D-CoRP: Differentiable Connectivity Refinement for Functional Brain Networks

• URL: http://arxiv.org/abs/2405.18658v1
• Date: Tue, 28 May 2024 23:49:52 GMT
• Title: D-CoRP: Differentiable Connectivity Refinement for Functional Brain Networks
• Authors: Haoyu Hu, Hongrun Zhang, Chao Li,
• Abstract summary: Existing models for brain networks typically focus on brain regions or overlook the complexity of brain connectivities. We develop a differentiable module for refining brain connectivity. Our experimental results show that the proposed method can significantly improve the performance of various baseline models.
• Score: 4.675640373196467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain network is an important tool for understanding the brain, offering insights for scientific research and clinical diagnosis. Existing models for brain networks typically primarily focus on brain regions or overlook the complexity of brain connectivities. MRI-derived brain network data is commonly susceptible to connectivity noise, underscoring the necessity of incorporating connectivities into the modeling of brain networks. To address this gap, we introduce a differentiable module for refining brain connectivity. We develop the multivariate optimization based on information bottleneck theory to address the complexity of the brain network and filter noisy or redundant connections. Also, our method functions as a flexible plugin that is adaptable to most graph neural networks. Our extensive experimental results show that the proposed method can significantly improve the performance of various baseline models and outperform other state-of-the-art methods, indicating the effectiveness and generalizability of the proposed method in refining brain network connectivity. The code will be released for public availability.

Related papers

• 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)
• BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model [7.894526238189559]
  We introduce BrainNetDiff, which combines a multi-head Transformer encoder to extract relevant features from fMRI time series. We validate applicability of this framework in the construction of brain network across healthy and neurologically impaired cohorts.
  arXiv  Detail & Related papers  (2023-11-09T08:27:12Z)
• 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)
• Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline [54.93591298333767]
  Brain diffuser is a diffusion based end-to-end brain network generative model. It exploits more structural connectivity features and disease-related information by analyzing disparities in structural brain networks across subjects. For the case of Alzheimer's disease, the proposed model performs better than the results from existing toolkits on the Alzheimer's Disease Neuroimaging Initiative database.
  arXiv  Detail & Related papers  (2023-03-11T14:04:58Z)
• A Structure-guided Effective and Temporal-lag Connectivity Network for Revealing Brain Disorder Mechanisms [8.459311736323572]
  We propose an effective temporal-lag neural network (termedN) to infer causal relationships and the temporal-lag values between brain regions. The evaluation results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-01T15:02:22Z)
• 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)
• Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis [61.53545734991802]
  We propose a novel brain network representation framework, namely BN-GNN, which searches for the optimal GNN architecture for each brain network. Our proposed BN-GNN improves the performance of traditional GNNs on different brain network analysis tasks.
  arXiv  Detail & Related papers  (2022-03-18T07:05:27Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis [17.48272758284748]
  Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. We develop a novel multiview GNN for multimodal brain networks. In particular, we regard each modality as a view for brain networks and employ contrastive learning for multimodal fusion.
  arXiv  Detail & Related papers  (2021-07-07T13:49:57Z)
• Ensembling complex network 'perspectives' for mild cognitive impairment detection with artificial neural networks [5.194561180498554]
  We propose a novel method for mild cognitive impairment detection based on jointly exploiting the complex network and the neural network paradigm. In particular, the method is based on ensembling different brain structural "perspectives" with artificial neural networks.
  arXiv  Detail & Related papers  (2021-01-26T08:38:11Z)
• Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks [81.74442855155843]
  We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification. The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
  arXiv  Detail & Related papers  (2021-01-18T13:28:08Z)

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.