Literature DB >> 34383233

Analysis of simultaneous visual and complex neural dynamics during cognitive learning to diagnose ASD.

Tanu Wadhera1, Deepti Kakkar2.   

Abstract

The interactions between gaze processing and neural activities mediate cognition. The present paper aims to identify the involvement of visual and neural dynamics in shaping the cognitive behavior in Autism Spectrum Disorder (ASD). Electroencephalogram (EEG) and Eye-tracker signals of ASD and Typically Developing (TD) are recorded while performing two difficulty levels of a maze-based experimental task. During task, the performance metrics, complex neural measures extracted from EEG data using Visibility Graph (VG) algorithm and visual measures extracted from eye-tracker data are analyzed and compared. For both task levels, the cognition processing is examined via performance metrics (reaction-time and poor accuracy), gaze measures (saccade, fixation duration and blinkrate) and VG-based metrics (average weighted degree, clustering coefficient, path length, global efficiency, mutual information). An engagement in cognitive processing in ASD is revealed statistically by high reaction time, poor accuracy, increased fixation duration, raised saccadic amplitude, higher blink rate, reduced average weighted degree, global efficiency, mutual information as well as higher eigenvector centrality and path length. Over the course of repetitive trials, the cognitive improvement is although poor in ASD compared to TDs, the reconfigurations of visual and neural network dynamics revealed activation of Cognitive Learning (CL) in ASD. Furthermore, the correlation of gaze-EEG measures reveal that independent brain region functioning is not impaired but declined mutual interaction of brain regions causes cognitive deficit in ASD. And correlation of EEG-gaze measures with clinical severity measured by Autism Diagnostic Observation Schedule(ADOS) suggest that visual-neural activities reveals social behavior/cognition in ASD. Thus, visual and neural dynamics together support the revelation of the cognitive behavior in ASD.
© 2021. Australasian College of Physical Scientists and Engineers in Medicine.

Entities:  

Keywords:  Cognition; EEG; Gaze patterns; Graph-theory; Learning; Visibility

Mesh:

Year:  2021        PMID: 34383233     DOI: 10.1007/s13246-021-01045-8

Source DB:  PubMed          Journal:  Phys Eng Sci Med        ISSN: 2662-4729


  17 in total

1.  From time series to complex networks: the visibility graph.

Authors:  Lucas Lacasa; Bartolo Luque; Fernando Ballesteros; Jordi Luque; Juan Carlos Nuño
Journal:  Proc Natl Acad Sci U S A       Date:  2008-03-24       Impact factor: 11.205

2.  Learning-induced autonomy of sensorimotor systems.

Authors:  Danielle S Bassett; Muzhi Yang; Nicholas F Wymbs; Scott T Grafton
Journal:  Nat Neurosci       Date:  2015-04-06       Impact factor: 24.884

Review 3.  Statistical learning as a basis for social understanding in children.

Authors:  Ted Ruffman; Mele Taumoepeau; Chris Perkins
Journal:  Br J Dev Psychol       Date:  2011-06-21

Review 4.  Implicit learning in individuals with autism spectrum disorders: a meta-analysis.

Authors:  F Foti; F De Crescenzo; G Vivanti; D Menghini; S Vicari
Journal:  Psychol Med       Date:  2014-08-15       Impact factor: 7.723

5.  Multiplex temporal measures reflecting neural underpinnings of brain functional connectivity under cognitive load in Autism Spectrum Disorder.

Authors:  Tanu Wadhera; Deepti Kakkar
Journal:  Neurol Res       Date:  2020-03-04       Impact factor: 2.448

6.  Statistical word learning in children with autism spectrum disorder and specific language impairment.

Authors:  Eileen Haebig; Jenny R Saffran; Susan Ellis Weismer
Journal:  J Child Psychol Psychiatry       Date:  2017-05-02       Impact factor: 8.982

Review 7.  Development of eye-movement control.

Authors:  Beatriz Luna; Katerina Velanova; Charles F Geier
Journal:  Brain Cogn       Date:  2008-10-19       Impact factor: 2.310

8.  Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD.

Authors:  Shafali S Jeste; Natasha Kirkham; Damla Senturk; Kyle Hasenstab; Catherine Sugar; Chloe Kupelian; Elizabeth Baker; Andrew J Sanders; Christina Shimizu; Amanda Norona; Tanya Paparella; Stephanny F N Freeman; Scott P Johnson
Journal:  Dev Sci       Date:  2014-05-13

9.  Electrophysiological signatures of visual statistical learning in 3-month-old infants at familial and low risk for autism spectrum disorder.

Authors:  Andrew Marin; Ted Hutman; Carolyn Ponting; Nicole M McDonald; Leslie Carver; Elizabeth Baker; Manjari Daniel; Abigail Dickinson; Mirella Dapretto; Scott P Johnson; Shafali S Jeste
Journal:  Dev Psychobiol       Date:  2020-03-25       Impact factor: 3.038

10.  Exploring the cognitive features in children with autism spectrum disorder, their co-twins, and typically developing children within a population-based sample.

Authors:  Victoria E A Brunsdon; Emma Colvert; Catherine Ames; Tracy Garnett; Nicola Gillan; Victoria Hallett; Stephanie Lietz; Emma Woodhouse; Patrick Bolton; Francesca Happé
Journal:  J Child Psychol Psychiatry       Date:  2014-11-24       Impact factor: 8.982

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  1 in total

1.  Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels.

Authors:  Hyeokjin Kwon; Johanna Inhyang Kim; Seung-Yeon Son; Yong Hun Jang; Bung-Nyun Kim; Hyun Ju Lee; Jong-Min Lee
Journal:  Front Neurosci       Date:  2022-07-07       Impact factor: 5.152

  1 in total

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