The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep learning methods applied to rs-fMRI either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brain activity. To overcome those shortcomings, we propose to formulate functional connectivity networks within the context of spatio-temporal graphs. We train a spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. Simultaneously, the model learns the importance of graph edges within ST-GCN to gain insight into the functional connectivities contributing to the prediction. In analyzing the rs-fMRI of the Human Connectome Project (HCP, N=1,091) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N=773), ST-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals. Furthermore, the brain regions and functional connections significantly contributing to the predictions of our model are important markers according to the neuroscience literature.
Fractals are self-similar and scale-invariant patterns found ubiquitously in nature. A lot of evidences implying fractal properties such as 1/f power spectrums have been also observed in resting state fMRI time series. To explain the fractal behavior in rs-fMRI, we have proposed the fractal-based model of resting state hemodynamic response function (rs-HRF) whose properties can be summarized by a fractal exponent. Here we show, through a simulation studies, that the fractal behavior of cerebral hemodynamics may cause significant distortion of network properties between neuronal activities and …
A Multiregression Dynamic Model (MDM) is a class of multivariate time series that represents various dynamic causal processes in a graphical way. One of the advantages of this class is that, in contrast to many other Dynamic Bayesian Networks, the hypothesised relationships accommodate conditional conjugate inference. We demonstrate for the first time how straightforward it is to search over all possible connectivity networks with dynamically changing intensity of transmission to find the Maximum a Posteriori Probability (MAP) model within this class. This search method is made feasible by usi…
Functional magnetic resonance imaging (fMRI) data provides information concerning activity in the brain and in particular the interactions between brain regions. Resting state fMRI data is widely used for inferring connectivities in the brain which are not due to external factors. As such analyzes strongly rely on stationarity, change point procedures can be applied in order to detect possible deviations from this crucial assumption. In this paper, we model fMRI data as functional time series and develop tools for the detection of deviations from covariance stationarity via change point altern…
BrainCast: A Spatio-Temporal Forecasting Model for Whole-Brain fMRI Time Series Prediction
Institute of Advanced Technology, University of Science and Technology of China（科学技术大学先进技术研究院）；MoE Key Laboratory of Brain-Inspired Intelligence Perception and Cognition, University of Science and Technology of China（科学技术大学脑启发智能感知与认知教育部重点实验室）；School of Mathematics and Computer Sciences, Nanchang University（南昌大学数学与计算机科学学院）；School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）；Department of Biomedical Engineering, Tulane University（路易斯安那大学医学工程系）
Cornell University（康奈尔大学）；Weill Cornell Medical College（威尔康奈尔医学院）；Brain and Mind Research Institute, Weill Cornell Medical College（威尔康奈尔医学院脑与心智研究所）；Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University（康奈尔大学南希·E·和彼得·C·迈尼希生物医学工程学院）
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature represent…
Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROI…
The objective of this paper is to provide a temporal dynamic model for resting state functional Magnetic Resonance Imaging (fMRI) trajectory to predict future brain images based on the given sequence. To this end, we came up with the model that takes advantage of representation learning and Neural Ordinary Differential Equation (Neural ODE) to compress the fMRI image data into latent representation and learn to predict the trajectory following differential equation. Latent space was analyzed by Gaussian Mixture Model. The learned fMRI trajectory embedding can be used to explain the variance of…
MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Cornell University（康奈尔大学）；Weill Cornell Medical College（威尔康奈尔医学院）
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional connectivity, there has been a recent surge in examining the temporal patterns in these data. In this paper, we explore two strategies for capturing the normal variability in resting-state activity across a healthy population: (a) an autoencoder approach on the rs-fMRI sequence, and (b) a next frame prediction strategy. We show that both…
Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Research Centre for Natural Sciences, Hungarian Academy of Sciences（匈牙利科学院自然科学研究中心）
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our C…
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…
