Autistic Spectrum Disorder Detection and Structural Biomarker Identification Using Self-Attention Model and Individual-Level Morphological Covariance Brain Networks
Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders, which brings enormous burdens to the families of patients and society. However, due to the lack of representation of variance for diseases and the absence of biomarkers for diagnosis, the early detection and intervention of A...
Main Authors: | Zhengning Wang, Dawei Peng, Yongbin Shang, Jingjing Gao |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-10-01
|
Series: | Frontiers in Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.756868/full |
Similar Items
-
Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks
by: Jingjing Gao, et al.
Published: (2021-01-01) -
Structural Covariance of Sensory Networks, the Cerebellum, and Amygdala in Autism Spectrum Disorder
by: Garrett J. Cardon, et al.
Published: (2017-11-01) -
THE STIMULATION OF LANGUAGE DEVELOPMENT IN CHILDREN WITH AUTIST SPECTRUM DISORDER
by: MAXIMCIUC Victoria, et al.
Published: (2017-11-01) -
The cerebellum and divided attention in autism spectrum disorders
by: Hsu, Julie Yong
Published: (2014) -
Classification and Biomarker Exploration of Autism Spectrum Disorders Based on Recurrent Attention Model
by: Fengkai Ke, et al.
Published: (2020-01-01)