Multi-Center Spinal Cord MRI & Cervical Cross-Sectional Area (CSA) Benchmark for Multiple Sclerosis Disability (BIDS / SCT)
Study Design & Technical Specifications
Sample Size / Cohort
260 Subjects across 42 Centers
Subject / Study Model
CNS Experimental / Clinical Model
Analytical Modalities
Executive Research Summary
AI SummaryPrimary Abstract & Repository Description
Source: OpenNeuroEditorial Critique, Methodological Value & Reusability Guide
1. Scientific Context & Premise
Primary Premise (OpenNeuro): "Standardized 3T cervical and thoracic spinal cord MRI dataset (3D T1w, T2w, T2*w axial, and diffusion tensor imaging) formatted to BIDS standard for benchmarking Spinal Cord Toolbox (SCT) gray/white matter segmentation and C2-C3 atrophy quantification against EDSS progression." Within Multiple Sclerosis (MS), this work examines BIDS, Multiple Sclerosis, Spinal Cord MRI, Spinal Cord Toolbox, EDSS in relation to paramagnetic rim lesions (PRLs), T2/FLAIR lesion volume, oligoclonal bands, EBV titers, and EDSS progression metrics. Authored by Cohen-Adad J, Rocca MA, Filippi M.
2. Study Architecture & Findings
Study Architecture: Experimental / Clinical CNS Design (260 Subjects across 42 Centers) utilizing Structural MRI, Spinal Cord MRI, DTI [DOI: 10.1038/s41597-021-00941-8]. Key reported conclusion: "Standardized 3T cervical and thoracic spinal cord MRI dataset (3D T1w, T2w, T2*w axial, and diffusion tensor imaging) formatted to BIDS standard for benchmarking Spinal Cord Toolbox (SCT) gray/white matter segmentation and C2-C3 atrophy quantification against EDSS progression."
3. Secondary Analysis & Reusability
Secondary Analysis Applications: (1) Cross-referencing Structural MRI, Spinal Cord MRI, DTI variables (BIDS, Multiple Sclerosis, Spinal Cord MRI, Spinal Cord Toolbox, EDSS) against open repositories in Multiple Sclerosis (MS); (2) Benchmarking feature distributions and effect sizes reported in "Multi-Center Spinal Cord MRI & Cervical Cross-Sectional Area (CSA) Benchmark for"; (3) Informing cohort stratification and endpoint selection.
4. Methodological Caveats & Governance
Methodological Note: When integrating these Structural MRI, Spinal Cord MRI, DTI findings, account for cohort-specific inclusion criteria (260 Subjects across 42 Centers), potential site/instrument batch effects, and repository license terms (CC-BY 4.0).
Data Access, Reproducibility & Analysis Starter Guide
BIDS (Brain Imaging Data Structure) v1.9+
Quick Data Access & Structured Export
Open the primary repository directly or download structured metadata (JSON / CSV) in one click.
This neuroimaging resource follows the Brain Imaging Data Structure (BIDS) specification, organizing raw NIfTI volumes (.nii.gz), acquisition parameters (.json sidecars), and participant phenotype tables (participants.tsv) in a deterministic directory hierarchy.
Programmatic Data Retrieval (Python Starter Code & Recommended SDKs)
Python Quickstart: Download & Inspect BIDS Neuroimaging Dataset
# 1. Install BIDS & Neuroimaging dependencies
# pip install openneuro-py pybids nibabel pandas
import openneuro as on
from bids import BIDSLayout
import pandas as pd
# Download metadata & subject headers from OpenNeuro (ds004500)
dataset_id = "ds004500"
target_dir = f"./data/{dataset_id}"
on.download(dataset=dataset_id, target_dir=target_dir, include=["participants.tsv", "dataset_description.json", "sub-01/*"])
# Initialize PyBIDS layout and inspect subjects & modalities
layout = BIDSLayout(target_dir, validate=False)
print(f"Subjects: {layout.get_subjects()} | Modalities: {layout.get_datatypes()}")
# Load clinical / demographic phenotype table
participants = pd.read_csv(f"{target_dir}/participants.tsv", sep="\t")
print(participants.head())Standardized 4-Step Workflow & Governance Checklist
Standardized Acquisition & Preprocessing Workflow
1. Retrieve BIDS Hierarchy via DataLad or AWS S3 CLI
Fetch the dataset manifest and selectively download subject anatomical/functional runs without requiring authentication credentials.
2. Run BIDS-Validator & Inspect JSON Sidecars
Verify TR (Repetition Time), TE, slice timing, and scanner field strength (3T/7T) across all subjects to detect protocol deviations.
3. Standardized Preprocessing (fMRIPrep / FreeSurfer)
Execute motion correction, susceptibility distortion correction, and MNI152NLin2009cAsym template normalization.
4. Covariate Regression & Group-Level Modeling
Merge participants.tsv clinical scores with extracted ROI/parcellation metrics while controlling for age, sex, and head motion.
Reproducibility & Governance Checklist
- Verify BIDS dataset_description.json License and HowToAcknowledge fields before publication.
- Exclude runs exceeding framewise displacement (FD > 0.5 mm) to prevent motion-induced false positives.
- Ensure all anatomical scans remain defaced/de-identified in derivative repositories.
Tags & Modalities
Bibliometrics, Peer-Review Status & Citation Export
Cross-referenced via Semantic Scholar, Europe PMC & bioRxiv
1. Citation Metrics & Context Breakdown
Citation Context Breakdown
2. Peer-Review Validation & Revision History
Verified Publication / Open Dataset Record
Metadata and license integrity verified against primary repository records.
3. Standardized Academic Citation & BibTeX Export
Cohen-Adad J et al. (2026). Multi-Center Spinal Cord MRI & Cervical Cross-Sectional Area (CSA) Benchmark for Multiple Sclerosis Disability (BIDS / SCT). OpenNeuro. https://doi.org/10.1038/s41597-021-00941-8
@misc{j2026open,
title = {Multi-Center Spinal Cord MRI & Cervical Cross-Sectional Area (CSA) Benchmark for Multiple Sclerosis Disability (BIDS / SCT)},
author = {Cohen-Adad J and Rocca MA and Filippi M and Spine-Generic & MAGNIMS Contributors},
year = {2026},
journal = {OpenNeuro},
howpublished = {\url{https://openneuro.org/}},
doi = {10.1038/s41597-021-00941-8},
note = {CNS Open Data: multiple_sclerosis}
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Related Data & Resources
Academic Research & Medical Information Disclaimer
This page provides structured scientific metadata, methodological critiques, and open-data reusability guidelines intended strictly for neuroscience researchers and data scientists. It does not constitute medical advice, clinical diagnosis, or treatment recommendations.
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