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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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2
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Li S, Zhang R. A novel interactive deep cascade spectral graph convolutional network with multi-relational graphs for disease prediction. Neural Netw 2024; 175:106285. [PMID: 38593556 DOI: 10.1016/j.neunet.2024.106285] [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: 10/14/2022] [Revised: 11/16/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024]
Abstract
Graph neural networks (GNNs) have recently grown in popularity for disease prediction. Existing GNN-based methods primarily build the graph topological structure around a single modality and combine it with other modalities to acquire feature representations of acquisitions. The complicated relationship in each modality, however, may not be well highlighted due to its specificity. Further, relatively shallow networks restrict adequate extraction of high-level features, affecting disease prediction performance. Accordingly, this paper develops a new interactive deep cascade spectral graph convolutional network with multi-relational graphs (IDCGN) for disease prediction tasks. Its crucial points lie in constructing multiple relational graphs and dual cascade spectral graph convolution branches with interaction (DCSGBI). Specifically, the former designs a pairwise imaging-based edge generator and a pairwise non-imaging-based edge generator from different modalities by devising two learnable networks, which adaptively capture graph structures and provide various views of the same acquisition to aid in disease diagnosis. Again, DCSGBI is established to enrich high-level semantic information and low-level details of disease data. It devises a cascade spectral graph convolution operator for each branch and incorporates the interaction strategy between different branches into the network, successfully forming a deep model and capturing complementary information from diverse branches. In this manner, more favorable and sufficient features are learned for a reliable diagnosis. Experiments on several disease datasets reveal that IDCGN exceeds state-of-the-art models and achieves promising results.
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Affiliation(s)
- Sihui Li
- Medical Big data Research Center, School of Mathematics, Northwest University, Xi'an 710127, Shaanxi, China.
| | - Rui Zhang
- Medical Big data Research Center, School of Mathematics, Northwest University, Xi'an 710127, Shaanxi, China.
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Ma C, Li W, Ke S, Lv J, Zhou T, Zou L. Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network. Med Biol Eng Comput 2024; 62:2133-2144. [PMID: 38457067 DOI: 10.1007/s11517-024-03060-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 02/23/2024] [Indexed: 03/09/2024]
Abstract
Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity-based graph convolutional network (MFC-GCN) framework is proposed, using not only full brain functional connectivity data but also the established functional connectivity data from networks of key brain subnetworks associated with ASD, and the GCN is adopted to acquire complementary feature information for the final classification task. Given the heterogeneity within the Autism Brain Imaging Data Exchange (ABIDE) dataset, a novel External Attention Network Readout (EANReadout) is introduced. This design enables the exploration of potential subject associations, effectively addressing the dataset's heterogeneity. Experiments were conducted on the ABIDE dataset using the proposed framework, involving 714 subjects, and the average accuracy of the framework was 70.31%. The experimental results show that the proposed EANReadout outperforms the best traditional readout layer and improves the average accuracy of the framework by 4.32%.
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Affiliation(s)
- Chaoran Ma
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, 213164, Jiangsu, China
| | - Wenjie Li
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164, Jiangsu, China
| | - Sheng Ke
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, 213164, Jiangsu, China
| | - Jidong Lv
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164, Jiangsu, China
| | - Tiantong Zhou
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164, Jiangsu, China
| | - Ling Zou
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, 213164, Jiangsu, China.
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164, Jiangsu, China.
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4
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Liu M, Zhang H, Liu M, Chen D, Zhuang Z, Wang X, Zhang L, Peng D, Wang Q. Randomizing Human Brain Function Representation for Brain Disease Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2537-2546. [PMID: 38376975 DOI: 10.1109/tmi.2024.3368064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.
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Tigga NP, Garg S, Goyal N, Raj J, Das B. Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network. Technol Health Care 2024:THC240550. [PMID: 38943414 DOI: 10.3233/thc-240550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2024]
Abstract
BACKGROUND Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities. OBJECTIVE This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals. METHODS In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction. RESULTS The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection. CONCLUSION The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis.
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Affiliation(s)
- Neha Prerna Tigga
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India
| | - Shruti Garg
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India
| | - Nishant Goyal
- Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, India
| | - Justin Raj
- Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, India
| | - Basudeb Das
- Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, India
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Zhang J, Guo J, Lu D, Cao Y. ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis. Sci Rep 2024; 14:13696. [PMID: 38871844 DOI: 10.1038/s41598-024-64299-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 06/06/2024] [Indexed: 06/15/2024] Open
Abstract
The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.
