Segregated Dynamical Networks for Biological Motion Perception in the Mu and Beta Range Underlie Social Deficits in Autism.

autism biological motion perception coherence segregation time-resolved Partial Directed Coherence

Journal

Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402

Informations de publication

Date de publication:
13 Feb 2024
Historique:
received: 19 11 2023
revised: 29 01 2024
accepted: 31 01 2024
medline: 24 2 2024
pubmed: 24 2 2024
entrez: 24 2 2024
Statut: epublish

Résumé

Biological motion perception (BMP) correlating with a mirror neuron system (MNS) is attenuated in underage individuals with autism spectrum disorder (ASD). While BMP in typically-developing controls (TDCs) encompasses interconnected MNS structures, ASD data hint at segregated form and motion processing. This coincides with less fewer long-range connections in ASD than TDC. Using BMP and electroencephalography (EEG) in ASD, we characterized directionality and coherence (mu and beta frequencies). Deficient BMP may stem from desynchronization thereof in MNS and may predict social-communicative deficits in ASD. Clinical considerations thus profit from brain-behavior associations. Point-like walkers elicited BMP using 15 white dots (walker vs. scramble in 21 ASD (mean: 11.3 ± 2.3 years) vs. 23 TDC (mean: 11.9 ± 2.5 years). Dynamic Imaging of Coherent Sources (DICS) characterized the underlying EEG time-frequency causality through time-resolved Partial Directed Coherence (tPDC). Support Vector Machine (SVM) classification validated the group effects (ASD vs. TDC). TDC showed MNS sources and long-distance paths (both feedback and bidirectional); ASD demonstrated distinct from and motion sources, predominantly local feedforward connectivity, and weaker coherence. Brain-behavior correlations point towards dysfunctional networks. SVM successfully classified ASD regarding EEG and performance. ASD participants showed segregated local networks for BMP potentially underlying thwarted complex social interactions. Alternative explanations include selective attention and global-local processing deficits. This is the first study applying source-based connectivity to reveal segregated BMP networks in ASD regarding structure, cognition, frequencies, and temporal dynamics that may explain socio-communicative aberrancies.

Identifiants

pubmed: 38396447
pii: diagnostics14040408
doi: 10.3390/diagnostics14040408
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Deutsche Forschungsgemeinschaft
ID : DFG:SFB-TR-128
Organisme : Mainz University
ID : MU 4354/1-1

Auteurs

Julia Siemann (J)

Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Evangelical Hospital Bielefeld, 33617 Bielefeld, Germany.

Anne Kroeger (A)

Clinic of Child and Adolescent Psychiatry, Goethe-University of Frankfurt am Main, 60389 Frankfurt, Germany.

Stephan Bender (S)

Clinic of Child and Adolescent Psychiatry, Goethe-University of Frankfurt am Main, 60389 Frankfurt, Germany.
Department for Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.

Muthuraman Muthuraman (M)

Department of Neurology, Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), University Clinic Würzburg, 97080 Würzburg, Germany.

Michael Siniatchkin (M)

Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Evangelical Hospital Bielefeld, 33617 Bielefeld, Germany.
University Clinic of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany.

Classifications MeSH