pith:67S2JBTB
NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning
NeuroMambaLLM derives dynamic latent graphs from raw fMRI BOLD signals and projects them into frozen LLM space for autism classification plus textual reports.
arxiv:2602.13770 v2 · 2026-02-14 · eess.IV · cs.LG
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Claims
The proposed method learns the functional connectivity dynamically from raw Blood-Oxygen-Level-Dependent (BOLD) time series, replacing fixed correlation graphs with adaptive latent connectivity while suppressing motion-related artifacts and capturing long-range temporal dependencies. The resulting dynamic brain representations are projected into the embedding space of an LLM model, where the base language model remains frozen and lightweight low-rank adaptation (LoRA) modules are trained for parameter-efficient alignment. This design enables the LLM to perform both diagnostic classification and language-based reasoning.
That dynamic latent graphs learned from raw BOLD signals, after Mamba processing, can be projected into LLM space such that a frozen base model plus LoRA adapters will produce accurate autism classifications and clinically meaningful textual reports without post-hoc tuning or external validation.
NeuroMambaLLM dynamically learns functional connectivity graphs from raw BOLD time series via Mamba, projects them into an LLM embedding space, and enables both diagnostic classification and generation of clinical text reports for autism.
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| First computed | 2026-05-17T23:39:16.157674Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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