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Affiliation(s)
- Jian Zhang
- School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China.
| | - Jifeng Guo
- College of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, 540004, China
| | - Donglei Lu
- School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China
| | - Yuanyuan Cao
- School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China
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7
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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 C, Xiao Z, Xu Y, Zhang Q, Chen J. A novel approach for ASD recognition based on graph attention networks. Front Comput Neurosci 2024; 18:1388083. [PMID: 38659616 PMCID: PMC11039788 DOI: 10.3389/fncom.2024.1388083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
Early detection and diagnosis of Autism Spectrum Disorder (ASD) can significantly improve the quality of life for affected individuals. Identifying ASD based on brain functional connectivity (FC) poses a challenge due to the high heterogeneity of subjects' fMRI data in different sites. Meanwhile, deep learning algorithms show efficacy in ASD identification but lack interpretability. In this paper, a novel approach for ASD recognition is proposed based on graph attention networks. Specifically, we treat the region of interest (ROI) of the subjects as node, conduct wavelet decomposition of the BOLD signal in each ROI, extract wavelet features, and utilize them along with the mean and variance of the BOLD signal as node features, and the optimized FC matrix as the adjacency matrix, respectively. We then employ the self-attention mechanism to capture long-range dependencies among features. To enhance interpretability, the node-selection pooling layers are designed to determine the importance of ROI for prediction. The proposed framework are applied to fMRI data of children (younger than 12 years old) from the Autism Brain Imaging Data Exchange datasets. Promising results demonstrate superior performance compared to recent similar studies. The obtained ROI detection results exhibit high correspondence with previous studies and offer good interpretability.
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Affiliation(s)
- Canhua Wang
- School of Computer, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Zhiyong Xiao
- School of Electronic & Information Engineering, Jiangxi Institute of Economic Administrators, Nanchang, China
| | - Yilu Xu
- School of Software, Jiangxi Agricultural University, Nanchang, China
| | - Qi Zhang
- Department of Medical Imaging, Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, China
| | - Jingfang Chen
- Department of Medical Imaging, The Second Affiliated Hospital of Nanchang University, Nanchang, 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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10
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Liu Y, Wang H, Ding Y. The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding. Interdiscip Sci 2024; 16:141-159. [PMID: 38060171 DOI: 10.1007/s12539-023-00592-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 11/02/2023] [Accepted: 11/02/2023] [Indexed: 12/08/2023]
Abstract
Autism spectrum disorder (ASD) is a neurological and developmental disorder and its early diagnosis is a challenging task. The dynamic brain network (DBN) offers a wealth of information for the diagnosis and treatment of ASD. Mining the spatio-temporal characteristics of DBN is critical for finding dynamic communication across brain regions and, ultimately, identifying the ASD diagnostic biomarker. We proposed the dgEmbed-KNN and the Aggregation-SVM diagnostic models, which use the spatio-temporal information from DBN and interactive information among brain regions represented by dynamic graph embedding. The classification accuracies show that dgEmbed-KNN model performs slightly better than traditional machine learning and deep learning methods, while the Aggregation-SVM model has a very good capacity to diagnose ASD using aggregation brain network connections as features. We discovered over- and under-connections in ASD at the level of dynamic connections, involving brain regions of the postcentral gyrus, the insula, the cerebellum, the caudate nucleus, and the temporal pole. We also found abnormal dynamic interactions associated with ASD within/between the functional subnetworks, including default mode network, visual network, auditory network and saliency network. These can provide potential DBN biomarkers for ASD identification.
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Affiliation(s)
- Yanting Liu
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Hao Wang
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Yanrui Ding
- School of Science, Jiangnan University, Wuxi, 214122, China.
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11
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Zhang P, Zhang W, Sun W, Xu J, Hu H, Wang L, Wong L. Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network. BMC Genomics 2024; 25:175. [PMID: 38350848 PMCID: PMC10865627 DOI: 10.1186/s12864-024-09967-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/03/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases. RESULTS In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback-Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD. CONCLUSION Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.
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Affiliation(s)
- Ping Zhang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277100, Shandong, China
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Weihan Zhang
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Hubei Hongshan Laboratory, Wuhan, 430074, China
| | - Weicheng Sun
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jinsheng Xu
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hua Hu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277100, Shandong, China.
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277100, Shandong, China.
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, 530007, China.
| | - Leon Wong
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
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12
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Yang J, Xu X, Sun M, Ruan Y, Sun C, Li W, Gao X. Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data. Cereb Cortex 2024; 34:bhad477. [PMID: 38100334 DOI: 10.1093/cercor/bhad477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/18/2023] [Accepted: 11/19/2023] [Indexed: 12/17/2023] Open
Abstract
Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).
