TY  - CONF
SP  - 17
ID  - scholars15363
TI  - Classification of ASD Subtypes Based on Coherence Features of BOLD Resting-state fMRI Signals
KW  - Biomedical signal processing; Classification (of information); Convolutional neural networks; Functional neuroimaging; Magnetic resonance imaging
KW  -  Autism spectrum disorder subtype; Autism spectrum disorders; Convolutional neural network; Functional connectivity; Functional magnetic resonance imaging; Multi-class classification; Normal controls; Resting state; Resting-state functional magnetic resonance imaging; Wavelet coherences
KW  -  Diseases
N2  - Resting-state brain functional connectivity (FC) patterns play an essential role in the development of autism spectrum disorder (ASD) classification models based on functional magnetic resonance imaging (fMRI) data. Due to the limited number of models in the literature for identifying ASD subtypes, a multiclass classification is introduced in this study. The aim of this study is to develop an ASD diagnosis model using convolutional neural networks (CNN) with dynamic FC as inputs. The rs-fMRI dataset used in this study consists of 35 individuals from multiple sites labeled based on autistic disorder subtypes (ASD, APD, and PDD-NOS) and normal control (NC). The Atlas for Automated Anatomical Labeling (AAL) is selected as the brain atlas for defining brain nodes. The BOLD signals of the nodes are extracted and then the dynamic FC between brain nodes is determined using our new metric wavelet coherence (WCF), where WCF quantifies the overall variability of coherence in specific low-frequency scales over the time. Based on the statistical analysis of WCF values between ASD and NC, 6 pairwise nodes are identified. Classification algorithm is developed using CNN, and wavelet coherence maps (scalogram) of pairwise nodes. The training and testing of the CNN is using a cross-validation framework. The results of the multiclass classification provided an average accuracy of 88.6. The results of this study illustrate the good potential of the wavelet coherence technique in analysing dynamics FC and open up possibilities for its application in diagnostic models, not only for ASD but also for other neuropsychiatric disorders. © 2021 IEEE.
N1  - cited By 1; Conference of 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021 ; Conference Date: 1 December 2021 Through 2 December 2021; Conference Code:176965
AV  - none
EP  - 22
A1  - Al-Hiyali, M.I.
A1  - Yahya, N.
A1  - Faye, I.
A1  - Al-Ezzi, A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126623641&doi=10.1109%2fICICyTA53712.2021.9689092&partnerID=40&md5=b6bca59d3c316fc8f2aa7a9134d97d1d
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781665417778
Y1  - 2021///
ER  -