A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

被引:55
|
作者
Subah, Faria Zarin [1 ]
Deb, Kaushik [1 ]
Dhar, Pranab Kumar [1 ]
Koshiba, Takeshi [2 ]
机构
[1] Chittagong Univ Engn & Technol, Dept Comp Sci & Engn, Chattogram 4349, Bangladesh
[2] Waseda Univ, Fac Educ & Integrated Arts & Sci, Shinjuku Ku, 1-6-1 Nishiwaseda, Tokyo 1698050, Japan
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 08期
关键词
autism spectrum disorder; resting-state fMRI; predefined brain atlas; ABIDE; functional connectivity; connectivity matrix; deep neural network; FUNCTIONAL CONNECTIVITY; CLASSIFICATION; INDIVIDUALS; CHILDREN; IDENTIFICATION; SCHIZOPHRENIA; DISEASE;
D O I
10.3390/app11083636
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network
    Huang, Zhi-An
    Zhu, Zexuan
    Yau, Chuen Heung
    Tan, Kay Chen
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (07) : 2847 - 2861
  • [2] Multi-site diagnostic classification of Autism spectrum disorder using adversarial deep learning on resting-state fMRI
    Tang, Yan
    Tong, Gan
    Xiong, Xing
    Zhang, Chengyuan
    Zhang, Hao
    Yang, Yuan
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 85
  • [3] Deep Learning-based Classification of Autism Spectrum Disorder Using Resting State fMRI Data
    Firouzi, M.H.
    Fadaei, S.
    [J]. International Journal of Engineering, Transactions A: Basics, 2025, 38 (04): : 785 - 795
  • [4] Non-Oscillatory Connectivity Approach for Classification of Autism Spectrum Disorder Subtypes Using Resting-State fMRI
    Sadiq, Alishba
    Al-Hiyali, Mohammed Isam
    Yahya, Norashikin
    Tang, Tong Boon
    Khan, Danish M.
    [J]. IEEE ACCESS, 2022, 10 : 14049 - 14061
  • [5] Deep learning in resting-state fMRI
    Abrol, Anees
    Hassanzadeh, Reihaneh
    Plis, Sergey
    Calhoun, Vince
    [J]. 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), 2021, : 3965 - 3969
  • [6] Prediction of Autism Spectrum Disorder Based on Imbalanced Resting-state fMRI Data Using Clustering Oversampling
    Yuan, Dan
    Zhu, Li
    Huang, Huifang
    [J]. TENTH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING SYSTEMS, 2019, 2019, 11071
  • [7] Classification of Autism Spectrum Disorder from Resting-State fMRI with Mutual Connectivity Analysis
    DSouza, Adora M.
    Abidin, Anas Z.
    Wismueller, Axel
    [J]. MEDICAL IMAGING 2019: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING, 2019, 10953
  • [8] Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?
    Nebel, Mary Beth
    Lidstone, Daniel E.
    Wang, Liwei
    Benkeser, David
    Mostofsky, Stewart H.
    Risk, Benjamin B.
    [J]. NEUROIMAGE, 2022, 257
  • [9] Classify Autism and Control Based on Deep Learning and Community Structure on Resting-state fMRI
    Liao, Dingan
    Lu, Hu
    [J]. PROCEEDINGS OF 2018 TENTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI), 2018, : 289 - 294
  • [10] Identification of autism spectrum disorder using multi-regional resting-state data through an attention learning approach
    Liu, Yaya
    Xu, Lingyu
    Yu, Jie
    Li, Jun
    Yu, Xuan
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 69