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Song R, Cao P, Wen G, Zhao P, Huang Z, Zhang X, Yang J, Zaiane OR. BrainDAS: Structure-aware domain adaptation network for multi-site brain network analysis. Med Image Anal 2024; 96:103211. [PMID: 38796945 DOI: 10.1016/j.media.2024.103211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/31/2024] [Accepted: 05/15/2024] [Indexed: 05/29/2024]
Abstract
In the medical field, datasets are mostly integrated across sites due to difficult data acquisition and insufficient data at a single site. The domain shift problem caused by the heterogeneous distribution among multi-site data makes autism spectrum disorder (ASD) hard to identify. Recently, domain adaptation has received considerable attention as a promising solution. However, domain adaptation on graph data like brain networks has not been fully studied. It faces two major challenges: (1) complex graph structure; and (2) multiple source domains. To overcome the issues, we propose an end-to-end structure-aware domain adaptation framework for brain network analysis (BrainDAS) using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed approach contains two stages: supervision-guided multi-site graph domain adaptation with dynamic kernel generation and graph classification with attention-based graph pooling. We evaluate our BrainDAS on a public dataset provided by Autism Brain Imaging Data Exchange (ABIDE) which includes 871 subjects from 17 different sites, surpassing state-of-the-art algorithms in several different evaluation settings. Furthermore, our promising results demonstrate the interpretability and generalization of the proposed method. Our code is available at https://github.com/songruoxian/BrainDAS.
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Affiliation(s)
- Ruoxian Song
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Peng Cao
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China.
| | - Guangqi Wen
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Pengfei Zhao
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing, China
| | - Ziheng Huang
- College of Software, Northeastern University, Shenyang, China
| | - Xizhe Zhang
- Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Jinzhu Yang
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China.
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Guan Z, Yu J, Shi Z, Liu X, Yu R, Lai T, Yang C, Dong H, Chen R, Wei L. Dynamic graph transformer network via dual-view connectivity for autism spectrum disorder identification. Comput Biol Med 2024; 174:108415. [PMID: 38599070 DOI: 10.1016/j.compbiomed.2024.108415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 03/17/2024] [Accepted: 04/03/2024] [Indexed: 04/12/2024]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that requires objective and accurate identification methods for effective early intervention. Previous population-based methods via functional connectivity (FC) analysis ignore the differences between positive and negative FCs, which provide the potential information complementarity. And they also require additional information to construct a pre-defined graph. Meanwhile, two challenging demand attentions are the imbalance of performance caused by the class distribution and the inherent heterogeneity of multi-site data. In this paper, we propose a novel dynamic graph Transformer network based on dual-view connectivity for ASD Identification. It is based on the Autoencoders, which regard the input feature as individual feature and without any inductive bias. First, a dual-view feature extractor is designed to extract individual and complementary information from positive and negative connectivity. Then Graph Transformer network is innovated with a hot plugging K-Nearest Neighbor (KNN) algorithm module which constructs a dynamic population graph without any additional information. Additionally, we introduce the PolyLoss function and the Vrex method to address the class imbalance and improve the model's generalizability. The evaluation experiment on 1102 subjects from the ABIDE I dataset demonstrates our method can achieve superior performance over several state-of-the-art methods and satisfying generalizability for ASD identification.
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Affiliation(s)
- Zihao Guan
- College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China; Digital Fujian Research Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Jiaming Yu
- College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China; Digital Fujian Research Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Zhenshan Shi
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350002, China
| | - Xiumei Liu
- Developmental and Behavior Pediatrics Department, Fujian Children's Hospital - Fujian Branch of Shanghai Children's Medical Center, Fuzhou, 350002, China; College of Clinical Medicine for Obstetrics Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350012, China
| | - Renping Yu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Taotao Lai
- College of Computer and Control Engineering, Minjiang University, Fuzhou, 350108, China
| | - Changcai Yang
- College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China; Digital Fujian Research Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Heng Dong
- College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China; Digital Fujian Research Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Riqing Chen
- College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China; Digital Fujian Research Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Lifang Wei
- College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China; Digital Fujian Research Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
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Wang Y, Long H, Bo T, Zheng J. Residual graph transformer for autism spectrum disorder prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108065. [PMID: 38428249 DOI: 10.1016/j.cmpb.2024.108065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 01/28/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
Brain functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been in vogue to predict Autism Spectrum Disorder (ASD), which is a neuropsychiatric disease up the plight of locating latent biomarkers for clinical diagnosis. Albeit massive endeavors have been made, most studies are fed up with several chronic issues, such as the intractability of harnessing the interaction flourishing within brain regions, the astriction of representation due to vanishing gradient within deeper network architecture, and the poor interpretability leading to unpersuasive diagnosis. To ameliorate these issues, a FC-learned Residual Graph Transformer Network, namely RGTNet, is proposed. Specifically, we design a Graph Encoder to extract temporal-related features with long-range dependencies, from which interpretable FC matrices would be modeled. Besides, the residual trick is introduced to deepen the GCN architecture, thereby learning the higher-level information. Moreover, a novel Graph Sparse Fitting followed by weighted aggregation is proposed to ease dimensionality explosion. Empirically, the results on two types of ABIDE data sets demonstrate the meliority of RGTNet. Notably, the achieved ACC metric reaches 73.4%, overwhelming most competitors with merely 70.9% on the AAL atlas using a five-fold cross-validation policy. Moreover, the investigated biomarkers concord closely with the authoritative medical knowledge, paving a viable way for ASD-clinical diagnosis. Our code is available at https://github.com/CodeGoat24/RGTNet.
