# NeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning

arXiv: https://arxiv.org/abs/2602.13770 · HTML: https://mlsanity.com/paper/2602.13770/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 14 Feb 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)
- State-space sequence models (S4, Mamba) / [Physiological signal modeling](https://mlsanity.com/ssm/physiological-signal-modeling/)

## Builds on (most cited first, 20 of 20)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis](https://mlsanity.com/paper/2003.10613/) (2020)
- [BrainMass: Advancing Brain Network Analysis for Diagnosis with Large-scale Self-Supervised Learning](https://mlsanity.com/paper/2403.01433/) (2024)
- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (2022)
- [Decoupling of brain function from structure reveals regional behavioral specialization in humans](https://mlsanity.com/paper/1905.07813/) (2019)
- [PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis](https://mlsanity.com/paper/2305.14376/) (2023)
- [BrainNPT: Pre-training of Transformer networks for brain network classification](https://mlsanity.com/paper/2305.01666/) (2023)
- [Latent Graph Inference with Limited Supervision](https://mlsanity.com/paper/2310.04314/) (2023)
- [BACE: Behavior-Adaptive Connectivity Estimation for Interpretable Graphs of Neural Dynamics](https://mlsanity.com/paper/2510.20831/) (2025)
- [A Feasibility Study of Task-Based fMRI at 0.55 T](https://mlsanity.com/paper/2505.20568/) (2025)
- [VRIQ: Benchmarking and Analyzing Visual-Reasoning IQ of VLMs](https://mlsanity.com/paper/2602.05382/) (2026)
- [A New Non-Negative Matrix Factorization Approach for Blind Source Separation of Cardiovascular and Respiratory Sound Based on the Periodicity of Heart and Lung Function](https://mlsanity.com/paper/2305.01889/) (2023)
- [From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance](https://mlsanity.com/paper/2510.03906/) (2025)
- [Chem-NMF: Multi-layer α-divergence Non-Negative Matrix Factorization for Cardiorespiratory Disease Clustering, with Improved Convergence Inspired by Chemical Catalysts and Rigorous Asymptotic Analysis](https://mlsanity.com/paper/2510.06632/) (2025)

## Built on by (most cited first, 3 of 3)

- [Can MLLMs Critique Like Humans? Evaluating Open-Ended Aesthetic Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2606.29689/) (2026)
- [CircuitSteer: Geometrically Aligned Multi-Layer Steering via Sparse Autoencoder Circuits](https://mlsanity.com/paper/2608.05732/) (2026)
- [Multimodal LLMs Can Learn to Read Brain Signals: A Vision--Language Model for Unified Multi-Task EEG Decoding](https://mlsanity.com/paper/2610.09355/) (2026)

## Abstract (arXiv)

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 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, allowing it to analyze dynamic fMRI patterns and generate clinically meaningful textual reports.