Release Notes

Ongoing Releases

A guiding principle for this collection is to release essential data for analysis. This collection will be updated with waves of data preparation and processing. As waves complete preparation or processing they will be uploaded and version-stamped with updated and versioned release notes.

User feedback will guide our course for future releases. Provide feedback on what you would like to see included in future releases using GitHub issues for this repository:

Release History

Release 2.0.0 (6/22/2022)

New updates to the ABCD BIDS Community Collection cover both revisions to existing datasets and new derivatives. Revisions include:

  1. Uploading 144 participants with new data due to revised fast track QC
  2. Providing Connectivity matrices for those participants with discrepancies in the number of timepoints used
  3. Uploading JSONs for the diffusion inputs in some participants.
  4. Updated version of the participants.tsv to v1.0.2 includes correction to site and sex designation for a small subset of subjects based on new information from the DAIC.

New additions include:

  1. Individual-specific network labels based on a template matching approach and infomap approaches
  2. Derivatives for the fmriprep pipeline, and
  3. Level-2 task files from the ABCD-task-fMRI pipeline.

Details about each update are given below.

fMRIPrep outputs

fMRIPrep v20.2.0 was run on all 10,038 participants whose visit one data was successfully converted to BIDS. The limited fMRIPrep processing errors were due to subjects that did not have any valid fMRI runs, but we did not do any manual quality control of outputs. 9,484 participants have at least one output. The data is available in 18 submissions (a summary, including number of files and submission size can be found here). Detailed information about the files included in each submission are on the second tab of that spreadsheet. Files with no submission name listed have not yet been uploaded. If additional outputs are desired, please reach out to Dylan Nielson at dylan.nielson@nih.gov. fMRIPrep was run in a singularity container on resources from the NIH High Performance Computing Biowulf cluster.

Replaced subjects

The initial release was processed prior to new updates to the fast track QC spreadsheet that affected the original inputs for 144 participants. This led to discrepancies in the number of timepoints reported for connectivity matrices (see below) relative to the inputs. The 144 participants were re-processed through the ABCD-BIDS pipeline at the Minnesota Supercomputing Institute (MSI), and being replaced, subsequent the required NDA review. The participants.tsv file indicates which subjects were reprocessed.

These subjects have had their connectivity matrices regenerated and replaced (see: Connectivity Matrices).

Connectivity matrices

The 144 participants with replaced fast track QC information mentioned above produced new Gordon 10 and 5 minute connectivity matrices. The old matrices remain valid, but may use different frames from the new matrices. The labels for these connectivity matrices were defined in Gordon, et al, 2017. These connectivity matrices were created using the DCAN Labs cifti connectivity wrapper. Timepoints used for connectivity calculations were thresholded based on data quality. Data quality was measured by the total frame displacement (FD) calculated from the frame-by-frame realignment parameters; Frames above an FD of 0.2 mm were excluded. An outlier detection procedure was used to exclude remaining frames that were 2 standard deviations away from the mean. These procedures match the original procedures used to generate the connectivity matrices in the November release.

Submission IDs: 36449 - 36452

DWI sidecar JSON patch (Diffusion inputs)

The DWI acquisition parameters from subjects scanned on Philips and GE with MR Software release versions 5.3.0_5.3.0.0 and DV25.0_R02_1549.b respectively (n=423) are missing the required field, PhaseEncodingDirection. This omission is because they reported the axis and not direction; therefore we did a manual check of these images to check the phase encoding direction, so that these JSON inputs are BIDS compatible and can be processed by pipelines like QSIprep. These JSONs have been updated and uploaded.

Submission ID: 36448

Individual-specific network maps using the Infomap algorithm

Infomap community detection is an unsupervised method of assigning nodes to communities in a graph based on information theory. Here, grayordinates are treated as nodes, and the edges are the correlation between the nodes. There are two versions of individual-specific maps available depending on whether not investigators are interested in the contribution of tasks to global network topography. 1) Maps are generated for subjects with at least 10 minutes of low-motion (See Hermosillo et al 2021) resting state data. 2)

Maps are generated with all available minutes below an FD threshold of 0.2mm (and corresponding BOLD outlier detection) using concatenated rest and task data. Because the tie density scales exponentially with the number of grayordinates, infomap community detection was only performed on the cortical surface and did not include subcortical structures (i.e. neither brainstem, cerebellum, nor diencephalon). Note, because infomap is an unsupervised community detection method, the subject may have more or fewer networks than a canonical network set. Where possible, we have attempted to assign networks based on the networks observed in an average dataset using the jaccard similarity (see Gordon et al. 2017), however in some instances the jaccard similarity sufficiently low (<0.1) such that the network did not resemble any of the canonical networks, in which case the network was provided a novel network assignment.

