Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD critical.With the deve...Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD critical.With the development of the machine learning and neuroimaging technology,extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging(s-MRI).However,most studies involve with datasets where participants'age are above 5 and lack interpretability.In this paper,we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years,based on s-MRI features extracted using Contrastive Variational AutoEncoder(CVAE).78 s-MRIs,collected from Shenzhen Children's Hospital,are used for training CVAE,which consists of both ASD-specific feature channel and common-shared feature channel.The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control(TC)participants represented by the common-shared features.In case of degraded predictive accuracy when data size is extremely small,a transfer learning strategy is proposed here as a potential solution.Finally,we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions,which discloses potential biomarkers that could help target treatments of ASD in the future.展开更多
基金supported by the Shenzhen Science and Technology Program(Nos.KQTD20200820113106007 and SGDX20201103095603009)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB38050100)+4 种基金the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)the Shenzhen Basic Research Fund(No.RCYX20200714114734194)the Key Research and Development Project of Guangdong Province(No.2021B0101310002)the National Natural Science Foundation of China(Nos.U22A2041 and 62272449)the Youth Innovation Promotion Association(No.Y2021101).
文摘Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD critical.With the development of the machine learning and neuroimaging technology,extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging(s-MRI).However,most studies involve with datasets where participants'age are above 5 and lack interpretability.In this paper,we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years,based on s-MRI features extracted using Contrastive Variational AutoEncoder(CVAE).78 s-MRIs,collected from Shenzhen Children's Hospital,are used for training CVAE,which consists of both ASD-specific feature channel and common-shared feature channel.The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control(TC)participants represented by the common-shared features.In case of degraded predictive accuracy when data size is extremely small,a transfer learning strategy is proposed here as a potential solution.Finally,we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions,which discloses potential biomarkers that could help target treatments of ASD in the future.
