BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
Emory University（埃默里大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Princeton University（普林斯顿大学）；Lehigh University（理海大学）
Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang
备注：
This paper has been accepted to ICML 2021 Workshop on Interpretable Machine Learning in Healthcare
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience. GNNs are promising to model complicated network data, but they are prone to overfitting and suffer from poor interpretability, which prevents their usage in decision-critical scenarios like healthcare. To bridge this gap, we propose BrainNNExplainer, an interpretable GNN framework for brain network analysis. It is mainly composed of two jointly learned modules: a backbone prediction model that is specifically designed for brain networks and an explanation generator that highlights disease-specific prominent brain network connections. Extensive experimental results with visualizations on two challenging disease prediction datasets demonstrate the unique interpretability and outstanding performance of BrainNNExplainer.
Emory University（埃默里大学）；University of California, Los Angeles（加州大学洛杉矶分校）；The University of British Columbia（不列颠哥伦比亚大学）；Lehigh University（理海大学）
Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged as a potential method for modeling complex network data. Deep models, on the other hand, have low interpretability, which prevents their us…
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks
University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；Georgia State University（佐治亚州立大学）；University of North Carolina, Charlotte（北卡罗来纳大学夏洛特分校）；Dartmouth College（达特茅斯学院）
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）
Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks constructed from fMRI data for clinical predictions. Traditional functional brain networks, however, are noisy and unaware of downstream prediction tasks, while also incompatible with the deep graph neural network (GNN) models. In order to fully unleash the power of GNNs in network-based fMRI analysis, we develop FBNETGEN, a task-aware and interpretable fMRI analysis framework via deep br…
BrainSymphony: A parameter-efficient multimodal foundation model for brain dynamics with limited data
Turner Institute for Brain and Mental Health（大脑与心理健康Turner研究所）；School of Psychological Sciences, Monash University（墨尔本大学心理学科学学院）；Cortical Labs（皮层实验室）；CIFAR Azrieli Global Scholars Program（CIFAR阿兹里埃利全球学者计划）
University of Pennsylvania（宾夕法尼亚大学）；University of Rochester（罗切斯特大学）
The deviation between chronological age and biological age is a well-recognized biomarker associated with cognitive decline and neurodegeneration. Age-related and pathology-driven changes to brain structure are captured by various neuroimaging modalities. These datasets are characterized by high dimensionality as well as collinearity, hence applications of graph neural networks in neuroimaging research routinely use sample covariance matrices as graphs. We have recently studied covariance neural networks (VNNs) that operate on sample covariance matrices using the architecture derived from grap…
Lehigh University（里海大学）；University of Pennsylvania（宾夕法尼亚大学）
Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting…
Tulane University（杜兰大学）；Central South University（中南大学）；Mind Research Network（心智研究网络）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；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）
Multimodal fusion benefits disease diagnosis by providing a more comprehensive perspective. Developing algorithms is challenging due to data heterogeneity and the complex within- and between-modality associations. Deep-network-based data-fusion models have been developed to capture the complex associations and the performance in diagnosis has been improved accordingly. Moving beyond diagnosis prediction, evaluation of disease mechanisms is critically important for biomedical research. Deep-network-based data-fusion models, however, are difficult to interpret, bringing about difficulties for st…
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（三校神经影像与数据科学转化研究中心（TReNDS））；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）
Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used for multiple purposes. ICA can identify patterns that can discriminate between healthy controls (HC) and patients with various mental disorders such as schizophrenia (Sz). Temporal functional network connectivity (tFNC) obtained from ICA can effectively explain the interactions between brain networks. On the other hand, dictionary learning (DL) enables the discovery of hidden information in data using learnable basi…
Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions. However, existing functional brain networks are noisy and unaware of downstream prediction tasks, while also incompatible with recent powerful machine learning models of GNNs. In this work, we develop an end-to-end trainable pipeline to extract prominent fMRI features, generate brain networks, and make predictions with GNNs, all under the guidance of downstream prediction tasks. Preliminary experiments on the PNC fMRI data show the superi…
Interpretable Information-Decomposed Brain Graph Learning for fMRI-based Disease Diagnosis
School of Mathematics and Statistics, Shandong University（山东大学数学与统计学院）；Data Science Institute, Shandong University（山东大学数据科学研究院）；Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences（中国科学院大学数学与系统科学研究院）
Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis
Dept. of Mathematics & Operational Research University of Mons（数学与运筹学系蒙斯大学）；Dept. of Computer Science & Engineering Lehigh University（计算机科学与工程系莱斯大学）；Dept. of Industrial & Systems Engineering Lehigh University（工业与系统工程系莱斯大学）
EVA-Net: Interpretable Anomaly Detection for Brain Health via Learning Continuous Aging Prototypes from One-Class EEG Cohorts
Qilu Hospital, ASU（青岛医院，亚利桑那州立大学）；Zhengzhou University（郑州大学）；University of Science and Technology Beijing（北京科技大学）；Southern University of Science and Technology（南方科技大学）；Qilu Hospital（青岛医院）
Interpretable 3D Multi-Modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis
School of Electrical Engineering and Computer Science, University of Queensland（昆士兰大学电气工程与计算机科学学院）；The Centre for Advanced Imaging, University of Queensland（昆士兰大学高级成像中心）；Australian Institute for Bioengineering and Nanotechnology, University of Queensland（昆士兰大学生物工程与纳米技术研究所）；ARC Training Centre for Innovation in Biomedical Imaging Technology（ARC生物医学成像技术创新培训中心）
Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation
University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；University of Maryland（马里兰大学）；University of Pittsburgh（匹兹堡大学）；Texas A&M University - Corpus Christi（德克萨斯农工大学科珀斯克里斯蒂分校）；Emory University（埃默里大学）；Lehigh University（利哈伊大学）；University of Illinois at Chicago（芝加哥伊利诺伊大学）；University of Southern California（南加州大学）
Centre for Addiction and Mental Health（成瘾与心理健康中心）；University of Edinburgh（爱丁堡大学）；Royal Infirmary of Edinburgh（爱丁堡皇家医院）
Prediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and responses to treatment. Better estimations of prognosis would support treatment planning and patient support. Advances in machine learning have informed development of clinical predictive models, but their integration into clinical practice is almost non-existent. One reasons for this is the lack of interpretability of models. In this paper, we use a novel brain tumour dataset to compare two interpretable rule list models against popular machine learning approaches for…
Imperial College London（帝国理工学院）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）；Cogitat Ltd.（Cogitat有限公司）
Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions. However, the relevant features are not readily apparent and accessible. To mine informative latent representations from multichannel recordings of ongoing EEG activity, we propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Specifically, we introduce filters parameterized by generalized Gaussian functions that offer a smooth derivative for stable end-to-end model …
PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
Zhengzhou University, Zhengzhou, China（郑州大学）；China University of Geosciences, Wuhan, China（中国地质大学（武汉））；University of Science and Technology Beijing, Beijing, China（北京科技大学）；Vrije Universiteit Amsterdam, Amsterdam, The Netherlands（阿姆斯特丹自由大学）
University of Pittsburgh（匹兹堡大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. Firstly, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits …
University of Trento（特伦托大学）；Mila - Quebec Artifical Intelligence Institute（Mila -魁北克人工智能研究所）；StonyBrook University（石溪大学）
Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorithms may hinder their applicability to medical decision support systems. This paper studies an interpretable deep learning technique, called SincNet. SincNet is a convolutional neural network that efficiently learns customized band-pass filters through trainable sinc-functions. In this study, we use SincNet to analyze the neural activity of individuals with Autism Spectrum Disorder (ASD), who experience characteristic differences in neural oscillat…
Interpretable Fuzzy Modeling Reveals Population-Level Representation Differences in P300 Brain Computer Interfaces Across Neurodivergent and Neurotypical Cohorts
Department of Management, Griffith Business School（管理学院，格里菲斯商学院）；Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania（神经科学系，佩尔曼医学学院，宾夕法尼亚大学）；Marketing Department, Wharton School of Business, University of Pennsylvania（营销系，沃顿商学院，宾夕法尼亚大学）；Department of Psychology, School of Arts and Sciences, University of Pennsylvania（心理学系，艺术与科学学院，宾夕法尼亚大学）
Identifying abnormal patterns in electroencephalography (EEG) remains the cornerstone of diagnosing several neurological diseases. The current clinical EEG review process relies heavily on expert visual review, which is unscalable and error-prone. In an effort to augment the expert review process, there is a significant interest in mining population-level EEG patterns using unsupervised approaches. Current approaches rely either on two-dimensional decompositions (e.g., principal and independent component analyses) or deep representation learning (e.g., auto-encoders, self-supervision). However…
CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data
Department of Electrical and Computer Engineering, Boston University（波士顿大学电气与计算机工程系）；School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）；Department of Psychology, University of Virginia（弗吉尼亚大学心理学系）；Department of Neurology, University of Virginia（弗吉尼亚大学神经病学系）；Departments of Computer Science and Biology, Johns Hopkins University（约翰霍普金斯大学计算机科学与生物学系）
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Automotive Engineering Research Institute, Jiangsu University（江苏大学汽车工程研究院）；Stanford University（斯坦福大学）；School of Mathematics Science, Liaocheng University（聊城大学数学科学学院）；School of Electric Engineering, Zhengzhou University（郑州大学电气工程学院）；Korea University（高丽大学）
Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently develop…
Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning
Duke University（杜克大学）；Harvard University（哈佛大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Massachusetts General Hospital（麻省总医院）；Johns Hopkins Medicine（约翰霍普金斯医学院）；Emory University（埃默里大学）；Yale University（耶鲁大学）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；University of Mississippi Medical Center（密西西比大学医学中心）；Mayo Clinic（梅奥诊所）
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration to a global template, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regre…
Okayama University（冈山大学）；Japan Science and Technology Agency（日本科学技术振兴机构）；Rikkyo University（立教大学）；National Institute for Physiological Sciences（生理学研究所）；Araya Inc.（Araya 公司）；Keio University（庆应义塾大学）
Deep neural networks (DNNs) can accurately decode task-related information from brain activations. However, because of the nonlinearity of the DNN, the decisions made by DNNs are hardly interpretable. One of the promising approaches for explaining such a black-box system is counterfactual explanation. In this framework, the behavior of a black-box system is explained by comparing real data and realistic synthetic data that are specifically generated such that the black-box system outputs an unreal outcome. Here we introduce a novel generative DNN (counterfactual activation generator, CAG) that…
Interpretable EEG-to-Image Generation with Semantic Prompts
Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada（太平洋帕金森研究中心、Djavad Mowafaghian脑健康中心、不列颠哥伦比亚大学，温哥华，加拿大）；Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran（电气与计算机工程系、伊朗德黑兰大学，德黑兰，伊朗）；Department of Medicine British Columbia, Vancouver, Canada（医学系、不列颠哥伦比亚大学，温哥华，加拿大）
Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present a novel framework for creating class specific FA maps through …
BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Cambridge（剑桥大学）；University of Sheffield（谢菲尔德大学）；The University of Georgia（佐治亚大学）
