BrainMAP: Learning Multiple Activation Pathways in Brain Networks

• URL: http://arxiv.org/abs/2412.17404v2
• Date: Sat, 01 Feb 2025 04:50:59 GMT
• Title: BrainMAP: Learning Multiple Activation Pathways in Brain Networks
• Authors: Song Wang, Zhenyu Lei, Zhen Tan, Jiaqi Ding, Xinyu Zhao, Yushun Dong, Guorong Wu, Tianlong Chen, Chen Chen, Aiying Zhang, Jundong Li,
• Abstract summary: We introduce a novel framework BrainMAP to learn Multiple Activation Pathways in Brain networks. Our framework enables explanatory analyses of crucial brain regions involved in tasks.
• Score: 77.15180533984947
• License:
• Abstract: Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and human behavior. To enhance analysis and comprehension of brain activity, Graph Neural Networks (GNNs) have been widely applied to the analysis of functional connectivities (FC) derived from fMRI data, due to their ability to capture the synergistic interactions among brain regions. However, in the human brain, performing complex tasks typically involves the activation of certain pathways, which could be represented as paths across graphs. As such, conventional GNNs struggle to learn from these pathways due to the long-range dependencies of multiple pathways. To address these challenges, we introduce a novel framework BrainMAP to learn Multiple Activation Pathways in Brain networks. BrainMAP leverages sequential models to identify long-range correlations among sequentialized brain regions and incorporates an aggregation module based on Mixture of Experts (MoE) to learn from multiple pathways. Our comprehensive experiments highlight BrainMAP's superior performance. Furthermore, our framework enables explanatory analyses of crucial brain regions involved in tasks. Our code is provided at https://github.com/LzyFischer/Graph-Mamba.

Related papers

• Brain-like Functional Organization within Large Language Models [58.93629121400745]
  The human brain has long inspired the pursuit of artificial intelligence (AI) Recent neuroimaging studies provide compelling evidence of alignment between the computational representation of artificial neural networks (ANNs) and the neural responses of the human brain to stimuli. In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs) This framework links the AN sub-groups to FBNs, enabling the delineation of brain-like functional organization within large language models (LLMs)
  arXiv  Detail & Related papers  (2024-10-25T13:15:17Z)
• 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)
• Graph Neural Networks for Brain Graph Learning: A Survey [53.74244221027981]
  Graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs.
  arXiv  Detail & Related papers  (2024-06-01T02:47:39Z)
• Transformer-Based Hierarchical Clustering for Brain Network Analysis [13.239896897835191]
  We propose a novel interpretable transformer-based model for joint hierarchical cluster identification and brain network classification. With the help of hierarchical clustering, the model achieves increased accuracy and reduced runtime complexity while providing plausible insight into the functional organization of brain regions.
  arXiv  Detail & Related papers  (2023-05-06T22:14:13Z)
• DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
  Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
  arXiv  Detail & Related papers  (2023-01-26T20:45:30Z)
• Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network [0.8399688944263843]
  Existing machine learning methods for fMRI-based brain decoding either suffer from low classification performance or poor explainability. We propose a biologically inspired architecture, Spatial Temporal-pyramid Graph Convolutional Network (STpGCN), to capture the spatial-temporal graph representation of functional brain activities. We conduct extensive experiments on fMRI data under 23 cognitive tasks from Human Connectome Project (HCP) S1200.
  arXiv  Detail & Related papers  (2022-10-08T12:14:33Z)
• Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis [31.281194583900998]
  We propose an interpretable framework to analyze disorder-specific Regions of Interest (ROIs) and prominent connections. The proposed framework consists of two modules: a brain-network-oriented backbone model for disease prediction and a globally shared explanation generator.
  arXiv  Detail & Related papers  (2022-06-30T08:02:05Z)
• Functional2Structural: Cross-Modality Brain Networks Representation Learning [55.24969686433101]
  Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-06T03:45:36Z)
• Can the brain use waves to solve planning problems? [62.997667081978825]
  We present a neural network model which can solve such tasks. The model is compatible with a broad range of empirical findings about the mammalian neocortex and hippocampus.
  arXiv  Detail & Related papers  (2021-10-11T11:07:05Z)

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.