Structure: - context: ... rule: rules.X.Y.Z does: pass - context: ... rule: rules.X.Y.Z does: notapply

Modular %%{init: {"flowchart": {"htmlLabels": false}} }%% graph LR classDef opt stroke-dasharray: 5 subgraph TemplateFlow template["Template"]:::opt end subgraph raw[Raw dataset]

Metadata Title fmriprep-next: Preprocessing as a fit-transform model Authors [x] Chris Markiewicz | markiewicz@stanford.edu | https://orcid.org/0000-0002-6533-164X [x] Mathias Goncalves | mathiasg@stanford.edu | https://orcid.org/0000-0002-7252-7771 [x] Ma Feilong | feilong.ma@dartmouth.edu | https://orcid.org/0000-0002-6838-3971 [x] Lea Waller | lea@fmri.science | https://orcid.org/0000-0002-3239-6957 [x] John Kruper | jk232@uw.edu | https://orcid.org/0000-0003-0081-391X

Here we consider the problem of applying susceptibility distortion correction to EPI imaging data. We derive an efficient algorithm for simultaneously correcting for head motion and susceptibility distortions. Background Consider a collection of MRI scans of a human brain. When collected, each image is in the "scanner space", which is to say that the elements of the data arrays (voxels) correspond to locations inside an MR scanner. When comparing images for the same subject, this space is generally unhelpful, as subjects may move within the scanner, and the scanner operator may select a different location within the scanner to consider the origin $(0, 0, 0)$. Therefore images must be brought into register with one another. Generally, one uses successive approximations of transformations, such as rotations and translations, to minimize a cost function. The resulting transformation may be used to transform coordinates in one image to the equivalent coordinates in another. Images contain an internal affine transformation that translates from indices in the data matrix into world coordinates, conventionally in millimeters (mm) right, anterior and superior of some origin. Because the world coordinate space is abstracted from the details of the data array, it is convenient to represent transformations between images as transformations between their world coordinates, not their array indices. Head motion

Authors: Peer Herholz, Oscar Esteban, Chris Markiewicz, Ariel Rokem, and Franco Pestilli Date: 2023-07-21 Abstract In this document, we propose a general principle for developing BIDS extension proposals for derivative data. The goal is to establish consensus so that parts of BEPs that propose terms in line with this document will be considered accepted in principle. The proposal is to ask feedback from the community, provide a timeline for the discussion, and settle on a decision making process. At the end of the timeline we request that a decision be reached. The proposal is RECOMMENDED not REQUIRED in that BEPs would be allowed to deviate when deemed necessary. Problem statement In working through BEPs 12 and 16, we have identified a repeated pattern in generating derivatives within several imaging modalities' workflows where: We require a reference map that is used to encode spatial features and parameters. There is an antecedent of this in BIDS with BEP23 (see below). In that BEP, the proposed naming takes the pattern _<suffix>ref (e.g., _boldref, _dwiref, etc.), and that solution has been suggested as a possibility in issue #1532 of the spec repository.

sub-X/func/sub-X_task-Y_run-Z_desc-hmc_boldref.nii.gz # BOLD contrast sub-X/func/sub-X_task-Y_run-Z_desc-coreg_boldref.nii.gz # aligned to hmcref, use sbref and fieldmaps, if possible sub-X/func/sub-X_task-Y_run-Z_from-orig_to-boldref_mode-image_xfm.txt sub-X/func/sub-X_task-Y_run-Z_from-boldref_to-T1w_mode-image_xfm.txt sub-X/func/sub-X_task-Y_run-Z_from-T1w_to-boldref_mode-image_xfm.txt sub-X/func/sub-X_task-Y_run-Z_from-fmap_to-boldref_mode-image_xfm.txt # MI metric Dependency structure graph TB; subgraph Head motion correction bold.nii --> desc-hmc_boldref.nii;

Option 1: Dense dereferenced objects (via Taylor) columns.yaml + values.yaml type__eeg_channels: name: type display_name: Channel type description: | Type of channel; MUST use the channel types listed below. Note that the type MUST be in upper-case. type: string enum:

The following API provides the basic data structures needed to represent a BIDS dataset. It aims to be simple enough to bear multiple implementations. Index = PaddedInt Value = str Entity = Literal['subject', 'session', ...] class Schema: """Representation of the state of BIDS schema

The checklist format The file checklists/checklist.json contains the description of the bronze, silver and gold tier checklists, which are rendered by the application in interactive/. For example, this snippet: { "id": "bronze_doc_1", "prompt": "Landing page (e.g., GitHub README, website) provides a link to documentation and brief description of what program does",

OpenNeuro dataset ds00WXYZ/ # Site XYZ dataset_description.json sub-<BIC0001>/ sub-<BIC0002>/ RedCap RedCapID BIC ...

❯ git annex whereis sub-jgrKCLmouse1/ses-1/anat/sub-jgrKCLmouse1_ses-1_acq-RARE_T2w.nii whereis sub-jgrKCLmouse1/ses-1/anat/sub-jgrKCLmouse1_ses-1_acq-RARE_T2w.nii (2 copies) 42dcfcdf-7499-4ebe-aef7-63a7e96bf0d8 -- root@620bbd8bb493:/datalad/ds001591 cc86b14d-dd87-49fe-ae07-b0f9fb23acb7 -- [s3-PUBLIC] s3-PUBLIC: https://s3.amazonaws.com/openneuro.org/ds001591/sub-jgrKCLmouse1/ses-1/anat/sub-jgrKCLmouse1_ses-1_acq-RARE_T2w.nii?versionId=OK3HMQS7xKP_2vs17wiE_shocwiyTuy0 ok

Revised list [x] 0:INTERNAL_ERROR -> Implementation detail [x] 1:NOT_INCLUDED -> errors.yaml [ ] 22:TSV_EMPTY_ROWS -> Instructions [x] 23:TSV_EMPTY_CELL -> type check [ ] 24:TSV_IMPROPER_NA -> todo [ ] 38:INCONSISTENT_SUBJECTS -> Implementor choice [ ] 39:INCONSISTENT_PARAMETERS -> Implementor choice [ ] 52:STIMULUS_FILE_MISSING -> todo [ ] 77:UNUSED_STIMULUS -> Instructions

This dataset is a BIDS Derivatives dataset resulting from running fMRIPrep on [{{ dataset_id }}](https://openneuro.org/datasets/{{ dataset_id }}). Methods fMRIPrep documentation can be found on https://fmriprep.org/en/stable/. For a complete description of the methods applied to these data, see logs/CITATION.md. This boilerplate text is released under CC0, and may be used in whole or part to describe the preparation of these data in publications. Jobs were submitted using ReproMan in order to improve reproducibility. Output and error logs can be found in .reproman/jobs/local/.

10 February 2022 State of the BIAP: https://github.com/effigies/nibabel/blob/biap/surfaces/doc/source/devel/biaps/biap_0009.rst Ready for final(ish) comment and move on to implementation. 28 January 2022 Attending: Chris, Joshua Teves MNE/STC