participants also proved to be highly reliable for the various derivatives; while this may point to site-related differences in the ability to detect differences across participants, it may also be some reflection of the specific populations obtained at a site (or the sample size). Overall, these site-related differences highlight the potential value of post-hoc statistical standardization approaches, which can be used to handle unaccounted for sources of variation within-site as well 43 .
Finally, in Figure 4 , we demonstrate the ability of the CoRR datasets to: (1) replicate prior work showing regional differences in inter-individual variation for the various derivatives that occur at ‘transition zones’ or boundaries between functional areas (even after mean-centering and variance normalization), and (2) show them to be highly reproducible across imaging sessions in the same sample. It is our hope that this demonstration will spark future work examining inter-individual variation in these boundaries and their functional relevance. These surface renderings and visualizations are carried out with the Connectome Computation System (CCS) documented at http://lfcd.psych.ac.cn/ccs.html and will be released to the public via github soon ( https://github.com/zuoxinian/CCS ).
To facilitate replication of our work, for each of Figures 1 , 2 , 3 and Supplementary Figures 1–4 , we include a variable in the COINS phenotypic data that indicates whether or not each dataset was included in the analyses depicted. We also included this information in the phenotypic files on NITRC.
Usage Notes While formal test-retest reliability or reproducibility analyses are beyond the scope of the present data description, we illustrate the broad range of potential questions that can be answered for rfMRI, dMRI and sMRI using the resource. These include the impact of: Acquisition parameters 7 , 38 , 44 Image quality 13 Head motion 7 , 30 , 38 , 43 , 45 Image processing decisions 13 , 30 , 38 , 43 , 46–48 (for example, nuisance signal regression for rfMRI, spatial normalization algorithms, computational space) Standardization approaches 43 Post-hoc analytic choices 13 , 49 , 50 Age 51–53 Of note, at present, the vast majority of studies do not collect physiological data, and this is reflected in the CoRR initiative. With that said, recent advances in model-free correction (for example, ICA-FIX 54 , 55 , CORSICA 56 , PESTICA 57 , PHYCAA 58 , 59 ) can be of particular value in the absence of physiological data.
Additional questions may include: How reliable are image quality metrics?
How does reliability and reproducibility impact prediction accuracy?
How do imaging modalities (for example, rfMRI, dMRI, sMRI) differ with respect to reproducibility and reliability? And within modality, are some derivatives more reliable than others?
Can reliability and reproducibility be used to optimize imaging analyses? How can such optimizations avoid being driven by artifacts such as motion?
How much information regarding inter-individual variation is shared and distinct among imaging metrics?
Which features best differentiate one individual from another?
One example analytic framework that can be used with the CoRR test-retest datasets is Non-Parametric Activation and Influence Reproducibility reSampling (NPAIRS 60 ). By combining prediction accuracy and reproducibility, this computational framework can be used to assess the relative merits of differing image modalities, image metrics, or processing pipelines, as well as the impact of artifacts 61–63 .
Open access connectivity analysis packages that may be useful (list adapted from http://RFMRI.org ): Brain Connectivity Toolbox (BCT; MATLAB) 64 BrainNet Viewer (BNV; MATLAB) 65 Configurable Pipeline for the Analysis of Connectomes (C-PAC; PYTHON) 66 CONN: functional connectivity toolbox (CONN; MATLAB) 67 Connectome Computation System (CCS; SHELL/MATLAB) 13 , 38 , 39 Dynamic Causal Model (DCM; MATLAB) as part of Statistical Parameter Mapping (SPM) 68 , 69 Data Processing Assistant for Resting-State FMRI (DPARSF; MATLAB) 70 Functional and Tractographic Connectivity Analysis Toolbox (FATCAT; C) as part of AFNI 71 , 72 Seed-based Functional Connectivity (FSFC; SHELL) as part of FreeSurfer 73 Graph Theory Toolkit for Network Analysis (GRETNA; MATLAB) 74 Group ICA of FMRI Toolbox (GIFT; MATLAB) 75 Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC; C) as part of FMRIB Software Library (FSL) 76 , 77 Neuroimaging Analysis Kit (NIAK: MATLAB/OCTAVE) 78 Ranking and averaging independent component analysis by reproducibility (RAICAR; MATLAB) 79 , 80 Resting-State fMRI Data Analysis Toolkit (REST; MATLAB) 81 Additional information Tables 2 , 3 , 4 , 5 , 6 are only available in the online version of this paper.
How to cite this article: ZuopersName, X.-N. et al. An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1:140049 doi: 10.1038/sdata.2014.49 (2014).
