Draft 2 of release 1.1.1.

Author: ericearl

docs/index.md

@@ -1,10 +1,14 @@
 # Releases
 
+## Curator's Latest Update
+
+- 10/6/2020: A small, but important release today.  Brain coverage quality control scores for the `derivatives.func.runs_task-(MID|nback|rest|SST)_volume` data subsets.
+
 ## Release 1.1.1
 
 Released 10/6/2020, this is a small version 1.0.0 release of the `derivatives_qc.(json|tsv)` with BIDS derivatives quality control data including a "brain coverage score" for the `derivatives.func.runs_task-(MID|nback|rest|SST)_volume` data subsets.
 
-**Note**: We unintentionally only uploaded runs 1 and 2 of all available volumes of the `derivatives.func.runs_task-(MID|nback|rest|SST)_volume` data subsets.  This is fine for task-based fMRI runs (of which there are always two), but it is short for many resting state fMRI tasks with 3 or more runs.  We are uploading these data to the NDA and we will post a data set Release 1.1.2 here soon.  We will also update the above `derivatives_qc.(json|tsv)` file as a version 1.0.1 after data are uploaded to include `task-rest` volume runs 3 and up.
+**Note**: We only uploaded runs 1 and 2 of the `derivatives.func.runs_task-(MID|nback|rest|SST)_volume` data subsets in Release 1.1.0.  We are uploading resting state fMRI tasks with 3 or more runs to the NDA now and we will post a data set Release 1.1.2 here soon.  We will also update the above `derivatives_qc.(json|tsv)` file as a version 1.0.1 after data are uploaded to include `task-rest` volume runs 3 and up.
 
 ## Release 1.1.0
 
@@ -24,9 +28,12 @@ Released 7/27/2020, this is the next big release with the addition of:
 
 Released 2/17/2020, this is 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**.
 
-## Updates from the Curators
+## Coming Soon
+
+### Release 1.1.2
 
-- 7/27/2020: Today marks another momentous release for the ABCD-BIDS Community Collection 3165 on the NDA.  The NDA has finished their quality assurance checks and the Release 1.1.0 inputs and derivatives are now available for download using [the nda-abcd-s3-downloader](https://github.com/ABCD-STUDY/nda-abcd-s3-downloader).
+- `derivatives_qc.(json|tsv)` version 1.0.1, including `task-rest` runs 3 and up
+- `derivatives.func.runs_task-rest_volume` data subset runs 3 and up
 
 ## Documentation Guide
 

docs/inputs.md

@@ -12,15 +12,18 @@ The data collection contains anatomical MRI images (T1w and T2w), functional MRI
 
 Sections 3 and onward of this document generally describe what each of the input data subsets are.  This section breaks down the exact contents of each of the input data subsets.  Subject and session identifiers are instead labeled as `#`.  Each input data subset comes with modality-agnostic BIDS-compatible `dataset_description.json`, `README`, and `CHANGES` files.
 
-`sourcedata.func.task_events`
-
-- `sourcedata/sub-#/ses-#/func/sub-#_ses-#_task-(MID|nback|SST)_run-#_bold_EventRelatedInformation.txt`
-
 `inputs.anat.(T1w|T2w)`
 
 - `sub-#/ses-#/anat/sub-#_ses-#(_rec-normalized)(_run-#)_(T1w|T2w).json`
 - `sub-#/ses-#/anat/sub-#_ses-#(_rec-normalized)(_run-#)_(T1w|T2w).nii.gz`
 
+`inputs.dwi.dwi`
+
+- `sub-#/ses-#/dwi/sub-#_ses-#_dwi.bval`
+- `sub-#/ses-#/dwi/sub-#_ses-#_dwi.bvec`
+- `sub-#/ses-#/dwi/sub-#_ses-#_dwi.json`
+- `sub-#/ses-#/dwi/sub-#_ses-#_dwi.nii.gz`
+
 `inputs.fmap.all`
 
 - `sub-#/ses-#/fmap/sub-#_ses-#_dir-(AP|PA)_run-#_epi.json`
@@ -33,12 +36,9 @@ Sections 3 and onward of this document generally describe what each of the input
 - `sub-#/ses-#/func/sub-#_ses-#_task-(MID|nback|SST|rest)_run-#_bold.json`
 - `sub-#/ses-#/func/sub-#_ses-#_task-(MID|nback|SST|rest)_run-#_bold.nii.gz`
 
-`inputs.dwi.dwi`
+`sourcedata.func.task_events`
 
-- `sub-#/ses-#/dwi/sub-#_ses-#_dwi.bval`
-- `sub-#/ses-#/dwi/sub-#_ses-#_dwi.bvec`
-- `sub-#/ses-#/dwi/sub-#_ses-#_dwi.json`
-- `sub-#/ses-#/dwi/sub-#_ses-#_dwi.nii.gz`
+- `sourcedata/sub-#/ses-#/func/sub-#_ses-#_task-(MID|nback|SST)_run-#_bold_EventRelatedInformation.txt`
 
 ## 3. DAIC Quality Control (QC) Selection Process
 

docs/pipeline.md

@@ -25,7 +25,7 @@ For full details read the following references:
 
 ### Stage 1: PreFreeSurfer
 
-The primary goal of PreFreeSurfer is to remove distortions from the anatomical data and align and extract the brain in the subject's native volume space. 
+The primary goal of PreFreeSurfer is to remove distortions from the anatomical data and align and extract the brain in the subject's native volume space.
 
