MCIC Multisite sMRI Dataset Preprocessed with VBM
Bibliographic record
Abstract
This is a multisite structural MRI dataset preprocessed with voxel-based morphometry (VBM). This data can be used to demonstrate how federated voxelwise regression works on the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). As described in Gazula et al, 2018, this data comes from the Mind Clinical Imaging Consortium (MCIC) collection, a publicly accessible, online data repository containing curated anatomical and functional MRI, in addition to other data, collected from individuals with and without a schizophrenia spectrum disorder (Gollub et al., 2013) and available via the COINS data exchange https://coins.mrn.org (Scott et al., 2011). The final cohort for whom data are available includes 146 patients and 160 controls with site distribution as follows: Site B (IA) 40 patients/67 controls; Site D (MGH) 32/23; Site C (UMN) 32/26; Site A (UNM) 42/44, respectively. These T1-weighted structural MRI (sMRI) images were acquired with the following scan parameters: TR = 2, 530ms for 3 T, TR = 12ms for 1.5 T; TE = 3.79ms for 3 T, TE = 4.76ms for 1.5 T; FA = 7° for 3 T, FA = 20° for 1.5 T; TI = 1100msfor 3 T; Bandwidth = 181 for 3 T, Bandwidth = 110 for 1.5 T; voxelsize = 0.625 × 0.625mm; slice thickness 1.5 mm; FOV= 16−18cm. The T1-weighted sMRI data were preprocessed using the Statistical Parametric Mapping software using unified segmentation (Ashburner and Friston, 2005), in which image registration, bias correction and tissue classification were performed using a single integrated algorithm resulting in individual brains segmented into gray matter, white matter and cerebrospinal fluid and nonlinearly warped to the Montreal Neurological Institute (MNI) standard space. The resulting gray matter concentration (GMC) images were re-sliced to 2 × 2 × 2mm, resulting in 91 × 109 × 91 voxels.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.061 |
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".