Supplementary Material Supplementary Information Data Citations Functional Connectomes Project International Neuroimaging Data-Sharing Initiative LiupersName X. NanpersName W. Z. WangpersName K. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.ipcas4 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative ZuopersName X. N. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.ipcas7 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative CastellanospersName F. X. Di MartinopersName A. KellypersName C. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.nyu1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative ChenpersName A. T. LeipersName X. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.swu3 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative ChenpersName A. T. ChenpersName J. T. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.swu2 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative ZangpersName Y. F. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.bnu3 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative MarguliespersName D. S. VillringerpersName A. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.bmb1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative JiangpersName T. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.iacas1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative BlautzikpersName J. Meidl T. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.lmu3 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative He Y. LinpersName Q. X. GongpersName G. Xia M. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.bnu1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative LiupersName J. ZhenpersName Z. L. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.bnu2 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative WeipersName G. X. Xu T. LuopersName J. ZuopersName X. N. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.ipcas8 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative Lu G. M. ZhangpersName Z. Q. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.jhnu1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative QiupersName J. WeipersName D. T. ZhangpersName Q. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.swu4 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative MilhampersName M. P. ColcombepersName S. J. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.nki1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative Li H. J. HoupersName X. H. Xu Y. JiangpersName Y. ZuopersName X. N. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.ipcas2 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative ChenpersName B. Ge Q. ZuopersName X. N. WengpersName X. C. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.hnu1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative Hallquist M. PaulsenpersName D. LunapersName B. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.upsm1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative BlautzikpersName J. MeindlpersName T. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.lmu1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative BlautzikpersName J. MeindlpersName T. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.lmu2 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative YangpersName Z. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.ipcas6 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative LaContepersName S. CraddockpersName C. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.ibatrt1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative ZhaopersName K. Qu F. ChenpersName Y. Fu X. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.ipcas1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative LiupersName X. WangpersName K. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.ipcas3 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative Di MartinopersName A. CastellanospersName F. X. KellypersName C. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.nyu2 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative AndersonpersName J. S. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.utah1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative ChenpersName A. T. LiupersName Y. J. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.swu1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative BellecpersName P. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.um1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative AndersonpersName J. S. NielsenpersName J. A. Ferguson M. A. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.utah2 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative BirnpersName R. M. PrabhakaranpersName V. MeyerandpersName M. E. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.uwm1 Functional Connectomes Project International Neuroimaging Data-Sharing Initiative MayerpersName A. R. CalhounpersName V. D. 2014 http://dx.doi.org/10.15387/fcp_indi.corr.mrn1 This work is partially supported by the National Basic Research Program (973) of China (2015CB351702, 2011CB707800, 2011CB302201, 2010CB833903), the Major Joint Fund for International Cooperation and Exchange of the National Natural Science Foundation (81220108014, 81020108022) and others from Natural Science Foundation of China (11204369, 81270023, 81171409, 81271553, 81422022, 81271652, 91132301, 81030027, 81227002, 81220108013, 31070900, 81025013, 31070987, 31328013, 81030028, 81225012, 31100808, 30800295, 31230031, 91132703, 31221003, 30770594, 31070905, 31371134, 91132301, 61075042, 31200794, 91132728, 31271079, 31170980, 81271477, 31070900, 31170983, 31271087), the National Social Science Foundation of China (11AZD119), the National Key Technologies R&D Program of China (2012BAI36B01, 2012BAI01B03), the National High Technology Program (863) of China (2008AA02Z405, 2014BAI04B05), the Key Research Program (KSZD-EW-TZ-002) of the Chinese Academy of Sciences, the NIH grants (BRAINS R01MH094639, R01MH081218, R01MH083246, R21MH084126, R01MH081218, R01MH083246, R01MH080243, K08MH092697, R24-HD050836, R21-NS064464-01A1, 3R21NS064464-01S1), the Stavros Niarchos Foundation and the Phyllis Green Randolph Cowen Endowment. Dr Xi-Nian ZuopersName acknowledges the Hundred Talents Program of the Chinese Academy of Sciences. Dr Michael P. MilhampersName acknowledges partial support for FCP/INDI from an R01 supplement by National Institute on Drug Abuse (NIDA; PAR-12-204), as well as gifts from Joseph P. Healey, Phyllis Green and Randolph Cowen to the Child Mind Institute. Dr JiangpersName QiupersName acknowledges the Program for New Century Excellent Talents in University (2011) by the Ministry of Education. Dr Antao ChenpersName acknowledges the support from the Foundation for the Author of National Excellent Doctoral Dissertation of PR China (201107) and the New Century Excellent Talents in University (NCET-11-0698). Dr Qiyong GongpersName would like to acknowledge the Program for Changjiang Scholars and Innovative Research Team in University of China (IRT1272) and his Visiting Professorship appointment in the Department of Psychiatry at the School of Medicine, Yale University. The Department of Energy (DE-FG02-99ER62764) supported the Mind Research Network. Drs Xi-Nian ZuopersName and Michael P. MilhampersName had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
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Figure 1 Summary map of brain coverage for rfMRI scans in CoRR ( N =5,093).
The color indicates the coverage ratio of rfMRI scans.
Figure 2 Test-retest plots of in-scanner head motion during rfMRI.
Total 1019 subjects who have at least two rfMRI sessions are selected. The green line indicates the correlation between the two sessions within the lower motion datasets (mean FD<0.2 mm). The blue line indicates the correlation for the higher motion datasets (mean FD >0.2 mm).
Figure 3 Individual differences in fALFFterm and the temporal sampling rateterm (TR).
Median fALFFterm values across each individual whole brains are plotted against the corresponding TR for each site. Different colors indicate labels of different sites.
Figure 4 Test-retest plots of individual variation-related functional boundaries.
Detection of functional boundaries was achieved via examination of voxel-wise coefficients of variation (CV) for fALFFterm, PCC, ReHo and VMHC maps. For the purpose of visualization, coefficients of variation were rank-ordered, whereby the relative degree of variation across participants at a given voxel, rather than the actual value, was plotted to better contrast brain regions. Ranking coefficients of variation (R-CV) efficiently identified regions of greatest inter-individual variability, thus delineating putative functional boundaries.
Table 1 CoRR sites and experimental design.
Site Age Range (Mean) % Female Retest Period DOI Within Session—Single Retest IPCAS (LiupersName)—Frames of Reference [IPCAS 4] 20 21–28 (23.1) 50 44 min http://dx.doi.org/10.15387/fcp_indi.corr.ipcas4 IPCAS (ZuopersName)—Intrasession [IPCAS 7] 74 6–17 (11.6) 57 8 min http://dx.doi.org/10.15387/fcp_indi.corr.ipcas7 NYU (CastellanospersName) [NYU 1] 49 19.1–48 (30.3) 47 60 min http://dx.doi.org/10.15387/fcp_indi.corr.nyu1 Southwest (ChenpersName)—Stroop [SWU 3] 24 18–25 (20.4) 34 90 min http://dx.doi.org/10.15387/fcp_indi.corr.swu3 Southwest (ChenpersName)—Emotion [SWU 2] 27 18–24 (20.9) 33 32 min http://dx.doi.org/10.15387/fcp_indi.corr.swu2 Site Age Range (Mean) % Female # Retests (Mean) Retest Period Range (Mean Interval) DOI Within Session — Multiple Retest Beijing Normal (ZangpersName) [BNU 3] 48 18–30 (22.5) 50 2 (2) 0–8 min (4 min) http://dx.doi.org/10.15387/fcp_indi.corr.bnu3 Berlin (MarguliespersName) [BMB 1] 50 19.9–59.7 (30.8) 52 1 or 3 (1.4) 10–25 min (8.3 min) http://dx.doi.org/10.15387/fcp_indi.corr.bmb1 IPCAS (WeipersName) [IPCAS 5] 22 18–19 (18.3) 0 1 or 2 (1.5) 10–40 min (30 min) http://dx.doi.org/10.15387/fcp_indi.corr.ipcas5 Site Age Range (Mean) % Female Retest Period Range (Mean) DOI Between Sessions — Single Retest IACAS (JiangpersName) [IACAS 1] 28 19–43 (26.4) 55 20–343 Days (75.2 Days) http://dx.doi.org/10.15387/fcp_indi.corr.iacas1 Munich (BlautzikpersName)—Yearly [LMU 3] 25 59–88 (69.8) 36 315–463 Days (399.6 Days) http://dx.doi.org/10.15387/fcp_indi.corr.lmu3 Beijing Normal (He) [BNU 1] 57 19–30 (23) 47 33–55 Days (40.9 Days) http://dx.doi.org/10.15387/fcp_indi.corr.bnu1 Beijing Normal (LiupersName) [BNU 2] 61 19.3–23.3 (21.3) 46 103–189 Days (160.5 Days) http://dx.doi.org/10.15387/fcp_indi.corr.bnu2 IPCAS (ZuopersName)—Tai Chi [IPCAS 8] 13 50–62 (57.6) 46 367–810 Days (516 Days) http://dx.doi.org/10.15387/fcp_indi.corr.ipcas8 Nanjing (Lu) [JHNU 1] 30 20–40 (23.3) 30 0–900 Days (202.6 Days) http://dx.doi.org/10.15387/fcp_indi.corr.jhnu1 Southwest (QiupersName) [SWU 4] 235 17–27 (20) 49 121–653 Days (302.1 Days) http://dx.doi.org/10.15387/fcp_indi.corr.swu4 NKI (MilhampersName) [NKI 1] 24 19–60 (34.4) 75 14 Days (14 Days) http://dx.doi.org/10.15387/fcp_indi.corr.nki1 IPCAS (JiangpersName) [IPCAS 2] 35 11–15 (13.3) 65 7–59 Days (33.6 Days) http://dx.doi.org/10.15387/fcp_indi.corr.ipcas2 MRN (MayerpersName, CalhounpersName) [MRN 1] 54 10–53 (24.9) 50 7–158 Days (109 Days) http://dx.doi.org/10.15387/fcp_indi.corr.mrn1 Site Age Range (Mean) % Female # Retests (Mean) Retest Period Range (Mean Interval) DOI Between Sessions — Multiple Retest Hangzhou (WengpersName) [HNU 1] 30 20–30 (24.4) 50 9 (9) 3–40 Days (3.65 Days) http://dx.doi.org/10.15387/fcp_indi.corr.hnu1 Pittsburgh (LunapersName) [UPSM 1] 100 10.1–19.7 (15.1) 48 1 or 2 (1.23) 473–1,404 Days (521 Days) http://dx.doi.org/10.15387/fcp_indi.corr.upsm1 Munich (BlautzikpersName)—Young Adult [LMU 1] 27 20–29 (24.3) 48 4 or 5 (4.7) 120–600 min (120 min) http://dx.doi.org/10.15387/fcp_indi.corr.lmu1 Munich (BlautzikpersName)—Aging [LMU 2] 40 20–79 (50.8) 45 3 (3) 150–450 min (150 min) http://dx.doi.org/10.15387/fcp_indi.corr.lmu2 Xuanwu (Li, Lu) [XHCUMS 1] 25 36–62 (52.05) 36 4 (4) 12–197 Days (77.6 Days) http://dx.doi.org/10.15387/fcp_indi.corr.xhcums1 Within + Between Sessions Within Between Site Age Range (Mean) % Female # Retests (Mean) Retest Period Range (Mean Interval) Retest Period Range (Mean Interval) DOI IPCAS (ZuopersName)—3 Day [IPCAS 6] 2 21 & 25 50 44 (44) 10–22 min (11.3 min) 83–3,298 min (210 min) http://dx.doi.org/10.15387/fcp_indi.corr.ipcas6 IBATRT (La Conte) [IBA TRT 1] 36 19–48 (26.8) 51 1 or 3 (1.4) 10 min (10 min) 51–183 Days (115.4 Days) http://dx.doi.org/10.15387/fcp_indi.corr.ibatrt1 IPCAS (Fu) [IPCAS 1] 30 18–24 (20.9) 30 3 (3) 29 min (29 min) 5–24 Days (13.9 Days) http://dx.doi.org/10.15387/fcp_indi.corr.ipcas1 IPCAS (LiupersName)—Conflict Adaptation [IPCAS 3] 36 17–25 (21) 34 1 or 3 (1.3) 40 min (40 min) 1-2 Days (1.4 Days) http://dx.doi.org/10.15387/fcp_indi.corr.ipcas3 NYU (Di MartinopersName) [NYU 2] 187 6.47–55.03 (20.2) 38 1, 2, 3, or 5 (1.6) 9–132 min (25.5 min) 1–203 Days (85.9 Days) http://dx.doi.org/10.15387/fcp_indi.corr.nyu2 Utah (AndersonpersName)—Longitudinal [Utah 1] 26 8–39 (20.2) 0 2 (2) 0 min (0 min) 733–1,187 Days (928.4 Days) http://dx.doi.org/10.15387/fcp_indi.corr.utah1 Southwest (ChenpersName)—Attentional Blink [SWU 1] 20 19–24 (21.5) 30 5 (5) 20 min (20 min) 20–2,900 min (1,460 min) http://dx.doi.org/10.15387/fcp_indi.corr.swu1 Montreal (BellecpersName) [UM 1] 80 55–84 (65.4) 27 3 (3) 1 min (1 min) 74–194 Days (111.4 Days) http://dx.doi.org/10.15387/fcp_indi.corr.um1 Utah Single [Utah 2] 1 39 0 100 (100) 1 min (1 min) 0–4 Days (1.75 Days) http://dx.doi.org/10.15387/fcp_indi.corr.utah2 Wisconsin (BirnpersName) [UWM 1] 25 21–32 (24.9) 44 2 (2) 30 min (30 min) 56–314 Days (110.4 Days) http://dx.doi.org/10.15387/fcp_indi.corr.uwm1 Table 2 Imaging parameters for sMRI scans in CoRR Site Manufacturer Model Headcoil Field Strength Sequence Flip Angle [Deg] Inversion Time (TI) [ms] Echo Time (TE) [ms] Repetition Time (TR) [ms] Bandwidth per Voxel (Readout) [Hz] Parallel Acquisition Number of Slices Orientation Slice Phase Encoding Direction Slice Acquisition Order Slice Thickness [mm] Slice Gap [mm] Field of View [mm] Acquisition Matrix Slice In-Place Resolution [mm2] Acquisition Time [min:sec] Fat Suppression Phase Partial Fourier Notes Beijing Normal University 3 (BNU 3) Siemens TrioTim 12 Chan 3T 3D MPRAGE 7 1,100 3.39 2,530 190 Off 128 s A-P int+ 1.33 0.6515 256 256×192 1.3×1.0 8:07 None Off Berlin Mind and Brain 1 (BMB 1) Siemens TrioTim 12 Chan 3T 3D MPRAGE 9 900 2.98 2,300 240 Off 176 s A-P int+ 1 0.5 256 256×256 1.0×1.0 9:50 None Off Hangzhou Normal University 1 (HNU 1) GE Discovery MR750 8 Chan 3T 3D SPGR 8 450 Min Full 8.06 125 A2 180 s A-P int+ 1 0 250 250×250 1.0×1.0 5:01 None Off Dartmouth College (DC 1) Philips N/A 32 Chan 3T 3D T1-TFE 8 900 3.7 2,375 191.4 S2.5 220 a R-L N/A 1 N/A 240 240×187 1.0×1.0 3:06 None N/A Reconstructed voxels at .94×.94 Institute of Automation, Chinese Academy of Sciences 1 (IACAS 1) GE Signa HDx 8 Chan 3T 3D BRAVO 7 1,100 2.984 7.788 122 A2 192 s R-L seq+ 1 0 256 256×256 1.0×1.0 5:02 None Off Intrinsic Brain Activity, Test-Retest Dataset (IBATRT) Siemens TrioTim 12 Chan 3T 3D MPRAGE 8 900 3.02 2,600 130 G2 176 s A-P seq+ 1 0.5 256 256×256 1.0×1.0 4:38 None 6/8 Institute of Psychology, Chinese Academy of Sciences 1 (IPCAS 1) Siemens TrioTim 8 Chan 3T MPRAGE 7 1,100 2.51 2,530 170 G2 128 s A-P seq+ 1.3 0.65 256 256×256 1.0×1.0 5:53 None Off Institute of Psychology, Chinese Academy of Sciences 2 (IPCAS 2) Siemens TrioTim 32 Chan 3T MPRAGE 9 900 2.95 2,300 130 Off 160 s A-P seq+ 1.2 0.6 240 240×226 0.9×0.9 9:14 None Off Institute of Psychology, Chinese Academy of Sciences 3 (IPCAS 3) Siemens TrioTim 8 Chan 3T 3D MPRAGE 7 1,100 2.51 2,530 170 Off 128 s A-P int+ 1.33 256 256×256 1.0×1.0 5:24 None Off Institute of Psychology, Chinese Academy of Sciences 4 (IPCAS 4) GE Discovery 8 Chan 3T 3D SPGR 8 450 3.136 8,068 31.25 A2 250 s A-P int+ 1 0 250 250×250 1.0×1.0 5:01 None Off Institute of Psychology, Chinese Academy of Sciences 5 (IPCAS 5) Siemens TrioTim 12 Chan 3T 3D MPRAGE 7 1,100 3.5 2,530 190 G2 176 s A-P int+ 1 0.5 256 256×256 1.0×1.0 6:03 None Off Institute of Psychology, Chinese Academy of Sciences 7 (IPCAS 7) Siemens TrioTim 8 Chan 3T 3D MPRAGE 8 900 3.02 2,600 180 Off 176 A-P seq+ 1 0.5 256 256×256 1.0×1.0 8:19 None 6/8 Institute of Psychology, Chinese Academy of Sciences 8 (IPCAS 8) Siemens TrioTim 12 Chan 3T 3D MPRAGE 7 1,100 3.39 2,530 190 Off 128 s A-P int+ 1.3 0.65 256 256×192 1.3×1.0 8:07 None Off Institute of Psychology, Chinese Academy of Sciences 6 (IPCAS 6) Siemens TrioTim 8 Chan 3T 3D MPRAGE 9 900 2.52 1,900 170 Off 176 s A-P seq+ 1 0.5 250 256×246 1.0×1.0 4:17 None Off University of Montreal 1 (UM 1) Siemens TrioTim 12 Chan 3T 3D