November 30, 2020

Digital Biomarkers of Peripheral Nervous Systems Activities to Forecast Central Nervous Systems Functions

The field of Neuroscience has largely focused on the study of the brain. Indeed all major brain initiatives in the US and the EU will fund brain-centered research to address important medical challenges of the 21st Century. Such research relies heavily on the description and measurements of behaviors. Yet, Behavioral Neuroscience, whether related to humans, or to animal models, relies on subjective descriptions of observed behaviors. Indeed, when it comes to basic research and clinical practices, the study of behaviors has been more an art than a science. Although heavy reliance on descriptions of what we consciously perceive makes up much of what neuroscientists use to help interpret brain-related data (cortical/subcortical spikes, EEG, fMRI, MEG, optogenetics, OMICS-data, etc.), a considerable portion of what we do occurs largely beneath awareness. As such, neuroscientists do not include that behavioral data in their statistical inference. Interpretation of scientific results from brain activities harnessed from animals and humans tends to miss those hidden aspects of behavior that are not apparent to the naked eye. This weakness extends to drug development in pre-clinical trials. Consequently, many drug trials fail owing to the lack of objective assessment of the treatment’s outcomes.

There are no outcome measures reflecting the fluctuations in the states of the nervous systems during drug intake and / or the possible transfer and generalization of potentially positive gains to activities of daily living in the home environment. It follows that there are no outcome measures of risk reflecting adverse effects during or after the trial.

The advent of new wearable sensors has the chance to capture those subtle aspects of behaviors that hide beneath conscious observation. New biosensors and the digital biomarkers we can derive from their output are bound to transform the ways in which we study the brain today. These instruments can non-invasively simultaneously co-register many biorhythms that are self-generated by the nervous systems and be used to characterize activities underlying automatic and spontaneous motions that complement the voluntary performance of deliberate actions. Since the brain and body interact through closed feedback loops, by examining the bodily activities we can non-invasively gain insights into brain functioning.

Using the readout from the peripheral nervous systems across the body in motion, we can infer with high certainty not only what the brain is intending to do next, but also characterize the spontaneous activity that emerges as a consequence of those impending intentional acts. In this sense, we can forecast behavior and brain-strategies beyond biased or incomplete inferences that emerge from exclusively relying on conscious observation. We can build new generations of digital biomarkers of behavior that more fully inform us of various degrees of mental intent.

The new book Objective Biometric Methods for the Diagnosis and Treatment of Nervous System Disorders encompasses innovative methods of personalized and objective behavioral analyses precisely aimed at providing this new generation of digital biomarkers capable of forecasting the conscious and unconscious processes of the individual’s brain as the person interacts with the environment, and as the individual engages in social exchange.

We are pleased to offer you a free chapter from this book – access this content by clicking here – The Closed Feedback Loops Between the Peripheral and the Central Nervous Systems, the Principle of Reafference and Its Contribution to the Definition of the Self. If you find this content interesting, please continue reading and browse the entire book on ScienceDirect.

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Dr. Elizabeth Torres is a Computational Neuroscientist who has been working on theoretical and empirical aspects of sensory motor integration and human cognition since the late 90’s. She graduated from Mathematics and Computer Science and spent a year at the NIH as a Pre-IRTA fellow, applying her skill set to the medical field. This work led to Pre-doctoral-fellowship funding (5 years) of graduate school. During her PhD at UCSD, she developed a new theoretical framework for the study of sensory motor integration, employing elements of Differential (Riemannian) geometry and tensor calculus adapted from Contemporary Mechanics and Dynamics to the realm of Cognitive Neuroscience. Upon PhD completion, she moved to CALTECH to receive postdoctoral training in electrophysiology and Computational Neural Systems as a Sloan-Swartz Fellow, a Della Martin Fellow and a Neuroscience Scholar. In parallel, she translated her models to work with humans suffering from pathologies of the nervous systems and built a new platform for personalized analyses of human naturalistic behaviors. She joined Rutgers University in 2008 and deployed her new platform to work on neurodevelopmental disorders with a focus on issues with social interactions. Under an NSF Cyber Enabled Discovery Award, she then launched a transformative research program in autism seeking to build synergies with industry, funded by the NSF Innovation Corps initiative. She filed four patent technologies and with the generous funding of the Nancy Lurie Marks Family Foundation and the New Jersey Governor’s Council for the treatment and research of autism, she extended the new platform to study natural dyadic and social behaviors in general. Her lab’s vision has paved the way to seek new frontiers in personalized mobile-Health, dynamic diagnostics systems and new objectively-driven drug development for clinical trials. The overarching goal of her group is to create the means to quantify and track improvements in the person’s quality of life. Learn more here:

Neuroscience – SciTech Connect

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