Classification of individuals based on Sparse Representation of brain cognitive patterns: A functional MRI study
Bibliographic record
Abstract
Many neurological disorders can change patterns of brain activity observed in functional imaging studies. These functional differences may be useful for classification of individuals into diagnostic categories. However, due to the high dimensionality of the input feature space and small set of subjects that are usually available, classification based on fMRI data is not trivial. Here, we evaluate the use of a Sparse Representation Analysis method within a Fisher Linear Discriminant (FLD) classification method, taking functional patterns characteristic of different cognitive tasks as the data input. As a test dataset, with a clear 'gold-standard' classification, we attempt to classify individuals as young, or older, based only on functional activation patterns in a speech listening task. Thirty two young (age: 19-26) and older (age: 57-73) adults (16 each) were scanned while listening to noise and to sentences degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Different functional contrast images were used within K-SVD to generate basis activation sources and their corresponding sparse modulation profiles. Sparse modulation profiles were used in a FLD framework to classify individuals into the young and older categories. The results demonstrate the feasibility of the general approach, and confirm the potential applicability of the proposed method for real-world diagnostic problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".