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Affiliation(s)
- Jie Yang
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China
- College of Information Science and Technology, Chongqing Jiaotong University, Chongqing 400074, China
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai 200444, China
| | - Xiaowen Xu
- Tongji University School of Medicine, Tongji University, Shanghai 200331, China
- Department of Medical Imaging, Tongji Hospital, Shanghai 430030, China
| | - Mingxiang Sun
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai 200444, China
| | - Yudi Ruan
- College of Information Science and Technology, Chongqing Jiaotong University, Chongqing 400074, China
| | - Chenhao Sun
- Department of Radiology, Rugao Jian'an Hospital, Rugao 226561, Jiangsu, China
| | - Weikai Li
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China
- College of Information Science and Technology, Chongqing Jiaotong University, Chongqing 400074, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Xin Gao
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai 200444, China
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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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14
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Shao L, Fu C, Chen X. A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder. BMC Bioinformatics 2023; 24:363. [PMID: 37759189 PMCID: PMC10536734 DOI: 10.1186/s12859-023-05495-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/21/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a serious developmental disorder of the brain. Recently, various deep learning methods based on functional magnetic resonance imaging (fMRI) data have been developed for the classification of ASD. Among them, graph neural networks, which generalize deep neural network models to graph structured data, have shown great advantages. However, in graph neural methods, because the graphs constructed are homogeneous, the phenotype information of the subjects cannot be fully utilized. This affects the improvement of the classification performance. METHODS To fully utilize the phenotype information, this paper proposes a heterogeneous graph convolutional attention network (HCAN) model to classify ASD. By combining an attention mechanism and a heterogeneous graph convolutional network, important aggregated features can be extracted in the HCAN. The model consists of a multilayer HCAN feature extractor and a multilayer perceptron (MLP) classifier. First, a heterogeneous population graph was constructed based on the fMRI and phenotypic data. Then, a multilayer HCAN is used to mine graph-based features from the heterogeneous graph. Finally, the extracted features are fed into an MLP for the final classification. RESULTS The proposed method is assessed on the autism brain imaging data exchange (ABIDE) repository. In total, 871 subjects in the ABIDE I dataset are used for the classification task. The best classification accuracy of 82.9% is achieved. Compared to the other methods using exactly the same subjects in the literature, the proposed method achieves superior performance to the best reported result. CONCLUSIONS The proposed method can effectively integrate heterogeneous graph convolutional networks with a semantic attention mechanism so that the phenotype features of the subjects can be fully utilized. Moreover, it shows great potential in the diagnosis of brain functional disorders with fMRI data.
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Affiliation(s)
- Lizhen Shao
- Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, Beijing, 100083, China.
- Lancaster University, Lancaster, LA1 4YX, UK.
| | - Cong Fu
- Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, Beijing, 100083, China
- Shunde Graduate School, University of Science and Technology Beijing, Foshan, 528399, China
| | - Xunying Chen
- Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, Beijing, 100083, China
- Shunde Graduate School, University of Science and Technology Beijing, Foshan, 528399, China
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15
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Wang M, Guo J, Wang Y, Yu M, Guo J. Multimodal Autism Spectrum Disorder Diagnosis Method Based on DeepGCN. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3664-3674. [PMID: 37698959 DOI: 10.1109/tnsre.2023.3314516] [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: 09/14/2023]
Abstract
Multimodal data play an important role in the diagnosis of brain diseases. This study constructs a whole-brain functional connectivity network based on functional MRI data, uses non-imaging data with demographic information to complement the classification task for diagnosing subjects, and proposes a multimodal and across-site WL-DeepGCN-based method for classification to diagnose autism spectrum disorder (ASD). This method is used to resolve the existing problem that deep learning ASD identification cannot efficiently utilize multimodal data. In the WL-DeepGCN, a weight-learning network is used to represent the similarity of non-imaging data in the latent space, introducing a new approach for constructing population graph edge weights, and we find that it is beneficial and robust to define pairwise associations in the latent space rather than the input space. We propose a graph convolutional neural network residual connectivity approach to reduce the information loss due to convolution operations by introducing residual units to avoid gradient disappearance and gradient explosion. Furthermore, an EdgeDrop strategy makes the node connections sparser by randomly dropping edges in the raw graph, and its introduction can alleviate the overfitting and oversmoothing problems in the DeepGCN training process. We compare the WL-DeepGCN model with competitive models based on the same topics and nested 10-fold cross-validation show that our method achieves 77.27% accuracy and 0.83 AUC for ASD identification, bringing substantial performance gains.
