17 February 2016: Towards Precision Psychiatry: Statistical Platform for the Personalized Characterization of Natural Behaviors

Elizabeth Torres
Psychology, Cognitive Science, Neuroscience, Biomedical Engineering
Computational Biomedical Imaging and Modeling
Rutgers University

There is a critical need for new analytics to personalize behavioral data analysis across different fields, including kinesiology, sports science and behavioral neuroscience. Specifically, to better translate and integrate basic research into patient care we need to radically transform the methods by which we describe and interpret movement data. We introduce a new statistical platform that enables the analyses of fluctuations in motor performance from natural movements and access various data sets to illustrate their use. These include data from newborns; data from large open-databases where we examine the effects of psychotropic medications on the volitional control of bodily rhythms and data from a large cohort involving various disorders of the nervous system. We show that hidden in the ‘noise’, smoothed out by averaging movement kinematics data, lies a wealth of information that can detect very early risk for neurodevelopmental derail/stagnation; and selectively differentiates neurological and mental disorders such as Parkinson’s disease (PD), deafferentation, Autism Spectrum Disorders (ASD), and Schizophrenia (SZ) from typically developing and typically aging controls. Further we show how to use these methods to assess medication effects on motor control. We empirically estimate the statistical parameters of the probability distributions for each individual in the various groups and report the parameter ranges for each clinical group after characterization of healthy developing and aging groups. We coin this newly proposed platform for individualized behavioral analyses ‘precision phenotyping’ to distinguish it from the type of observational-behavioral phenotyping prevalent in clinical studies or from the ‘one-size-fits-all’ model in basic movement science. We further propose the use of this platform as a unifying statistical framework to characterize brain disorders of known etiology in relation to idiopathic neurological disorders with similar phenotypic manifestations.

For background, please see: http://journal.frontiersin.org/article/10.3389/fneur.2016.00008/full