Link Function: information

Database for open data sharing of resting-state fMRI and DTI images collected from over 50 sites around the world. These data collections now contain comprehensive phentoypic information, openly available via data usage agreements.

Collection of resting state fMRI (R-fMRI) datasets from sites around world. It demonstrates open sharing of R-fMRI data and aims to emphasize aggregation and sharing of well-phenotyped datasets.

The Autism Brain Imaging Data Exchange (ABIDE) contains data from 17 international sites, sharing resting state functional magnetic resonance imaging (R-fMRI) and anatomical and phenotypic datasets. This effort contains 1112 datasets, including 539 from individuals with ASD and 573 from typical controls (ages 7-64 years, median 14.7 years across groups).

COBRE dataset, preprocessed and functional connectivity features extracted at 7 resolutions (7,12,20,36,64,122,197,325,444). Pearson correlation was used to compute functional connectivity between time series. The resolution are based on a partition using Cambridge dataset availlable at http://dx.doi.org/10.6084/m9.figshare.1285615

Content

This work is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (INDI), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 72 patients diagnosed with schizophrenia (58 males, age range = 18-65 yrs) and 74 healthy controls (51 males, age range = 18-65 yrs). The fMRI dataset for each subject are single nifti files (.nii.gz), featuring 150 EPI blood-oxygenation level dependent (BOLD) volumes were obtained in 5 mns (TR = 2 s, TE = 29 ms, FA = 75°, 32 slices, voxel size = 3x3x4 mm3 , matrix size = 64x64, FOV = mm2 ).

• cobre_model_group.csv A comma-separated value file, with the sz (1: patient with schizophrenia, 0: control), age, sex, and FD (frame displacement, as defined by Power et al. 2012) variables. Each column codes for one variable, starting with the label, and each line has the label of the corresponding subject.
• cobre_resolution_xx.mat: a .mat (octave/matlab) structure with two variables: data a NxF (N subjects x F features) and a subj_idx the subject id of each row. The features are a vetororized for of the connectome. ### Preprocessing The datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.12.14, under CentOS version 6.3 with Octave(http://gnu.octave.org) version 3.8.1 and the Minc toolkit (http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) version 0.3.18.Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs, using the median volume of the first run as a target. The median volume of one selected fMRI run for each subject was coregistered with a T1 individual scan using Minctracc (Collins and Evans, 1998), which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011) using the CIVET pipeline (Ad-Dabbagh et al., 2006). The MNI symmetric template was generated from the ICBM152 sample of 152 young adults, after 40 iterations of non-linear coregistration. The rigid-bodytransform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 3 mm isotropic resolution. The “scrubbing” method of (Power et al., 2012), was used to remove the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~180 s of acquisition, was then required for further analysis. For this reason, 16 controls and 29 schizophrenia patients were rejected from the subsequent analyses. The following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares (Giove et al., 2009). The fMRI volumes were finally spatially smoothed with a 6 mm isotropic Gaussian blurring kernel. ### References Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J. S., Mok, K., Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., Evans, A. C., 2006. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neu-roImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Jan. 2011.Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54 (1), 313–327.URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033 Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064. URL http://dx.doi.org/10.1016/j.mri.2009.06.004 Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Feb. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154. URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018

A project which systematically preprocess the data from the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) and openly share the results. Data is currently hosted in an Amazon Web Services Public S3 Bucket and at NITRC.

