Diagnosing Autism Spectrum Disorder (ASD) is a challenging problem, and is based purely on behavioral descriptions of symptomology (DSM-5/ICD-10), and requires informants to observe children with disorder across different settings (e.g. home, school). Numerous limitations (e.g., informant discrepancies, lack of adherence to assessment guidelines, informant biases) to current diagnostic practices have the potential to result in over-, under-, or misdiagnosis of the disorder. Advances in neuroimaging technologies are providing a critical step towards a more objective assessment of the disorder. Prior research provides strong evidence that structural and functional magnetic resonance imaging (MRI) data collected from individuals with ASD exhibit distinguishing characteristics that differ in local and global spatial, and temporal neural-patterns of the brain. Our proposed deep-learning model ASD-DiagNet exhibits consistently high accuracy for classification of ASD brain scans from neurotypical scans. We have for the first time integrated traditional machine-learning and deep-learning techniques that allows us to isolate ASD biomarkers from MRI data sets. Our method, called Auto-ASD-Network, uses a combination of deep-learning and Support Vector Machines (SVM) to classify ASD scans from neurotypical scans. Such interpretable models would help explain the decisions made by deep-learning techniques leading to knowledge discovery for neuroscientists, and transparent analysis for clinicians.
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…
In this paper we review the preprocessing pipeline through which fMRI data is transformed into a network. We discuss three parameters that mostly affect our understanding of the existence of functional correlations between the brain regions. In the end, we conclude that the existence of functional correlations between pairs of the brain's regions can be modeled with probabilistic edges, not to lose the uncertainty that is inherent in the network generation process.
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；New York University Langone Medical Center（纽约大学朗格尼医学中心）；Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
Purpose : Because functional MRI (fMRI) data sets are in general small, we sought a data efficient approach to resting state fMRI classification of autism spectrum disorder (ASD) versus neurotypical (NT) controls. We hypothesized that a Deep Reinforcement Learning (DRL) classifier could learn effectively on a small fMRI training set. Methods : We trained a Deep Reinforcement Learning (DRL) classifier on 100 graph-label pairs from the Autism Brain Imaging Data Exchange (ABIDE) database. For comparison, we trained a Supervised Deep Learning (SDL) classifier on the same training set. Results : DR…
a Division of Medical Informatics, Department of Biomedical Engineering b Division of Statistics \& Machine Learning, Department of Computer；Information Science c Center for Medical Image Science；Visualization (CMIV) \ \" o ping University, Sweden
Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years. Here we apply deep learning to derivatives from resting state fMRI data, and investigate how different 3D augmentation techniques affect the test accuracy. Specifically, we use resting state derivatives from 1,112 subjects in ABIDE preprocessed to train a 3D convolutional neural network (CNN) to perform the classification. Our results show that augmentation only provide minor improvements to the test accuracy.
University of Cambridge（剑桥大学）；University of Granada（格拉纳达大学）
Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box problem"). In this paper, we train a convolutional neural network (CNN) with the largest multi-source, functional MRI (fMRI) connectomic dataset ever compiled, consisting of 43,858 datapoints. We apply this model to a cross-sectional comparison of autism (ASD) vs typically developing (TD) controls that has proved difficult to characterise with inferential statistics. To contextualise these findings, we additionally pe…
Middle Tennessee State University（中田纳西州立大学）；St. Ambrose University（圣安布罗斯大学）
The objective of this study is to derive functional networks for the autism spectrum disorder (ASD) population using the group ICA and dictionary learning model together and to classify ASD and typically developing (TD) participants using the functional connectivity calculated from the derived functional networks. In our experiments, the ASD functional networks were derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. We downloaded a total of 120 training samples, including 58 ASD and 62 TD participants, which were obtained from the public repository: Autism Brain I…
Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes to compensate for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to those face in realistic diagnostic applications. Here, we demonstrate the feasibility of inter-site classification of neuropsychiatric status, with an …
Autism spectrum disorder (ASD) is one of the major developmental disorders affecting children. Recently, it has been hypothesized that ASD is associated with atypical brain connectivities. A substantial body of researches use Pearson's correlation coefficients, mutual information, or partial correlation to investigate the differences in brain connectivities between ASD and typical controls from functional Magnetic Resonance Imaging (fMRI). However, correlation or partial correlation does not directly reveal causal influences - the information flow - between brain regions. Comparing to correlat…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functional MRI (fMRI) characterizes brain connectivity and may be used to inform diagnoses and democratize medicine. However, successful construction of deep learning models from fMRI requires addressing key choices about the model's architecture, including the number of layers and number of neurons per layer. Meanwhile, deriving functional connectivity (FC) features from fMRI requires choosing an atlas with an appropriate…
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁医学院）
Recurrent neural networks (RNNs) were designed for dealing with time-series data and have recently been used for creating predictive models from functional magnetic resonance imaging (fMRI) data. However, gathering large fMRI datasets for learning is a difficult task. Furthermore, network interpretability is unclear. To address these issues, we utilize multitask learning and design a novel RNN-based model that learns to discriminate between classes while simultaneously learning to generate the fMRI time-series data. Employing the long short-term memory (LSTM) structure, we develop a discrimina…
