Autism spectrum disorder studies using fMRI data and machine learning: a review
M Liu, B Li, D Hu - Frontiers in Neuroscience, 2021 - frontiersin.org
Machine learning methods have been frequently applied in the field of cognitive
neuroscience in the last decade. A great deal of attention has been attracted to introduce …
neuroscience in the last decade. A great deal of attention has been attracted to introduce …
Functional connectome–based predictive modeling in autism
Autism is a heterogeneous neurodevelopmental condition, and functional magnetic
resonance imaging–based studies have helped advance our understanding of its effects on …
resonance imaging–based studies have helped advance our understanding of its effects on …
The diagnosis of ASD with MRI: a systematic review and meta-analysis
While diagnosing autism spectrum disorder (ASD) based on an objective test is desired, the
current diagnostic practice involves observation-based criteria. This study is a systematic …
current diagnostic practice involves observation-based criteria. This study is a systematic …
Brain imaging-based machine learning in autism spectrum disorder: methods and applications
Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood
onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is …
onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is …
CNNG: a convolutional neural networks with gated recurrent units for autism spectrum disorder classification
As a neurodevelopmental disorder, autism spectrum disorder (ASD) severely affects the
living conditions of patients and their families. Early diagnosis of ASD can enable the …
living conditions of patients and their families. Early diagnosis of ASD can enable the …
Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder
Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information.
Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study …
Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study …
Multi-view feature enhancement based on self-attention mechanism graph convolutional network for autism spectrum disorder diagnosis
F Zhao, N Li, H Pan, X Chen, Y Li, H Zhang… - Frontiers in human …, 2022 - frontiersin.org
Functional connectivity (FC) network based on resting-state functional magnetic resonance
imaging (rs-fMRI) has become an important tool to explore and understand the brain, which …
imaging (rs-fMRI) has become an important tool to explore and understand the brain, which …
Classification of major depression disorder via using minimum spanning tree of individual high-order morphological brain network
Y Li, T Chu, Y Liu, H Zhang, F Dong, Q Gai… - Journal of Affective …, 2023 - Elsevier
Background Major depressive disorder (MDD) is an overbroad and heterogeneous
diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning …
diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning …
A review of methods for classification and recognition of ASD using fMRI data
W Feng, G Liu, K Zeng, M Zeng, Y Liu - Journal of neuroscience methods, 2022 - Elsevier
Autism spectrum disorder (ASD) is a severe neuropsychiatric brain disorder that affects
people's social communication and daily routine. Considering the phenomenon of abnormal …
people's social communication and daily routine. Considering the phenomenon of abnormal …
Constructing multi-view high-order functional connectivity networks for diagnosis of autism spectrum disorder
F Zhao, X Zhang, KH Thung, N Mao… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Brain functional connectivity network (FCN) based on resting-state functional magnetic
resonance imaging (rs-fMRI) has been widely used to identify neuropsychiatric disorders …
resonance imaging (rs-fMRI) has been widely used to identify neuropsychiatric disorders …