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）
Yi Hao Chan, Deepank Girish, Sukrit Gupta, Jing Xia, Chockalingam Kasi, Yinan He, Conghao Wang, Jagath C. Rajapakse
Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. However, existing methods of evaluating their robustness are often limited to cross-referencing with existing literature, which is a subjective and inconsistent process. In this review, we provide an overview of how GNN and model explainability techniques (specifically, feature attributors) have been applied to fMRI datasets for disorder prediction tasks, with an emphasis on evaluating the robustness of potential biomarkers produced for psychiatric disorders. Then, 65 studies using GNNs that reported potential fMRI biomarkers for psychiatric disorders (attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, schizophrenia) published before 9 October 2024 were identified from 2 online databases (Scopus, PubMed). We found that while most studies have performant models, salient features highlighted in these studies (as determined by feature attribution scores) vary greatly across studies on the same disorder. Reproducibility of biomarkers is only limited to a small subset at the level of regions and few transdiagnostic biomarkers were identified. To address these issues, we suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers. We further highlight gaps in the existing literature and put together a prediction-attribution-evaluation framework that could set the foundations for future research on discovering robust biomarkers of psychiatric disorders via GNNs.
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）
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）
Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosis
Department of Computer Science, Vrije Universiteit Amsterdam（范德比大学计算机科学系）；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）；Department of Physics and Technology, UiT - The Arctic University of Norway（北挪威大学物理与技术系）
Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks
Electric and Computer Engineering Department, Universidad Nacional del Sur（国家南方大学电子与计算机工程系）；Institute of Computer Science and Engineering, National Scientific and Technological Research Council of Argentina (CONICET)（阿根廷国家科学与技术研究理事会计算机科学与工程研究所）；Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra（庞培法布拉大学脑认知中心、信息与通信技术系计算神经科学组）；Institució Catalana de la Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究与教育研究所）；Department of Information and Communication Technologies, Universitat Pompeu Fabra（庞培法布拉大学信息与通信技术系）；ViRVIG, University of Girona（吉罗纳大学ViRVIG）
Background: Depression has become a major health burden worldwide, and effective detection depression is a great public-health challenge. This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Methods: Resting state EEG data was collected from 24 major depressive patients (MDD) and 29 normal controls using 128 channel HydroCel Geodesic Sensor Net (HCGSN). To better identify depression, we extracted different types of EEG features including linear features, nonlinear features and functional connectivity features phase lagging index (P…
Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin Institute of Health（柏林健康研究所）
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the devel…
Single subject prediction of brain disorders from neuroimaging data has gained increasing attention in recent years. Yet, for some heterogeneous disorders such as major depression disorder (MDD) and autism spectrum disorder (ASD), the performance of prediction models on large-scale multi-site datasets remains poor. We present a two-stage framework to improve the diagnosis of heterogeneous psychiatric disorders from resting-state functional magnetic resonance imaging (rs-fMRI). First, we propose a self-supervised mask prediction task on data from healthy individuals that can exploit differences…
Deep learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders. For psychiatric disorders in particular, it is believed that one of the most promising modality is the resting-state functional MRI (rsfMRI), which captures the intrinsic connectivity between regions in the brain. Because rsfMRI data points are inherently high-dimensional (~1M), it is impossible to process the entire input in its raw form. In this paper, we propose a very simple transforma…
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Many studies have been conducted on machine learning techniques that seek brain imaging data for specific biomarkers of disorders. These studies have encountered the following dilemma: A direct classification overfits to a small number of high-dimensional samples but unsupervised feature-extraction has the risk of extracting a signal of no interest. In addition, such studies often provided only diagnoses for patients without…