Template Matching

[Template Matching] Template matching is a supervised algorithm for identifying neural networks using resting state connectivity data, based on the spatial topography. Click here for documentation of source code as well as a written tutorial. Multiple versions of the time series are provided, to allow investigator flexibility in their desired analysis: either exactly 10 minutes of randomly sampled frames, all available frames below the 0.2mm FD threshold, or concatenated rest and task time series data in the following order: rest, MID, n-back, and SST (provided that the participant had an available scan for the task). For full details of inter- and intra- participant reliability, and motion correction, see Hermosillo et al. 2021 (in prep).

Submission IDs: 36458 - 36630

Task outputs

abcd-bids-tfmripipeline a modified version of the TaskfMRIAnalysis stage of the HCP-pipeline (Glasser et al., 2013) developed at University of Vermont by Anthony Juliano, was used to process task-fmri data from the minimally processed ABCD-BIDS (Feczko et al., 2020b) processing pipeline (v.1.0) data, as well as derived ABCC data (Feczko, 2020; ABCD-3165). An example fsf file template for ABCD's MID task is made available for users to review on ABCC (https://osf.io/psv5m/). MID, Nback, and SST level-2 task outputs are available for the baseline sessions for all data that passed task QC. These outputs include the fully-processed dtseries data that are subsequently ready for the user to perform their desired third-level or group-wise analyses.

Release 1.1.1 (10/7/2020)

This was a small version 1.0.0 release of the derivatives_qc.(json|tsv) with additional BIDS derivatives quality control data including a "brain coverage score" for the derivatives.func.runs_task-(MID|nback|rest|SST)_volume data subsets.

Release 1.1.0 (7/27/2020)

This was the next big release with the addition of:

  1. 157 additional subjects due to updated fast track QC spreadsheet
  2. participants.(json|tsv) version 1.0.0: BIDS standard participants files with matched groups
  3. sourcedata.func.task_events: Task-based fMRI E-Prime files
  4. inputs.dwi.dwi: DWI BIDS input data
  5. derivatives.anat.stats: FreeSurfer stats files
  6. derivatives.anat.(T1w|T2w): T1 and T2 volumes
  7. derivatives.anat.wmparc: white-matter volume ROIs
  8. derivatives.func.updated_motion_task-(MID|nback|SST|rest): Improved motion files (including outlier calculation)
  9. derivatives.func.pconns: Curated parcellated connectivity files
  10. derivatives.func.runs_task-(MID|nback|SST|rest)_volume: Minimally-processed fMRI volumes

Release 1.0.0 (2/17/2020)

This was the initial release of DCAN Labs ABCD-BIDS inputs and derivatives containing 10,038 MRI sessions worth of NDA imagingcollection01 data and 9,647 MRI sessions worth of NDA fmriresults01 data.

Corrections

task-rest_bold.json

Discovered in the middle of June 2020, the modality-specific BIDS inherited task-rest_bold.json file at the top of the directory tree which is nested in almost every task-rest associated record in the NDA database has a typo in it. The "TaskDescription" key has a value of "See http://www.cognitiveatlas.org/task/id/tsk_4a57abb949e1a/". However, this link goes to the stop signal task page on the Cognitive Atlas website. Instead you should refer to the Cognitive Atlas website for "rest eyes open". This website describes the task as:

"Subjects rest passively with their eyes open. Often used as a baseline for comparison for other tasks."

derivatives.func.runs_task-rest_volume

This data subset was originally uploaded in Release 1.1.0, but was missing all runs chronologically numbered 3 and up. We are uploading these missing data in Release 1.1.2.

updated_dwi_input_json

The DWI acquisition parameters from all subjects scanned on GE with MR Software release DV25.0_R02_1549.b (n=281) are missing the required field, PhaseEncodingDirection. This omission is because they reported the axis and not direction.