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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Related Experiment Video

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Diagnosing autism spectrum disorder using brain entropy: A fast entropy method.

Liangliang Zhang1, Xun-Heng Wang2, Lihua Li3

  • 1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

Computer Methods and Programs in Biomedicine
|December 7, 2019
PubMed
Summary

This study introduces a novel fast entropy algorithm for analyzing brain activity in autism spectrum disorder (ASD). The new method effectively distinguishes ASD patients from controls, offering potential biomarkers for diagnosis.

Keywords:
Approximate entropyFunctional magnetic resonance imagingSample entropySignal complexitySupport-vector machine

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • Autism spectrum disorder (ASD) research has primarily used linear models for fMRI analysis.
  • The nonlinear neural complexity in ASD remains largely unexplored.
  • Resting-state fMRI (rs-fMRI) offers insights into brain function.

Purpose of the Study:

  • To explore nonlinear neural mechanisms in ASD using entropy analysis.
  • To develop and validate a fast entropy algorithm for ASD diagnosis.
  • To compare the efficacy of entropy-based methods with traditional functional connectivity (FC) methods.

Main Methods:

  • Analyzed rs-fMRI data from 21 ASD patients and 26 typically developing (TD) individuals.
  • Applied approximate entropy (ApEn) and sample entropy (SampEn) methods.
  • Developed a fast matrix computation-based entropy algorithm and combined it with support-vector machine (SVM) for classification.

Main Results:

  • The fast entropy method achieved higher diagnostic accuracy (AUC for ApEn=0.79, SampEn=0.89) compared to FC (AUC=0.62).
  • ASD patients exhibited lower brain entropy.
  • Entropy measures showed significant negative correlations with Autism Diagnostic Observation Schedule scores in ASD patients.

Conclusions:

  • Brain entropy analysis using a fast algorithm provides a novel approach for distinguishing ASD individuals.
  • ApEn and SampEn demonstrate potential as reliable biomarkers for ASD.
  • The developed algorithm offers a computationally efficient method for entropy analysis in neuroimaging.