Intrinsic functional connectivity variance and state-specific under-connectivity in autism.

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Brain Connectivity

Background:

• Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by altered brain connectivity.
• Previous neuroimaging studies on functional connectivity in ASD have yielded inconsistent results, often attributed to static analytical approaches.
• Emerging research highlights the dynamic nature of brain connections, suggesting static metrics overlook crucial time-varying properties.

Purpose of the Study:

• To investigate differences in the strength and variance of dynamic functional connections between individuals with ASD and healthy controls (HCs).
• To explore the relationship between dynamic functional connectivity patterns and ASD symptom severity.
• To identify potential neuroimaging biomarkers for ASD using a dynamic functional connectivity approach.

Main Methods:

• Utilized a dynamic functional connectivity approach on a large, multicenter dataset (507 male subjects, 209 with ASD, 298 HC) from the Autism Brain Imaging Data Exchange (ABIDE).
• Identified six distinct whole-brain dynamic functional connectivity states.
• Analyzed the variance (standard deviation across time) and strength of these dynamic connections.

Main Results:

• Individuals with ASD exhibited greater variance in widespread long-range dynamic functional connections compared to HCs.
• Weaker dynamic functional connections were observed in ASD within specific whole-brain connectivity states.
• Increased variance in dynamic functional connections correlated with higher ASD symptom severity (ADOS total score), particularly involving the medial superior frontal gyrus and temporal pole.

Conclusions:

• Greater intraindividual dynamic variance in functional brain connectivity is a potential neuroimaging biomarker for ASD.
• Dynamic functional connectivity analysis offers a more nuanced understanding of brain alterations in ASD than static approaches.
• These findings contribute to identifying objective markers for ASD diagnosis and understanding its neural underpinnings.