Guest Lecture by Dr. Levin Kuhlmann, Monash University

Despite decades of research on the neurobiological correlates of global states, or level, of consciousness, the role of dynamics, connectivity and stability of cortical local circuits remain unclear. To address this issue, we used a whole-cortex computational model, which consists of local neural mass models that are fully connected with each other. Parameters of the local mass models and inter-regional connectivity can be uniquely constrained by observed neural dynamics and inferred in a space-time resolved fashion across cortex using novel inference methods that allow for sample by sample correlation analysis between behavioural and neural variable time-series. This study applied this framework to the magnetoencephalography data from human subjects who were performing a behavioral task until they lost consciousness due to Xenon anaesthesia and recovered from it. Notable correlates with loss of consciousness were related to the posterior parietal and occipital regions, in terms of both inter-regional connectivity and regional parameters. Additionally, levels of consciousness were negatively correlated with dynamic cortical stability defined in the context of dynamical systems theory, suggesting greater brain flexibility when awake. Regional parameters, rather than inter-regional connectivity, had more influence on dynamic cortical stability. The posterior parietal and prefrontal regions also demonstrated an impact on steering dynamic cortical stability. This study offers new insights into the time-resolved neurobiological mechanism correlates of consciousness by examining local dynamics and inter-regional connectivity using mathematical models applied to human experimental data.

Levin Kuhlmann, PhD, is a data scientist, computational neuroscientist and neural engineer. His research areas include data science, machine learning, signal processing, control theory and computational neuroscience applications to digital health, neural engineering and neuroimaging.

Lern more about his research here.