A new study details the use of brain imaging and virtual reality (VR) to look at cortical network activity to identify biomarkers for brain disorders. Using this method, researchers found differences between the functional connectivity of cortical networks of mice with autism spectrum disorder (ASD) and those of wild-type mice during changes in behavior related to motor activities. This tool found that ASD mice have hyperconnected, less modular cortical networks and show that motor areas provide contributing features for the classification of ASD.
The research article, titled “Virtual reality-based real-time imaging reveals abnormal cortical dynamics during behavioral transitions in a mouse model of autism,” was published in Cell Reports.
ASD classification with VR-based imaging
Functional connectivity, which is the time dependence of neuronal activity between anatomically separate brain regions, can be used to study how brain networks work in people with ASD.
Recent human functional magnetic resonance imaging (fMRI) studies have started to look at how dynamic resting-state functional connectivity can be used to find abnormal brain network activity that is unique to ASD. Typically, functional magnetic resonance imaging (fMRI) is used to measure functional connectivity as the amount of coactivation between spontaneous blood-oxygen level-dependent (BOLD) signals during rest or tasks that don’t require much movement.
However, people can’t move in an MRI scanner and BOLD signals in resting-state fMRI are slow, making it hard to study the motor coordination deficits and impairments of movement planning in goal-directed locomotion that are exhibited by individuals with ASD. Furthermore, accumulating evidence suggests that the sensorimotor difficulties seen in ASD are strongly associated with the development and maintenance of social and non-social core symptoms.
In this study, researchers from the RIKEN Brain Science Institute and the Kobe University School of Medicine sought to characterize the dynamics of functional cortical networks during locomotion initiation.
To do so, the Japanese research team used transcranial Ca2+ imaging with a head-fixed VR system to measure cortical functional connectivity in mice navigating a virtual environment of a real open-field enclosure that mimicked real-world situations. The imaging data was then looked at with several tools for analysis, such as a support vector machine (SVM) for machine learning to classify functional connectivity patterns.
The researchers used a copy number variation mouse model for human 15q11–13 duplication, which has social communication problems like people with ASD. These mice also have abnormal somatosensory tuning when they are asleep and whole-brain functional hypoconnectivity when they are awake and in a resting state.
They found that these mice had poor dynamics of functional connectivity that depended on how they moved, as well as abnormal patterns of functional connectivity in which the motor areas were overconnected. This shows how important motor areas are for cortical functional connectivity dysfunction during spontaneous behavioral switching in ASD.
These results show that real-time imaging systems that are based on virtual reality give important information that helps us understand how functional connectivity dynamics are linked to abnormal behavior in neuropsychiatric disorders. Also, it will be very interesting to find out in future studies if the abnormalities in functional connectivity seen in ASD model mice can be fixed by pharmacological treatment during postnatal development or adulthood. The researchers think it would be interesting to make a multimodal “metaverse” where mice interact with other mice of the same species through their avatars. This would help them figure out how cortical functional connectivity changes during virtual social interaction.