The study of hierarchy in networks of the human brain has been of significant interest among the researchers as numerous studies have pointed out towards a functional hierarchical organization of the human brain. This paper provides a novel method for the extraction of hierarchical connectivity components in the human brain using resting-state fMRI. The method builds upon prior work of Sparse Connectivity Patterns (SCPs) by introducing a hierarchy of sparse overlapping patterns. The components are estimated by deep factorization of correlation matrices generated from fMRI. The goal of the pape…
National Technical University of Athens（雅典国立理工大学）；Università degli Studi di Napoli Federico II（那不勒斯费德里科二世大学）
We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the const…
Texas Tech University（得克萨斯理工大学）；Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆利斯特·希尔国家生物医学传播中心）
Purpose: Functional Magnetic Resonance Imaging (fMRI) data acquired through resting-state studies have been used to obtain information about the spontaneous activations inside the brain. One of the approaches for analysis and interpretation of resting-state fMRI data require spatially and functionally homogenous parcellation of the whole brain based on underlying temporal fluctuations. Clustering is often used to generate functional parcellation. However, major clustering algorithms, when used for fMRI data, have their limitations. Among commonly used parcellation schemes, a tradeoff exists be…
Deep Learning Inductive Biases for fMRI Time Series Classification during Resting-state and Movie-watching
School of Electrical and Computer Engineering, University of Tehran（埃特瓦尔大学电气与计算机工程学院）；School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM)（基础科学研究所（IPM）认知科学学院）
National Tsing Hua University（国立清华大学）；MOST Joint Research Center for AI Technology and All Vista Healthcare（科技部人工智能技术与全视野医疗联合研究中心）；National Health Research Institutes（国家卫生研究院）
The ability to accurately detect onset of dementia is important in the treatment of the disease. Clinically, the diagnosis of Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) patients are based on an integrated assessment of psychological tests and brain imaging such as positron emission tomography (PET) and anatomical magnetic resonance imaging (MRI). In this work using two different datasets, we propose a behavior score-embedded encoder network (BSEN) that integrates regularly adminstrated psychological tests information into the encoding procedure of representing subject's resting…
VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
Department of Biomedical Engineering and Biotechnology, and the Health Engineering Innovation Group (HEIG), Khalifa University（生物医学工程与生物技术系，以及健康工程创新小组（HEIG），卡利法大学）；Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki（电气与计算机工程系，阿尔基洛斯大学）
An Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI
School of Medical Science and Technology（医学科学与技术学院）；Dr B C Roy Multi Speciality Medical Research Centre（B C Roy多专科医学研究中心）；Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Indian Institute of Technology Gandhinagar（印度理工学院甘地纳格尔分校）
Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls
University of Bojnord（博季努尔德大学）；Munster Technological University（芒斯特理工大学）
Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals
Old Dominion University（奥多明尼昂大学）；Institute of Drug and Alcohol Studies（药物与酒精研究所）；Virginia Commonwealth University（弗吉尼亚联邦大学）；C. Kenneth and Dianne Wright Center for Clinical and Translational Research（C.肯尼斯和黛安·赖特临床与转化研究中心）；Data Science Institute（数据科学研究所）
University of Wisconsin（威斯康星大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
A key strength of twin studies arises from the fact that there are two types of twins, monozygotic and dizygotic, that share differing amounts of genetic information. Accurate differentiation of twin types allows efficient inference on genetic influences in a population. However, identification of zygosity is often prone to errors without genotying. In this study, we propose a novel pairwise feature representation to classify the zygosity of twin pairs of resting state functional magnetic resonance images (rs-fMRI). For this, we project an fMRI signal to a set of basis functions and use the pr…
Johns Hopkins University（约翰斯·霍普金斯大学）；Microsoft（微软）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
Resting state fMRI is an imaging modality which reveals brain activity localization through signal changes, in what is known as Resting State Networks (RSNs). This technique is gaining popularity in neurosurgical pre-planning to visualize the functional regions and assess regional activity. Labeling of rs-fMRI networks require subject-matter expertise and is time consuming, creating a need for an automated classification algorithm. While the impact of AI in medical diagnosis has shown great progress; deploying and maintaining these in a clinical setting is an unmet need. We propose an end-to-e…
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…
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…
