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pith:2026:67S2JBTBUKDJ6UOGPDZIRTITDA
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NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning

Bardia Baraeinejad, Hamed Ajorlou, Parsa Razmara, Yasaman Torabi

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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.

References

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[1] Overview of functional magnetic resonance imaging, 2011
[2] Spurious but systematic correlations in functional connectivity mri networks arise from subject motion, 2012
[3] Resting-state fmri confounds and cleanup, 2013
[4] Building better biomarkers: brain models in translational neuroimaging, 2017
[5] A feasibility study of task-based fmri at 0.55 t, 2025

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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

f7e5a48661a2869f51c678f288cd131838639d6c522598a885ae5efbd35615ec

Aliases

arxiv: 2602.13770 · arxiv_version: 2602.13770v2 · doi: 10.48550/arxiv.2602.13770 · pith_short_12: 67S2JBTBUKDJ · pith_short_16: 67S2JBTBUKDJ6UOG · pith_short_8: 67S2JBTB
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/67S2JBTBUKDJ6UOGPDZIRTITDA \
  | jq -c '.canonical_record' \
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Canonical record JSON
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