Using Virtual Reality to Investigate Autism’s Neural Network Dynamics

An international research collaboration has developed a VR imaging system that can measure a wide range of neural activity in the cortices of mice during active behavior. This enabled them to illuminate the abnormalities in cortical functional network dynamics that are found in autism model mice.

Using machine learning, they were also able to highly accurately distinguish between autism model mice and wild-type mice based on the cortical functional network patterns when the mice start or stop running.

The research group was led by Professor Toru Takumi and Assistant Professor Nobuhiro Nakai (both of the Department of Physiology, Kobe University Graduate School of Medicine), and Masaaki Sato, (Lecturer, Department of Pharmacology, Graduate School of Medicine, Hokkaido University). Professor Takumi is also a Visiting Senior Scientist at RIKEN Center for Biosystems Dynamics Research.

Future research on functional brain network dynamics in autism is expected to lead to the development of new biomarkers for autism diagnosis.

The results of this research will be published in Cell Reports

Main Points

The researchers developed a VR imaging system that can measure a wide range of cortical activity from mice in action.

Mice models of autism have dense cortical functional networks and reduced modularity after motor initiation.

Machine learning can highly accurately identify autism model mice from their cortical functional network patterns.

Research Background

Autism (autism spectrum disorder) is a neurodevelopmental disorder with many unexplored aspects, characterized by poor social communication, intense preoccupation with certain things, and repetitive behaviors. The number of autistic individuals is markedly increasing, which is considered to be a significant social issue.

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