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16
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Wang Y, Long H, Zhou Q, Bo T, Zheng J. PLSNet: Position-aware GCN-based autism spectrum disorder diagnosis via FC learning and ROIs sifting. Comput Biol Med 2023; 163:107184. [PMID: 37356292 DOI: 10.1016/j.compbiomed.2023.107184] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/25/2023] [Accepted: 06/13/2023] [Indexed: 06/27/2023]
Abstract
Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in the studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of modeling the sophisticated brain neuronal connectivity; (2) the mismatch of identically graph node setup to the variations of different brain regions; (3) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (4) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a position-aware graph-convolution-network-based model, namely PLSNet, with superior accuracy and compelling built-in interpretability for ASD diagnosis. Specifically, a time-series encoder is designed for context-rich feature extraction, followed by a function connectivity generator to model the correlation with long range dependencies. In addition, to discriminate the brain nodes with different locations, the position embedding technique is adopted, giving a unique identity to each graph region. We then embed a rarefying method to sift the salient nodes during message diffusion, which would also benefit the reduction of the dimensionality complexity. Extensive experiments conducted on Autism Brain Imaging Data Exchange demonstrate that our PLSNet achieves state-of-the-art performance. Notably, on CC200 atlas, PLSNet reaches an accuracy of 76.4% and a specificity of 78.6%, overwhelming the previous state-of-the-art with 2.5% and 6.5% under five-fold cross-validation policy. Moreover, the most salient brain regions predicted by PLSNet are closely consistent with the theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis. Our code is available at https://github.com/CodeGoat24/PLSNet.
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Affiliation(s)
- Yibin Wang
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Haixia Long
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Qianwei Zhou
- College of Computer Science and Engineering, 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 Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China.
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17
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Wen G, Cao P, Liu L, Yang J, Zhang X, Wang F, Zaiane OR. Graph Self-Supervised Learning With Application to Brain Networks Analysis. IEEE J Biomed Health Inform 2023; 27:4154-4165. [PMID: 37159311 DOI: 10.1109/jbhi.2023.3274531] [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: 05/11/2023]
Abstract
The less training data and insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is significant to construct a learning framework that can capture more information in limited data and insufficient supervision. To address these issues, we focus on self-supervised learning and aim to generalize the self-supervised learning to the brain networks, which are non-Euclidean graph data. More specifically, we propose an ensemble masked graph self-supervised framework named BrainGSLs, which incorporates 1) a local topological-aware encoder that takes the partially visible nodes as input and learns these latent representations, 2) a node-edge bi-decoder that reconstructs the masked edges by the representations of both the masked and visible nodes, 3) a signal representation learning module for capturing temporal representations from BOLD signals and 4) a classifier used for the classification. We evaluate our model on three real medical clinical applications: diagnosis of Autism Spectrum Disorder (ASD), diagnosis of Bipolar Disorder (BD) and diagnosis of Major Depressive Disorder (MDD). The results suggest that the proposed self-supervised training has led to remarkable improvement and outperforms state-of-the-art methods. Moreover, our method is able to identify the biomarkers associated with the diseases, which is consistent with the previous studies. We also explore the correlation of these three diseases and find the strong association between ASD and BD. To the best of our knowledge, our work is the first attempt of applying the idea of self-supervised learning with masked autoencoder on the brain network analysis.
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18
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Zhang C, Ma Y, Qiao L, Zhang L, Liu M. Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification. BIOLOGY 2023; 12:971. [PMID: 37508401 PMCID: PMC10376072 DOI: 10.3390/biology12070971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/03/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023]
Abstract
Functional connectivity network (FCN) has become a popular tool to identify potential biomarkers for brain dysfunction, such as autism spectrum disorder (ASD). Due to its importance, researchers have proposed many methods to estimate FCNs from resting-state functional MRI (rs-fMRI) data. However, the existing FCN estimation methods usually only capture a single relationship between brain regions of interest (ROIs), e.g., linear correlation, nonlinear correlation, or higher-order correlation, thus failing to model the complex interaction among ROIs in the brain. Additionally, such traditional methods estimate FCNs in an unsupervised way, and the estimation process is independent of the downstream tasks, which makes it difficult to guarantee the optimal performance for ASD identification. To address these issues, in this paper, we propose a multi-FCN fusion framework for rs-fMRI-based ASD classification. Specifically, for each subject, we first estimate multiple FCNs using different methods to encode rich interactions among ROIs from different perspectives. Then, we use the label information (ASD vs. healthy control (HC)) to learn a set of fusion weights for measuring the importance/discrimination of those estimated FCNs. Finally, we apply the adaptively weighted fused FCN on the ABIDE dataset to identify subjects with ASD from HCs. The proposed FCN fusion framework is straightforward to implement and can significantly improve diagnostic accuracy compared to traditional and state-of-the-art methods.
