Innovations In Clinical Neuroscience

NOV-DEC 2017

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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59 ICNS INNOVATIONS IN CLINICAL NEUROSCIENCE November-December 2017 • Volume 14 • Number 11–12 O R I G I N A L R E S E A R C H T The Positive and Negative Syndrome Scale (PANSS) has become the gold-standard assessment for examining the efficacy of antipsychotic medications in the treatment of psychotic disorders. 1 The PANSS has demonstrated sensitivity to change in many clinical trials to date. 2–7 Studies using the PANSS have used traditional univariate statistical approaches to examine treatment effects, demonstrating that several first- and second-generation antipsychotics reduce mean PANSS domain scores relative to baseline and placebo. 8,9 These studies support the utility of the PANSS as an outcome measure in clinical trials by focusing on the change in each individual symptom. However, less emphasis has been put on the collective behavior of symptoms (i.e., the impact of the symptoms on each other) and how changes in the interdependency patterns of symptoms could influence an individual's psychopathology (e.g., their resistance or responsiveness to the treatment, vulnerability to develop a comorbid disorder). In this regard, network science has gained a lot of attention in studying psychiatric disorders by considering them as a network of interacting components (symptoms) represented by G=(V,E), where V is the set of symptoms connected to each other by the set of edges E that represent the relationship between these symptoms (usually the statistical dependency between the measured intensity of symptoms). Representing the psychiatric disorders as networks allows us to analyze different patterns of interaction between these symptoms using various network properties. Network properties can be categorized as macroscopic, mesoscopic, and microscopic based on the type of information they provide. 15 Macroscopic properties, such as network density, characteristic path length, and average clustering coefficient, provide information about the overall connectedness of the network as a whole. Networks with a higher density, average clustering coefficient, and lower average shortest path length are tightly connected, which, regarding symptom networks, means that the symptoms are highly interdependent. The mesoscopic properties, on the other hand, characterize how different subsets of nodes in the network are connected with each other. The nodes that belong to one community tend to have a stronger connection with each other while having a weaker connection with the nodes in other communities. This notion of communities A B S T R A C T ABSTRACT: Objective: The Positive and Negative Syndrome Scale is a primary outcome measure in clinical trials examining the efficacy of antipsychotic medications. Although the Positive and Negative Syndrome Scale has demonstrated sensitivity as a measure of treatment change in studies using traditional univariate statistical approaches, its sensitivity to detecting network-level changes in dynamic relationships among symptoms has yet to be demonstrated using more sophisticated multivariate analyses. In the current study, we examined the sensitivity of the Positive and Negative Syndrome Scale to detecting antipsychotic treatment effects as revealed through network analysis. Design: Participants included 1,049 individuals diagnosed with psychotic disorders from the Phase I portion of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Of these participants, 733 were clinically determined to be treatment-responsive and 316 were found to be treatment-resistant. Item level data from the Positive and Negative Syndrome Scale were submitted to network analysis, and macroscopic, mesoscopic, and microscopic network properties were evaluated for the treatment-responsive and treatment-resistant groups at baseline and post-phase I antipsychotic treatment. Results: Network analysis indicated that treatment-responsive patients had more densely connected symptom networks after antipsychotic treatment than did treatment-responsive patients at baseline, and that symptom centralities increased following treatment. In contrast, symptom networks of treatment-resistant patients behaved more randomly before and after treatment. Conclusions: These results suggest that the Positive and Negative Syndrome Scale is sensitive to detecting treatment effects as revealed through network analysis. Its findings also provide compelling new evidence that strongly interconnected symptom networks confer an overall greater probability of treatment responsiveness in patients with psychosis, suggesting that antipsychotics achieve their effect by enhancing a number of central symptoms, which then facilitate reduction of other highly coupled symptoms in a network-like fashion. KEYWORDS: Schizophrenia, psychosis, antipsychotic, treatment resistance, network analysis, PANSS, Positive and Negative Syndrome Scale Sensitivity of the Positive and Negative Syndrome Scale (PANSS) in Detecting Treatment Effects via Network Analysis by FARNAZ ZAMANI ESFAHLANI, MS; HIROKI SAYAMA, DSc; KATHERINE FROST VISSER, MS; and GREGORY P. STRAUSS, PhD Ms. Zamani Esfahlani is a PhD candidate in the Systems Science program at the State University of New York at Binghamton. Dr. Sayama is the director of the Center for Collective Dynamics of Complex Systems (CoCo), professor in the Department of Systems Science and Industrial Engineering, and director of Graduate Programs in Systems Science at the State University of New York at Binghamton. Ms. Visser is a graduate student at the State University of New York at Binghamton. Dr. Strauss is an assistant professor at the State University of New York at Binghamton. Innov Clin Neurosci. 2017;14(11–12):59–67 FUNDING: No funding was provided for this study. DISCLOSURES: The authors have no conflicts of interest relevant to the content of this article. CORRESPONDENCE: Gregory P. Strauss, PhD; Email: gstrauss@uga.edu

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