Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient regions is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, e.g. brain networks constructed by functional magnetic resonance imaging (fMRI). We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders. Specifically, we design novel regularized pooling layers that highlight salient regions of interests (ROIs) so that we can infer which ROIs are important to identify a certain disease based on the node pooling scores calculated by the pooling layers. Our proposed framework, Pooling Regularized-GNN (PR-GNN), encourages reasonable ROI-selection and provides flexibility to preserve either individual- or group-level patterns. We apply the PR-GNN framework on a Biopoint Autism Spectral Disorder (ASD) fMRI dataset. We investigate different choices of the hyperparameters and show that PR-GNN outperforms baseline methods in terms of classification accuracy. The salient ROI detection results show high correspondence with the previous neuroimaging-derived biomarkers for ASD.
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁医学院）
Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promising approach to identify biomarkers is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, i.e. brain networks constructed by fMRI. One way to interpret important features is through looking at how the classification probability changes if the features are occluded or replaced. The major limitation of this approach is that replacing values may change the distribution of the data an…
Contrastive Graph Pooling for Explainable Classification of Brain Networks
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；School of Computer Science, the University of Auckland（奥克兰大学计算机学院）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光前医学院）
Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Department of Computer Science and Engineering, Indian Institute of Technology（印度理工学院计算机科学与工程系）
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Tulane University（杜兰大学）；Xavier University of Louisiana（路易斯安那泽维尔大学）；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust brain regional fMRI representations for both graph-level classification and region-level functional difference detection tasks between ASD and healthy control (HC) groups is difficult. Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-st…
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁医学院）
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. Although Deep Neural Networks (DNNs) have been applied in functional magnetic resonance imaging (fMRI) to identify ASD, understanding the data-driven computational decision making procedure has not been previously explored. Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specific…
scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；The Hong Kong Polytechnic University（香港理工大学）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁医学院）
Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifiers have recently been used for identifying ASD from functional magnetic resonance imaging (fMRI) with higher accuracy than traditional learning strategies. However, a key challenge with deep learning models is understanding just what image features the network is using, which can in turn be used to define the biomarkers. Current methods extract biomarkers, i.e., important features, by looking at how the prediction c…
University of Electronic Science and Technology of China（电子科技大学）；Cornell University（康奈尔大学）
Feature selection is among the most important components because it not only helps enhance the classification accuracy, but also or even more important provides potential biomarker discovery. However, traditional multivariate methods is likely to obtain unstable and unreliable results in case of an extremely high dimensional feature space and very limited training samples, where the features are often correlated or redundant. In order to improve the stability, generalization and interpretations of the discovered potential biomarker and enhance the robustness of the resultant classifier, the re…
Hierarchical Pooling and Explainability in Graph Neural Networks for Tumor and Tissue-of-Origin Classification Using RNA-seq Data
Institute of Informatics（信息学院）；Universidade Federal do Rio Grande do Sul（鲁汶联邦大学）；Bioinformatics Core（生物信息学核心）；Hospital de Clínicas de Porto Alegre（波多利亚临床医院）
BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia
Vrije University Amsterdam（荷兰阿姆斯特丹自由大学）；Tri-institutional Center for Translational Research in Neuroimaging（转化神经影像研究联合中心）；Emory University（埃默里大学）；Key Laboratory of Genetic Evolution and Animal Models（遗传进化与动物模型重点实验室）；Kunming Institute of Zoology（昆明动物研究所）；Chinese Academy of Sciences Kunming（中国科学院昆明分院）；Department of Psychiatry, Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学精神病科）；Department of Physics and Technology, UiT The Arctic University of Norway（北极大学挪威理工学院物理与技术系）
Yale School of Medicine（耶鲁医学院）；Yale University（耶鲁大学）
Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification task of ASD using fMRI data. However, due to the black-box nature of most deep learning models, it's hard to perform biomarker selection and interpret model decisions. The recently proposed invertible networks can accurately reconstruct the input from its output, and have the potential to unravel the black-box representation. Therefore, we propose a novel method to classify ASD and identify biomarkers for ASD using the …
