An automated framework for emotional fMRI data analysis using covariance matrix
Bibliographic record
Abstract
Classifying a particular affective state from the patterns of fMRI data is challenging. This is because of difficulty in finding discriminative and computationally inexpensive features and methods. In the present work, we employed a kernel-based machine learning framework utilizing covariance features and support vector machine (SVM) to classify 3 emotional states: pleasant (Ple), neutral (Neu), and unpleasant (Unp). We examined task functional magnetic resonance imaging (fMRI) of 5 healthy subjects who passively viewed a series of relevant pictures. Kernels (covariance matrix) per subject were extracted, mean-centered, and normalized by standard deviation. A standard leave-one-out cross validation was employed for generalization error. A balanced accuracy was computed to report overall accuracy. Discrimination maps derived from SVM weights were used to evaluate contributions of the input voxels. Best accuracy was achieved in Ple-vs-Neu with 88%, followed by Ple-vs-Unp (79%), and and Unp-vs-Neu (68%). Discrimination and t-maps suggested highly contributed regions close to dorsal attention network. The proposed machine learning framework is efficient and can be generalized to differentiate other cognitive tasks and emotions in fMRI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".