System Identification Software for Cognitive Electrophysiology

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DESCRIPTION (provided by applicant): Cognitive-behavioral neuroscientists lack adequate computational tools for identifying linear and nonlinear dynamical system models, both deterministic and stochastic from human electrophysiological data, including electro- and magneto-encephalography. This makes it difficult or impossible to investigate systematically many scientifically and clinically significant phenomena. These phenomena include modification of evoked response components by preceding stimuli (e.g., response suppression or facilitation, recovery functions, sensory gating, echoic memory lifetimes, priming), modification by following stimuli (e.g., masking), multisensory evoked responses (e.g., auditory-visual facilitation), and reaction time dependence of sensory-related and motor-related brain responses (e.g., psychological refractory period). Commercial software tools to be developed under this SBIR project will enable cognitive-behavioral neurophysiologists to characterize these modulations of variable event-related transients within the framework of event-related Volterra modeling. These novel modeling tools will facilitate new experimental designs that harness a largely unexploited source of information about brain dynamics: variation of inter-event interval sequences. The software will be validated using simulated and experimental data, including a pilot study that will lay the basis for identifying candidate biomarkers for schizophrenia research. These tools will be integrated into our existing EMSE Suite software product using an FDA-compliant quality management process for use initially by basic and clinical neuroscience researchers. PUBLIC HEALTH RELEVANCE: Cognitive-behavioral neuroscientists lack adequate computational tools for identifying linear and nonlinear dynamical system models, from human electrophysiological data, making it difficult or impossible to investigate systematically many scientifically and clinically significant phenomena, including response suppression or facilitation, recovery functions, sensory gating, echoic memory lifetimes, and priming. Commercial software tools to be developed under this SBIR project will enable cognitive-behavioral neurophysiologists to characterize these modulations of variable event-related transients. The software will be validated using simulated and experimental data, including a pilot study that will lay the basis for identifying candidate biomarkers for schizophrenia research.