ContentThis work is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (INDI), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 72 patients diagnosed with schizophrenia (58 males, age range = 18-65 yrs) and 74 healthy controls (51 males, age range = 18-65 yrs). The fMRI dataset for each subject are single nifti files (.nii.gz), featuring 150 EPI blood-oxygenation level dependent (BOLD) volumes were obtained in 5 mns (TR = 2 s, TE = 29 ms, FA = 75°, 32 slices, voxel size = 3x3x4 mm3 , matrix size = 64x64, FOV = mm2 ). The data processing as well as packaging was implemented by Pierre Bellec, CRIUGM, Department of Computer Science and Operations Research, University of Montreal, 2016.The COBRE preprocessed fMRI release more specifically contains the following files:README.md: a markdown (text) description of the release.phenotypic_data.tsv.gz: A gzipped tabular-separated value file, with each column representing a phenotypic variable as well as measures of data quality (related to motions). Each row corresponds to one participant, except the first row which contains the names of the variables (see file below for a description).keys_phenotypic_data.json: a json file describing each variable found in phenotypic_data.tsv.gz.fmri_XXXXXXX.tsv.gz: A gzipped tabular-separated value file, with each column representing a confounding variable for the time series of participant XXXXXXX (which is the same participant ID found in phenotypic_data.tsv.gz). Each row corresponds to a time frame, except for the first row, which contains the names of the variables (see file below for a definition).keys_confounds.json: a json file describing each variable found in the files fmri_XXXXXXX.tsv.gz.fmri_XXXXXXX.nii.gz: a 3D+t nifti volume at 6 mm isotropic resolution, stored as short (16 bits) integers, in the MNI non-linear 2009a symmetric space (http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Each fMRI data features 150 volumes.Usage recommendationsIndividual analyses: You may want to remove some time frames with excessive motion for each subject, see the confounding variable called scrub in fmri_XXXXXXX.tsv.gz. Also, after removing these time frames there may not be enough usable data. We recommend a minimum number of 60 time frames. A fairly large number of confounds have been made available as part of the release (slow time drifts, motion paramaters, frame displacement, scrubbing, average WM/Vent signal, COMPCOR, global signal). We strongly recommend regression of slow time drifts. Everything else is optional.Group analyses: There will also be some residuals effect of motion, which you may want to regress out from connectivity measures at the group level. The number of acceptable time frames as well as a measure of residual motion (called frame displacement, as described by Power et al., Neuroimage 2012), can be found in the variables Frames OK and FD scrubbed in phenotypic_data.tsv.gz. Finally, the simplest use case with these data is to predict the overall presence of a diagnosis of schizophrenia (values Control or Patient in the phenotypic variable Subject Type). You may want to try to match the control and patient samples in terms of amounts of motion, as well as age and sex. Note that more detailed diagnostic categories are available in the variable Diagnosis.PreprocessingThe datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.17, under CentOS version 6.3 with Octave (http://gnu.octave.org) version 4.0.2 and the Minc toolkit (http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) version 0.3.18. Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs, using the median volume of the first run as a target. The median volume of one selected fMRI run for each subject was coregistered with a T1 individual scan using Minctracc (Collins and Evans, 1998), which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011) using the CIVET pipeline (Ad-Dabbagh et al., 2006). The MNI symmetric template was generated from the ICBM152 sample of 152 young adults, after 40 iterations of non-linear coregistration. The rigid-body transform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 6 mm isotropic resolution.Note that a number of confounding variables were estimated and are made available as part of the release. WARNING: no confounds were actually regressed from the data, so it can be done interactively by the user who will be able to explore different analytical paths easily. The “scrubbing” method of (Power et al., 2012), was used to identify the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~120 s of acquisition, is recommended for further analysis. The following nuisance parameters were estimated: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the six rigid-body motion parameters (Giove et al., 2009), anatomical COMPCOR signal in the ventricles and white matter (Chai et al., 2012), PCA-based estimator of the global signal (Carbonell et al., 2011). The fMRI volumes were not spatially smoothed.ReferencesAd-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J. S., Mok, K., Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., Evans, A. C., 2006. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy.Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082F. Carbonell, P. Bellec, A. Shmuel. Validation of a superposition model of global and system-specific resting state activity reveals anti-correlated networks. Brain Connectivity 2011 1(6): 496-510. doi:10.1089/brain.2011.0065Chai, X. J., Castan, A. N. N., Ongr, D., Whitfield-Gabrieli, S., Jan. 2012. Anticorrelations in resting state networks without global signal regression. NeuroImage 59 (2), 1420-1428. http://dx.doi.org/10.1016/j.neuroimage.2011.08.048 Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294.Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Jan. 2011. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54 (1), 313–327. URLhttp://dx.doi.org/10.1016/j.neuroimage.2010.07.033Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064. URLhttp://dx.doi.org/10.1016/j.mri.2009.06.004Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Feb. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154. URLhttp://dx.doi.org/10.1016/j.neuroimage.2011.10.018

# ABIDE I FreeSurfer 6 'brain volume' data

This archive contains the following ABIDE I FreeSurfer 6 volumes:
mri/brain.mgz, mri/brain_mask.mgz, mri/aseg.mgz, mri/wm.mgz.