MPRAGE 9 900 2.98 2,300 240 G2 176 s A-P int+ 1 0.5 256 256×256 1.0×1.0 5:12 None Off Mind Research Network (MRN 1) Siemens TrioTim 12 Chan 3T 3D MEMPR 7 1,200 1.64/3.5/5.36/7.22/9.08 2,530 651 G2 192 s oblique A-P int+ 1 0.5 256 256×256 1.0×1.0 6:03 None Off Ludwig-Maximilians-University 2 (LMU 2) Siemens Verio 12 Chan 3T 3D MPRAGE 9 900 3.06 2,400 230 G2 160 s A-P int+ 1 0.5 256 256×246 1.0×1.0 4:45 None 7/8 Ludwig-Maximilians-University 1 (LMU 1) Philips Achieva 32 Chan 3T 3D T1-TFE 8 900 N/A 2,375 191.5 S2/2.5 220 a R-L seq+ 1 0 240 240×187 1.0×1.0 3:06 None None Reconstructed voxels at .94×.94 Ludwig-Maximilians-University 3 (LMU 3) Siemens TrioTim 12 Chan 3T 3D MPRAGE 9 900 3.06 2,400 230 G2 256 s A-P int+ 1 0.5 256 256×246 1.0×1.0 4:45 None 7/8 Jinling Hospital, Nanjing University 1 (JHNU 1) Siemens TrioTim 8 Chan 3T 3D MPRAGE 9 900 2.98 2,300 240 Off 176 s A-P seq+ 1 0 256 256×256 1.0×1.0 9:50 None Off Nathan Kline Institute 1 (NKI 1) Siemens TrioTim 32 Chan 3T 3D MPRAGE 9 900 2.52 1,900 170 G2 176 s A-P seq+ 1 0.5 250 256×246 1.0×1.0 4:18 None Off New York University 2 (NYU 2) Siemens Allegra 1 Chan 3T 3D MPRAGE 7 1,100 3.25 2,530 200 Off 128 s A-P seq+ 1.3 0.65 256 256×192 1.3×1.0 8:07 None Off New York University 1 (NYU 1) Siemens Allegra 1 Chan 3T MPRAGE 8 900 3.93 2,500 N/A N/A 176 N/A N/A N/A 1 N/A 256 256×256 1.0×1.0 N/A N/A N/A University of Pittsburgh School of Medicine (UPSM) Siemens TrioTim 12 Chan 3T 3D MPRAGE 8 1,050 3.43 2,100 240 G2 192 a oblique R-L int+ 1 0.5 256 256×256 1.0×1.0 3:59 None Off Southwest University 1 (SWU 1) Siemens TrioTim 8 Chan 3T 3D MPRAGE 9 900 2.52 1,900 170 G2 176 s A-P seq+ 1 0.5 250 256×246 1.0×1.0 4:18 None Off Southwest University 3 (SWU 3) Siemens TrioTim 8 Chan 3T 3D MPRAGE 9 900 2.52 1,900 170 G2 176 s A-P seq+ 1 0.5 250 256×246 1.0×1.0 4:18 None Off Southwest University 2 (SWU 2) Siemens TrioTim 8 Chan 3T 3D MPRAGE 9 900 2.52 1,900 170 G2 176 s A-P seq+ 1 0.5 250 256×246 1.0×1.0 4:18 None Off Southwest University 4 (SWU 4) Siemens TrioTim 8 Chan 3T 3D MPRAGE 9 900 2.52 1,900 170 G2 176 s A-P seq+ 1 0.5 256 256×256 1.0×1.0 4:26 None Off Beijing Normal University 1 (BNU 1) Siemens TrioTim 12 Chan 3T 3D MPRAGE 7 1,100 3.39 2,530 256 Off 144 s A-P int+ 1.3 0.65 256 256×192 1.3×1.0 8:07 None Off Beijing Normal University 2 (BNU 2) (Test) Siemens TrioTim 12 Chan 3T 3D MPRAGE 7 1,100 3.39 2,530 256 Off 128 s A-P int+ 1.3 0.65 256 256×192 1.3×1.0 8:07 None Off Beijing Normal University 2 (BNU 2) (Retest) Siemens TrioTim 12 Chan 3T 3D MPRAGE 7 1,100 3.45 2,530 256 Off 176 s A-P int+ 1 0.5 256 256×256 1.0×1.0 10:49 None Off University of Utah 1 (Utah 1) Siemens TrioTim 12 Chan 3T 3D MPRAGE 9 900 2.91 2,300 240 Off 160 s A-P int+ 1.2 0.6 256 256×256 1.0×1.0 9:14 None Off University of Utah 2 (Utah 2) Siemens TrioTim 12 Chan 3T 3D MPRAGE 9 900 2.91 2,300 240 Off 160 s A-P int+ 1.2 0.6 256 256×256 1.0×1.0 9:14 None Off University of Washington—Madison 1 (UWM 1) GE Discovery 8 Chan 3T 3D MPRAGE 12 450 3.18 8.13 244 Off 160 a R-L Simultaneous (3D) 1 0 256 256×256 1.0×1.0 7:30 None Off Xuanwu Hospital, Capital University of Medical Sciences 1 (XHCUMS 1) Siemens TrioTim 12 Chan 3T 3D MPRAGE 9 800 2.15 1,600 200 Off 176 s oblique A-P seq+ 1 0.5 256 256×256 1.0×1.0 5:09 None 6/8 Table 3 Imaging parameters for rfMRI scans in CoRR Site Manufacturer Model Headcoil Field Strength Sequence Flip Angle [Deg] Echo Time (TE) [ms] Repetition Time (TR) [ms] Bandwidth per Voxel (Readout) [Hz] Parallel Acquisition Number of Slices Orientation Slice Phase Encoding Direction Slice Acquisition Order Slice Thickness [mm] Slice Gap [mm] Field of View [mm] Acquisition Matrix Slice In-Place Resolution [mm2] Number of Measurements Acquisition Time [min:sec] Fat Suppression Prospective Motion Correction Retrospective Motion Correction Notes Beijing Normal University 3 (BNU 3) Siemens TrioTim 12 Chan 3T EPI 90 30 2,000 2,520 Off 34 a A-P int+ 3.5 0.7 200 64×64 3.5×3.5 150 8:06 Yes No No Berlin Mind and Brain 1 (BMB 1) Siemens TrioTim 12 Chan 3T EPI 90 30 2,300 2,232 Off 34 a A-P int+ 4 0 192 64×64 3.0×3.0 200 7:45 Yes No No Hangzhou Normal University 1 (HNU 1) GE Discovery MR750 8 Chan 3T EPI 90 30 2,000 3437.5 On 43 a A-P int+ 3.4 0 220 64×64 3.4×3.4 300 10:00 Yes No No Dartmouth College (DC 1) Philips N/A 32 Chan 3T EPI 90 35 2,500 3,625 S2 36 a A-P N/A 3.5 0.5 240 80×80 3.0×3.0 120 5:10 Yes No N/A Institute of Automation, Chinese Academy of Sciences 1 (IACAS 1) GE Signa HDx 8 Chan 3T EPI 90 30 2,000 7812.5 Off 32 N/A R-L int+ 4 0.6 220 64×64 3.4×3.4 240 8:00 No N/A N/A Intrinsic Brain Activity, Test-Retest Dataset (IBATRT) Siemens TrioTim 12 Chan 3T EPI 90 30 1,750 2,442 Off 29 a A-P seq+ 3.6 0.36 220 64×64 3.4×3.4 343 10:04 Yes No No Institute of Psychology, Chinese Academy of Sciences 1 (IPCAS 1) Siemens TrioTim 8 Chan 3T EPI 90 30 2,000 2,232 Off 32 a A-P int+ 4 0.8 256 64×64 4.0×4.0 205 6:54 Yes No N/A Institute of Psychology, Chinese Academy of Sciences 2 (IPCAS 2) Siemens TrioTim 32 Chan 3T EPI 90 30 2,500 2,232 Off 32 a A-P int+ 3 0.99 240 64×64 3.8×3.8 212 8:57 Yes Yes No Institute of Psychology, Chinese Academy of Sciences 3 (IPCAS 3) Siemens TrioTim 8 Chan 3T EPI 90 30 2,000 2,232 Off 64 a A-P int+ 3 0.99 220 64×64 3.4×3.4 180 6:00 Yes No No Institute of Psychology, Chinese Academy of Sciences 4 (IPCAS 4) GE Discovery MR750 8 Chan 3T EPI 90 30 2,000 250 Off 37 a A-P int+ 3.5 0 224 64×64 3.5×3.5 180 6:04 Yes No