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Affiliation(s)
- Yibin Wang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China.
| | - Haixia Long
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Tao Bo
- Scientific Center, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Jianwei Zheng
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China.
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4
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Cui L, Bai L, Bai X, Wang Y, Hancock ER. Learning Aligned Vertex Convolutional Networks for Graph Classification. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:4423-4437. [PMID: 34890333 DOI: 10.1109/tnnls.2021.3129649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Graph convolutional networks (GCNs) are powerful tools for graph structure data analysis. One main drawback arising in most existing GCN models is that of the oversmoothing problem, i.e., the vertex features abstracted from the existing graph convolution operation have previously tended to be indistinguishable if the GCN model has many convolutional layers (e.g., more than two layers). To address this problem, in this article, we propose a family of aligned vertex convolutional network (AVCN) models that focus on learning multiscale features from local-level vertices for graph classification. This is done by adopting a transitive vertex alignment algorithm to transform arbitrary-sized graphs into fixed-size grid structures. Furthermore, we define a new aligned vertex convolution operation that can effectively learn multiscale vertex characteristics by gradually aggregating local-level neighboring aligned vertices residing on the original grid structures into a new packed aligned vertex. With the new vertex convolution operation to hand, we propose two architectures for the AVCN models to extract different hierarchical multiscale vertex feature representations for graph classification. We show that the proposed models can avoid iteratively propagating redundant information between specific neighboring vertices, restricting the notorious oversmoothing problem arising in most spatial-based GCN models. Experimental evaluations on benchmark datasets demonstrate the effectiveness.
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Zhang Y, Gao Y, Xu J, Zhao G, Shi L, Kong L. Unsupervised Joint Domain Adaptation for Decoding Brain Cognitive States From tfMRI Images. IEEE J Biomed Health Inform 2024; 28:1494-1503. [PMID: 38157464 DOI: 10.1109/jbhi.2023.3348130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Recent advances in large model and neuroscience have enabled exploration of the mechanism of brain activity by using neuroimaging data. Brain decoding is one of the most promising researches to further understand the human cognitive function. However, current methods excessively depends on high-quality labeled data, which brings enormous expense of collection and annotation of neural images by experts. Besides, the performance of cross-individual decoding suffers from inconsistency in data distribution caused by individual variation and different collection equipments. To address mentioned above issues, a Join Domain Adapative Decoding (JDAD) framework is proposed for unsupervised decoding specific brain cognitive state related to behavioral task. Based on the volumetric feature extraction from task-based functional Magnetic Resonance Imaging (tfMRI) data, a novel objective loss function is designed by the combination of joint distribution regularizer, which aims to restrict the distance of both the conditional and marginal probability distribution of labeled and unlabeled samples. Experimental results on the public Human Connectome Project (HCP) S1200 dataset show that JDAD achieves superior performance than other prevalent methods, especially for fine-grained task with 11.5%-21.6% improvements of decoding accuracy. The learned 3D features are visualized by Grad-CAM to build a combination with brain functional regions, which provides a novel path to learn the function of brain cortex regions related to specific cognitive task in group level.
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Wang M, Ma Z, Wang Y, Liu J, Guo J. A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder. PLoS One 2023; 18:e0295621. [PMID: 38064474 PMCID: PMC10707567 DOI: 10.1371/journal.pone.0295621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 11/21/2023] [Indexed: 12/18/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy. This study proposes the use of MAACNN, a method that utilizes multi-view convolutional neural networks (CNNs) in conjunction with attention mechanisms for identifying ASD in multi-scale fMRI. The proposed algorithm effectively combines unsupervised and supervised learning. In the initial stage, we employ stacked denoising autoencoders, an unsupervised learning method for feature extraction, which provides different nodes to adapt to multi-scale data. In the subsequent stage, we perform supervised learning by employing multi-view CNNs for classification and obtain the final results. Finally, multi-scale data fusion is achieved by using the attention fusion mechanism. The ABIDE dataset is used to evaluate the model we proposed., and the experimental results show that MAACNN achieves superior performance with 75.12% accuracy and 0.79 AUC on ABIDE-I, and 72.88% accuracy and 0.76 AUC on ABIDE-II. The proposed method significantly contributes to the clinical diagnosis of ASD.
