Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy.

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Abstract

Functional near-infrared spectroscopy (fNIRS) was used to investigate spontaneous hemodynamic fluctuations in the bilateral temporal cortices for typically developing (TD) children and children with autism spectrum disorder (ASD).
This paper proposed an approach to estimate the global time-varying behavior of brain activity through the measurement on change in first-order statistical properties directly from fNIRS time series. Then, a deep learning model combining the long-short term memory (LSTM) and convolutional neural network (CNN) was constructed based on the integration strategy with improved bagging algorithm, with the purpose to explore the potential patterns of temporal variation for ASD identification.
Based on the theory of stationarity, analysis on the global time-varying behavior of hemodynamic fluctuations in oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) demonstrated that children with ASD showed weaker internal logic, but stronger memory and persistence to random shocks than TD children. Differentiating between ASD and TD with the proposed deep learning approach resulted in high accurate classification with sensitivity of 97.1% and specificity of 94.3%.
Using fNIRS time series of Hb from single optical channel, we achieved a better classification accuracy of 95.7% that was about 8% higher than previous methods with similar data.
The characterization on time-varying behavior of brain activity holds promise for better understanding the underlying causes to ASD. And the deployed deep learning framework with an integration manner has the potential for screening children with risk of ASD.
Copyright © 2019. Published by Elsevier B.V.