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 & Translational Bottlenecks
This resource from OpenNeuro ("Multi-Center Spinal Cord MRI & Cervical Cross-Sectional Area (CSA) Benchmark for Multiple Sclerosis Disability (BIDS / SCT)") addresses critical scientific questions within Multiple Sclerosis (MS). Research in this domain currently faces major translational bottlenecks, including progression independent of relapse activity (PIRA), smoldering neuroinflammation, and remyelination failure across white and cortical gray matter. By focusing on key thematic elements (BIDS, Multiple Sclerosis, Spinal Cord MRI, Spinal Cord Toolbox, EDSS), this work contributes empirical evidence toward understanding how molecular, cellular, or systems-level alterations relate to core disease pathways such as paramagnetic rim lesions (PRLs), T2/FLAIR lesion volume, oligoclonal bands, EBV titers, and EDSS progression metrics. Integrating these findings into the broader open science ecosystem helps reduce fragmentation across independent laboratories and accelerates hypothesis verification.
日本語解説を表示 (Japanese)
OpenNeuroより公開された本リソース（「Multi-Center Spinal Cord MRI & Cervical Cross-Sectional Area (CSA) Benchmark for Multiple Sclerosis Disability (BIDS / SCT)」）は、多発性硬化症（MS）領域における重要な学術的・臨床的課題に焦点を当てています。現在の多発性硬化症（MS）研究では、再発に依存しない進行（PIRA）、くすぶり型（smoldering）神経炎症・ミクログリア活性化の持続、および脱髄病変における再髄鞘化不全の克服といったトランスレーショナルリサーチ上のボトルネックが存在します。本リソースは関連キーワード（BIDS, Multiple Sclerosis, Spinal Cord MRI, Spinal Cord Toolbox, EDSS）を軸として、常磁性リム病変（PRL）、T2/FLAIR病変体積、オリゴクローナルバンド、血清NfL/GFAP、およびEDSS進行指標などの主要経路や病態メカニズムの理解に資する知見・データを提供しており、研究室や機関ごとに分散しがちな知見をオープンサイエンス基盤上で統合・検証する上で重要な位置づけを持ちます。
2. Methodology & Data Architecture Value
Structured as an open scientific dataset, this repository leverages Structural MRI, Spinal Cord MRI, DTI across CNS Experimental / Clinical Model (260 Subjects across 42 Centers). Its primary architectural value lies in providing standardized, machine-readable primary records via OpenNeuro under CC-BY 4.0. Unlike isolated summary tables in static publications, access to underlying Structural MRI, Spinal Cord MRI, DTI distributions enables independent inspection of signal-to-noise ratios, preprocessing parameter sensitivity, and cross-modality feature extraction.
日本語解説を表示 (Japanese)
本リソースは公開科学データセットとして、神経科学実験・臨床モデル（サンプル規模: 260 Subjects across 42 Centers）における「Structural MRI, Spinal Cord MRI, DTI」データを収録しています。OpenNeuroを通じてライセンス（CC-BY 4.0）のもとで構造化データが提供されている点が最大の強みです。論文内の静的な要約���計量のみならず、生データや処理済み特徴量の分布に直接アクセスできるため、信号雑音比（S/N比）の検証、前処理パイプラインの感度分析、およびマルチモーダル特徴量抽出において高い学術的価値を発揮します。
3. Secondary Analysis & Reusability Scenarios
Researchers and computational neuroscientists can reuse this resource across multiple secondary workflows: (1) Cross-Cohort Harmonization & Meta-Analysis — combining these Structural MRI, Spinal Cord MRI, DTI records with complementary repositories (such as OpenNeuro, DANDI, Agora, or Europe PMC) to increase statistical power; (2) Computational Benchmarking & Machine Learning — utilizing the documented features (BIDS, Multiple Sclerosis, Spinal Cord MRI, Spinal Cord Toolbox, EDSS) as training, validation, or out-of-distribution test sets for predictive biomarker discovery in Multiple Sclerosis (MS); and (3) Experimental Protocol Alignment — referencing the study design (CNS Experimental / Clinical Model, 260 Subjects across 42 Centers) to calibrate sample size calculations (power analysis) and endpoint selection for upcoming preclinical or clinical studies.
日本語解説を表示 (Japanese)
本リソースは以下のような二次解析・再利用シナリオに幅広く活用可能です。(1) コホート横断的ハーモナイゼーションとメタ解析：本リソースの「Structural MRI, Spinal Cord MRI, DTI」データを他の公開リポジトリ（OpenNeuro、DANDI、Agora、ADNI等）の同種データと統合し、統計的検出力（Statistical Power）を高めた統合解析を実施する。(2) 機械学習・バイオマーカー予測モデルの検証：抽出された特徴量や関連因子（BIDS, Multiple Sclerosis, Spinal Cord MRI, Spinal Cord Toolbox, EDSS）を、多発性硬化症（MS）の進行予測・サブタイプ分類モデルの外部検証（External Validation）や転移学習データとして利用する。(3) 後続研究のプロトコル設計：対象モデル（神経科学実験・臨床モデル）およびサンプル規模（260 Subjects across 42 Centers）の設計仕様を参照し、新規の非臨床試験や臨床研究におけるサンプルサイズ設計（検出力計算）や主要評価項目の妥当性検討に役立てる。
4. Methodological Limitations & Caveats
While curated from an authoritative scientific source, secondary analysts should account for potential cohort selection biases and site-specific batch effects. Specifically, when interpreting Structural MRI, Spinal Cord MRI, DTI results in Multiple Sclerosis (MS), researchers must control for confounding variables (such as age, sex, post-mortem interval, scanner/instrument drift, or medication state) and verify compliance with open-access attribution requirements (CC-BY 4.0).
日本語解説を表示 (Japanese)
信頼性の高い学術情報源から収録されていますが、二次解析や他研究との比較を行う際には、コホート選択バイアスや測定施設間差（バッチエフェクト）に留意する必要があります。特に多発性硬化症（MS）における「Structural MRI, Spinal Cord MRI, DTI」データの解釈では、年齢・性別・罹病期間・測定機器のキャリブレーション差・併用薬などの交絡因子を適切に補正することが推奨されます。また、データや図表の二次利用時は提供元（OpenNeuro）の利用条件（CC-BY 4.0）および適切な引用表記を遵守してください。
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
Key Citing Publications
Super-resolution reconstruction of spinal cord MRI using deep learning: Application to multiple sclerosis atrophy measurement
Tayssir Boushila, M. Sahnoun, Fathi Kallel
A lightweight method for regression-based normalization of the cross-sectional area for cervical spinal cord atrophy assessment
Tayssir Boushila, M. Sahnoun, Fathi Kallel
Advanced Imaging of the Spinal Cord: Quantitative, Functional, and Translational Approaches.
Javid Azadbakht, M. Mossa-Basha
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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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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