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Functional alterations in the relevant neural circuits occur from drug addiction over a certain period. And these significant alterations are also revealed by analyzing fMRI. However, because of fMRI's high dimensionality and poor signal-to-noise ratio, it is challenging to encode efficient and robust brain regional embeddings for both graph-level identification and region-level biomarkers detection tasks between nicotine addiction (NA) and healthy control (HC) groups. In this work, we represent the fMRI of the rat brain as a graph with biological attributes and propose a novel feature-selecte…
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck
Xi’an Jiaotong University（西安交通大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；UiT - Arctic University of Norway（挪威北极大学）；Fourth Military Medical University（空军军医大学）
Generative AI Enables Structural Brain Network Construction from fMRI via Symmetric Diffusion Learning
Department of Computing, Hong Kong Polytechnic University（香港理工大学计算学系）；School of Information Engineering, Nanchang University（南昌大学信息工程学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
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 biologicall…
NeuroGAN-3D: Enhancing Intrinsic Functional Brain Networks via High-Fidelity 3D Generative Super-Resolution
Georgia State University（佐治亚州立大学）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（神经影像与数据科学转化研究三机构中心（TReNDS））；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Shanghai University（上海大学）；Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；University of Science and Technology of China（中国科学技术大学）；Nanjing Medical University（南京医科大学）；The University of Electronic Science and Technology of China（电子科技大学）
FDG-PET reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, the studies of identification of early MCI (EMCI) and late MCI (LMCI) with FDG-PET images are still insufficient. Compared with studies based on fMRI and DTI images, the researches of the inter-region representation features in FDG-PET images are insufficient. Moreover, considering the variability…
Arizona State University（亚利桑那州立大学）；Washington University in St. Louis（圣路易斯华盛顿大学）
Convolutional neural networks (CNN) have been broadly studied on images, videos, graphs, and triangular meshes. However, it has seldom been studied on tetrahedral meshes. Given the merits of using volumetric meshes in applications like brain image analysis, we introduce a novel interpretable graph CNN framework for the tetrahedral mesh structure. Inspired by ChebyNet, our model exploits the volumetric Laplace-Beltrami Operator (LBO) to define filters over commonly used graph Laplacian which lacks the Riemannian metric information of 3D manifolds. For pooling adaptation, we introduce new object…
A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis
School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）；Department of Electrical and Computer Engineering, University of Pittsburgh（匹兹堡大学电气与计算机工程系）
TReND: Transformer derived features and Regularized NMF for neonatal functional network Delineation
University of Pennsylvania（宾夕法尼亚大学）；Children’s Hospital of Philadelphia（费城儿童医院）；Peking University（北京大学）；Beijing Children’s Hospital（北京儿童医院）；Beijing Normal University（北京师范大学）
Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis
Department of Language Science and Technology, The Hong Kong Polytechnic University（香港理工大学语言科学与技术系）；Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University（上海 Maritime 大学数字图像与智能计算实验室）
City University of Hong Kong（香港城市大学）；Yunnan University of Chinese Medicine（云南中医药大学）；City of Hope（希望之城）；Brown University（布朗大学）
Autism Spectrum Disorder(ASD) is a set of neurodevelopmental conditions that affect patients' social abilities. In recent years, many studies have employed deep learning to diagnose this brain dysfunction through functional MRI (fMRI). However, existing approaches solely focused on the abnormal brain functional connections but ignored the impact of regional activities. Due to this biased prior knowledge, previous diagnosis models suffered from inter-site measurement heterogeneity and inter-individual phenotypic differences. To address this issue, we propose a novel feature extraction method fo…
National University of Singapore（新加坡国立大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；NUS (Suzhou) Research Institute（新国大苏研院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；The N.1 Institute for Health, National University of Singapore（新加坡国立大学N.1健康研究所）；The Johns Hopkins University（约翰斯·霍普金斯大学）
This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels. We introduce a generic formulation of spectral filters on heterogeneous graphs by introducing the $k-th$ Hodge-Laplacian (HL) operator. In particular, we propose Laguerre polynomial approximations of HL spectral filters and prove that their spatial localization on graphs is related to the polynomial order. Furthermore, based on the bijection property of boundary operators on simplex graphs, we introduce a generic topological graph pooling …
In the research, we developed a computer vision solution to support diagnostic radiology in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. The chest radiograph appearance of COVID-19 pneumonia is thought to be nonspecific, having presented a challenge to identify an optimal architecture of a convolutional neural network (CNN) that would classify with a high sensitivity among the pulmonary inflammation features of COVID-19 and non-COVID-19 types of pneumonia. Rahman (2021) states that COVID-19 radiography images observe unavailability and quality i…
BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）；Saw Swee Hock School of Public Health at National University of Singapore, Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）；Xi’an Jiaotong University, Xi’an, Shaanxi（西安交通大学）；S-Lab, Nanyang Technological University, Singapore（南洋理工大学 S 实验室）