## Credits

This data is derived from the MRI scans of the ABIDE I dataset:

* ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/

Quoting from that website:

"The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first
ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17
international sites, sharing previously collected resting state functional
magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets
made available for data sharing with the broader scientific community.
This effort yielded 1112 dataset, including 539 from individuals with
ASD and 573 from typical controls (ages 7-64 years, median 14.7 years
across groups). This aggregate was released in August 2012. Its
establishment demonstrated the feasibility of aggregating resting
state fMRI and structural MRI data across sites; the rate of these
data use and resulting publications (see Manuscripts) have shown its
utility for capturing whole brain and regional properties of the brain
connectome in Autism Spectrum Disorder (ASD). In accordance with
HIPAA guidelines and 1000 Functional Connectomes Project / INDI
protocols, all datasets have been anonymized, with no protected
health information included."

Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

## How this data was produced

The following steps were used to create the data:

* We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).
* We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.
- We did not run any quality metrics on the scans or exclude any subjects.

## Contained files

* In order to reduce the size of this dataset, for each subject, we only included the following files:

-

-

-

-

All files are in FreeSurfer MGZ format.

## What is NOT contained

* The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.

## Author and License

Note: For the authors of the original ABIDE I dataset, see the Credits section above.

This mri volume data was created by:

Dr. Tim Schäfer
Postdoc Computational Neuroimaging
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy
University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
http://rcmd.org/ts

The data is published under the following license:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.

See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

# ABIDE I FreeSurfer 6 'local gyrification index' (lGI) data

DOI of this dataset: 10.5281/zenodo.7132610

This directory 'abide_freesurfer6_lgi' contains the ABIDE I FreeSurfer 6 'local gyrification index' (lGI) data and meshes.

## Credits

This data is derived from the MRI scans of the ABIDE I dataset:

* ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/

Quoting from that website:

"The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first
ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17
international sites, sharing previously collected resting state functional
magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets
made available for data sharing with the broader scientific community.
This effort yielded 1112 dataset, including 539 from individuals with
ASD and 573 from typical controls (ages 7-64 years, median 14.7 years
across groups). This aggregate was released in August 2012. Its
establishment demonstrated the feasibility of aggregating resting
state fMRI and structural MRI data across sites; the rate of these
data use and resulting publications (see Manuscripts) have shown its
utility for capturing whole brain and regional properties of the brain
connectome in Autism Spectrum Disorder (ASD). In accordance with
HIPAA guidelines and 1000 Functional Connectomes Project / INDI
protocols, all datasets have been anonymized, with no protected
health information included."

Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

## How this data was produced

The following steps were used to create the data:

* We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).
* We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.
- We did not run any quality metrics on the scans or exclude any subjects.
* We computed pial-lgi (Schaer et al. 2008, https://doi.org/10.1109/TMI.2007.903576) as implemented in FreeSurfer 6 for all subjects.
- For some of the subjects, MRI data was not available or lgi could not be computed due to very bad quality of the (or a completely failed) surface reconstruction. These subjects are listed in the file 'subjects_lgi_computation_failed.txt' (14 of 1035 subjects).
- All subjects for which lgi computation succeeded for both hemispheres are listed in the file 'subjects.txt' (1021 of 1035 subjects).

## Contained files

* In order to reduce the size of this dataset, for each subject, we only included the following files:
-

See the section 'How this data was produced' for information on the files 'subjects.txt' and 'subjects_lgi_computation_failed.txt'.

## What is NOT contained

* The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.
* The lgi values are only contained in native space. Standard space data is available as a separate download on Zenodo.

## Author and License

Note: For the authors of the original ABIDE I dataset, see the Credits section above.

This lgi data was created by:

Dr. Tim Schäfer
Postdoc Computational Neuroimaging
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy
University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
http://rcmd.org/ts

The data is published under the following license:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.

See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

# ABIDE I FreeSurfer 6 'native space descriptors' data

This archive contains the following ABIDE I FreeSurfer 6 native space mesh descriptors:
thickness, area, volume, sulc, curv, jacobian_white.

## Credits

This data is derived from the MRI scans of the ABIDE I dataset:

* ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/

Quoting from that website:

"The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first
ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17
international sites, sharing previously collected resting state functional
magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets
made available for data sharing with the broader scientific community.
This effort yielded 1112 dataset, including 539 from individuals with
ASD and 573 from typical controls (ages 7-64 years, median 14.7 years
across groups). This aggregate was released in August 2012. Its
establishment demonstrated the feasibility of aggregating resting
state fMRI and structural MRI data across sites; the rate of these
data use and resulting publications (see Manuscripts) have shown its
utility for capturing whole brain and regional properties of the brain
connectome in Autism Spectrum Disorder (ASD). In accordance with
HIPAA guidelines and 1000 Functional Connectomes Project / INDI
protocols, all datasets have been anonymized, with no protected
health information included."

Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

## How this data was produced

The following steps were used to create the data:

* We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).
* We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.
- We did not run any quality metrics on the scans or exclude any subjects.

## Contained files

* In order to reduce the size of this dataset, for each subject, we only included the following files:
-

All files are in binary FreeSurfer curv format.

## What is NOT contained

* The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.

## Author and License

Note: For the authors of the original ABIDE I dataset, see the Credits section above.

This lgi data was created by:

Dr. Tim Schäfer
Postdoc Computational Neuroimaging
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy
University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
http://rcmd.org/ts

The data is published under the following license:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.

See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

Content

This work is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (INDI), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 72 patients diagnosed with schizophrenia (58 males, age range = 18-65 yrs) and 74 healthy controls (51 males, age range = 18-65 yrs). The fMRI dataset for each subject are single nifti files (.nii.gz), featuring 150 EPI blood-oxygenation level dependent (BOLD) volumes were obtained in 5 mns (TR = 2 s, TE = 29 ms, FA = 75°, 32 slices, voxel size = 3x3x4 mm3 , matrix size = 64x64, FOV = mm2 ). The COBRE preprocessed fMRI release more specifically contains the following files:* README.md: a markdown (text) description of the release. * cobre_model_group.csv A comma-separated value file, with the sz (1: patient with schizophrenia, 0: control), age, sex, and FD (frame displacement, as defined by Power et al. 2012) variables. Each column codes for one variable, starting with the label, and each line has the label of the corresponding subject.* fmri_szxxxSUBJECT_session1_run1.nii.gz: a 3D+t nifti volume at 3 mm isotropic resolution, in the MNI non-linear 2009a symmetric space(http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Note that the number of time samples may vary, as some samples have beenremoved if tagged with excessive motion. See the _extra.mat file below for more info.* fmri_szxxxSUBJECT_session1_run1_extra.mat: a matlab/octave file for each subject. Each .mat file contains the following variables:* confounds: a TxK array. Each row corresponds to a time sample, and each column to one confound that was regressed out from the time series during preprocessing.* labels_confounds: cell of strings. Each entry is the label of a confound that was regressed out from the time series.* mask_suppressed: a T2x1 vector. T2 is the number of time samples in the raw time series (before preprocessing), T2=119. Each entry corresponds to a time sample, and is 1 if the corresponding sample was removed due to excessive motion (or to wait for magnetic equilibrium at the beginning of the series). Samples that were kept are tagged with 0s.* time_frames: a Tx1 vector. Each entry is the time of acquisition (in s) of the corresponding volume.

Preprocessing

The datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.12.14, under CentOS version 6.3 with Octave(http://gnu.octave.org) version 3.8.1 and the Minc toolkit (http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) version 0.3.18.Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs, using the median volume of the first run as a target. The median volume of one selected fMRI run for each subject was coregistered with a T1 individual scan using Minctracc (Collins and Evans, 1998), which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011) using the CIVET pipeline (Ad-Dabbagh et al., 2006). The MNI symmetric template was generated from the ICBM152 sample of 152 young adults, after 40 iterations of non-linear coregistration. The rigid-bodytransform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 3 mm isotropic resolution. The “scrubbing” method of (Power et al., 2012), was used to remove the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~180 s of acquisition, was then required for further analysis. For this reason, 16 controls and 29 schizophrenia patients were rejected from the subsequent analyses. The following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares (Giove et al., 2009). The fMRI volumes were finally spatially smoothed with a 6 mm isotropic Gaussian blurring kernel.

References

Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J. S., Mok, K., Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., Evans, A. C., 2006. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neu-roImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Jan. 2011. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54 (1), 313–327.URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033 Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064. URL http://dx.doi.org/10.1016/j.mri.2009.06.004 Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Feb. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154. URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018

Other derivatives

This dataset was used in a publication, see the link below.https://github.com/SIMEXP/glm_connectome

The Dallas Lifespan Brain Study (DLBS) is a major effort designed to understand the antecedents of preservation and decline of cognitive function at different stages of the adult lifespan, with a particular interest in the early stages of a healthy brain's march towards Alzheimer Disease.

