Innovations In Clinical Neuroscience

NOV-DEC 2017

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

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66 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 secondary features in terms of treatment response to these other symptoms, which, when improved, spur improvements in positive symptoms. Why these particular symptoms would be so central is unclear, and future studies are needed to replicate and extend these findings by detecting pathophysiological mechanisms that allow for improvements in these symptoms to precipitate changes in the entire symptom network. Knowing that these symptoms are central suggests that adjunctive treatment with other types of medication (e.g., mood stabilizers, antidepressants, anxiolytics) might be an appropriate approach to facilitating rapid response in patients who will respond to antipsychotics. In contrast, treatment- resistant patients had a different pattern of symptom centrality, with Suspiciousness/ Persecution, Hostility, Depression, and Passive/ Apathetic Social Withdrawal being the most central ones. These are more classic symptoms, suggesting that these features were core to the treatment-resistant patients' pathology and unchanged by antipsychotic treatment. Their dense connections with other symptoms might explain why they are so resistant to treatment. Essentially, these patients might require more momentum to experience global symptom improvement, since all other symptoms hinge on the improvement of these positive symptoms. These findings extend prior work on antipsychotic treatment resistance in several ways. Prior studies indicate that factors such as obstetric complications, age of onset of psychosis, age of first antipsychotic treatment, premorbid functioning, substance abuse history, male sex, family history of schizophrenia, and number of hospitalizations predict treatment resistance. 25,26 The current findings provided novel evidence that treatment response might best be considered from a network perspective, rather than conceptualizing individual symptoms or symptom domains (e.g., positive, negative, disorganized) as mutually exclusive entities. Antipsychotic treatment response appears to be the result of interactions among a range of interacting symptoms that traverse traditional symptom dimensions. Moreover, consistent with prior studies of treatment- resistant schizophrenia, our microscopic analysis suggests that negative and general psychotic symptoms (e.g., excitement, hostility, depression) could be as important as positive symptoms (traditionally known as predictors of treatment-resistant schizophrenia) in better understanding the psychopathology of treatment-resistant schizophrenia patients. 26,27 Certain limitations should be considered. First, we wanted our findings to be broadly applicable to the use of antipsychotics. We did not evaluate network dynamics resulting from individual antipsychotics in the CATIE study; however, this could be an important future direction because the antipsychotics examined in the trial do have some differences in the pathophysiological mechanisms they target. Second, the macroscopic and mesoscopic analyses do not allow for a statistical comparison of differences among treatment- responsive and -resistant groups. In this study, this is due to the nature of data where one single value is obtained for the whole network from macroscopic and microscopic analysis. Nonetheless, the information obtained from these analyses is essential to gain insight into the network structure of psychotic disorders and subsequently develop appropriate fine- grained analysis. Finally, our results are specific to the phase of illness studied in CATIE. Future studies should take a network science approach to exploring treatment response in the first episode and in prodromal participants as well. The current findings suggest that the PANSS might be an optimal measure for detecting treatment effects from a network perspective. REFERENCES 1. Kay SR, Fiszbein A, Opler LA. The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13(2):2: 261–276. 2. Furukawa TA, Levine SZ, Tanaka S, et al. Initial severity of schizophrenia and efficacy of antipsychotics: participant-level meta-analysis of 6 placebo-controlled studies. JAMA Psychiatry. 2015;72(1):14–21. 3. Johnsen E, Kroken RA, Wentzel-Larsen T, et al. Effectiveness of second-generation antipsychotics: a naturalistic, randomized comparison of olanzapine, quetiapine, risperidone, and ziprasidone. BMC Psychiatry. 2010;10:10–26. 4. Leucht S, Arbter D, Engel R, et al. How effective are second-generation antipsychotic drugs? a meta-analysis of placebo-controlled trials. Molecular psychiatry. 2009;14(4):429–447. 5. Lieberman JA, Stroup TS, McEvoy JP, et al. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N Engl J Med. 2005;353(12):1209–1223. 6. McEvoy JP, Lieberman JA, Stroup TS, et al. Effectiveness of clozapine versus olanzapine, quetiapine, and risperidone in patients with chronic schizophrenia who did not respond to prior atypical antipsychotic treatment. Am J Psychiatry. 2006;163(4):600–610. 7. Stroup TS, Lieberman JA, McEvoy JP, et al. Effectiveness of olanzapine, quetiapine, and risperidone in patients with chronic schizophrenia after discontinuing perphenazine: a CATIE study. Am J Psychiatry. 2007;164(3):415–427. 8. Zacher JL, Holmes JC. Second-generation antipsychotics: A review of recently-approved agents and drugs in the pipeline. Formulary. 2012;47:106–121. 9. Citrome L. A review of aripiprazole in the treatment of patients with schizophrenia or bipolar I disorder. Neuropsychiatr Dis Treat. 2006;2(4):427–443. 10. Frewen PA, Schmittmann VD, Bringmann LF, et al. Perceived causal relations between anxiety, posttraumatic stress and depression: extension to TABLE 3. Kolmogorov-Smirnov test results for microscopic network properties TIME POINTS KOLMOGOROV-SMIRNOV-VALUES CLOSENESS CENTRALITY DEGREE CENTRALITY Treatment-resistant (baseline) vs. treatment-resistant (18-month follow-up) 0.17 0.17 Treatment-responsive (baseline) vs. treatment-responsive (18-month follow-up) 0.33* 0.47** Treatment-resistant (baseline) vs. treatment-responsive (baseline) 0.93*** 0.87*** Treatment-resistant (18-month follow-up) vs. treatment- responsive (18-month follow-up) 0.77*** 0.77*** *p<0.09, **p<0.01, ***p<0.0001

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