Brain decoding of the Human Connectome Project Tasks in a Dense Individual fMRI Dataset.
Bibliographic record
Abstract
Brain decoding aims to infer cognitive states from patterns of brain activity. Substantialinter-individual variations in functional brain organization challenge accurate decodingperformed at the group level. In this paper, we tested whether accurate brain decodingmodels can be trained entirely at the individual level. We trained several classifiers ona dense individual functional magnetic resonance imaging (fMRI) dataset for which sixparticipants completed the entire Human Connectome Project (HCP) task battery >13times over ten separate fMRI sessions. We evaluated nine decoding methods, fromSupport Vector Machines (SVM) and Multi-Layer Perceptron (MLP) to GraphConvolutional Neural Networks (GCN). All decoders were trained to classify singlefMRI volumes into 21 experimental conditions simultaneously, using ~7h of fMRI dataper participant. The best prediction accuracies were achieved with GCN and MLPmodels, whose performance (57-67% accuracy) approached state-of-the-art accuracy(76%) with models trained at the group level on >1k hours of data from the originalHCP sample. Our SVM model also performed very well (54-62% accuracy). Featureimportance maps derived from MLP —our best-performing model— revealedinformative features in regions relevant to particular cognitive domains, notably in themotor cortex. We also observed that inter-subject classification achieved substantiallylower accuracy than subject-specific models, indicating that our decoders learnedindividual-specific features. This work demonstrates that densely-sampledneuroimaging datasets can be used to train accurate brain decoding models at theindividual level. We expect this work to become a useful benchmark for techniques thatimprove model generalization across multiple subjects and acquisition conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".