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Affiliation(s)
- Mingzhi Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, China
| | - Zhiqiang Ma
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, China
| | - Yongjie Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, China
| | - Jing Liu
- College of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, China
| | - Jifeng Guo
- College of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, China
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Alharthi AG, Alzahrani SM. Do it the transformer way: A comprehensive review of brain and vision transformers for autism spectrum disorder diagnosis and classification. Comput Biol Med 2023; 167:107667. [PMID: 37939407 DOI: 10.1016/j.compbiomed.2023.107667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 10/25/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
Autism spectrum disorder (ASD) is a condition observed in children who display abnormal patterns of interaction, behavior, and communication with others. Despite extensive research efforts, the underlying causes of this neurodevelopmental disorder and its biomarkers remain unknown. However, advancements in artificial intelligence and machine learning have improved clinicians' ability to diagnose ASD. This review paper investigates various MRI modalities to identify distinct features that characterize individuals with ASD compared to typical control subjects. The review then moves on to explore deep learning models for ASD diagnosis, including convolutional neural networks (CNNs), autoencoders, graph convolutions, attention networks, and other models. CNNs and their variations are particularly effective due to their capacity to learn structured image representations and identify reliable biomarkers for brain disorders. Computer vision transformers often employ CNN architectures with transfer learning techniques like fine-tuning and layer freezing to enhance image classification performance, surpassing traditional machine learning models. This review paper contributes in three main ways. Firstly, it provides a comprehensive overview of a recommended architecture for using vision transformers in the systematic ASD diagnostic process. To this end, the paper investigates various pre-trained vision architectures such as VGG, ResNet, Inception, InceptionResNet, DenseNet, and Swin models that were fine-tuned for ASD diagnosis and classification. Secondly, it discusses the vision transformers of 2020th like BiT, ViT, MobileViT, and ConvNeXt, and applying transfer learning methods in relation to their prospective practicality in ASD classification. Thirdly, it explores brain transformers that are pre-trained on medically rich data and MRI neuroimaging datasets. The paper recommends a systematic architecture for ASD diagnosis using brain transformers. It also reviews recently developed brain transformer-based models, such as METAFormer, Com-BrainTF, Brain Network, ST-Transformer, STCAL, BolT, and BrainFormer, discussing their deep transfer learning architectures and results in ASD detection. Additionally, the paper summarizes and discusses brain-related transformers for various brain disorders, such as MSGTN, STAGIN, and MedTransformer, in relation to their potential usefulness in ASD. The study suggests that developing specialized transformer-based models, following the success of natural language processing (NLP), can offer new directions for image classification problems in ASD brain biomarkers learning and classification. By incorporating the attention mechanism, treating MRI modalities as sequence prediction tasks trained on brain disorder classification problems, and fine-tuned on ASD datasets, brain transformers can show a great promise in ASD diagnosis.
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Affiliation(s)
- Asrar G Alharthi
- Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia.
| | - Salha M Alzahrani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia
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8
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Hu Y, Huang ZA, Liu R, Xue X, Sun X, Song L, Tan KC. Source Free Semi-Supervised Transfer Learning for Diagnosis of Mental Disorders on fMRI Scans. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:13778-13795. [PMID: 37486851 DOI: 10.1109/tpami.2023.3298332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
The high prevalence of mental disorders gradually poses a huge pressure on the public healthcare services. Deep learning-based computer-aided diagnosis (CAD) has emerged to relieve the tension in healthcare institutions by detecting abnormal neuroimaging-derived phenotypes. However, training deep learning models relies on sufficient annotated datasets, which can be costly and laborious. Semi-supervised learning (SSL) and transfer learning (TL) can mitigate this challenge by leveraging unlabeled data within the same institution and advantageous information from source domain, respectively. This work is the first attempt to propose an effective semi-supervised transfer learning (SSTL) framework dubbed S3TL for CAD of mental disorders on fMRI data. Within S3TL, a secure cross-domain feature alignment method is developed to generate target-related source model in SSL. Subsequently, we propose an enhanced dual-stage pseudo-labeling approach to assign pseudo-labels for unlabeled samples in target domain. Finally, an advantageous knowledge transfer method is conducted to improve the generalization capability of the target model. Comprehensive experimental results demonstrate that S3TL achieves competitive accuracies of 69.14%, 69.65%, and 72.62% on ABIDE-I, ABIDE-II, and ADHD-200 datasets, respectively. Furthermore, the simulation experiments also demonstrate the application potential of S3TL through model interpretation analysis and federated learning extension.