 Notable deviations from the original HCP minimal preprocessing pipeline include the use of [Advanced Normalization Tools (ANTs)](http://stnava.github.io/ANTs/) to perform denoising and N4 bias field correction, which significantly improves results for subjects scanned on General Electric (GE) and Philips scanners that tend to have more noise and are not always normalized following the scan.  We moved Montreal Neurological Institute (MNI) standard space registration to the PostFreeSurfer stage to make use of the refined brain mask created in the FreeSurfer stage.
 
@@ -60,23 +60,23 @@ This stage involves four broad steps:
 1. Motion censoring followed by standard re-processing
 1. Construction of parcellated timeseries
 
-#### DBP 1. Standard pre-processing
+#### 1. DBP Standard pre-processing
 
 Standard pre-processing comprises three steps.  First all fMRI data are de-meaned and de-trended with respect to time.  Next a general linear model is used to denoise the processed fMRI data.  Denoising regressors comprise signal and movement variables.  Signal variables comprise mean time series and first derivative for white matter, CSF, and the global signal, which are derived from Individualized segmentations generated during PostFreesurfer.  Movement variables comprise translational (X,Y,Z) and rotational (roll, pitch, and yaw) measures estimated by re-alignment during FMRIVolume and their Volterra expansion.  The inclusion of GSR is critical for most resting-state functional MRI comparisons, as demonstrated empirically by multiple independent labs (Ciric et al., 2017; Power et al., 2017, 2019b; Satterthwaite et al., 2013).  After denoising the fMRI data, the time series are band-pass filtered between 0.008 and 0.09 Hz using a 2nd order Butterworth filter.  Such a band-pass filter is softer than other filters, and avoids potential aliasing of the time series signal.
 
 ##### On Global Signal Regression
 
 Global signal regression (GSR) has been consistently shown to reduce the effects of motion on BOLD signals and eliminate known batch effects that directly impact group  comparisons (Ciric et al., 2017; Power et al., 2015, 2019b). Motion censoring (see below) combined with GSR has been shown to be the best existing method for eliminating artifacts produced by motion.
 
-#### DBP 2. Respiratory Motion Filter
+#### 2. DBP Respiratory Motion Filter
 
 In working with ABCD data, we have found that a respiratory artifact is produced within multi-band data (Fair et al., 2020).  While this artifact occurs outside the brain, it can affect estimates of frame alignment, leading to inappropriate motion censoring.  By filtering the frequencies of the respiratory signal from the motion realignment data, our respiratory motion filter produces better estimates of FD.
 
-#### DBP 3. Motion censoring
+#### 3. DBP Motion censoring
 
 Our motion censoring procedure is used for performing the standard pre-processing and for the final construction of parcellated timeseries.  For standard pre-processing, data are labeled as "bad" frames if they exceed an FD threshold of 0.3 mm.  Such "bad" frames are removed when demeaning and detrending, and betas for the denoising are calculated using only the "good" frames. For band-pass filtering, interpolation is used initially to replace the "bad" frames and the residuals are extracted from the denoising GLM.  In such a way, standard pre-processing of the timeseries only uses the "good" data but avoids potential aliasing due to missing timepoints.  When extracting time series for data analysis only data above an FD threshold of 0.2 mm are extracted.  After motion censoring, timepoints are further censored using an outlier detection approach.
 
-#### DBP 4. Generation of parcellated timeseries for specific atlases
+#### 4. DBP Generation of parcellated timeseries for specific atlases
 
 Using the processed resting-state fMRI data, this stage constructs parcellated time series for pre-defined atlases making it easy to construct correlation matrices or perform time series analysis on putative brain areas defined by independent datasets.  The atlases comprise recent parcellations of brain regions that comprise different networks.  In particular, parcellated timeseries are extracted for Evan Gordon’s 333 ROI atlas template (Gordon et al., 2014), Jonathan Power’s 264 ROI atlas template (Power et al., 2011), Thomas Yeo’s 118 ROI atlas template (Yeo et al., 2011), and the HCP’s 360 ROI atlas template (Glasser et al., 2016).  Since we anticipate newer parcellated atlases as data acquisition, analytic techniques, and knowledge all improve, it is trivial to add new templates for this final stage.
 

docs/recommendations.md

@@ -58,7 +58,7 @@ The brain coverage score is an estimate of how much overlap exists between the f
 
 There are two ways to download ABCD Study data and get BIDS inputs or derivatives:
 
-1. (***PREFERRED***) Downloading from NDA Collection 3165 will provide you an "data structure manifest" spreadsheet with AWS S3 links and other key information.  DCAN Labs has designed [a GitHub repository for selectively downloading only parts of the BIDS input and derivative data, the "nda-abcd-s3-downloader"](https://github.com/ABCD-STUDY/nda-abcd-s3-downloader).
+1. (***PREFERRED***) Downloading from NDA Collection 3165 will provide a "data structure manifest" spreadsheet with AWS S3 links and other key information.  DCAN Labs has designed [a GitHub repository for selectively downloading only parts of the BIDS input and derivative data, the "nda-abcd-s3-downloader"](https://github.com/ABCD-STUDY/nda-abcd-s3-downloader).
 1. [ABCD Fast Track Data on the NDA](https://nda.nih.gov/abcd/query/abcd-fast-track-data.html) can alternatively be downloaded and unpacked into BIDS with the [ABCD-STUDY abcd-dicom2bids GitHub repository](https://github.com/ABCD-STUDY/abcd-dicom2bids).  This is if you need DICOM files specifically.
 
 ### [`nda-abcd-s3-downloader`](https://github.com/ABCD-STUDY/nda-abcd-s3-downloader)