Using fMRI Time Series and Functional Connectivity for Autism Classification: Integrating Mamba and KAN in Domain-Adversarial Neural Networks
Bibliographic record
Abstract
Domain differences in fMRI analysis often cause biases that negatively affect Autism classification. To address this, we propose a novel pipeline leveraging a Domain Adversarial Neural Network (DANN) architecture to extract domain-invariant yet classification-informative features by integrating Mamba and Kolmogorov-Arnold Network (KAN) models. The DANN framework consists of an extractor, a domain classifier, and a label classifier, trained in an adversarial way to reduce domain bias while maintaining classification accuracy. The extractor employs two parallel paths: one processes fMRI time series with the Mamba model, and the other analyzes functional connectivity data using the KAN. The extracted features are concatenated and utilized by KAN-based domain and label classifiers. Adversarial training ensures the domain classifier cannot distinguish domain labels, confirming the domain invariance of the features. Experimental results show that this method achieves an accuracy of 72.56% and an AUC of 72.46%, demonstrating comparability to state-of-the-art methods which rely solely on fMRI data without utilizing phenotype information. Source code and implementation details are available at https://github.com/fatemehghanadi/fMRI-Based-Autism-Classification-Mamba-KAN-with-DANN.Clinical Relevance- The proposed approach effectively mitigates domain-induced biases and offers a robust solution for fMRI-based Autism classification tasks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".