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9
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Fan L, Gong X, Guo Y. General Multiscenario Ultrasound Image Tumor Diagnosis Method Based on Unsupervised Domain Adaptation. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2291-2301. [PMID: 37532633 DOI: 10.1016/j.ultrasmedbio.2023.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 06/18/2023] [Accepted: 06/23/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVE The utilization of computer-aided diagnosis (CAD) in breast ultrasound image classification has been limited by small sample sizes and domain shift. Current ultrasound classification methods perform inadequately when exposed to cross-domain scenarios, as they struggle with data sets from unobserved domains. In the medical field, there are situations in which all images must share the same networks as they capture the same symptom of the same participant, implying that they share identical structural content. Nevertheless, most domain adaptation methods are not suitable for medical images as they overlook the common features among the images. METHODS To overcome these challenges, we propose a novel diverse-domain 2-D feature selection network (FSN), which uses the similarities among medical images and extracts features with a reconstruction network with shared weights. Additionally, it penalizes the feature domain distance through two adversarial learning modules that align the feature space and select common features. Our experiments illustrate that the proposed method is robust and can be applied to ultrasound images of various diseases. RESULTS Compared with the latest domain adaptive methods, 2-D FSN markedly enhances the accuracy of classification of breast, thyroid and endoscopic ultrasound images, achieving accuracies of 82.4%, 96.4% and 89.7%, respectively. Furthermore, the model was evaluated on an unsupervised domain adaptation task using ultrasound images from multiple sources and achieved an average accuracy of 77.3% across widely varying domains. CONCLUSION In general, 2-D FSN improves the classification ability of the model on multidomain ultrasound data sets through the learning of common features and the combination of multimodule intelligence. The algorithm has good clinical guidance value.
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Affiliation(s)
- Lin Fan
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, P.R. China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, P.R. China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, P.R. China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, P.R. China
| | - Xun Gong
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, P.R. China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, P.R. China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, P.R. China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, P.R. China.
| | - Ying Guo
- North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China
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Cui W, Du J, Sun M, Zhu S, Zhao S, Peng Z, Tan L, Li Y. Dynamic multi-site graph convolutional network for autism spectrum disorder identification. Comput Biol Med 2023; 157:106749. [PMID: 36921455 DOI: 10.1016/j.compbiomed.2023.106749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 02/13/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023]
Abstract
Multi-site learning has attracted increasing interests in autism spectrum disorder (ASD) identification tasks by its efficacy on capturing data heterogeneity of neuroimaging taken from different medical sites. However, existing multi-site graph convolutional network (MSGCN) often ignores the correlations between different sites, and may obtain suboptimal identification results. Moreover, current feature extraction methods characterizing temporal variations of functional magnetic resonance imaging (fMRI) signals require the time series to be of the same length and cannot be directly applied to multi-site fMRI datasets. To address these problems, we propose a dual graph based dynamic multi-site graph convolutional network (DG-DMSGCN) for multi-site ASD identification. First, a sliding-window dual-graph convolutional network (SW-DGCN) is introduced for feature extraction, simultaneously capturing temporal and spatial features of fMRI data with different series lengths. Then we aggregate the features extracted from multiple medical sites through a novel dynamic multi-site graph convolutional network (DMSGCN), which effectively considers the correlations between different sites and is beneficial to improve identification performance. We evaluate the proposed DG-DMSGCN on public ABIDE I dataset containing data from 17 medical sites. The promising results obtained by our framework outperforms the state-of-the-art methods with increase in identification accuracy, indicating that it has a potential clinical prospect for practical ASD diagnosis. Our codes are available on https://github.com/Junling-Du/DG-DMSGCN.
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Affiliation(s)
- Weigang Cui
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
| | - Junling Du
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
| | - Mingyi Sun
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
| | - Shimao Zhu
- South China Hospital of Shenzhen University, Shenzhen University, Shenzhen, 518111, China.
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China.
| | - Ziwen Peng
- Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen University School of Medicine, Shenzhen, 518020, China.
| | - Li Tan
- School of Computer Science and Engineering, Beijing Technology and Business Universtiy, Beijing, 100048, China.
| | - Yang Li
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China; State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China.
