An international collaboration has developed a VR imaging system that can measure a wide range of neural activity in the cortices of mice during activity, say researchers at Kobe University.
The study helped show the abnormalities in cortical functional network dynamics found in autism model mice. Using machine learning, the researchers were also able to 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 results of this research were published in Cell Reports in March 2023.
Autism (autism spectrum disorder) is a neurodevelopmental disorder with many unexplored aspects, characterised by poor social communication, intense preoccupation with certain things, and repetitive behaviours.
The number of autistic individuals is markedly increasing, and is considered to be a significant social issue. Even now, an autism diagnosis is based on behavioural characteristics, which is far from a quantitative perspective, and there is great demand for the discovery of a new biomarker.
Recent research has sought to identify functional brain abnormalities unique to autistic individuals. Resting-state fMRI 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. As the analysis was conducted when the participants were in a resting state, it was unclear how abnormalities in functional brain networks affect behaviour.
Genetics contribute significantly to autism, and genomic abnormalities such as copy number variations (CNV) are thought to be involved in neuropathology. Recently, animals (mainly mice) modelling human genomic aberrations are often used to elucidate the neuropathology of autism.
In this study, the researchers developed a VR imaging system that can measure the brain activity of autism model mice in real-time during active behaviour. By investigating brain functional network dynamics, they aimed to clarify autism-specific phenomena in the brain during behaviour.
A VR imaging system was constructed and a mouse with its head fixed in place was put on a treadmill and shown an image of a virtual space projected on a screen. The virtual space reproduced the field used for mouse behavioural experiments. The mice were allowed to freely explore the virtual space while a wide range of functional area activity in the cerebral cortex was measured in real time.
The result was a successful visualisation of the network dynamics during the switch from rest to locomotion and from locomotion to rest.
The VR imaging system then analysed 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.
Based on these differences in network patterns, the researchers attempted to identify autism model mice by cortical function networks using support vector machines (SVM), a type of machine learning. The SVM was able to distinguish whether individual test data was from an autism model mouse or not with a 78%~89% accuracy rate. This result suggests that the functional brain network during behaviour 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.
In summary, the autism model mice, had a dense functional cortical network during locomotion and reduced modularity. The researchers also found that machine learning can identify autism model mice in a highly accurate manner based on their functional cortical network patterns that are associated with behavioural changes.
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.
The functional brain network in mouse models of autism is characterised by the functional connectivity of the motor cortex, which is crucial for determining autism. 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 behaviour is expected to lead to the discovery of new biomarkers for the diagnosis of autism.
By analysing the extensive cortical activity recorded from active mice, the researchers were able to visualize the dynamic behaviour-dependent changes in the functional cortical network of the brain. VR allows for the creation of multimodal environments that utilise multiple sensory information, including visual, auditory, and olfactory senses. Since a significant symptom of autism in people is impaired social communication, the researchers would like to construct a social environment for mice in the virtual space and investigate how the functional network dynamics change when autism model mice perform social behaviours.