Neural correlates of facial recognition deficits in autism spectrum disorder: a comprehensive review

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by significant impairments in social interaction, often manifested in facial recognition deficits. These deficits hinder individuals with ASD from recognizing facial identities and interpreting emotions, further complicat...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Frontiers in psychiatry Ročník 15; s. 1464142
Hlavní autori: Liu, Jianmei, Chen, Huihui, Wang, Haijing, Wang, Zhidan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Media S.A 06.01.2025
Predmet:
amygdala
ASD
facial recognition deficits
PFC
Psychiatry
STS
FG
facial recognition deficits
ASD
amygdala
STS
social cognition
PFC
ISSN:1664-0640, 1664-0640
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by significant impairments in social interaction, often manifested in facial recognition deficits. These deficits hinder individuals with ASD from recognizing facial identities and interpreting emotions, further complicating social communication. This review explores the neural mechanisms underlying these deficits, focusing on both functional anomalies and anatomical differences in key brain regions such as the fusiform gyrus (FG), amygdala, superior temporal sulcus (STS), and prefrontal cortex (PFC). It has been found that the reduced activation in the FG and atypical activation of the amygdala and STS contribute to difficulties in processing facial cues, while increased reliance on the PFC for facial recognition tasks imposes a cognitive load. Additionally, disrupted functional and structural connectivity between these regions further exacerbates facial recognition challenges. Future research should emphasize longitudinal, multimodal neuroimaging approaches to better understand developmental trajectories and design personalized interventions, leveraging AI and machine learning to optimize therapeutic outcomes for individuals with ASD.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
Makoto Wada, National Rehabilitation Center for Persons with Disabilities, Japan
Edited by: Margaret Lang Bauman, Boston University, United States
Reviewed by: Marco Leo, National Research Council (CNR), Italy
ISSN:1664-0640
1664-0640
DOI:10.3389/fpsyt.2024.1464142