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11
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Song X, Zhou F, Frangi AF, Cao J, Xiao X, Lei Y, Wang T, Lei B. Multicenter and Multichannel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:354-367. [PMID: 35767511 DOI: 10.1109/tmi.2022.3187141] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose three mechanisms in the current graph convolutional network (GCN) to improve classifier performance. First, we introduce a DTI-strength penalty term for constructing functional connectivity networks. Stronger structural connectivity and bigger structural strength diversity between groups provide a higher opportunity for retaining connectivity information. Second, a multi-center attention graph with each node representing a subject is proposed to consider the influence of data source, gender, acquisition equipment, and disease status of those training samples in GCN. The attention mechanism captures their different impacts on edge weights. Third, we propose a multi-channel mechanism to improve filter performance, assigning different filters to features based on feature statistics. Applying those nodes with low-quality features to perform convolution would also deteriorate filter performance. Therefore, we further propose a pooling mechanism, which introduces the disease status information of those training samples to evaluate the quality of nodes. Finally, we obtain the final classification results by inputting the multi-center attention graph into the multi-channel pooling GCN. The proposed method is tested on three datasets (i.e., an ADNI 2 dataset, an ADNI 3 dataset, and an in-house dataset). Experimental results indicate that the proposed method is effective and superior to other related algorithms, with a mean classification accuracy of 93.05% in our binary classification tasks. Our code is available at: https://github.com/Xuegang-S.
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12
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Fang Y, Wang M, Potter GG, Liu M. Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification. Med Image Anal 2023; 84:102707. [PMID: 36512941 PMCID: PMC9850278 DOI: 10.1016/j.media.2022.102707] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 11/21/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols. To address this issue, we develop a novel discrepancy-based unsupervised cross-domain fMRI adaptation framework (called UFA-Net) for automated MDD identification. The proposed UFA-Net is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation for cross-site MDD identification. Extensive evaluation on 681 subjects from two imaging sites shows that the proposed method outperforms several state-of-the-art methods. Our method helps localize disease-associated functional connectivity abnormalities and is therefore well interpretable and can facilitate fMRI-based analysis of MDD in clinical practice.
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Affiliation(s)
- Yuqi Fang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Mingliang Wang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Guy G Potter
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, United States.
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
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13
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Lu Z, Wang J, Mao R, Lu M, Shi J. Jointly Composite Feature Learning and Autism Spectrum Disorder Classification Using Deep Multi-Output Takagi-Sugeno-Kang Fuzzy Inference Systems. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:476-488. [PMID: 35349448 DOI: 10.1109/tcbb.2022.3163140] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Autism spectrum disorder (ASD) is characterized by poor social communication abilities and repetitive behaviors or restrictive interests, which has brought a heavy burden to families and society. In many attempts to understand ASD neurobiology, resting-state functional magnetic resonance imaging (rs-fMRI) has been an effective tool. However, current ASD diagnosis methods based on rs-fMRI have two major defects. First, the instability of rs-fMRI leads to functional connectivity (FC) uncertainty, affecting the performance of ASD diagnosis. Second, many FCs are involved in brain activity, making it difficult to determine effective features in ASD classification. In this study, we propose an interpretable ASD classifier DeepTSK, which combines a multi-output Takagi-Sugeno-Kang (MO-TSK) fuzzy inference system (FIS) for composite feature learning and a deep belief network (DBN) for ASD classification in a unified network. To avoid the suboptimal solution of DeepTSK, a joint optimization procedure is employed to simultaneously learn the parameters of MO-TSK and DBN. The proposed DeepTSK was evaluated on datasets collected from three sites of the Autism Brain Imaging Data Exchange (ABIDE) database. The experimental results showed the effectiveness of the proposed method, and the discriminant FCs are presented by analyzing the consequent parameters of Deep MO-TSK.
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14
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Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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15
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Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Shoeibi A, Khosravi A, Ling SH, Subasi A, Alizadehsani R, Gorriz JM, Abdulla SA, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front Mol Neurosci 2022; 15:999605. [PMID: 36267703 PMCID: PMC9577321 DOI: 10.3389/fnmol.2022.999605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022] Open
Abstract
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 structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
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Affiliation(s)
- Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Navid Ghassemi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Delaram Sadeghi
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Abdulhamit Subasi
- Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Juan M. Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Sara A. Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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16
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Qin C, Zhu X, Ye L, Peng L, Li L, Wang J, Ma J, Liu T. Autism detection based on multiple time scale model. J Neural Eng 2022; 19. [PMID: 35985297 DOI: 10.1088/1741-2552/ac8b39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 08/19/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Current autism clinical detection relies on doctor observation and filling of clinical scales, which is subjective and easily misdetection. Existing autism research of functional magnetic resonance imaging (fMRI) over-compresses the time-scale information and has poor generalization ability. This study extracts multiple time scale brain features of fMRI, providing objective detection. APPROACH We first use least absolute shrinkage and selection operator (LASSO) to build a sparse network and extract features with a time scale of 1. Then, we use hidden markov model (HMM) to extract features that describe the dynamic changes of the brain, with a time scale of 2. Additionally, to analyze the features of the potential network activity of autism from a higher time scale, we use long short-term memory (LSTM) to construct an auto-encoder to re-encode the original data and extract the features of the at a higher time scale, with a time scale of T, and T is the time length of fMRI. We use Recursive Feature Elimination (RFE) for feature selection for three different time scale features, merge them into multiple time scale features, and finally use one-dimensional convolution neural network (1DCNN) for classification. MAIN RESULTS Compared with well-established models, our method has achieved better results. The accuracy of our method is 76.0%, and the area under the roc curve is 0.83, tested on the completely independent data, so our method has better generalization ability. SIGNIFICANCE This research analyzes fMRI sequences from multiple time scale to detect autism, and it also provides a new framework and research ideas for subsequent fMRI analysis.