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Affiliation(s)
- Chaojun Zhang
- The School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China
- The School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Yunling Ma
- The School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Lishan Qiao
- The School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Limei Zhang
- The School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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19
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Samanta A, Sarma M, Samanta D. ALERT: Atlas-Based Low Estimation Rank Tensor Approach to Detect Autism Spectrum Disorder . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083014 DOI: 10.1109/embc40787.2023.10340610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
In response to a stimulus, distinct areas of the human brain are activated. Also, it is known that the regions interact with one another. This functional connectivity is helpful to diagnose any neurological abnormality, such as autism spectrum disorder (ASD). This work proposes an approach to construct a functional connectivity network from fMRI image data. For obtaining a functional connectivity network, the time series component of fMRI data is used and from it correlation matrix is calculated showing the degree of interaction among the brain regions. To map the different regions of a brain, the brain atlas is considered. This essentially yields a low-rank tensor approximation of the functional connectivity matrix. A 2D convolutional deep neural network model is built to categorize topological similarity in the functional connectivity matrices related to ASD and typically developing control. The proposed approach has been tested with ABIDE dataset of fMRI data for autism spectrum disorder. Several brain atlases have been considered in the experiment. With a majority voting concept on the results from the atlases, the proposed technique reveals an ASD detection accuracy of 84.79%, which is significantly comparable to the state of the art techniques.Clinical Relevance- ASD is one of the least understood neurological disorders that has been recently recognized to have major sociological consequences on an affected individual's life. A symptom-based diagnosis is in practice. However, this requires prolonged behavioural examinations under the supervision of a highly skilled multidisciplinary team. An early and cost-effective detection using an fMRI image is considered an appropriate, comprehensive, and advanced treatment plan.
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20
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Qian J, Li H, Wang J, He L. Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging. Diagnostics (Basel) 2023; 13:1571. [PMID: 37174962 PMCID: PMC10178221 DOI: 10.3390/diagnostics13091571] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/29/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as "black boxes". There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.
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Affiliation(s)
- Jinzhao Qian
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Junqi Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
- Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45221, USA
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21
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Liu S, Liang B, Wang S, Li B, Pan L, Wang SH. NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 5:428-433. [PMID: 38899023 PMCID: PMC11186657 DOI: 10.1109/ojemb.2023.3267612] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/21/2023] [Accepted: 04/12/2023] [Indexed: 06/21/2024] Open
Abstract
Goal: The purpose of this paper is to recognize autism spectrum disorders (ASD) using graph attention network. Methods: we propose a node features graph attention network (NF-GAT) for learning functional connectivity (FC) features to achieve ASD diagnosis. Firstly, node features are modelled based on functional magnetic resonance imaging (fMRI) data, with each subject modelled as a graph. Next, we use the graph attention layer to learn the node features and gets the node information of different nodes for ASD classification. Results: Compared with other models, the NF-GAT has significant advantages in terms of classification results. Conclusions: NF-GAT can be effectively used for ASD classification.
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Affiliation(s)
- Shuaiqi Liu
- College of Electronic and Information Engineering, Machine Vision Engineering Research Center of Hebei ProvinceHebei UniversityBaoding071002China
- National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijing100190China
| | - Beibei Liang
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information EngineeringHebei UniversityBaoding071002China
| | - Siqi Wang
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information EngineeringHebei UniversityBaoding071002China
| | - Bing Li
- National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijing100190China
| | - Lidong Pan
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information EngineeringHebei UniversityBaoding071002China
| | - Shui-Hua Wang
- School of Computer Science and TechnologyHenan Polytechnic UniversityJiaozuo454000China
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22
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Li L, Wen G, Cao P, Liu X, R Zaiane O, Yang J. Exploring interpretable graph convolutional networks for autism spectrum disorder diagnosis. Int J Comput Assist Radiol Surg 2023; 18:663-673. [PMID: 36333597 DOI: 10.1007/s11548-022-02780-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE Finding the biomarkers associated with autism spectrum disorder (ASD) is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatments. In essence, we are faced with two challenges (i) how to learn a node representation and a clean graph structure from original graph data with high dimensionality and (ii) how to jointly model the procedure of node representation learning, structure learning and graph classification. METHODS We propose FSL-BrainNet, an interpretable graph convolution network (GCN) model for jointly Learning of node Features and clean Structures in brain networks for automatic brain network classification and interpretation. We formulate an end-to-end trainable and interpretable framework for graph classification and biomarkers (salient brain regions and potential subnetworks) identification. RESULTS The experimental results on the ABIDE dataset show that our proposed methods not only achieve improved prediction performance compared with the state-of-the-art methods, but also find a compact set of highly suggestive biomarkers including relevant brain regions and subnetworks to ASD. CONCLUSION Through node feature learning and structure learning, our model can simultaneously select important brain regions and identify subnetworks.
