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. - Provides methods and their implementation using real data and simple computer programs that students and researchers from less technically trained fields can use - Describes the motivation for methods according to the problem domain in light of existing methods for each chapter, along with their lack of neuroscientific foundation and invalid statistical assumptions - Accompanied by a companion website which contains Appendices with MATLAB codes and data samples to generate the graphics displayed in all chapter figures - Features videos illustrating the experimental set up for scenarios and methods described in each chapter - Includes step-by-step explanations of paradigms in each clinical or typical sample population to enable reproducibility of the study across different clinical phenotypes and levels of expertise in sports, the performing arts, or mere individual academic predispositions/preferences

Dr. 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. Photo credit - Roy Groething. Video https://www.youtube.com/watch?v=e27da3rxnMg