MICA-MICs: a dataset for Microstructure-Informed Connectomics
Bibliographic record
Abstract
The MICA-MICs dataset provides raw and fully processed multimodal neuroimaging data acquired in 50 healthy control participants at a field strength of 3T. Modalities include high-resolution anatomical (T1-weighted), microstructurally-sensitive (quantitative T1), diffusion-weighted and resting-state functional imaging. In addition, MICA-MICs provides ready-to-use connectomes built across multiple parcellation schemes based on brain anatomy, function, and histology (18 parcellations in total). Processed matrices are available for each imaging modality across a range of parcellation scales. MICA-MICs can also be accessed from the Canadian Open Neuroscience Platform's data portal: https://portal.conp.ca/dataset?id=projects/mica-mics Please cite the following reference if you use this dataset: Royer, J., Rodriguez-Cruces, R., Tavakol, S., Lariviere, S., Herholz, P., Li, Q., Vos de Wael, R., Paquola, C., Benkarim, O., Park, B., Lowe, A.J., Margulies, D.S., Smallwood, J., Bernasconi, A., Bernasconi, N., Frauscher, B., Bernhardt, B.C. (2022). An open MRI dataset for multiscale neuroscience. Scientific Data, 9(1), 569.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.039 |
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".