Literature DB >> 26315980

Baseline Striatal Functional Connectivity as a Predictor of Response to Antipsychotic Drug Treatment.

Deepak K Sarpal1, Miklos Argyelan1, Delbert G Robinson1, Philip R Szeszko1, Katherine H Karlsgodt1, Majnu John1, Noah Weissman1, Juan A Gallego1, John M Kane1, Todd Lencz1, Anil K Malhotra1.   

Abstract

OBJECTIVE: Clinical response to antipsychotic drug treatment is highly variable, yet prognostic biomarkers are lacking. The authors recently demonstrated that successful antipsychotic drug treatment alters resting-state functional connectivity of the striatum. The goal of the present study was to test whether intrinsic striatal connectivity patterns provide prognostic information and can serve as a potential biomarker of treatment response to antipsychotic drugs.
METHOD: The authors used resting-state functional MRI (fMRI) to develop a prognostic index in a discovery cohort of 41 first-episode schizophrenia patients, then tested this index in an independent cohort of 40 newly hospitalized chronic patients with acute psychosis. In the discovery cohort, patients underwent resting-state fMRI scanning at the initiation of randomized controlled treatment with a second-generation antipsychotic. Whole-brain functional connectivity maps were generated for each subject from striatal seed regions. A stringent measure of clinical response was calculated that required sustained improvement over two consecutive study visits. Clinical response was entered into a survival analysis, and Cox regression was applied to the functional connectivity data. A striatal connectivity index was created, comprising functional connections of the striatum that predicted treatment response. This striatal connectivity index was tested on a generalizability cohort of patients with psychotic disorders who were hospitalized for an acute psychotic episode.
RESULTS: A total of 91 regions functionally connected with the striatum provided significant prognostic information. Connectivity in these regions was used to create a baseline striatal connectivity index that predicted response to antipsychotic treatment with high sensitivity and specificity in both the discovery and generalizability cohorts.
CONCLUSIONS: These results provide evidence that individual differences in striatal functional connectivity predict response to antipsychotic drug treatment in acutely psychotic patients. With further development, this has the potential to serve as a prognostic biomarker with clinical utility and to reduce the overall burden associated with psychotic illnesses.

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Year:  2015        PMID: 26315980      PMCID: PMC4845897          DOI: 10.1176/appi.ajp.2015.14121571

Source DB:  PubMed          Journal:  Am J Psychiatry        ISSN: 0002-953X            Impact factor:   18.112


  34 in total

1.  Early prediction of antipsychotic response in schizophrenia.

Authors:  Christoph U Correll; Anil K Malhotra; Saurabh Kaushik; Marjorie McMeniman; John M Kane
Journal:  Am J Psychiatry       Date:  2003-11       Impact factor: 18.112

2.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

Authors:  Jonathan D Power; Kelly A Barnes; Abraham Z Snyder; Bradley L Schlaggar; Steven E Petersen
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3.  Predictors of treatment response from a first episode of schizophrenia or schizoaffective disorder.

Authors:  D G Robinson; M G Woerner; J M Alvir; S Geisler; A Koreen; B Sheitman; M Chakos; D Mayerhoff; R Bilder; R Goldman; J A Lieberman
Journal:  Am J Psychiatry       Date:  1999-04       Impact factor: 18.112

4.  D2 receptor genetic variation and clinical response to antipsychotic drug treatment: a meta-analysis.

Authors:  Jian-Ping Zhang; Todd Lencz; Anil K Malhotra
Journal:  Am J Psychiatry       Date:  2010-03-01       Impact factor: 18.112

5.  Brain imaging correlates of depressive symptom severity and predictors of symptom improvement after antidepressant treatment.

Authors:  Chi-Hua Chen; Khanum Ridler; John Suckling; Steve Williams; Cynthia H Y Fu; Emilio Merlo-Pich; Ed Bullmore
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6.  Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

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Jessica Singleton; David A Sleet; Karen Sliwa; Emma Smith; Jennifer L Smith; Nicolas J C Stapelberg; Andrew Steer; Timothy Steiner; Wilma A Stolk; Lars Jacob Stovner; Christopher Sudfeld; Sana Syed; Giorgio Tamburlini; Mohammad Tavakkoli; Hugh R Taylor; Jennifer A Taylor; William J Taylor; Bernadette Thomas; W Murray Thomson; George D Thurston; Imad M Tleyjeh; Marcello Tonelli; Jeffrey A Towbin; Thomas Truelsen; Miltiadis K Tsilimbaris; Clotilde Ubeda; Eduardo A Undurraga; Marieke J van der Werf; Jim van Os; Monica S Vavilala; N Venketasubramanian; Mengru Wang; Wenzhi Wang; Kerrianne Watt; David J Weatherall; Martin A Weinstock; Robert Weintraub; Marc G Weisskopf; Myrna M Weissman; Richard A White; Harvey Whiteford; Steven T Wiersma; James D Wilkinson; Hywel C Williams; Sean R M Williams; Emma Witt; Frederick Wolfe; Anthony D Woolf; Sarah Wulf; Pon-Hsiu Yeh; Anita K M Zaidi; Zhi-Jie Zheng; David Zonies; Alan D Lopez; Christopher J L Murray; Mohammad A AlMazroa; Ziad A Memish
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9.  Cortical folding defects as markers of poor treatment response in first-episode psychosis.

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Journal:  JAMA Psychiatry       Date:  2013-10       Impact factor: 25.911

10.  Clinical, functional, and economic ramifications of early nonresponse to antipsychotics in the naturalistic treatment of schizophrenia.

Authors:  Haya Ascher-Svanum; Allen W Nyhuis; Douglas E Faries; Bruce J Kinon; Robert W Baker; Anantha Shekhar
Journal:  Schizophr Bull       Date:  2007-12-21       Impact factor: 9.306

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2.  What is the risk-benefit ratio of long-term antipsychotic treatment in people with schizophrenia?

Authors:  Christoph U Correll; Jose M Rubio; John M Kane
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Authors:  Goda Tarcijonas; Deepak K Sarpal
Journal:  Neurobiol Dis       Date:  2018-06-25       Impact factor: 5.996

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10.  Structural similarity networks predict clinical outcome in early-phase psychosis.

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Journal:  Neuropsychopharmacology       Date:  2019-01-24       Impact factor: 7.853

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