Graph-Based Method for Anomaly Detection in Functional Brain Network using Variational Autoencoder

Abstract

Functional neuroimaging techniques using resting-state functional MRI (rs-fMRI) have accelerated progress in brain disorders and dysfunction studies. Since, there are the slight differences between healthy and disorder brains, investigation in the complex topology of human brain functional networks is difficult and complicated task with the growth of evaluation criteria. Recently, graph theory and deep learning applications have spread widely to understanding human cognitive functions that are linked to gene expression and related distributed spatial patterns. Irregular graph analysis has been widely applied in many brain recognition domains, these applications might involve both node-centric and graph-centric tasks. In this paper, we discuss about individual Variational Autoencoder and Graph Convolutional Network (GCN) for the region of interest identification areas of brain which do not have normal connection when apply certain tasks. Here, we identified a framework of Graph Auto-Encoder (GAE) with hyper sphere distributer for functional data analysis in brain imaging studies that is underlying non-Euclidean structure, in learning of strong rigid graphs among large scale data. In addition, we distinguish the possible mode correlations in abnormal brain connections.

Graph Neural Networks and Their Current Applications in Bioinformatics

“…Finally, for heterogeneous networks containing multi-source information, GNNs can deeply integrate their topological information. Nevertheless, current GNNs mainly focus on the processing of isomorphic graphs and cannot sufficiently capture the heterogeneity of nodes and edges in Parisot et al, 2017;Kazi et al, 2018Kazi et al, , 2019aAnirudh and Thiagarajan, 2019;Yang et al, 2019;Arya et al, 2020;Stankevičiūtė et al, 2020 √ Brain connection research Ktena et al, 2017Ktena et al, , 2018Li X. et al, 2019a;Mirakhorli and Mirakhorli, 2019;Grigis et al, 2020 heterogeneous networks (Zhang C. et al, 2019). So, a new architecture needs to be studied, which can consider the feature of data in heterogeneous biological networks.…”

“…Meanwhile, image data can be represented as a graph structure appropriate for the use of GNNs. Therefore, GNNs have an extensive application space in the field of medical imaging, such as image segmentation ( Gopinath et al, 2019 ; Wang et al, 2019b ; Tian et al, 2020a , b ), abnormal detection ( Wu et al, 2019 ) of MRI images and pathological images, classification ( Shi et al, 2019 ; Zhou et al, 2019 ; Adnan et al, 2020 ) and visualization ( Levy et al, 2020 ; Sureka et al, 2020 ) of histological images, analysis of surgical images ( Zhang et al, 2018 ), image enhancement ( Hu et al, 2020 ), registration ( Hansen et al, 2019 ), retrieval ( Zhai et al, 2019 ), brain connection ( Ktena et al, 2017 , 2018 ; Li X. et al, 2019a ; Mirakhorli and Mirakhorli, 2019 ; Grigis et al, 2020 ; Li et al, 2020 ; Zhang and Pierre, 2020 ; Zhang et al, 2021 ) and disease prediction ( Parisot et al, 2017 ; Kazi et al, 2018 , 2019a , b , c ; Anirudh and Thiagarajan, 2019 ; Yang et al, 2019 ; Stankevičiūtė et al, 2020 ; Zhang and Pierre, 2020 ; Zhang et al, 2021 ), etc.…”

“…At present, most models in deep learning use regular images as the default input data, but the processing of irregular brain connection images remains a problem. The pioneering work of depth graph networks in brain connection research was done by Ktena et al (2018) , and then this work was further extended about the way of cross-validation ( Mirakhorli and Mirakhorli, 2019 ). Figure 9 shows the process for the conversion and analysis of functional magnetic resonance imaging (fMRI) in the study of Ktena et al which has been used in some later research of GNNs for brain connection.…”

A Survey of Deep Learning for Alzheimer’s Disease

“…where A is an adjacency matrix that contains the connection information between graph nodes; X is the feature matrix of the graph; and W and Θ are matrices of learnable parameters. A key aspect of utilizing GNNs is the generation of graphs or manifolds, e.g., structural connectivity graphs derived from DTI [298] and hypersphere projections of brain-functional networks extracted from fMRI [302]. The graph generation component can also be incorporated into the neural network with embedding and attention-based mechanisms [303], while global attention mechanisms can also be used to build resilience against noise and variance [304].…”