Individual Brain Charting dataset extension, fourth release of high-resolution fMRI data for cognitive mapping
Bibliographic record
Abstract
Functional Magnetic Resonance Imaging (fMRI) constitutes the primary imaging technique that allows us to investigate brain-functional organization at the millimeter scale. We thus present herein another extension of the Individual Brain Charting (IBC) dataset – a long-running project dedicated to map cognition in twelve individuals brains. In this release, we provide more multi-task fMRI data at high-spatial resolution –i.e. 1.5mm– from the same individuals. Concretely, the data refer to the performance of eighteen distinct tasks across seven fMRI sessions, comprising a wide range of cognitive processes. The data release pertains to both raw data and derived statistical maps that can be found in OpenNeuro plus EBRAINS and NeuroVault, respectively. In addition to fMRI, we provide paradigm descriptors of the tasks’ designs compliant with the Brain Imaging data Structure (BIDS) conventions, as well as code to reproduce the tasks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.030 |
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".