Towards Disease-Aware Self-Supervised Dynamic Brain Network Learning For Mental Diagnosis
Bibliographic record
Abstract
The dynamic brain network learning methods ignored the separation of redundant disease-irrelevant information, resulting in the model only achieving suboptimal diagnosis results. Meanwhile, the supervised learning scheme inevitably suffers from poor generalization due to the limited data. To address these problems, we propose a Self-supervised Dynamic Brain network Disentangled representation learning framework named SDBD, which incorporates 1) a dynamic topology-aware encoder for capturing diverse topological information, 2) a cross decoder for reconstructing the graph structure and 3) a spatio-temporal learning model based on the multi-head self-attention mechanism for classification. To disentangle the disease-related information from the dynamic brain networks, we design a temporal contrastive loss and a structure reconstruction loss. We evaluate our model on three real-world mental diseases: Autism Spectrum Disorder (ASD), Major Depressive Disorder (MDD), and Bipolar Disorder (BD). The results indicate significant improvements in our SDBD over the state-of-the-art methods owing to the disentangled disease-related information. Moreover, our method can identify the biomarkers associated with the diseases, which is consistent with the previous studies. To the best of our knowledge, our work is the first attempt to disentangle the disease-related information for the dynamic brain network analysis. The code is available at https://github.com/IntelliDAL/Graph/tree/main/SDBD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".