Background: Functional magnetic resonance imaging (fMRI) provides non-invasive measures of neuronal activity using an endogenous Blood Oxygenation-Level Dependent (BOLD) contrast. This article introduces a nonlinear dimensionality reduction (Locally Linear Embedding) to extract informative measures of the underlying neuronal activity from BOLD time-series. The method is validated using the Leave-One-Out-Cross-Validation (LOOCV) accuracy of classifying psychiatric diagnoses using resting-state and task-related fMRI. Methods: Locally Linear Embedding of BOLD time-series (into each voxel's respec…
Resting-state fMRI Analysis using Quantum Time-series Transformer
Interdisciplinary Program in Artificial Intelligence（人工智能交叉学科项目）；Department of Brain and Cognitive Sciences（脑科学与认知科学系）；Wells Fargo（沃伦·巴菲特）；Artificial Intelligence Department（人工智能部门）；Brookhaven National Laboratory（布鲁赫斯林国家实验室）；Department of Psychology（心理学系）
Resting-state fMRI has been shown to provide surrogate biomarkers for the analysis of various diseases. In addition, fMRI data helps in understanding the brain's functional working during resting state and task-induced activity. To improve the statistical power of biomarkers and the understanding mechanism of the brain, pooling of multi-center studies has become increasingly popular. But pooling the data from multiple sites introduces variations due to hardware, software, and environment. In this paper, we look at the estimation problem of hierarchical Sparsity Connectivity Patterns (hSCPs) in…
Parkinson's disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it is challenging to classify early stages 1 and 2 and detect brain function alterations. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. Some machine learning approaches like support vector machine and logistic regression have been successfully applied in the early diagnosis of PD using fMRI data, which outperform classifiers based on manual…
COMSATS University Islamabad（伊斯兰堡COMSATS大学）；University of Okara（奥卡拉大学）；Ca’ Foscari University of Venice（威尼斯大学）；Ege University（埃格大学）；Université Paris-Est Créteil (UPEC)（巴黎东克雷泰伊大学（UPEC））
Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve under…
Johns Hopkins University（约翰斯·霍普金斯大学）；Kennedy Krieger Institute（肯尼迪·克里格研究所）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures. Our specialized autoencoder infers a low dimensional manifold embedding for the rs-fMRI correlation matrices that mimics a canonical outer-product decomposition. The embedding is simultaneously used to reconstruct DTI tractography matrices via a second manifold alignment decoder and to predict inter-subject phenotypic variability via an artificial neural network. We validate our frame…
University of Amsterdam（阿姆斯特丹大学）；Amsterdam Neuroscience（阿姆斯特丹神经科学研究所）
Single-subject mapping of resting-state brain functional activity to non-imaging phenotypes is a major goal of neuroimaging. The large majority of learning approaches applied today rely either on static representations or on short-term temporal correlations. This is at odds with the nature of brain activity which is dynamic and exhibit both short- and long-range dependencies. Further, new sophisticated deep learning approaches have been developed and validated on single tasks/datasets. The application of these models for the study of a different targets typically require exhaustive hyperparame…
Over the last twenty five years, advances in the collection and analysis of fMRI data have enabled new insights into the brain basis of human health and disease. Individual behavioral variation can now be visualized at a neural level as patterns of connectivity among brain regions. Functional brain imaging is enhancing our understanding of clinical psychiatric disorders by revealing ties between regional and network abnormalities and psychiatric symptoms. Initial success in this arena has recently motivated collection of larger datasets which are needed to leverage fMRI to generate brain-based…
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）；King’s College Hospital（国王学院医院）
Deep learning is attracting significant interest in the neuroimaging community as a means to diagnose psychiatric and neurological disorders from structural magnetic resonance images. However, there is a tendency amongst researchers to adopt architectures optimized for traditional computer vision tasks, rather than design networks customized for neuroimaging data. We address this by introducing NEURO-DRAM, a 3D recurrent visual attention model tailored for neuroimaging classification. The model comprises an agent which, trained by reinforcement learning, learns to navigate through volumetric i…
University of Rochester（罗切斯特大学）；Ludwig Maximilian University（路德维希-马克西米利安大学）
The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. The lsXGC is a multivariate approach since it identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here lsXGC serves a…
Department of Computer Science and Engineering, University of Texas at Arlington, United States（德克萨斯理工大学计算机科学与工程系，美国）；School of Computing, University of Georgia, United States（佐治亚大学计算机学院，美国）
Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides insight into certain neurodegenerative diseases and psychiatric disorders, and thus is of clinical importance. To help inform physicians regarding patient diagnoses, unsupervised clustering of subjects based on FC is desired, allowing the data to inform us of groupings of patients based on shared features of connectivity. Since heterogeneity in FC is present ev…
University of Rochester（罗切斯特大学）；Ludwig Maximilian University（路德维希·马克西米利安大学）
It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We propose large-scale Extended Granger Causality (lsXGC) and investigate whether it can capture such changes using resting-state fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other time-series of the underlying dyn…
University of Cambridge（剑桥大学）；Shanghai General Hospital, Shanghai Jiao Tong University（上海交通大学附属第一人民医院）；University of Bath（巴斯大学）
Alzheimer's disease (AD) is the most common age-related dementia. It remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a noninvasive biomarker to detect brain aging. Previous evidence shows that the brain structural change detected by diffusion MRI is associated with dementia. Mounting studies has conceptualised the brain as a complex network, which has shown the utility of this approach in characterising various neurological and psychiatric disorders. Therefore, the structural connectivity shows promise in dementia classification. The…
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）
The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popular in the last years. Although this representation has advanced our understanding of brain function, it represents a simplified model of brain connectivity that has a complex dynamic spatio-temporal nature. Oversimplification of the data may hinder the merits of applying advanced non-linear feature extraction algorithms. To this end, we propose a dynamic adaptive spatio-temporal graph convolution (DAST-GCN) model to…
Multi-feature concatenation and multi-classifier stacking: an interpretable and generalizable machine learning method for MDD discrimination with rsfMRI
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；Key Laboratory of Cognition and Personality(SWU), Ministry of Education（教育部认知与人格重点实验室（西南大学））；School of Psychology, Southwest University (SWU)（西南大学心理学部）；Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University（北京师范大学基础教育质量评价协同创新中心西南大学分中心）
Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning
Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University（教育部医学图像智能计算重点实验室）；College of Artificial Intelligence, Shandong Normal University（山东师范大学人工智能学院）；National Frontiers Science Center for Industrial Intelligence and Systems Optimization（工业智能与系统优化国家级前沿科学中心）；AiShiWeiLai AI Research（AiShiWeiLai人工智能研究）；Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University（南京医科大学附属南京脑科医院精神科早期干预单元）；Amii, University of Alberta（阿尔伯塔大学Amii）
Southwest University（西南大学）；Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University（北京师范大学基础教育质量评价协同创新中心）
Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies reveal that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data.…
Michigan State University（密歇根州立大学）；Florida International University（佛罗里达国际大学）
Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous disorders that are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioural observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing of drugs due to misdiagnosis. In order to move the field towards more quantitative fashion, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet…
Brain gender differences have been known for a long time and are the possible reason for many psychological, psychiatric and behavioral differences between males and females. Predicting genders from brain functional connectivity (FC) can build the relationship between brain activities and gender, and extracting important gender related FC features from the prediction model offers a way to investigate the brain gender difference. Current predictive models applied to gender prediction demonstrate good accuracies, but usually extract individual functional connections instead of connectivity patte…
Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
Lyda Hill Department of Bioinformatics（Lyda Hill 生物信息学系）；Department of Biomedical Engineering（生物医学工程系）；University of Texas Southwestern Medical Center, Dallas, Texas, USA（德克萨斯西南医学中心，德克萨斯州达拉斯）
Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations
Johns Hopkins University（约翰斯·霍普金斯大学）；Kennedy Krieger Institute（肯尼迪·克里格研究所）；Johns Hopkins School of Medicine（约翰斯·霍普金斯医学院）
Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification
Monash University Malaysia（莫纳什大学马来西亚校区）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
Multiscale Neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（三机构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）；Lawson Health Research Institute（劳森健康研究所）；London Health Sciences Centre（伦敦健康科学中心）；FEMAP；Schulich School of Medicine and Dentistry（舒立克医学与牙科学院）；Western University（韦仕敦大学）
Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California（南加州大学凯克医学院马克与玛丽史蒂文斯神经成像与信息学研究所成像遗传学中心）；University Medical Center Groningen, University of Groningen（格罗宁根大学医学中心、格罗宁根大学）；University of Cape Town（开普敦大学）
