Tracking statistical signals from both mental and physical activities
Cognitive Neuroscience research uses cortical-related activity to infer various functional and anatomical aspects of the brain. However, this is done in a rather disembodied way, discounting the feedback from bodily biorhythms. In contrast, fields like Behavioral Neuroscience examine bodily biorhythms without considering brain-related activity reflecting cognitive states. As such, we have either a ‘disembodied’ notion of the brain, or a ‘headless’ notion of the body in action.
Part of the problem is the lack of a unifying statistical platform that allows examination of the coupled dynamics of the brain and the body under a common scale. Indeed, a proper metric permits to track closed-loop brain-body interactions in real time and make inferences about the contributions of bodily biorhythms to brain activity (and vice-versa) in a personalized manner.
Scientists at Rutgers have developed a new analytical platform to study the brain-body interactions using real-time tracking of statistical signals from intentional thoughts and physical activities. In this invention, the stochastic trajectories of brain-, body- and coupled-network dynamics are tracked moment by moment to determine lead-lag interactions across self-emerging synergies as a common code is shared across the brain, the body and the coupled-network nodes (see Figure).
The information exchange across these networks are tracked within a phylogenetically orderly taxonomy of the nervous system that includes deliberate, spontaneous and inevitable process shared by the central, peripheral and autonomic nervous systems respectively.
Cross talks among these processes are indicative of plasticity, stability, controllability and predictability of mental-physical states. As such, it is possible to not only classify highly controllable and predictable states (normal condition) vs. dysregulated states (pathological condition); but also, to identify critical transitions across dissimilar states for quick detection of change. This is particularly relevant to track the evolution of nascent nervous systems under accelerated rates of change (e.g. young infants and children) as well as nervous systems under markedly non-uniform hormonal changes (puberty and male/female menopause). Further, detection of slow change in neurodegenerative disorders (e.g. Parkinson’s disease) is also possible under the common statistical framework assessing multiple layers of the nervous systems innervating the coupled brain - body closed loop networks.
Analytical Tool for:
- Age-dependent neurological diseases diagnostics
- Outcome metrics of treatment assessment
- Sleep, adaptive learning or sports tracking
- Wearable devices and laboratory settings
- Characterization of nervous systems (CNS, PNS-ANS)
- Common scale for personalized assessment
- High temporal resolution (in milliseconds)
- Naturalistic motions of daily living
Intellectual Property & Development Status:
Patent pending. Available for licensing and/or research collaboration.