No Institute of Psychology, Chinese Academy of Sciences 5 (IPCAS 5) Siemens TrioTim 12 Chan 3T EPI 90 30 2,000 2,298 Off 33 c F-H int+ 5 0 200 64×64 3.1×3.1 170 5:44 Yes No No Institute of Psychology, Chinese Academy of Sciences 7 (IPCAS 7) Siemens TrioTim 8 Chan 3T EPI 80 30 2,500 2,240 Off 38 a A-P int+ 3 0.33 216 72×72 3.0×3.0 184 7:45 Yes No No Institute of Psychology, Chinese Academy of Sciences 8 (IPCAS 8) Siemens TrioTim 12 Chan 3T EPI 90 30 2,000 2,520 Off 33 a A-P int+ 3 0.9 220 64×64 3.4×3.4 240 8:06 Yes Yes No Institute of Psychology, Chinese Academy of Sciences 6 (IPCAS 6) Siemens TrioTim 8 Chan 3T EPI 90 30 2,500 2,298 Off 25 a A-P int+ 3.5 3.5 224 64×64 3.5×3.5 242 10:05 Yes No No University of Montreal 1 (UM 1) Siemens TrioTim 12 Chan 3T EPI 90 30 2,000 2,442 Off 32 a A-P seq- 4 0 256 64×64 4.0×4.0 150 5:04 Yes No No Mind Research Network (MRN 1) Siemens TrioTim 12 Chan 3T EPI 75 29 2,000 2,170 Off 33 a oblique A-P int+ 3.5 1.05 240 64×64 3.8×3.8 150 5:04 Yes No No Ludwig-Maximilians-University 2 (LMU 2) Siemens Verio 12 Chan 3T EPI 80 30 3,000 2,232 Off 28 a A-P int+ 4 0.4 192 64×64 3.0×3.0 120 6:06 Yes No Yes Ludwig-Maximilians-University 1 (LMU 1) Philips Achieva 32 Chan 3T EPI 90 30 2,500 2,032 S3 52 a A-P seq+ 3 0 224×233 76×79 2.95×2.95 180 7:35 Yes Yes N/A Data Reconstructed at 1.65×1.65 in plane resolution Ludwig-Maximilians-University 3 (LMU 3) Siemens TrioTim 12 Chan 3T EPI 80 30 3,000 2,232 Off 36 a A-P int+ 4 0.4 192 64×64 3.0×3.0 120 6:06 Yes No No Jinling Hospital, Nanjing University 1 (JHNU 1) Siemens TrioTim 8 Chan 3T EPI 90 30 2,000 2,230 2 30 a A-P int+ 4 0.4 240 64×64 3.75×3.75 250 8:20 Yes No No Nathan Kline Institute 1 (NKI 1) (2500) Siemens TrioTim 32 Chan 3T EPI 80 30 2,500 2,240 Off 38 a A-P int+ 3 0.33 216 72×72 3.0×3.0 120 5:05 Yes No No Nathan Kline Institute 1 (NKI 1) (1400) Siemens TrioTim 32 Chan 3T EPI 65 30 1,400 1,786 Off 64 a A-P int+ 2 0 224 112×112 2.0×2.0 404 9:35 Yes No No Nathan Kline Institute 1 (NKI 1) (645) Siemens TrioTim 32 Chan 3T EPI 60 30 645 2,598 Off 40 a A-P int+ 3 0 222 74×74 3.0×3.0 900 9:46 Yes No No New York University 2 (NYU 2) Siemens Allegra 1 Chan 3T EPI 90 15 2,000 3,906 Off 33 a oblique R-L int+ 4 0 240 80×80 3.0×3.0 180 6:00 Yes No N/A New York University 1 (NYU 1) Siemens Allegra 1 Chan 3T EPI 90 25 2,000 N/A N/A 39 N/A N/A N/A 3 N/A 192 64×64 3.0×3.0 197 6:34 N/A N/A N/A University of Pittsburgh School of Medicine (UPSM) Siemens TrioTim 12 Chan 3T EPI 70 29 1,500 2,694 G2 29 a oblique P-A seq+ 4 0 200 64×64 3.1×3.1 200 5:06 Yes No Yes Southwest University 1 (SWU 1) Siemens TrioTim 8 Chan 3T EPI 90 30 2,000 2,232 Off 33 a A-P int+ 3 0.6 200 64×64 3.1×3.1 240 8:06 Yes Yes No Southwest University 3 (SWU 3) Siemens TrioTim 8 Chan 3T EPI 90 30 2,000 2,232 Off 32 a oblique A-P int+ 3 0.99 220 64×64 3.4×3.4 242 8:08 Yes Yes No Southwest University 2 (SWU 2) Siemens TrioTim 8 Chan 3T EPI 90 30 2,000 2,232 Off 32 a oblique A-P int+ 3 0.99 220 64×64 3.4×3.4 300 10:04 Yes Yes No Southwest University 4 (SWU 4) Siemens TrioTim 8 Chan 3T EPI 90 30 2,000 2,232 Off 32 a A-P int+ 3 1 220 64×64 3.4×3.4 242 8:06 Yes Yes No Beijing Normal University 1 (BNU 1) Siemens TrioTim 12 Chan 3T EPI 90 30 2,000 2,520 Off 33 a A-P int+ 3.5 0.7 200 64×64 3.1×3.1 200 6:46 Yes No No Beijing Normal University 2 (BNU 2) (Test) Siemens TrioTim 12 Chan 3T EPI 90 30 2,000 2,520 Off 33 a A-P int+ 3 0.6 200 64×64 3.1×3.1 240 8:06 Yes No No Beijing Normal University 2 (BNU 2) (Retest) Siemens TrioTim 12 Chan 3T EPI 90 30 1,500 2,520 Off 25 a A-P int+ 4 0.8 200 64×64 3.1×3.1 420 10:36 Yes No Yes University of Utah 1 (Utah 1) Siemens TrioTim 12 Chan 3T EPI 90 28 2,000 2,894 Off 40 a A-P int+ 3 0.3 220 64×64 3.4×3.4 240 8:06 Yes No Yes University of Utah 2 (Utah 2) Siemens TrioTim 12 Chan 3T EPI 90 28 2,000 2,894 Off 40 a A-P int+ 3 0.3 220 64×64 3.4×3.4 240 8:06 Yes No Yes University of Washington—Madison 1 (UWM 1) GE Discovery MR750 8 chan 3T EPI 60 25 2,600 N/A Off 40 N/A A-P int+ 3.5 0 224 64×64 3.5×3.5 231 10:01 No (Spectral Spatial RF pulse) N/A N/A Xuanwu Hospital, Capital University of Medical Sciences 1 (XHCUMS 1) Siemens TrioTim 12 Chan 3T EPI 90 30 3,000 2,232 Off 43 a oblique A-P int+ 3 0.48 192 64×64 3.0×3.0 124 6:20 Yes No N/A Table 4 Imaging parameters for dMRI scans in CoRR Site Manufacturer Model Sequence Headcoil Field Strength Flip Angle [Deg] Echo Time (TE) [ms] Repetition Time (TR) [ms] Bandwidth per Voxel (Readout) [Hz] Parallel Acquisition Number of Slices Orientation Slice Phase Encoding Direction Slice Acquisition Order Slice Thickness [mm] Slice Gap [mm] Field of View [mm] Acquisition Matrix Slice In-Place Resolution [mm 2 ] Number of Measurements Acquisition Time [min:sec] Fat Suppression Phase Partial Fourier Number of Directions Number of B Zeros B Value(s) [s/mm 2 ] Averages Notes Beijing Normal University 3 (BNU 3) Siemens TrioTim EPI 3T N/A 104 7,200 1,396 G2 49 a A-P int+ 2.5 0 230 128×128 1.8×1.8 65 8:11 Yes None 64 1 1,000 1 Hangzhou Normal University 1 (HNU 1) GE Min 8,600 68 R-L int+ 1.5 0 192 128×128 1.5×1.5 33 Yes 30 1,000 Institute of Psychology, Chinese Academy of Sciences (IPCAS 1) Siemens TrioTim Institute of Psychology, Chinese Academy of Sciences (IPCAS 2) Institute of Psychology, Chinese Academy of Sciences (IPCAS 8) Siemens TrioTim EPI 3T 104 6,600 1,396 G2 45 a A-P int+ 3 0 230 128×128 1.8×1.8 65 7:30 Yes None 64 1 1,000 1 Mind Research Network 1 (MRN 1) Siemens