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Affiliation(s)
- Lanting Li
- College of Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Shenyang, China
| | - Guangqi Wen
- College of Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Shenyang, China
| | - Peng Cao
- College of Computer Science and Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Shenyang, China.
| | | | - Osmar R Zaiane
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada
| | - Jinzhu Yang
- College of Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Shenyang, China
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23
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Manikantan K, Jaganathan S. A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks. Diagnostics (Basel) 2023; 13:1143. [PMID: 36980452 PMCID: PMC10047680 DOI: 10.3390/diagnostics13061143] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/09/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
This article proposes a model to diagnose autism patients using graphical neural networks. A graphical neural network relates the subjects (nodes) using the features (edges). In our model, radiomic features obtained from sMRI are used as edges, and spatial-temporal data obtained through rs-fMRI are used as nodes. The similarity between first-order and texture features from the sMRI data of subjects are derived using radiomics to construct the edges of a graph. The features from brain summaries are assembled and learned using 3DCNN to represent the features of each node of the graph. Using the structural similarities of the brain rather than phenotypic data or graph kernel functions provides better accuracy. The proposed model was applied to a standard dataset, ABIDE, and it was shown that the classification results improved with the use of both spatial (sMRI) and statistical measures (brain summaries of rs-fMRI) instead of using only medical images.
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24
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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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25
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Li Y, Feng L. Patient multi-relational graph structure learning for diabetes clinical assistant diagnosis. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:8428-8445. [PMID: 37161205 DOI: 10.3934/mbe.2023369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The rapid accumulation of electronic health records (EHRs) and the advancements in data analysis technology have laid the foundation for research and clinical decision-making in the healthcare community. Graph neural networks (GNNs), a deep learning model family for graph embedding representations, have been widely used in the field of smart healthcare. However, traditional GNNs rely on the basic assumption that the graph structure extracted from the complex interactions among the EHRs must be a real topology. Noisy connections or false topology in the graph structure leads to inefficient disease prediction. We devise a new model named PM-GSL to improve diabetes clinical assistant diagnosis based on patient multi-relational graph structure learning. Specifically, we first build a patient multi-relational graph based on patient demographics, diagnostic information, laboratory tests, and complex interactions between medicines in EHRs. Second, to fully consider the heterogeneity of the patient multi-relational graph, we consider the node characteristics and the higher-order semantics of nodes. Thus, three candidate graphs are generated in the PM-GSL model: original subgraph, overall feature graph, and higher-order semantic graph. Finally, we fuse the three candidate graphs into a new heterogeneous graph and jointly optimize the graph structure with GNNs in the disease prediction task. The experimental results indicate that PM-GSL outperforms other state-of-the-art models in diabetes clinical assistant diagnosis tasks.
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Affiliation(s)
- Yong Li
- College of Computer Science and Engineering, Northwest Normal University, 967 Anning East Road, Lanzhou 730070, China
| | - Li Feng
- College of Computer Science and Engineering, Northwest Normal University, 967 Anning East Road, Lanzhou 730070, China
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26
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Zhang H, Song R, Wang L, Zhang L, Wang D, Wang C, Zhang W. Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:444-455. [PMID: 36327188 DOI: 10.1109/tmi.2022.3219260] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Recently, functional brain network has been used for the classification of brain disorders, such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods either ignore the non-imaging information associated with the subjects and the relationship between the subjects, or cannot identify and analyze disease-related local brain regions and biomarkers, leading to inaccurate classification results. This paper proposes a local-to-global graph neural network (LG-GNN) to address this issue. A local ROI-GNN is designed to learn feature embeddings of local brain regions and identify biomarkers, and a global Subject-GNN is then established to learn the relationship between the subjects with the embeddings generated by the local ROI-GNN and the non-imaging information. The local ROI-GNN contains a self-attention based pooling module to preserve the embeddings most important for the classification. The global Subject-GNN contains an adaptive weight aggregation block to generate the multi-scale feature embedding corresponding to each subject. The proposed LG-GNN is thoroughly validated using two public datasets for ASD and AD classification. The experimental results demonstrated that it achieves the state-of-the-art performance in terms of various evaluation metrics.