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Affiliation(s)
- Chi Qin
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
| | - Xiaofei Zhu
- Tangdu Hospital Fourth Military Medical University, Department of Radiology, Xi'an, Shaanxi, 710038, CHINA
| | - Lin Ye
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
| | - Li Peng
- Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Department of Radiology, Wuhan, Hubei, 430030, CHINA
| | - Long Li
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
| | - Jue Wang
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
| | - Jin Ma
- Air Force Medical University, School of Aerospace Medicine, Xi'an, 710032, CHINA
| | - Tian Liu
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
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17
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Han X, Fei X, Wang J, Zhou T, Ying S, Shi J, Shen D. Doubly Supervised Transfer Classifier for Computer-Aided Diagnosis With Imbalanced Modalities. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2009-2020. [PMID: 35171766 DOI: 10.1109/tmi.2022.3152157] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Transfer learning (TL) can effectively improve diagnosis accuracy of single-modal-imaging-based computer-aided diagnosis (CAD) by transferring knowledge from other related imaging modalities, which offers a way to alleviate the small-sample-size problem. However, medical imaging data generally have the following characteristics for the TL-based CAD: 1) The source domain generally has limited data, which increases the difficulty to explore transferable information for the target domain; 2) Samples in both domains often have been labeled for training the CAD model, but the existing TL methods cannot make full use of label information to improve knowledge transfer. In this work, we propose a novel doubly supervised transfer classifier (DSTC) algorithm. In particular, DSTC integrates the support vector machine plus (SVM+) classifier and the low-rank representation (LRR) into a unified framework. The former makes full use of the shared labels to guide the knowledge transfer between the paired data, while the latter adopts the block-diagonal low-rank (BLR) to perform supervised TL between the unpaired data. Furthermore, we introduce the Schatten-p norm for BLR to obtain a tighter approximation to the rank function. The proposed DSTC algorithm is evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) dataset and the bimodal breast ultrasound image (BBUI) dataset. The experimental results verify the effectiveness of the proposed DSTC algorithm.
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18
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Jiang X, Zhou Y, Zhang Y, Zhang L, Qiao L, De Leone R. Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification. Front Neurosci 2022; 16:872848. [PMID: 35573311 PMCID: PMC9094041 DOI: 10.3389/fnins.2022.872848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Brain functional network (BFN) has become an increasingly important tool to understand the inherent organization of the brain and explore informative biomarkers of neurological disorders. Pearson’s correlation (PC) is the most widely accepted method for constructing BFNs and provides a basis for designing new BFN estimation schemes. Particularly, a recent study proposes to use two sequential PC operations, namely, correlation’s correlation (CC), for constructing the high-order BFN. Despite its empirical effectiveness in identifying neurological disorders and detecting subtle changes of connections in different subject groups, CC is defined intuitively without a solid and sustainable theoretical foundation. For understanding CC more rigorously and providing a systematic BFN learning framework, in this paper, we reformulate it in the Bayesian view with a prior of matrix-variate normal distribution. As a result, we obtain a probabilistic explanation of CC. In addition, we develop a Bayesian high-order method (BHM) to automatically and simultaneously estimate the high- and low-order BFN based on the probabilistic framework. An efficient optimization algorithm is also proposed. Finally, we evaluate BHM in identifying subjects with autism spectrum disorder (ASD) from typical controls based on the estimated BFNs. Experimental results suggest that the automatically learned high- and low-order BFNs yield a superior performance over the artificially defined BFNs via conventional CC and PC.
