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[2602.13770] NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning
Summary
The paper presents NeuroMambaLLM, an innovative framework that integrates dynamic graph learning and language model reasoning to analyze fMRI data in autistic brains, enhancing diagnostic capabilities.
Why It Matters
This research addresses the limitations of static fMRI analysis methods by introducing a dynamic approach that captures transient neural dynamics, which is crucial for understanding neurodevelopmental disorders like autism. The integration of language models allows for improved diagnostic classification and the generation of meaningful reports, potentially transforming clinical practices in neuroscience.
Key Takeaways
- NeuroMambaLLM combines dynamic graph learning with language model reasoning.
- It replaces static functional connectivity representations with adaptive latent graphs.
- The framework enhances the analysis of fMRI data by capturing long-range temporal dependencies.
- It enables the generation of clinically relevant textual reports from fMRI patterns.
- This approach may improve diagnostic accuracy for neurodevelopmental disorders.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.13770 (eess) [Submitted on 14 Feb 2026] Title:NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning Authors:Yasaman Torabi, Parsa Razmara, Hamed Ajorlou, Bardia Baraeinejad View a PDF of the paper titled NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning, by Yasaman Torabi and 3 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have demonstrated strong semantic reasoning across multimodal domains. However, their integration with graph-based models of brain connectivity remains limited. In addition, most existing fMRI analysis methods rely on static Functional Connectivity (FC) representations, which obscure transient neural dynamics critical for neurodevelopmental disorders such as autism. Recent state-space approaches, including Mamba, model temporal structure efficiently, but are typically used as standalone feature extractors without explicit high-level reasoning. We propose NeuroMambaLLM, an end-to-end framework that integrates dynamic latent graph learning and selective state-space temporal modelling with LLMs. 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 wh...