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

Characterizing autism spectrum disorder by deep learning spontaneous brain activity from... Analyzing on the time-varying behavior of spontaneous hemodynamic fluctuations from fNIRS was performed to investigate the possible functional patterns associated with autism spectrum disorder (ASD) and potential contribution of optical channels to ASD/TD classification; Then a deep learning model combining the long-short term memory (LSTM) and convolutional neural network (CNN) was conducted to represent the temporal variation of brain activity for accurate identification of ASD. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Neuroscience Methods Elsevier

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

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Publisher
Elsevier
Copyright
Copyright © 2019 Elsevier B.V.
ISSN
0165-0270
eISSN
1872-678X
DOI
10.1016/j.jneumeth.2019.108538
Publisher site
See Article on Publisher Site

Abstract

Analyzing on the time-varying behavior of spontaneous hemodynamic fluctuations from fNIRS was performed to investigate the possible functional patterns associated with autism spectrum disorder (ASD) and potential contribution of optical channels to ASD/TD classification; Then a deep learning model combining the long-short term memory (LSTM) and convolutional neural network (CNN) was conducted to represent the temporal variation of brain activity for accurate identification of ASD.

Journal

Journal of Neuroscience MethodsElsevier

Published: Feb 1, 2020

References

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