Learning image derived PDE-phenotypes from fMRI data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Partial differential equations (PDEs) model various physical phenomena, such as electromagnetic fields and fluid mechanics. Methods such as sparse identification of nonlinear dynamics (SINDy) and PDE-Net 2.0 have been developed to identify and model PDEs on the basis of data via sparse optimization and deep neural networks, respectively. While PDE models are less commonly applied to fMRI data, they have the potential to uncover hidden connections and essential components in brain activity. Using the ADHD200 dataset, we applied canonical independent component analysis (CanICA) and uniform manifold approximation (UMAP) for dimensionality reduction of fMRI data. We then used sparse ridge regression to identify PDEs from the reduced data, and applied significant PDE features for classification achieving high accuracy in distinguishing individuals with attention deficit hyperactivity disorder (ADHD). This study demonstrates a novel approach for extracting meaningful features from fMRI data for neurological disorder analysis to understand the role of oxygen transport (delivery & consumption) in the brain during neural activity, which is relevant for studying intracranial pathologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.038 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it