Variability of Resting‐State Functional MRI Graph Theory Metrics across 3T Platforms
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Graph theory analysis of brain connectivity data is a promising tool for studying the function of the healthy and diseased brain. The consistency of resting-state functional MRI (rsfMRI) connectivity measures across multiple scanner types is an important factor in designing multi-institutional research studies and has important implications for the potential use of this technique in a heterogeneous clinical setting. We sought to quantitatively study the interscanner variability of rsfMRI graph theory metrics obtained from healthy volunteers scanned on three different scanner platforms. METHODS: In this prospective Institutional Review Board approved study, 9 healthy volunteers were enrolled for brain MRI on three 3T scanners (Magnetom Prisma, Skyra, and Trio, Siemens, Erlangen, Germany) in three separate scan sessions within approximately 1 week. Standard preprocessing of rsfMRI was performed with SPM12. Subject scans were normalized to Montreal Neurologic Institute (MNI) space, and connectivity of 116 regions-of-interests based on the automated anatomic labeling (AAL) atlas was calculated using Conn toolbox. Whole-network graph theory metrics were calculated using Brain Connectivity Toolbox, and intraclass correlation (ICC) across three scan sessions was assessed. RESULTS: A total of 25 rsfMRI exams were completed in 9 subjects with a median-intersession time of 3 days. Among all three sessions, there was good to excellent agreement in characteristic path length and global efficiency (ICC: .79, .79) and good agreement in the transitivity, local efficiency, and clustering coefficient (ICC = .72, .69, .62). CONCLUSIONS: There was high consistency of graph theory metrics of rsfMRI connectivity networks among healthy volunteers scanned on three different generation 3T MRI scanners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".