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Affiliation(s)
- Xiao Jiang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
- School of Science and Technology, University of Camerino, Camerino, Italy
| | - Yueying Zhou
- College of Computer Science and Technology, Nanjing University of Aeronautics, Nanjing, China
| | - Yining Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Limei Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
- *Correspondence: Lishan Qiao,
| | - Renato De Leone
- School of Science and Technology, University of Camerino, Camerino, Italy
- Renato De Leone,
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19
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Zhang Y, Yuan N, Zhang Z, Du J, Wang T, Liu B, Yang A, Lv K, Ma G, Lei B. Unsupervised Domain Selective Graph Convolutional Network for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer. Med Image Anal 2022; 79:102467. [PMID: 35537338 DOI: 10.1016/j.media.2022.102467] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 12/24/2022]
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20
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Abstract
Machine learning techniques used in computer-aided medical image analysis usually suffer from the domain shift problem caused by different distributions between source/reference data and target data. As a promising solution, domain adaptation has attracted considerable attention in recent years. The aim of this paper is to survey the recent advances of domain adaptation methods in medical image analysis. We first present the motivation of introducing domain adaptation techniques to tackle domain heterogeneity issues for medical image analysis. Then we provide a review of recent domain adaptation models in various medical image analysis tasks. We categorize the existing methods into shallow and deep models, and each of them is further divided into supervised, semi-supervised and unsupervised methods. We also provide a brief summary of the benchmark medical image datasets that support current domain adaptation research. This survey will enable researchers to gain a better understanding of the current status, challenges and future directions of this energetic research field.
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21
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Shi CL, Xin XW, Zhang JC. Domain adaptation based on rough adjoint inconsistency and optimal transport for identifying autistic patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106615. [PMID: 35016084 DOI: 10.1016/j.cmpb.2021.106615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/09/2021] [Accepted: 12/30/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer aided diagnosis technology has been widely used to diagnose autism spectrum disorder (ASD) from neural images. The performance of the model usually depends largely on a sufficient number of training samples that reflect the real sample distribution. Due to the lack of labelled neural images data, multisite data are often pooled together to expand the sample size. However, the heterogeneity among sites will inevitably lead to a decline in the generalization of models. To solve this problem, we propose a multisource unsupervised domain adaptation method using rough adjoint inconsistency and optimal transport. METHODS First, we define the concept of rough adjoint inconsistency and propose a double quantization method based on rough adjoint inconsistency and Dempster-Shafer (D-S) evidence theory to estimate the weight coefficient of each source domain to accurately describe the importance of each source domain to the target domain. Second, using optimal transport theory, we weaken the data distribution differences between domains and solve the problem of class imbalance by adjusting the sampling weights among classes. RESULTS The ASD recognition accuracy of the proposed method is improved on all eight tasks, which are 70.67%, 64.86%, 62.50%, 70.80%, 73.08%, 71.19%, 75.41% and 75.76%, respectively. Our proposed model achieves superior performance compared to traditional machine learning methods and other recently proposed deep learning model. CONCLUSIONS Our method demonstrates that the fusion of rough adjoint inconsistency and optimal transport can be a powerful tool for identifying ASD and quantifying the correlations between domains.
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Affiliation(s)
- Chun-Lei Shi
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Xian-Wei Xin
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Jia-Cai Zhang
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China; Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing, 100875, China.
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22
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Liu M, Li B, Hu D. Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review. Front Neurosci 2021; 15:697870. [PMID: 34602966 PMCID: PMC8480393 DOI: 10.3389/fnins.2021.697870] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/09/2021] [Indexed: 01/01/2023] Open
Abstract
Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD.
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Affiliation(s)
- Meijie Liu
- Engineering Training Center, Xi'an University of Science and Technology, Xi'an, China.,College of Missile Engineering, Rocket Force University of Engineering, Xi'an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
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23
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Xie Q, Zhang X, Rekik I, Chen X, Mao N, Shen D, Zhao F. Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder. PeerJ 2021; 9:e11692. [PMID: 34268010 PMCID: PMC8269664 DOI: 10.7717/peerj.11692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 06/08/2021] [Indexed: 01/23/2023] Open
Abstract
The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%.
