Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clustering

• URL: http://arxiv.org/abs/2403.08203v1
• Date: Wed, 13 Mar 2024 02:55:27 GMT
• Title: Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clustering
• Authors: Yanting Yang, Beidi Zhao, Zhuohao Ni, Yize Zhao, Xiaoxiao Li
• Abstract summary: We present a token clustering brain transformer-based model ($texttTC-BrainTF$) for joint community clustering and classification. Our results demonstrate that our learnable community-aware model $textttTC-BrainTF$ offers improved accuracy in identifying Autism Spectrum Disorder (ASD) and classifying genders.
• Score: 18.248669456724116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neuroscientific research has revealed that the complex brain network can be organized into distinct functional communities, each characterized by a cohesive group of regions of interest (ROIs) with strong interconnections. These communities play a crucial role in comprehending the functional organization of the brain and its implications for neurological conditions, including Autism Spectrum Disorder (ASD) and biological differences, such as in gender. Traditional models have been constrained by the necessity of predefined community clusters, limiting their flexibility and adaptability in deciphering the brain's functional organization. Furthermore, these models were restricted by a fixed number of communities, hindering their ability to accurately represent the brain's dynamic nature. In this study, we present a token clustering brain transformer-based model ($\texttt{TC-BrainTF}$) for joint community clustering and classification. Our approach proposes a novel token clustering (TC) module based on the transformer architecture, which utilizes learnable prompt tokens with orthogonal loss where each ROI embedding is projected onto the prompt embedding space, effectively clustering ROIs into communities and reducing the dimensions of the node representation via merging with communities. Our results demonstrate that our learnable community-aware model $\texttt{TC-BrainTF}$ offers improved accuracy in identifying ASD and classifying genders through rigorous testing on ABIDE and HCP datasets. Additionally, the qualitative analysis on $\texttt{TC-BrainTF}$ has demonstrated the effectiveness of the designed TC module and its relevance to neuroscience interpretations.

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)
• Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
  We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI. Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels. The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
  arXiv  Detail & Related papers  (2024-07-31T04:32:43Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• 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)
• Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks [18.375521792153112]
  Recurrent neural networks (RNNs) trained on compositional tasks can exhibit functional modularity. We apply a recent machine learning method, brain-inspired modular training, to a network being trained to solve a set of compositional cognitive tasks. We find that functional and anatomical clustering emerge together, such that functionally similar neurons also become spatially localized and interconnected.
  arXiv  Detail & Related papers  (2023-10-11T17:58:25Z)
• Community-Aware Transformer for Autism Prediction in fMRI Connectome [12.433556885503243]
  Com-BrainTF is a hierarchical local-global transformer architecture that learns intra and inter-community aware node embeddings for ASD prediction task. Our model outperforms state-of-the-art (SOTA) architecture on ABIDE dataset and has high interpretability, evident from the attention module.
  arXiv  Detail & Related papers  (2023-06-24T23:52:57Z)
• Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition [71.35205097460124]
  How humans understand and recognize the actions of others is a complex neuroscientific problem. LA-GCN proposes a graph convolution network using large-scale language models (LLM) knowledge assistance.
  arXiv  Detail & Related papers  (2023-05-21T08:29:16Z)
• 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)
• A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification [5.563162319586206]
  Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) are shifting towards acknowledging aspects of brain connectivity across time. In this paper, a deep non-temporalal variation Bayes framework is proposed to learn to identify autism spectrum disorder (ASD) in human participants. The framework incorporates a spatial-aware recurrent neural network with an attention-based message passing scheme to capture richtemporal patterns across dynamic FC networks.
  arXiv  Detail & Related papers  (2023-02-14T18:42:17Z)
• 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)
• Identification of brain states, transitions, and communities using functional MRI [0.5872014229110214]
  We propose a Bayesian model-based characterization of latent brain states and showcase a novel method based on posterior predictive discrepancy. Our results obtained through an analysis of task-fMRI data show appropriate lags between external task demands and change-points between brain states.
  arXiv  Detail & Related papers  (2021-01-26T08:10:00Z)

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.