Individual Brain Charting third release, probing brain activity during Movie Watching and Retinotopic Mapping
Bibliographic record
Abstract
The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatialresolution and dedicated to the cognitive mapping of the human brain.It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications.Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation.We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features.We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems.We also present the topographic organization of the visual system through retinotopy.In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex.IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories. Background & SummaryMapping cognition across the whole human brain requires the multi-dimensional analysis of the correlates of behavior corresponding to a wide range of psychological domains.This phenotyping of behavioral responses relies on brain activation maps obtained from functional Magnetic Resonance Imaging (fMRI), that quantify the underlying neural correlates modulated by mental functions across tasks [1][2][3][4][5] .In order to achieve generalisation of cognitive processes, behavioural phenotyping with fMRI can be carried out through data pooling analyses, in which data from different sources, and therefore different tasks, from different participants in different studies, are aggregated.In this context, it becomes difficult to dissociate effects elicited by: (1) common functional signatures of cognitive processes (even if different tasks performed by different participants share some psychological domains); (2) withinsubject variability in task performance; (3) individual functional differences in both between-and within-task performance; and (4) different feature distributions across multiple data-acquisition sites.Inter-subject variability arising from individual functional differences has been widely recognized in neuroimaging and
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".