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Affiliation(s)
- Qingsong Xie
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
| | - Xiangfei Zhang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
| | - Islem Rekik
- School of Science and Engineering, Computing, University of Dundee, Dundee, Dundee, United Kingdom.,BASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Istanbul, Turkey
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
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24
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Xu M, Calhoun V, Jiang R, Yan W, Sui J. Brain imaging-based machine learning in autism spectrum disorder: methods and applications. J Neurosci Methods 2021; 361:109271. [PMID: 34174282 DOI: 10.1016/j.jneumeth.2021.109271] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/25/2021] [Accepted: 06/19/2021] [Indexed: 01/09/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is comprised of a constellation of behavioral symptoms. Non-invasive brain imaging techniques, such as magnetic resonance imaging (MRI), provide a valuable objective measurement of the brain. Many efforts have been devoted to developing imaging-based diagnostic tools for ASD based on machine learning (ML) technologies. In this survey, we review recent advances that utilize machine learning approaches to classify individuals with and without ASD. First, we provide a brief overview of neuroimaging-based ASD classification studies, including the analysis of publications and general classification pipeline. Next, representative studies are highlighted and discussed in detail regarding different imaging modalities, methods and sample sizes. Finally, we highlight several common challenges and provide recommendations on future directions. In summary, identifying discriminative biomarkers for ASD diagnosis is challenging, and further establishing more comprehensive datasets and dissecting the individual and group heterogeneity will be critical to achieve better ADS diagnosis performance. Machine learning methods will continue to be developed and are poised to help advance the field in this regard.
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Affiliation(s)
- Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 100049
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA 30303
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190
| | - Weizheng Yan
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA 30303
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China 100088.
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25
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Hu Z, Wang J, Zhang C, Luo Z, Luo X, Xiao L, Shi J. Uncertainty Modeling for Multi center Autism Spectrum Disorder Classification Using Takagi-Sugeno-Kang Fuzzy Systems. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3073368] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Zhongyi Hu
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China. (e-mail: )
| | - Jun Wang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Ins titute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Chunxiang Zhang
- School of Artificial Intelligence and Computer Science, Jiangnan University, WuXi 214122, China
| | - Zhenzhen Luo
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
| | - Xiaoqing Luo
- School of Artificial Intelligence and Computer Science, Jiangnan University, WuXi 214122, China
| | - Lei Xiao
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
| | - Jun Shi
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Ins titute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
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26
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Yang T, Zhu J, Li Q, Chen L, Wu LJ, Jia FY, Hao Y, Ke XY, Yi MJ, Jin CH, Chen J, Li TY. China Multi-Center Preschool Autism Project (CMPAP): Design and Methodologies to Identify Clinical Symptom Features and Biomarkers of Autism Spectrum Disorders. Front Psychiatry 2020; 11:613519. [PMID: 33633597 PMCID: PMC7900150 DOI: 10.3389/fpsyt.2020.613519] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/15/2020] [Indexed: 12/27/2022] Open
Abstract
Background: The etiology of autism spectrum disorder (ASD) has not yet been fully identified, but it seems to be triggered by complex genetic and environmental risk factors. Moreover, the tremendous etiological and clinical differences among individuals with ASD has had a major negative impact on early diagnosis and individualized treatment. Earlier diagnosis of precise clinical subtypes of ASD could lead to individualized treatment and a better prognosis. However, few large-scale epidemiological studies have explored precise clinical subtypes and clinically meaningful biomarkers, especially in China. Methods and Design: The China Multi-center Preschool Autism Project (CMPAP) includes nearly 3,000 children-1,469 individuals with ASD and 1,499 typically-developing (TD) controls-from 13 cities in China. Using a case-control design, each participant was comprehensively characterized in terms of feeding and disease history, maternal history, family history, clinical core symptoms, comorbidities, biochemical markers, genomics, urine/fecal metabonomics, and intestinal flora. In addition, data on environmental risk factors were obtained using interviews and electronic medical records. Conclusion: The study was designed to: (1) investigate age at diagnosis and treatment and family and social support for preschool children with ASD in China, (2) develop a more accurate clinical subtype and intervention system for the ICD-11, and (3) find the specific genes and environmental markers of different subtypes, which will help in the development of early diagnosis and individual intervention programs for preschool children with ASD. This study will provide the basis for improving national health policies for ASD in China.
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Affiliation(s)
- Ting Yang
- Children's Nutrition Research Center, Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Childhood Nutrition and Health, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Jiang Zhu
- Children's Nutrition Research Center, Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Childhood Nutrition and Health, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Qiu Li
- Children's Nutrition Research Center, Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Childhood Nutrition and Health, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Li Chen
- Children's Nutrition Research Center, Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Childhood Nutrition and Health, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Li-Jie Wu
- Department of Child and Adolescent Health, School of Public Health, China Medical University, Shenyang, China
| | - Fei-Yong Jia
- Department of Rehabilitation Medicine, The First Hospital of Jilin University, Changchun, China
| | - Yan Hao
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao-Yan Ke
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Ming-Ji Yi
- Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chun-Hua Jin
- Department of Child Health Care, Capital Institute of Pediatrics, Beijing, China
| | - Jie Chen
- Children's Nutrition Research Center, Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Childhood Nutrition and Health, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Ting Yu Li
- Children's Nutrition Research Center, Children's Hospital of Chongqing Medical University, Chongqing Key Laboratory of Childhood Nutrition and Health, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
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