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Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
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Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation.

Runa Bhaumik1, Ashish Pradhan2, Soptik Das2

  • 1Department of Psychiatry, Bio-Statistical Research Center, The University of Illinois at Chicago, Chicago, IL, USA. rbhaumik@uic.edu.

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Summary

This study explored neuroimaging biomarkers for autism spectrum disorder (ASD) using resting-state functional MRI (rs-fMRI). While partial least square regression (PLS) did not significantly improve classification accuracy, key brain regions were identified for potential early ASD diagnosis.

Keywords:
ABIDEAutism spectrum disorderElastic netLassoPartial least square regressionRs-fMRISupport vector machine

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Psychiatry

Background:

  • Resting-state functional MRI (rs-fMRI) connectivity shows promise as a diagnostic biomarker for autism spectrum disorder (ASD).
  • Replicating biomarker robustness across sites and acquisition conditions is crucial for clinical validation.
  • Multicenter data variations necessitate domain-adaptive methods for reliable ASD diagnosis.

Purpose of the Study:

  • To investigate the efficacy of partial least square regression (PLS) in adjusting for multicenter acquisition effects in ASD diagnosis.
  • To discriminate between individuals with ASD and healthy controls using a sparse Multivariate Pattern Analysis (MVVPA) framework.
  • To identify neurobiological markers for early clinical diagnosis or risk determination in ASD.

Main Methods:

  • Utilized a leave-one-site-out cross-validation (LOSOCV) approach on data from six sites in the Autism Brain Imaging Data Exchange (ABIDE).
  • Employed partial least square regression (PLS) within a sparse Multivariate Pattern Analysis (MVVPA) framework.
  • Extracted classification features from 42 bilateral Brodmann areas.

Main Results:

  • Support Vector Machine (SVM) classification accuracy was 62% with PLS, which was not significantly different from classification without PLS.
  • Identified key brain regions involved in executive function, speech, visual perception, and language, including Dorsolateral Prefrontal Cortex and Primary Auditory Cortex.
  • These identified regions showed interrupted connectivity patterns associated with ASD.

Conclusions:

  • Partial least square regression (PLS) did not significantly enhance SVM classification accuracy for ASD diagnosis in this multicenter study.
  • Specific brain regions with altered connectivity are implicated in ASD, offering potential neurobiological markers.
  • Findings support the potential for identifying neurobiological markers for early ASD diagnosis and risk assessment.