MRI/Dev.: Instrument that Monitors Behaviors Associated with OCD and Schizophrenia Project Proposed: This project, developing a new instrument to facilitate data collection associated with clinical assessment of complex mental health disorders such as obsessive-compulsive behaviors (known as OCD), aims to enable long-term research advances in computer vision, activity recognition and tridimensional reconstruction algorithms to automate the identification of behaviors typical of OCD subjects. The immersion of selected subjects in a virtual reality room (CAVE) is used to trigger specific behaviors to be captured and analyzed. The sophisticated sensor system under development will serve to collect these data and provide intelligent data processing capabilities that would enable future exploration and testing of new diagnostic and therapeutic protocols, leading to the establishment of the basis for, and utility in, seeking early at-risk markers in children and adolescents. This instrument initiative is based on the premise that expertise can accurately identify useful diagnostic markers and on the belief that technologies can now be developed to collect massive behavioral data in ways not previously done and discover behavioral patterns. The instrument is expected to - Provide extensive data collection associated with subjects diagnosed with the respective disorders (data useful not only to clinicians but also to computer vision and machine learning researchers among others), - Capture interactions, behaviors, and physiological reactions to real and/or synthetic multimodal stimuli (optical, acoustical, etc.), - Allow computer and information scientists to develop computational tools and algorithms to generate quantitative, adequate, and cost-effective norms for screening a broad population, - Enhance Cognitive Behavior Therapy (CBT) procedures and diagnostic protocols by integration of technologies that can excite or inhibit triggers for schizophrenic or OCD episodes, - Assess particular Augmented and Virtual Environments (AE/VEs) and social media devices (smart phones and tablets) and their impacts on the cognitive presence of normal versus afflicted subjects, and - Evaluate whether an enhanced cognitive presence via an AE/VE can increase or suppress (habituate) the intensity of behavioral symptoms detectable by sensors. Broader Impacts: Among these we have: - Creation of large and complex datasets that will enable computer scientists to apply the newest computational tools on them, Development of a potentially transformative technology-driven instrument for detecting early risk markers of OCD, - Exploration of a platform well-suited to new directions for a better characterization of mental disorders, - Systematic database development of quantified, multimodal data and a sounder and more precise basis for earlier detection, - Reduction of overall costs and a parallelizing reduction in the long-term costs due to previously delayed or incorrect diagnoses, - Reduction of anxiety, disruption, stress, and sometimes real tragedy on patients and their families, - Earlier detection and reduced need for drug-based, later stage interventions enabled by the ease of testing, and associated societal benefits, and - Student education and training in the use of the instrument.