The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing well into adolescence and even adulthood before finally confirming the disorder. Recently, graph-based learning techniques have demonstrated impressive results on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). We introduce IMAGIN, a multI-granular, Multi-Atlas spatio-temporal attention Graph Isomorphism Network, which we use to learn graph repres…
Autism spectrum disorder (ASD) is regarded as a brain disease with globally disrupted neuronal networks. Even though fMRI studies have revealed abnormal functional connectivity in ASD, they have not reached a consensus of the disrupted patterns. Here, a deep learning-based feature extraction method identifies multivariate and nonlinear functional connectivity patterns of ASD. Resting-state fMRI data of 972 subjects (465 ASD 507 normal controls) acquired from the Autism Brain Imaging Data Exchange were used. A functional connectivity matrix of each subject was generated using 90 predefined brai…
University of Rochester（罗切斯特大学）；Ludwig Maximilian University（路德维希·马克西米利安大学）
We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems. The lsAGC algorithm combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. We quantitatively evaluate the performance of lsAGC on synthetic directional time…
Indian Institute of Technology Mandi（印度理工学院曼迪分校）；Indian Institute of Technology Jodhpur（印度理工学院焦特布尔分校）
Resting-state fMRI is commonly used for diagnosing Autism Spectrum Disorder (ASD) by using network-based functional connectivity. It has been shown that ASD is associated with brain regions and their inter-connections. However, discriminating based on connectivity patterns among imaging data of the control population and that of ASD patients' brains is a non-trivial task. In order to tackle said classification task, we propose a novel deep learning architecture (MHATC) consisting of multi-head attention and temporal consolidation modules for classifying an individual as a patient of ASD. The d…
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）
Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph
Southwestern University of Finance and Economics（西南财经大学）；Osaka University（大阪大学）；West China Hospital of Sichuan University（四川大学华西医院）；The University of Hong Kong（香港大学）；General Hospital of Ningxia Medical University（宁夏医科大学总医院）
Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI-based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by proposing a biologically inspired architecture, Spatial Temporal-pyramid Graph Convolutional Network (STpGCN), to capture the spatial-temporal graph representation of functional brain activities. By designing multi-scale spatial-temporal pathways and bottom-up pathways that mimic th…
Islamic Azad University（伊斯兰阿扎德大学）；Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；Semnan University（塞姆南大学）；Qatar Biomedical Research Institute（卡塔尔生物医学研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；University of Granada（格拉纳达大学）；Deakin University（迪肯大学）；University of Technology Sydney（悉尼科技大学）；University of Turku（图尔库大学）；Effat University（埃法特大学）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）；Singapore University of Social Sciences（新加坡社会科学大学）
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and struc…
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）
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…
The autism dataset is studied to identify the differences between autistic and healthy groups. For this, the resting-state Functional Magnetic Resonance Imaging (rs-fMRI) data of the two groups are analyzed, and networks of connections between brain regions were created. Several classification frameworks are developed to distinguish the connectivity patterns between the groups. The best models for statistical inference and precision were compared, and the tradeoff between precision and model interpretability was analyzed. Finally, the classification accuracy measures were reported to justify t…
Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders
Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis（广东省多模态大数据智能分析重点实验室）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome. Early diagnosis and precise treatment are essential for ASD patients. Although researchers have built many analytical models, there has been limited progress in accurate predictive models for early diagnosis. In this project, we aim to build an accurate model to predict treatment outcome and ASD severity from early stage functional magnetic resonance imaging (fMRI) scans. The difficulty in building large databases of patients who have received specific treatments and the high dimensionality of medical image analysis probl…
HOI-Brain: a novel multi-channel transformers framework for brain disorder diagnosis by accurately extracting signed higher-order interactions from fMRI
School of Mathematics and Statistics, Shandong University（山东大学数学与统计学院）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）
Coevolutionary balance of resting-state brain networks in autism
Institute for Cognitive and Brain Sciences, Shahid Beheshti University（认知与脑科学学院，沙希德贝赫什提大学）；Department of Physics, Shahid Beheshti University（物理系，沙希德贝赫什提大学）
Mining human-brain networks to discover patterns that can be used to discriminate between healthy individuals and patients affected by some neurological disorder, is a fundamental task in neuroscience. Learning simple and interpretable models is as important as mere classification accuracy. In this paper we introduce a novel approach for classifying brain networks based on extracting contrast subgraphs, i.e., a set of vertices whose induced subgraphs are dense in one class of graphs and sparse in the other. We formally define the problem and present an algorithmic solution for extracting contr…
Psychiatric disorders have historically been classified using symptom information alone. With the advent of new technologies that allowed researchers to investigate brain mechanisms more directly, interest in the mechanistic rationale behind defined pathologies and aetiology redefinition has greatly increased. This is particularly appealing for the field of personalised medicine, which searches for data-driven approaches to improve individual diagnosis, prognosis and treatment selection. Here we intend to systematically analyse the usage of functional MRI on both the elucidation of psychiatric…
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）
Over the last years, increasing evidence has fuelled the hypothesis that Autism Spectrum Disorder (ASD) is a condition of altered brain functional connectivity. The great majority of these empirical studies rely on functional magnetic resonance imaging (fMRI) which has a relatively poor temporal resolution. Only a handful of studies have examined networks emerging from dynamic coherence at the millisecond resolution and there are no investigations of coherence at the lowest frequencies in the power spectrum - which has recently been shown to reflect long-range cortico-cortical connections. Her…
