Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational...

Area of Science:

• Neuroscience
• Computational Psychiatry
• Developmental Neuroscience

Background:

• Autism spectrum disorder (ASD) symptoms may be influenced by the interplay between functional connectivity (FC) and neural excitability.
• Understanding these neural dynamics is crucial for developing effective interventions.

Purpose of the Study:

• To investigate the interaction between FC and neural excitability in the context of ASD.
• To model cognitive alterations in ASD using neural network simulations and validate findings with fMRI data.

Main Methods:

• A hierarchical recurrent neural network model based on predictive processing theory was used for simulations.
• The model performed a facial emotion recognition task under varying conditions of FC and neural excitability.
• Functional magnetic resonance imaging (fMRI) data from ASD and neurotypical subjects were analyzed and mapped to simulation conditions.

Main Results:

• Simulations showed that homogeneous neural excitability in lower-level networks led to ASD-like performance, characterized by reduced generalization and emotion recognition.
• An interaction between FC and neural excitability was observed: higher FC exacerbated ASD-like performance in homogeneous networks but ameliorated it in heterogeneous networks.
• fMRI data confirmed a higher prevalence of ASD in subject subgroups corresponding to simulated conditions exhibiting ASD-like performance.

Conclusions:

• The study demonstrates a significant interaction between functional connectivity and neural excitability in influencing cognitive functions relevant to ASD.
• A novel computational framework integrating neural network simulations and biological data is proposed for understanding developmental learning processes in ASD.
• Findings suggest that specific patterns of neural excitability and FC may underlie cognitive alterations observed in autism spectrum disorder.