BrainMT: A Hybrid Mamba-Transformer Architecture for Modeling Long-Range Dependencies in Functional MRI Data

• URL: http://arxiv.org/abs/2506.22591v1
• Date: Fri, 27 Jun 2025 19:20:41 GMT
• Title: BrainMT: A Hybrid Mamba-Transformer Architecture for Modeling Long-Range Dependencies in Functional MRI Data
• Authors: Arunkumar Kannan, Martin A. Lindquist, Brian Caffo,
• Abstract summary: Recent advances in deep learning have made it possible to predict phenotypic measures directly from functional magnetic resonance imaging (fMRI) brain volumes.<n>We introduce BrainMT, a novel hybrid framework designed to efficiently learn and integrate long-rangetemporal attributes in fMRI data.<n>Our framework operates in two stages: (1) a bidirectional Mamba block with a temporal-first scanning mechanism to capture global temporal interactions in a computationally efficient manner; and (2) a transformer block leveraging self-attention to model global spatial relationships.
• Score: 0.09363323206192666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in deep learning have made it possible to predict phenotypic measures directly from functional magnetic resonance imaging (fMRI) brain volumes, sparking significant interest in the neuroimaging community. However, existing approaches, primarily based on convolutional neural networks or transformer architectures, often struggle to model the complex relationships inherent in fMRI data, limited by their inability to capture long-range spatial and temporal dependencies. To overcome these shortcomings, we introduce BrainMT, a novel hybrid framework designed to efficiently learn and integrate long-range spatiotemporal attributes in fMRI data. Our framework operates in two stages: (1) a bidirectional Mamba block with a temporal-first scanning mechanism to capture global temporal interactions in a computationally efficient manner; and (2) a transformer block leveraging self-attention to model global spatial relationships across the deep features processed by the Mamba block. Extensive experiments on two large-scale public datasets, UKBioBank and the Human Connectome Project, demonstrate that BrainMT achieves state-of-the-art performance on both classification (sex prediction) and regression (cognitive intelligence prediction) tasks, outperforming existing methods by a significant margin. Our code and implementation details will be made publicly available at this https://github.com/arunkumar-kannan/BrainMT-fMRI

Related papers

• BrainSymphony: A Transformer-Driven Fusion of fMRI Time Series and Structural Connectivity [2.3486335708866606]
  BrainSymphony is a lightweight, parameter-efficient foundation model for neuroimaging.<n>It achieves state-of-the-art performance while being pre-trained on significantly smaller public datasets.<n>BrainSymphony establishes that architecturally-aware, multimodal models can surpass their larger counterparts.
  arXiv  Detail & Related papers  (2025-06-23T06:00:21Z)
• Voxel-Level Brain States Prediction Using Swin Transformer [65.9194533414066]
  We propose a novel architecture which employs a 4D Shifted Window (Swin) Transformer as encoder to efficiently learn-temporal information and a convolutional decoder to enable brain state prediction at the same spatial and temporal resolution as the input fMRI data.<n>Our model has shown high accuracy when predicting 7.2s resting-state brain activities based on the prior 23.04s fMRI time series.<n>This shows promising evidence that thetemporal organization of the human brain can be learned by a Swin Transformer model, at high resolution, which provides a potential for reducing fMRI scan time and the development of brain-computer interfaces
  arXiv  Detail & Related papers  (2025-06-13T04:14:38Z)
• Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer [30.044545011553172]
  We propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC.<n> Experimental results on 345 subjects with 570 scans from the Alzheimers Disease Neuroimaging Initiative (ADNI) demonstrate the superiority of our proposed method.
  arXiv  Detail & Related papers  (2025-01-27T18:20:33Z)
• Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis [1.89314691075208]
  Experimental results demonstrate significant improvements in the proposed FST-Mamba model on various brain-based classification and regression tasks.<n>Our work reveals the substantial potential of attention-free sequence modeling in brain discovery.
  arXiv  Detail & Related papers  (2024-08-23T13:58:14Z)
• BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals [11.030708270737964]
  We propose Brain Masked Auto-Encoder (BrainMAE) for learning representations directly from fMRI time-series data. BrainMAE consistently outperforms established baseline methods by significant margins in four distinct downstream tasks.
  arXiv  Detail & Related papers  (2024-06-24T19:16:24Z)
• MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding [50.55024115943266]
  We introduce a novel semantic alignment method of multi-subject fMRI signals using so-called MindFormer. This model is specifically designed to generate fMRI-conditioned feature vectors that can be used for conditioning Stable Diffusion model for fMRI- to-image generation or large language model (LLM) for fMRI-to-text generation. Our experimental results demonstrate that MindFormer generates semantically consistent images and text across different subjects.
  arXiv  Detail & Related papers  (2024-05-28T00:36:25Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
  Brain decoding aims to reconstruct stimuli from acquired brain signals. Currently, brain decoding is confined to a per-subject-per-model paradigm. We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
  arXiv  Detail & Related papers  (2024-04-11T15:46:42Z)
• Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data [54.55126643084341]
  Existing deep learning solutions suffer from three major limitations. We introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets.
  arXiv  Detail & Related papers  (2024-01-01T10:20:01Z)
• DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI [15.40111168345568]
  It is challenging to bridge the reliable non-linear mapping relations between structural connectivity (SC) and functional magnetic resonance imaging (fMRI) A novel diffusision generative adversarial network-based fMRI-to-SC model is proposed to predict SC from brain fMRI in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-09-28T06:55:50Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.