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Yang F, Wang H, Wei S, Sun G, Chen Y, Tao L. Multi-model adaptive fusion-based graph network for Alzheimer's disease prediction. Comput Biol Med 2023; 153:106518. [PMID: 36641934 DOI: 10.1016/j.compbiomed.2022.106518] [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: 09/05/2022] [Revised: 12/22/2022] [Accepted: 12/31/2022] [Indexed: 01/04/2023]
Abstract
Alzheimer's disease (AD) is a common cognitive disorder. Recently, many computer-aided diagnostic techniques have been used for AD prediction utilizing deep learning technology, among which graph neural networks have received increasing attention owing to their ability to model sample relationships on large population graphs. Most of the existing graph-based methods predict diseases according to a single model, which makes it difficult to select an appropriate node embedding algorithm for a certain classification task. Moreover, integrating data from different patterns into a unified model to improve the quality of disease diagnosis remains a challenge. Hence, in this study, we aimed to develop a multi-model fusion framework for AD prediction. A spectral graph attention model was used to aggregate intra- and inter-cluster node embeddings of normal and diseased populations, whereafter, a bilinear aggregation model was applied as an auxiliary model to enhance the abnormality degree in different categories of populations, and finally, an adaptive fusion module was designed to dynamically fuse the results of both models and enhance AD prediction. Compared to other comparison methods, the model proposed in this study provides the best results.
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Affiliation(s)
- Fusheng Yang
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, 230601, China.
| | - Huabin Wang
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, 230601, China.
| | - Shicheng Wei
- School of Electrical and Information Engineering, University of Sydney, Sydney, 2006, Australia.
| | - Guangming Sun
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, 230601, China.
| | - Yonglin Chen
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, 230601, China.
| | - Liang Tao
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, 230601, China.
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Classification of schizophrenia patients using a graph convolutional network: A combined functional MRI and connectomics analysis. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104293] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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29
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Wang C, Zhang L, Zhang J, Qiao L, Liu M. Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification. J Pers Med 2023; 13:jpm13020251. [PMID: 36836485 PMCID: PMC9958959 DOI: 10.3390/jpm13020251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 12/27/2022] [Accepted: 01/13/2023] [Indexed: 01/31/2023] Open
Abstract
Background: Functional brain networks (FBNs) derived from resting-state functional MRI (rs-fMRI) have shown great potential in identifying brain disorders, such as autistic spectrum disorder (ASD). Therefore, many FBN estimation methods have been proposed in recent years. Most existing methods only model the functional connections between brain regions of interest (ROIs) from a single view (e.g., by estimating FBNs through a specific strategy), failing to capture the complex interactions among ROIs in the brain. Methods: To address this problem, we propose fusion of multiview FBNs through joint embedding, which can make full use of the common information of multiview FBNs estimated by different strategies. More specifically, we first stack the adjacency matrices of FBNs estimated by different methods into a tensor and use tensor factorization to learn the joint embedding (i.e., a common factor of all FBNs) for each ROI. Then, we use Pearson's correlation to calculate the connections between each embedded ROI in order to reconstruct a new FBN. Results: Experimental results obtained on the public ABIDE dataset with rs-fMRI data reveal that our method is superior to several state-of-the-art methods in automated ASD diagnosis. Moreover, by exploring FBN "features" that contributed most to ASD identification, we discovered potential biomarkers for ASD diagnosis. The proposed framework achieves an accuracy of 74.46%, which is generally better than the compared individual FBN methods. In addition, our method achieves the best performance compared to other multinetwork methods, i.e., an accuracy improvement of at least 2.72%. Conclusions: We present a multiview FBN fusion strategy through joint embedding for fMRI-based ASD identification. The proposed fusion method has an elegant theoretical explanation from the perspective of eigenvector centrality.
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Affiliation(s)
- Chengcheng Wang
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Limei Zhang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China
- Correspondence: (L.Z.); (M.L.)
| | - Jinshan Zhang
- College of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong 643000, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Correspondence: (L.Z.); (M.L.)
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Wang G, Zhang L, Qiao L. The effect of node features on GCN-based brain network classification: an empirical study. PeerJ 2023; 11:e14835. [PMID: 36967986 PMCID: PMC10035427 DOI: 10.7717/peerj.14835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/10/2023] [Indexed: 03/29/2023] Open
Abstract
Brain functional network (BFN) analysis has become a popular technique for identifying neurological/mental diseases. Due to the fact that BFN is a graph, a graph convolutional network (GCN) can be naturally used in the classification of BFN. Different from traditional methods that directly use the adjacency matrices of BFNs to train a classifier, GCN requires an additional input-node features. To our best knowledge, however, there is no systematic study to analyze their influence on the performance of GCN-based brain disorder classification. Therefore, in this study, we conduct an empirical study on various node feature measures, including (1) original fMRI signals, (2) one-hot encoding, (3) node statistics, (4) node correlation, and (5) their combination. Experimental results on two benchmark databases show that different node feature inputs to GCN significantly affect the brain disease classification performance, and node correlation usually contributes higher accuracy compared to original signals and manually extracted statistical features.
