Identification of autism spectrum disorder based on short -term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network

Objective: To classify children with autism spectrum disorder (ASD) and typical development (TD) using short-term spontaneous hemodynamic fluctuations and to explore the abnormality of inferior frontal gyrus and temporal lobe in ASD. Methods: 25 ASD children and 22 TD children were measured with functional near-infrared spectroscopy located on the inferior frontal gyrus and temporal lobe. To extract features used to classify ASD and TD, a multi-layer neural network was applied, combining with a three-layer convolutional neural network, a layer of long and short-term memory network (LSTM) and a layer of LSTM with Attention mechanism. In order to shorten the time of data collection and get more information from limited samples, a sliding window with 3.5 s width was utilized after comparisons, and numerous short (3.5 s) fNIRS time series were then obtained and used as the input of the multi-layer neural network. Results: A good classification between ASD and TD was obtained with considerably high accuracy by using a multi-layer neural network in different brain regions, especially in the left temporal lobe, where sensitivity of 90.6% and specificity of 97.5% achieved. Conclusions: The "CLAttention" multi-layer neural network has the potential to excavate more meaningful features to distinguish between ASD and TD. Moreover, the temporal lobe may be worth further study. Significance: The findings in this study may have implications for rapid diagnosis of children with ASD and provide a new perspective for future medical diagnosis. (c) 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.