Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network.

IEEE Transactions on Neural Networks and Learning Systems
Zhi-An HuangKay Chen Tan

Abstract

With the increasing prevalence of autism spectrum disorder (ASD), it is important to identify ASD patients for effective treatment and intervention, especially in early childhood. Neuroimaging techniques have been used to characterize the complex biomarkers based on the functional connectivity anomalies in the ASD. However, the diagnosis of ASD still adopts the symptom-based criteria by clinical observation. The existing computational models tend to achieve unreliable diagnostic classification on the large-scale aggregated data sets. In this work, we propose a novel graph-based classification model using the deep belief network (DBN) and the Autism Brain Imaging Data Exchange (ABIDE) database, which is a worldwide multisite functional and structural brain imaging data aggregation. The remarkable connectivity features are selected through a graph extension of K -nearest neighbors and then refined by a restricted path-based depth-first search algorithm. Thanks to the feature reduction, lower computational complexity could contribute to the shortening of the training time. The automatic hyperparameter-tuning technique is introduced to optimize the hyperparameters of the DBN by exploring the potential parameter space. The simulatio...Continue Reading

Software Mentioned

scikit
DE
qquad
Configurable Pipeline for the Analysis of Connectomes ( C - PAC )
CompCor
MAX
ABIDE
PSO
GBFS
MUN

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