 | Objective Biometric Methods for the Diagnosis and Treatment of Nervous System Disorders Objective Biometric Methods for the Diagnosis and Treatment of Nervous System Disorders provides a new and unifying methodological framework, introducing new objective biometrics to characterize patterns of sensory motor control underlying symptoms. Its goal is to radically transform the ways in which disorders of the nervous system are currently diagnosed, tracked, researched and treated. This book introduces new ways to bring the laboratory to the clinical setting, to schools and to settings of occupational and physical therapy. Ready-to-use, graphic user interfaces are introduced to provide outcome measures from wearable sensors that automatically assess in near real time the effectiveness of interventions. Lastly, examples of how the new framework has been effectively utilized in the context of clinical trials are provided. Dr. Torres is a Computational Neuroscientist who has been working on computational aspects of sensory motor integration since the late 90s. She graduated from Mathematics and Computer Science and spent a year at the NIH as a Pre-IRTA fellow working on various computational models of Cognition which led to a Pre-doctoral fellowship funding 5 years of graduate school. During her PhD, she developed a new theoretical framework for the study of sensory motor integration using Differential Geometry and in particular elements of Riemannian geometry and tensor calculus used in Contemporary Mechanics and Dynamics. Here Dr. Torres provided a model for movement in closed loop with cognition, targeting volitional control during intentional behaviors of the nervous system. She went on to CALTECH to receive postdoctoral training in electrophysiology and Computational Neural Systems. During those years, she was a Sloan-Swartz Fellow, a Della Martin Fellow and a Neuroscience Scholar. In parallel she worked on translational applications of her models to address issues in patients with Parkinson's disease, stroke, and neuropathy. After joining Rutgers University in 2008, she applied a stochastic extension of such models to autism spectrum disorders and schizophrenia-related disorders with a focus on spontaneous and autonomic behaviors. The new extension characterizes a readout of somatosensation in spontaneous fluctuations of behaviors so as to enable the tracking of performance even in non-verbal individuals. Under an NSF Cyber Enabled Discovery Award, she developed a new set of objective biomarkers to classify autism and Parkinson's severity, discriminate gender-based differences, track treatment outcomes in real time and induce longitudinal changes in sensory motor control. This framework deals well with the heterogeneity of disorders on a spectrum and allows the design of personalized treatments. The outcome of this work has resulted in multiple ongoing collaborative efforts across various institutions that use the metrics developed in my laboratory to assess natural behaviors in neurological disorders such as Parkinson's disease, autism, and mental illnesses such as schizophrenia and bipolar disorder (e.g. UCSD, IU, UMDNJ, the Mount Sinai Medical School NYC). Recently non-profit research in industry (SRI-International at Princeton, NJ branch) has also initiated a collaborative link resulting as well in a sub-contract with the DARPA groups leading the Strategic Social Interactions Module. Two patents have been filed at Rutgers University Office of Technology Transfer for a diagnostic tool, the tracking of treatments in disorders on a spectrum as well as for the development of a new science of sports training, the performing arts and the objective study of perception in brain sciences. Her group is at present investigating the use of their platform on clinical trials for a radically different personalized approach to the evaluation of drug effectiveness vs. drawbacks. |