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Affiliation(s)
| | - Limei Zhang
- Liaocheng University, Liaocheng, China
- Shandong Jianzhu University, Jinan, China
| | - Lishan Qiao
- Liaocheng University, Liaocheng, China
- Shandong Jianzhu University, Jinan, China
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Deng X, Zhang J, Liu R, Liu K. Classifying ASD based on time-series fMRI using spatial-temporal transformer. Comput Biol Med 2022; 151:106320. [PMID: 36442277 DOI: 10.1016/j.compbiomed.2022.106320] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 10/11/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022]
Abstract
As the prevalence of autism spectrum disorder (ASD) increases globally, more and more patients need to receive timely diagnosis and treatment to alleviate their suffering. However, the current diagnosis method of ASD still adopts the subjective symptom-based criteria through clinical observation, which is time-consuming and costly. In recent years, functional magnetic resonance imaging (fMRI) neuroimaging techniques have emerged to facilitate the identification of potential biomarkers for diagnosing ASD. In this study, we developed a deep learning framework named spatial-temporal Transformer (ST-Transformer) to distinguish ASD subjects from typical controls based on fMRI data. Specifically, a linear spatial-temporal multi-headed attention unit is proposed to obtain the spatial and temporal representation of fMRI data. Moreover, a Gaussian GAN-based data balancing method is introduced to solve the data unbalance problem in real-world ASD datasets for subtype ASD diagnosis. Our proposed ST-Transformer is evaluated on a large cohort of subjects from two independent datasets (ABIDE I and ABIDE II) and achieves robust accuracies of 71.0% and 70.6%, respectively. Compared with state-of-the-art methods, our results demonstrate competitive performance in ASD diagnosis.
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Affiliation(s)
- Xin Deng
- The Key Laboratory of Data Engineering and Visual Computing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Jiahao Zhang
- The Key Laboratory of Data Engineering and Visual Computing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Rui Liu
- Department of Computer Science, City University of Hong Kong, 999077, Hong Kong, China.
| | - Ke Liu
- The Key Laboratory of Data Engineering and Visual Computing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
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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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A unified framework of graph structure learning, graph generation and classification for brain network analysis. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03891-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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34
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Yang W, Wen G, Cao P, Yang J, Zaiane OR. Collaborative learning of graph generation, clustering and classification for brain networks diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106772. [PMID: 35395591 DOI: 10.1016/j.cmpb.2022.106772] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
PURPOSE Accurate diagnosis of autism spectrum disorder (ASD) plays a key role in improving the condition and quality of life for patients. In this study, we mainly focus on ASD diagnosis with functional brain networks (FBNs). The major challenge for brain networks modeling is the high dimensional connectivity in brain networks and limited number of subjects, which hinders the classification capability of graph convolutional networks (GCNs). METHOD To alleviate the influence of the limited data and high dimensional connectivity, we introduce a unified three-stage graph learning framework for brain network classification, involving multi-graph clustering, graph generation and graph classification. The framework combining Graph Generation, Clustering and Classification Networks (GraphCGC-Net) enhances the critical connections by multi-graph clustering (MGC) with a supervision scheme, and generates realistic brain networks by simultaneously preserving the global consistent distribution and local topology properties. RESULTS To demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Autism Brain Imaging Data Exchange (ABIDE) dataset and conduct extensive experiments on the ASD classification problem. Our proposed method achieves an average accuracy of 70.45% and an AUC of 72.76% on ABIDE. Compared with the traditional GCN model, the proposed GraphCGC-Net obtains 9.3%, and 10.64% improvement in terms of accuracy and AUC metrics, respectively. CONCLUSION The comprehensive experiments demonstrate that our GraphCGC-Net is effective for graph classification in brain disorders diagnosis. Moreover, we find that MGC can generate biologically meaningful subnetworks, which is highly consistent with the previous neuroimaging-derived biomarker evidence of ASD. More importantly, the promising results suggest that applying generative adversarial networks (GANs) in brain networks to improve the classification performance is worth further investigation.
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Affiliation(s)
- Wenju Yang
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Guangqi Wen
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Peng Cao
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Jinzhu Yang
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Osmar R Zaiane
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada
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