Atypical hierarchical brain connectivity in autism: Insights from stepwise causal analysis using Liang information...

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Computational Neuroscience

Background:

• Autism spectrum disorder (ASD) is characterized by atypical brain connectivity.
• The hierarchical organization of this connectivity in ASD is not well understood.
• Novel analytical methods are needed to explore brain network interactions in ASD.

Purpose of the Study:

• To investigate hierarchical causal functional connectivity in autism spectrum disorder.
• To identify specific brain networks and hierarchical levels affected by connectivity alterations in ASD.
• To assess the potential of hierarchical connectivity features for ASD classification and symptom correlation.

Main Methods:

• Applied the Liang information flow method to analyze stepwise causal functional connectivity.
• Utilized resting-state functional magnetic resonance imaging (fMRI) data from individuals with ASD and healthy controls.
• Examined alterations in positive and negative causal connections across key brain networks.

Main Results:

• Observed significant disruptions in causal connectivity across the ventral attention, limbic, frontal-parietal, and default mode networks at multiple hierarchical levels.
• Hierarchical causal connectivity features achieved high classification accuracy between ASD and control groups.
• Changes in network node degrees correlated with ASD clinical symptoms, especially social and communication deficits.

Conclusions:

• Disrupted hierarchical brain connectivity is a key feature of autism spectrum disorder.
• The Liang information flow method offers a novel approach to understanding ASD neurobiology.
• Hierarchical causal connectivity analysis shows promise for objective ASD diagnosis and understanding symptom relationships.