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Browsing by Author "Silverstein, Steven M."
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Item Predicting Attention Shaping Response in People with Schizophrenia(Wolters Kluwer, 2021) Beaudette, Danielle M.; Gold, James M.; Waltz, James; Thompson, Judy L.; Cherneski, Lindsay; Martin, Victoria; Monteiro, Brian; Cruz, Lisa N.; Silverstein, Steven M.; Psychology, School of SciencePeople with schizophrenia often experience attentional impairments that hinder learning during psychological interventions. Attention shaping is a behavioral technique that improves attentiveness in this population. Because reinforcement learning (RL) is thought to be the mechanism by which attention shaping operates, we investigated if preshaping RL performance predicted level of response to attention shaping in people with schizophrenia. Contrary to hypotheses, a steeper attentiveness growth curve was predicted by less intact pretreatment RL ability and lower baseline attentiveness, accounting for 59% of the variance. Moreover, baseline attentiveness accounted for over 13 times more variance in response to attention shaping than did RL ability. Results suggest attention shaping is most effective for lower-functioning patients, and those high in RL ability may already be close to ceiling in terms of their response to reinforcers. Attention shaping may not be a primarily RL-driven intervention, and other mechanisms of its effects should be considered.Item Relationships Between Working Alliance and Outcomes in Group Therapy for People Diagnosed with Schizophrenia(Taylor & Francis, 2020) Beaudette, Danielle M.; Cruz, Lisa N.; Lukachko, Alicia; Roché, Matthew; Silverstein, Steven M.; Psychology, School of ScienceWorking alliance (WA) is an important predictor of treatment outcomes in therapy. Forming a strong WA can be challenging with people diagnosed with schizophrenia, and differences between client-rated and clinician-rated WA have been found in this population. This project examined WA in people diagnosed with schizophrenia who completed a skills training and attention shaping group intervention. Paired samples t-tests revealed differences between client and clinician ratings on the Working Alliance Inventory Short Form (WAI-S). Clinician-rated WAI-S scores were related to symptom severity, cognitive functioning, and attention during group sessions. Yet, the primary hypothesis was not supported as WAI-S scores were unrelated to clients’ treatment response. Clinician-rated WAI-S was found to partially mediate the relationship between negative symptoms and overall attention. Client-rated WAI-S scores were associated with client measures of self-efficacy and mastery. Results reinforce the importance of working alliance in the treatment of those diagnosed with schizophrenia and indicate clinical and functional factors that may influence the quality of WA.Item Toward Precision Psychiatry: Statistical Platform for the Personalized Characterization of Natural Behaviors.(Frontiers, 2016) Torres, Elizabeth B.; Isenhower, Robert W.; Nguyen, Jillian; Whyatt, Caroline; Nurnberger, John I.; Jose, Jorge V.; Silverstein, Steven M.; Papathomas, Thomas V.; Sage, Jacob; Cole, Jonathan; Department of Psychiatry, IU School of MedicineThere 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. Here, we show that hidden in the “noise,” smoothed out by averaging movement kinematics data, lies a wealth of information that selectively differentiates neurological and mental disorders such as Parkinson’s disease, deafferentation, autism spectrum disorders, and schizophrenia from typically developing and typically aging controls. In this report, we quantify the continuous forward-and-back pointing movements of participants from a large heterogeneous cohort comprising typical and pathological cases. We empirically estimate the statistical parameters of the probability distributions for each individual in the cohort 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.