TrioTim EPI 3T N/A 84 9,000 1,562 G2 72 a A-P int+ 2 0 256 128×128 2.0×2.0 35 5:42 Yes 6/8 35 0 800 1 Nathan Kline Institute 1 (NKI 1) Siemens TrioTim EPI 3T 90 85 2,400 1,814 Off 64 a A-P int+ 2 0 212 106×106 2.0×2.0 137 5:58 Yes 6/8 137 0 1,500 1 Southwest University 4 (SWU 4) Siemens TrioTim Beijing Normal University 1 (BNU 1) Siemens TrioTim EPI 3T 89 8,000 1,562 G2 62 a A-P int+ 2.2 0 282 128×128 2.2×2.2 31 4:34 Yes 6/8 30 1 1,000 1 Xuanwu Hospital, Capital University of Medical Sciences (XHCUMS 1) Siemens TrioTim EPI 3T 83 8,000 1,396 G2 64 a A-P int+ 2 0 256 128×128 2.0×2.0 65 9:06 Yes 6/8 64 1 700 1 Table 5 Phenotypic protocols in CoRR CoRR Data Legend COLUMN LABEL DESCRIPTION DATA TYPE REQUIREMENT LEVEL SUBID INDI Subject ID integer core AGE_AT_SCAN_1 Age at scan session 1 in years (1 decimal place) float core SEX sex (1: female, 2: male) integer core DSM_IV_TR DSM-based Psychiatric Diagnosis (CPT Code) integer optional FIQ Full-scale IQ integer optional VIQ Verbal IQ integer optional PIQ Peformance IQ integer optional BMI Body Mass Index float optional RESTING STATE_INSTRUCTION Instruction string core VISUAL_STIMULATION_CONDITION Visual stimulation for rest (1: fixation, 2: blank screen, 3: word, 4: eyes closed, 5: other) integer core RETEST DESIGN 1: Within Session, 2: Between Session, 3: Within + Between integer core baseline PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (−1: male, 0: unknown) integer preferred retest 1 RETEST DURATION Time since baseline real core RETEST_UNITS m: min, d: days string core PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (−1: male, 0: unknown) integer preferred retest 2 RETEST DURATION Time since baseline real core RETEST_UNITS m: min, d: days string core PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (−1: male, 0: unknown) integer preferred retest 3 RETEST DURATION Time since baseline real core RETEST_UNITS m: min, d: days string core PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (−1: male, 0: unknown) integer preferred retest 4 RETEST DURATION Time since baseline real core RETEST_UNITS m: min, d: days string core PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (-1: male, 0: unknown) integer preferred retest 5 RETEST DURATION Time since baseline real core RETEST_UNITS m: min, d: days string core PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (−1: male, 0: unknown) integer preferred retest 6 RETEST DURATION Time since baseline real core RETEST_UNITS m: min, d: days string core PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (−1: male, 0: unknown) integer preferred retest 7 RETEST DURATION Time since baseline real core RETEST_UNITS m: min, d: days string core PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (-1: male, 0: unknown) integer preferred retest 8 RETEST DURATION Time since baseline real core RETEST_UNITS m: min, d: days string core PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (−1: male, 0: unknown) integer preferred retest 9 RETEST DURATION Time since baseline real core RETEST_UNITS m: min, d: days string core PRECEDING_CONDITION 0: No active task, 1: active task, 2: music listening, 3: video watching, 4: unknown integer core TIME_OF_DAY 0[0-5:59], 1[6:00-11:59], 2[12:00-17:59], 3[18:00-23:59] integer preferred SATIETY 0: unknown, 1: post-prandial, 2: fasting integer preferred LMP Number of days since start of last menstrual period (−1: male, 0: unknown) integer preferred Table 6 Descriptive statistics for common derivatives site falffterm_50_mean falffterm_50_std falffterm_75_mean falffterm_75_std falffterm_90_mean falffterm_90_std reho_50_mean reho_50_std reho_75_mean reho_75_std reho_90_mean reho_90_std vmhc_50_mean vmhc_50_std vmhc_75_mean vmhc_75_std vmhc_90_mean vmhc_90_std BMB_1 0.67187915 0.01095439 0.71275284 0.01375119 0.75674746 0.01764103 0.11483976 0.02234383 0.17367675 0.03054432 0.23555456 0.03477065 0.40005245 0.05349399 0.59060775 0.05345469 0.74120895 0.04415967 UPSM_1 0.53848629 0.01365502 0.58279827 0.01723196 0.63421968 0.02156525 0.10928463 0.01861684 0.15879749 0.02548246 0.21595553 0.03151972 0.36652558 0.08059337 0.55897114 0.07633087 0.72308106 0.05822021 LMU_1 0.68131285 0.07205763 0.71701862 0.07185605 0.75380735 0.07318461 0.19503535 0.01811093 0.26123735 0.02698287 0.34628153 0.0460762 0.36543758 0.08438397 0.57967558 0.10305263 0.75077383 0.07739737 LMU_2 0.75128582 0.01154888 0.78492683 0.01157899 0.81470764 0.01302299 0.08188113 0.07398937 0.11549643 0.07391351 0.15802806 0.07467288 0.27874734 0.09491569 0.47506016 0.09555446 0.6654087 0.07868861 LMU_3 0.75300309 0.01130565 0.78767158 0.01167477 0.81832079 0.0126824 0.08800187 0.01217619 0.12622061 0.020186 0.17269627 0.02807193 0.3391815 0.06357559 0.53838804 0.06600959 0.70707509 0.05175325 HNU_1 0.65986927 0.02010203 0.72070762 0.02451698 0.77651227 0.02363225 0.2038152 0.03514323 0.29749165 0.04497476 0.38325751 0.05052365 0.4588192 0.05497453 0.63538063 0.04934746 0.77230292 0.03885562 IPCAS_1 0.67044235 0.016092 0.73265578 0.02054316 0.78949274 0.02176515 0.16103934 0.01787039 0.23594238 0.02272643 0.30939742 0.0268952 0.46294367 0.04570625 0.64824828 0.03803198 0.7883282 0.0275335 IPCAS_8 0.62967971 0.01925352 0.67052505 0.02387977 0.71691484 0.02876108 0.09530096 0.01353402 0.14184759 0.02166288 0.1948324 0.02725064 0.36661959 0.07089762 0.55197346 0.07581143 0.70837986 0.06042681 IPCAS_3 0.63595724 0.01076589 0.68743886 0.01482221 