Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity

• URL: http://arxiv.org/abs/2209.11232v1
• Date: Thu, 22 Sep 2022 04:17:57 GMT
• Title: Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity
• Authors: Mianxin Liu, Han Zhang, Feng Shi, and Dinggang Shen
• Abstract summary: We propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling.
• Score: 48.75665245214903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnoses of brain disorders. However, state-of-the-art studies used to build the FCN using a single brain parcellation atlas at a certain spatial scale, which largely neglected functional interactions across different spatial scales in hierarchical manners. In this study, we propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling across multiple spatial scales, namely "Atlas-guided Pooling". Accordingly, we propose a Multiscale-Atlases-based Hierarchical Graph Convolutional Network (MAHGCN), built on the stacked layers of graph convolution and the atlas-guided pooling, for a comprehensive extraction of diagnostic information from multiscale FCNs. Experiments on neuroimaging data from 1792 subjects demonstrate the effectiveness of our proposed method in the diagnoses of Alzheimer's disease (AD), the prodromal stage of AD (i.e., mild cognitive impairment [MCI]), as well as autism spectrum disorder (ASD), with accuracy of 88.9%, 78.6%, and 72.7% respectively. All results show significant advantages of our proposed method over other competing methods. This study not only demonstrates the feasibility of brain disorder diagnosis using resting-state fMRI empowered by deep learning, but also highlights that the functional interactions in the multiscale brain hierarchy are worth being explored and integrated into deep learning network architectures for better understanding the neuropathology of brain disorders.

Related papers

• Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion [16.622075098468002]
  There is no standard atlas for brain network classification, leading to limitations in detecting abnormalities in disorders. Some recent methods have proposed utilizing multiple atlases, but they neglect consistency across atlases and lack ROI-level information exchange. We propose an Atlas-Integrated Distillation and Fusion network (AIDFusion) to improve brain network classification using fMRI data.
  arXiv  Detail & Related papers  (2024-09-28T11:51:35Z)
• 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)
• Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification [42.82118108887965]
  Building comprehensive brain connectomes has proved to be fundamental importance in resting-state fMRI (rs-fMRI) analysis. We model the brain network as a directed acyclic graph (DAG) to discover direct causal connections between brain regions. We propose Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer effective connectivity and classify rs-fMRI time series.
  arXiv  Detail & Related papers  (2023-12-16T04:31:51Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI [9.916963496386089]
  A novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices. Results from a wide range of tests performed on the public Alzheimer's Disease Neuroimaging Initiative database show that the proposed model performs better than competing approaches.
  arXiv  Detail & Related papers  (2023-06-16T05:22:25Z)
• Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects [70.37277191524755]
  We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge.
  arXiv  Detail & Related papers  (2023-06-15T16:22:57Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• 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)
• Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learning [4.310840361752551]
  We propose a novel feature extraction method for fMRI that can learn a personalized lowe-resolution representation of the entire brain networking. The present model has achieved a mean classification accuracy of 85.95% and a mean AUC of 0.92, which is better than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-09-19T14:22:55Z)
• Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis [30.99183477161096]
  multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis. We propose a novel Hypergraph Generative Adversarial Networks(HGGAN) to generate multimodal connectivity of Brain Network from rs-fMRI combination with DTI.
  arXiv  Detail & Related papers  (2021-07-21T09:02:29Z)

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.