CCNP takes its pilot stage (2013 – 2022) of the first ten-year. It aims at establishing protocols on the Chinese normative brain development trajectories across the human lifespan. It implements a structured multi-cohort longitudinal design (or accelerated longitudinal design), which is particularly viable for lifespan trajectory studies, and optimal for recoverable missing data. The CCNP pilot comprises three connected components: developing CCNP (devCCNP, baseline age = 6-18 years, 12 age cohorts, 3 waves, interval = 15 months), maturing CCNP (matCCNP, baseline age = 18-60 years, 14 age cohorts, 3 waves, interval = 39 months) and ageing CCNP (ageCCNP, baseline age = 60-84 years, 12 age cohorts, 3 waves, interval = 27 months). The developmental component of CCNP (devCCNP, 2013-2022), also known as "Growing Up in China", a ten-year's pilot stage of CCNP, has established follow-up cohorts in Chongqing (CKG, Southwest China) and Beijing (PEK, Northeast China). It is an ongoing project focused on longitudinal developmental research as creating and sharing a large-scale multimodal dataset for typically developing Chinese children and adolescents (ages 6.0-17.9 at enrollment) carried out in both school- and community-based samples. The devCCNP houses longitudinal data about demographics, biophysical measures, psychological and behavioral assessments, cognitive phenotyping, ocular-tracking, as well as multimodal magnetic resonance imaging (MRI) of brain's resting and naturalistic viewing function, diffusion structure and morphometry. With the collection of longitudinal structured images and psychobehavioral samples from school-age children and adolescents in multiple cohorts, devCCNP has constructed a full school-age brain template and its growth curve reference for Han Chinese which demonstrated the difference in brain development between Chinese and American school-aged children.*This dataset contains only T1-weighted MRI, Resting-state fMRI and Diffusion Tensor MRI data of devCCNP.To access the devCCNP Lite data, investigators must complete the application file Data Use Agreement on CCNP (DUA-CCNP) at http://deepneuro.bnu.edu.cn/?p=163 and have it reviewed and approved by the Chinese Color Nest Consortium (CCNC). All terms specified by the DUA-CCNP must be complied with. Meanwhile, the baseline CKG Sample on brain imaging are available to researchers via the International Data-sharing Neuroimaging Initiative (INDI) through the Consortium for Reliability and Reproducibility (CoRR). More information about CCNP can be found at: http://deepneuro.bnu.edu.cn/?p=163 or https://github.com/zuoxinian/CCNP. Requests for further information and collaboration are encouraged and considered by the CCNC, and please read the Data Use Agreement and contact us via deepneuro@bnu.edu.cn.

# ABIDE I FreeSurfer 6 'surface transforms' data

This archive contains files needed to map surface-based subject data to other subjects, templates or spaces.

## Credits

This data is derived from the MRI scans of the ABIDE I dataset:

* ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/

Quoting from that website:

"The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first
ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17
international sites, sharing previously collected resting state functional
magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets
made available for data sharing with the broader scientific community.
This effort yielded 1112 dataset, including 539 from individuals with
ASD and 573 from typical controls (ages 7-64 years, median 14.7 years
across groups). This aggregate was released in August 2012. Its
establishment demonstrated the feasibility of aggregating resting
state fMRI and structural MRI data across sites; the rate of these
data use and resulting publications (see Manuscripts) have shown its
utility for capturing whole brain and regional properties of the brain
connectome in Autism Spectrum Disorder (ASD). In accordance with
HIPAA guidelines and 1000 Functional Connectomes Project / INDI
protocols, all datasets have been anonymized, with no protected
health information included."

Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

## How this data was produced

The following steps were used to create the data:

* We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).
* We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.
- We did not run any quality metrics on the scans or exclude any subjects.

## Contained files

* In order to reduce the size of this dataset, for each subject, we only included the following files:

-

-

## What is NOT contained

* The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.

## Author and License

Note: For the authors of the original ABIDE I dataset, see the Credits section above.

This data was created by:

Dr. Tim Schäfer
Postdoc Computational Neuroimaging
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy
University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
http://rcmd.org/ts

The data is published under the following license:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.

See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

ABIDE I FreeSurfer 6 'label' data

This archive contains the following ABIDE I FreeSurfer 6 labels: cortex, aparc, aparc.a2009s.