Columbia University（哥伦比亚大学）；Stanford University（斯坦福大学）
We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data. Our model allows us to uncover shared network patterns across disease conditions. We evaluate our method on the ADNI2 dataset by inferring latent state patterns corresponding to altered neural circuits in individuals with Mild Cognitive Impairment (MCI). In addition to states shared across healthy and individuals with MCI, we discover latent states that are predominantly observed in individuals with MCI. Our model outperforms current state of the art deep learn…
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…
University of Regensburg（雷根斯堡大学）；Universidade de Aveiro（阿威罗大学）
Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is not compatible to the analysis of group data in general. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA-based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be in…
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…
Sabanci University（萨班哲大学）；Hacettepe University（哈塞特佩大学）；University of Rochester（罗切斯特大学）
Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectrospatial features of the pre-task, resting-state electroencephalograms (EEG). We asked ten healthy volunteers (6 females, 4 males) to participate in 105-minute fixed-sequence-varying-duration sessions of sustained attention to response task (SART). A novel and adaptive vigilance scoring scheme was designed based on the performance and response time in consecutive t…
University of Rajshahi（拉杰沙希大学）；Hajee Mohammad Danesh Science and Technology University（哈吉·穆罕默德·达内什科技大学）；Deakin University（迪肯大学）；COMSATS University Islamabad（伊斯兰堡COMSATS大学）；Charles Sturt University（查尔斯特大学）
In the last two decades, functional magnetic resonance imaging (fMRI) has emerged as one of the most effective technologies in clinical research of the human brain. fMRI allows researchers to study healthy and pathological brains while they perform various neuropsychological functions. Beyond task-related activations, the human brain has some intrinsic activity at a task-negative (resting) state that surprisingly consumes a lot of energy to support communication among neurons. Recent neuroimaging research has also seen an increase in modeling and analyzing brain activity in terms of a graph or…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data. Despite recent progresses, a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way. Here, we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network (GIN), which was recently proposed as a powerful GNN for graph classification. One of the important contributions of this paper…
Johns Hopkins University（约翰斯·霍普金斯大学）；Microsoft（微软公司）；Johns Hopkins School of Medicine（约翰斯·霍普金斯医学院）；Malone Center for Engineering in Healthcare（马龙医疗保健工程中心）
Wayne State University（韦恩州立大学）；New York University（纽约大学）
Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have provided new insight into development of the human brain before birth, but these studies have predominately focused on brain functional connectivity (i.e. Fisher z-score), which requires manual processing steps for feature extraction from fMRI images. Deep learning approaches (i.e., Convolutional Neural Networks) have achieved remarkable success on learning directly fro…
Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the Human Connectome Development Cohort
University of Calgary（卡尔加里大学）；Cumming School of Medicine（卡明医学院）；Charles Darwin University（查尔斯达尔文大学）
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）
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals
École de technologie supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Dibrugarh University（Dibrugarh大学）；Vytautas Magnus University（维陶塔斯马格努斯大学）；University of Southern Queensland（南昆士兰大学）
INESC-ID（里斯本系统与计算机工程研究所）；Instituto Superior Técnico（高等技术学院）
Topographical structures represent connections between entities and provide a comprehensive design of complex systems. Currently these structures are used to discover correlates of neuronal and haemodynamical activity. In this work, we incorporate them with neural processing techniques to perform regression, using electrophysiological activity to retrieve haemodynamics. To this end, we use Fourier features, attention mechanisms, shared space between modalities and incorporation of style in the latent representation. By combining these techniques, we propose several models that significantly ou…
Graph Neural Networks (GNNs) have been shown to be a powerful tool for generating predictions from biological data. Their application to neuroimaging data such as functional magnetic resonance imaging (fMRI) scans has been limited. However, applying GNNs to fMRI scans may substantially improve predictive accuracy and could be used to inform clinical diagnosis in the future. In this paper, we present a novel approach to representing resting-state fMRI data as a graph containing nodes and edges without omitting any of the voxels and thus reducing information loss. We compare multiple GNN archite…