0.74423368 0.01892027 0.11979874 0.01402373 0.18396178 0.01845976 0.24977681 0.02327644 0.40684854 0.06845373 0.60042805 0.06228979 0.75188672 0.04717628 BNU_2 0.60229242 0.02920188 0.65412869 0.02554877 0.71380426 0.02530338 0.11757821 0.02282137 0.17708686 0.03426596 0.23930029 0.04184171 0.39161112 0.06085424 0.57577467 0.06092372 0.72323258 0.05214133 Utah_2 0.39387042 0.00795506 0.43954022 0.01013165 0.48927946 0.01422875 0.09003811 0.0073556 0.13869799 0.01255276 0.199258 0.01605418 0.29095939 0.03879641 0.51411575 0.04272958 0.696097 0.03564367 IPCAS_2 0.72243191 0.0181438 0.7615492 0.02058029 0.799527 0.02245114 0.11424405 0.01332137 0.17020604 0.01854308 0.22841676 0.02230091 0.38997572 0.0479311 0.57226243 0.04429783 0.72028097 0.03543431 IPCAS_7 0.70453551 0.01044921 0.74220274 0.01239562 0.78069057 0.01499633 0.11302486 0.01391228 0.16486068 0.01887637 0.22047912 0.02306602 0.44019481 0.05222028 0.61713186 0.04193198 0.75632372 0.0310469 IPCAS_4 0.61859584 0.00500133 0.67050929 0.00733894 0.73398443 0.00864194 0.15711392 0.01341702 0.24042446 0.01581406 0.32192849 0.01555375 0.33438623 0.04830237 0.53106513 0.03968627 0.70159497 0.02432706 IBA_TRT 0.61697087 0.01698959 0.67181163 0.02379427 0.73267667 0.02564145 0.15428888 0.01943454 0.22296525 0.02621619 0.28959599 0.03162183 0.49319222 0.06024092 0.66792044 0.05406984 0.79630123 0.04112627 NYU_1 0.60403584 0.00560845 0.63578872 0.00704961 0.66602103 0.01062687 0.06655346 0.00876404 0.08898365 0.01330434 0.11775377 0.01752209 0.24062456 0.06428724 0.4098162 0.07451068 0.58509115 0.06925713 SWU_3 0.64630782 0.01126605 0.69593592 0.0152442 0.75454278 0.01890379 0.124959 0.01111077 0.18522821 0.01390574 0.24769718 0.01560898 0.42335421 0.05311485 0.59702631 0.05107783 0.73650282 0.04191661 JHNU_1 0.65301786 0.01257395 0.70823791 0.01853576 0.7656215 0.02257707 0.14548168 0.01738676 0.21816082 0.02548086 0.29086169 0.03104211 0.43962768 0.04908573 0.62718721 0.04797892 0.76920497 0.04042416 IPCAS_6 0.70658553 0.0123221 0.74488307 0.01826713 0.78379522 0.02078967 0.10545752 0.01273462 0.15660753 0.02326668 0.21339069 0.03337394 0.34452537 0.04373743 0.53229765 0.04768446 0.69326661 0.04242879 IPCAS_5 0.64256233 0.01487854 0.69059087 0.02347256 0.74449512 0.02796 0.11943758 0.0155287 0.17868678 0.02501478 0.23846551 0.03047227 0.4077564 0.05064864 0.59247696 0.05081532 0.73817232 0.04471268 SWU_2 0.64974047 0.01289791 0.70310073 0.0188145 0.76135469 0.02277764 0.12797104 0.01927335 0.19042776 0.02500273 0.25444691 0.03008468 0.45193177 0.05525688 0.63140079 0.05077888 0.76819185 0.04285344 BNU_1 0.63211946 0.00972767 0.67600309 0.01378115 0.72653647 0.01881311 0.10428446 0.01308989 0.15421598 0.01991249 0.21087879 0.02547435 0.35300216 0.05553608 0.53670762 0.05706393 0.69495155 0.04838126 SWU_4 0.64154444 0.01429541 0.69082036 0.0203162 0.74711214 0.02426893 0.11653525 0.01383388 0.17615494 0.01984331 0.2391432 0.02410923 0.39455079 0.06615914 0.58018861 0.06404032 0.73106233 0.0485205 XHCUMS_1 0.74545799 0.0078227 0.77938982 0.0088829 0.81059326 0.0107497 0.07256239 0.01079506 0.10624958 0.01963938 0.1511244 0.02958381 0.30188529 0.06824965 0.50331368 0.08017147 0.68419305 0.07082655 IACAS_1 0.6895231 0.0300066 0.75245037 0.03322049 0.8018579 0.0332941 0.24198074 0.02482892 0.33039185 0.03241646 0.41397883 0.04006509 0.52029517 0.06943379 0.69257685 0.05949973 0.81463929 0.0401705 UWM_1 0.73885091 0.02085548 0.78182404 0.0217158 0.82110711 0.02122855 0.18033792 0.02375627 0.26637009 0.03235537 0.34847208 0.03830241 0.43066953 0.06329518 0.62068197 0.05952193 0.76718269 0.04542059 Utah_1 0.43180294 0.01134049 0.47441855 0.01467621 0.52603847 0.02023981 0.09954567 0.01276878 0.14814368 0.01833839 0.20506961 0.02249829 0.32673934 0.06492514 0.53635086 0.06411349 0.71313155 0.04848159 MRN_1 0.65478119 0.01662275 0.7120571 0.02363398 0.76604944 0.02670331 0.15111643 0.02284248 0.2216864 0.02877418 0.29443578 0.03425655 0.48563286 0.06569892 0.67372623 0.0542015 0.80905918 0.0375586 BNU_3 0.62995743 0.01655295 0.6739848 0.02082506 0.72541729 0.02575076 0.1084434 0.01530951 0.16294329 0.02273817 0.222565 0.02686687 0.36913241 0.0628315 0.5559278 0.06363683 0.71159108 0.05368372 NYU_2 0.60924566 0.00834139 0.64618221 0.01101985 0.68359507 0.01529646 0.09548649 0.01852433 0.13417889 0.02440248 0.17756895 0.02930165 0.31585856 0.07920283 0.5005881 0.07993809 0.66899026 0.06534853 UM_1 0.64465695 0.01904965 0.69641997 0.02274992 0.7489135 0.02579444 0.18495672 0.0263751 0.25570424 0.03319021 0.32440271 0.03714822 0.5210892 0.06289882 0.7000433 0.05041404 0.8275942 0.03329554 SWU_1 0.63444905 0.01143149 0.6768667 0.01638089 0.72632754 0.02163575 0.12247162 0.01482465 0.17643713 0.02030604 0.23413699 0.02418896 0.38041606 0.05010822 0.55571521 0.0510355 0.70154697 0.04606569 nki_rest_645 0.42075741 0.03601592 0.51325902 0.04760785 0.62189991 0.05329492 0.16792067 0.02582188 0.26346495 0.03860451 0.35137484 0.04565719 0.53317793 0.08586873 0.72076799 0.07001122 0.83808126 0.04968654 nki_rest_1400 0.5329489 0.0157548 0.58441685 0.02376856 0.6584884 0.03356512 0.13901774 0.01651447 0.21431023 0.03082392 0.30258362 0.04254909 0.47995716 0.08000626 0.68036714 0.06892006 0.81266311 0.05198713 nki_rest_2500 0.69885965 0.01782518 0.74602209 0.02094921 0.78988815 0.02365705 0.10835936 0.01536915 0.16690148 0.02466222 0.23075883 0.0325903 0.45334 0.06787562 0.65129144 0.05635185 0.79011133 0.03912288