Credits

This data is derived from the MRI scans of the ABIDE I dataset:

• ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/

Quoting from that website:

"The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first
 ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17
 international sites, sharing previously collected resting state functional
 magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets
 made available for data sharing with the broader scientific community.
 This effort yielded 1112 dataset, including 539 from individuals with
 ASD and 573 from typical controls (ages 7-64 years, median 14.7 years
 across groups). This aggregate was released in August 2012. Its
 establishment demonstrated the feasibility of aggregating resting
 state fMRI and structural MRI data across sites; the rate of these
 data use and resulting publications (see Manuscripts) have shown its
 utility for capturing whole brain and regional properties of the brain
 connectome in Autism Spectrum Disorder (ASD). In accordance with
 HIPAA guidelines and 1000 Functional Connectomes Project / INDI
 protocols, all datasets have been anonymized, with no protected
 health information included."

Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

How this data was produced

The following steps were used to create the data:

• We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).
• We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.
  • We did not run any quality metrics on the scans or exclude any subjects.

Contained files

• In order to reduce the size of this dataset, for each subject, we only included the following files:
  • /label/lh.cortex.label : label identifying which mesh vertices belong to cortex versus medial wall, for left hemisphere.
  • /label/rh.cortex.label : label identifying which mesh vertices belong to cortex versus medial wall, for right hemisphere.
  • /label/lh.aparc.annot : Desikan atlas surface parcellation for left hemisphere.
  • /label/rh.aparc.annot : Desikan atlas surface parcellation for right hemisphere.
  • /label/lh.aparc.a2009s.annot : Destrieux atlas surface parcellation for left hemisphere.
  • /label/rh.aparc.a2900s.annot : Destrieux atlas surface parcellation for right hemisphere.

All files are in ASCII FreeSurfer label format. For the annot files, see FreeSurferColorLut.txt that comes with FreeSurfer for meaning of integers specifying a region.

What is NOT contained

• The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.

Author and License

Note: For the authors of the original ABIDE I dataset, see the Credits section above.

This label data was created by:

Dr. Tim Schäfer
Postdoc Computational Neuroimaging
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy
University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
http://rcmd.org/ts

The data is published under the following license:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.

See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

Resting state functional magnetic resonance imaging (R-fMRI) datasets from 539 individuals with autism spectrum disorder (ASD) and 573 typical controls. This initiative involved 16 international sites, sharing 20 samples yielding 1112 datasets composed of both MRI data and an extensive array of phenotypic information common across nearly all sites. This effort is expected to facilitate discovery science and comparisons across samples. All datasets are anonymous, with no protected health information included.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that often persists into adulthood. One hallmark in the characterization of pathological processing in ADHD is that attention skills are not impaired per se but more inconsistent and with higher variability compared to typically developing children (TDC). Increased variability in ADHD patients has been found in reaction times, as well as resting-state fMRI (rs-fMRI) brain signals. High variability has been assumed to reflect occasional lapses in attention, linked to intrusions of distracting activity during task performance and/or reduced anti-correlation between regions in the DMN and attention networks. Therefore, Dajani et al. (2019) concluded that it is more likely the dynamics between and within neural networks [i.e., the variability of network processing across time- and frequency-scales], that are affected in ADHD, than functional connectivity [in terms of one coefficient describing the (averaged) correlation between two regions over time].

We determined wavelet variance to quantify these dynamics. We determined wVar at rest and under task in fMRI timeseries of regions of the DMN and the FPN in three different frequency bands: 0.02 to 0.04Hz, 0.04 to 0.08Hz, and 0.08-0.16Hz.

We found that wVar differed group specifically between rest and task (significant group X condition interaction: whereas wVar was higher at rest compared to task in TDC, wVar was comparable or even decreased at rest in ADHD. For an external validation of group comparisons in wVar at rest, we determined wVar in rs-fMRI timeseries of a subsample of the Child Mind Institute data set (Functional Connectomes Project International Neuroimaging Data-Sharing Initiative http://dx.doi.org/10.15387/CMI_HBN (2017)). Results replicated our findings in terms of no significant group differences in wVar at rest in combination with similar or lower absolute values in ADHD patients compared to control subjects.

In normal processing, high wVar at rest was interpreted as reflecting free fluctuating brain signaling, in comparison to small wVar under task indicating focussed processing. Thus, we conclude that wVar is a sensitive measure of cognitive processing and is even capable of detecting deviant processing in pathological brain function.