Autism is a less explored neurodevelopmental disorder characterized by poor social communication, intense preoccupation with certain things, and repetitive behaviors. The number of autistic individuals is markedly increasing, which is a significant social issue.
Even now, autism diagnosis is based on behavioral characteristics, which is far from a quantitative perspective, and there is great demand for the discovery of a new biomarker. In recent years, research has been conducted to identify functional brain abnormalities unique to autistic individuals.
These studies suggest that the density of functional brain networks increases in young autistic individuals and decreases in adults. However, these changes vary widely from individual to individual.
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As the analysis was conducted when the participants were in a resting state, it was unclear how abnormalities in functional brain networks affect behavior. Genetics also contribute significantly to autism. Recently, animals (mainly mice) modeling human genomic aberrations are often used to elucidate the neuropathology of autism.
In the new study published in the journal Cell Reports, researchers developed a VR imaging system that can measure the brain activity of autism model mice in real-time during active behavior. By investigating brain functional network dynamics, they aimed to clarify autism-specific phenomena in the brain during behavior.
Virtual Reality-Based Real-Time Imaging Reveals Abnormal Cortical Dynamics in Autism
First, the virtual space was prepared so that it reproduced the field used for mouse behavioral experiments. Alongside behavioral measurements was performed simultaneously so that a wide range of functional area activity in the cerebral cortex could be measured in real time.
They calculated correlations between functional areas from one-second neural activity data obtained via imaging, and visualized the functional network using graph theory. Later, they analyzed the 3 second time windows before and after when the mouse spontaneously started or stopped moving on the treadmill (locomotion) and examined the network characteristics in each time window.
The results revealed that the network structure changes with the onset of locomotion and that modularity increases. It was also found that the network structure returns to the resting state when locomotion is stopped. Thus, they succeeded in visualizing the network dynamics during the switch from rest to locomotion and from locomotion to rest.
Next, the researchers used this VR imaging system to analyze the functional cortical network of autism model mice. Examination of the functional cortical network revealed higher network connections after locomotion initiation, decreased network centrality, and decreased modularity of the functional network.
These results suggest that the functional brain network during behavior contains versatile information about the genotype identification. The researchers also examined which information was influential in the brain and found that functional connectivity in the motor cortex was essential for identification in autism model mice.
Machine learning can identify autism model mice in a highly accurate manner based on their functional cortical network patterns that are associated with behavioral changes. Detailed studies of these anatomical connections and neurophysiology will help elucidate which networks between the motor cortex and other brain regions play critical roles in autism pathology.
In addition, further research on the functional brain network dynamics of autism during active behavior is expected to lead to the discovery of new biomarkers for the diagnosis of autism.
Source: Eurekalert
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