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      Dopamine and Glutamate in Antipsychotic-Responsive Compared With Antipsychotic-Nonresponsive Psychosis: A Multicenter Positron Emission Tomography and Magnetic Resonance Spectroscopy Study (STRATA)

      1 , 2 , 3 , 1 , 3 , 4 , 2 , 5 , 1 , 2 , 3 , 6 , 7 , 2 , 8 , 3 , 1 , 1 , 2 , 9 , 1 , 2 , 10 , 10 , 7 , 6 ,   6 , 2 , 5 , 3 , 1 , 2 , 7 , 11 , 10 , 10 , 6 , 3 , 2 , 5 , 7 , 12 , 13 , 1 , 2 , 1 , 2 , 14
      Schizophrenia Bulletin
      Oxford University Press
      1H-MRS, PET, antipsychotic response, treatment resistance, schizophrenia

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          The variability in the response to antipsychotic medication in schizophrenia may reflect between-patient differences in neurobiology. Recent cross-sectional neuroimaging studies suggest that a poorer therapeutic response is associated with relatively normal striatal dopamine synthesis capacity but elevated anterior cingulate cortex (ACC) glutamate levels. We sought to test whether these measures can differentiate patients with psychosis who are antipsychotic responsive from those who are antipsychotic nonresponsive in a multicenter cross-sectional study. 1H-magnetic resonance spectroscopy ( 1H-MRS) was used to measure glutamate levels (Glu corr) in the ACC and in the right striatum in 92 patients across 4 sites (48 responders [R] and 44 nonresponders [NR]). In 54 patients at 2 sites (25 R and 29 NR), we additionally acquired 3,4-dihydroxy-6-[18F]fluoro- l-phenylalanine ( 18F-DOPA) positron emission tomography (PET) to index striatal dopamine function ( K i cer, min −1). The mean ACC Glu corr was higher in the NR than the R group after adjustment for age and sex ( F 1,80 = 4.27; P = .04). This was associated with an area under the curve for the group discrimination of 0.59. There were no group differences in striatal dopamine function or striatal Glu corr. The results provide partial further support for a role of ACC glutamate, but not striatal dopamine synthesis, in determining the nature of the response to antipsychotic medication. The low discriminative accuracy might be improved in groups with greater clinical separation or increased in future studies that focus on the antipsychotic response at an earlier stage of the disorder and integrate other candidate predictive biomarkers. Greater harmonization of multicenter PET and 1H-MRS may also improve sensitivity.

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          Most cited references76

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          The positive and negative syndrome scale (PANSS) for schizophrenia.

          The variable results of positive-negative research with schizophrenics underscore the importance of well-characterized, standardized measurement techniques. We report on the development and initial standardization of the Positive and Negative Syndrome Scale (PANSS) for typological and dimensional assessment. Based on two established psychiatric rating systems, the 30-item PANSS was conceived as an operationalized, drug-sensitive instrument that provides balanced representation of positive and negative symptoms and gauges their relationship to one another and to global psychopathology. It thus constitutes four scales measuring positive and negative syndromes, their differential, and general severity of illness. Study of 101 schizophrenics found the four scales to be normally distributed and supported their reliability and stability. Positive and negative scores were inversely correlated once their common association with general psychopathology was extracted, suggesting that they represent mutually exclusive constructs. Review of five studies involving the PANSS provided evidence of its criterion-related validity with antecedent, genealogical, and concurrent measures, its predictive validity, its drug sensitivity, and its utility for both typological and dimensional assessment.
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            Effectiveness of antipsychotic drugs in patients with chronic schizophrenia.

            The relative effectiveness of second-generation (atypical) antipsychotic drugs as compared with that of older agents has been incompletely addressed, though newer agents are currently used far more commonly. We compared a first-generation antipsychotic, perphenazine, with several newer drugs in a double-blind study. A total of 1493 patients with schizophrenia were recruited at 57 U.S. sites and randomly assigned to receive olanzapine (7.5 to 30 mg per day), perphenazine (8 to 32 mg per day), quetiapine (200 to 800 mg per day), or risperidone (1.5 to 6.0 mg per day) for up to 18 months. Ziprasidone (40 to 160 mg per day) was included after its approval by the Food and Drug Administration. The primary aim was to delineate differences in the overall effectiveness of these five treatments. Overall, 74 percent of patients discontinued the study medication before 18 months (1061 of the 1432 patients who received at least one dose): 64 percent of those assigned to olanzapine, 75 percent of those assigned to perphenazine, 82 percent of those assigned to quetiapine, 74 percent of those assigned to risperidone, and 79 percent of those assigned to ziprasidone. The time to the discontinuation of treatment for any cause was significantly longer in the olanzapine group than in the quetiapine (P<0.001) or risperidone (P=0.002) group, but not in the perphenazine (P=0.021) or ziprasidone (P=0.028) group. The times to discontinuation because of intolerable side effects were similar among the groups, but the rates differed (P=0.04); olanzapine was associated with more discontinuation for weight gain or metabolic effects, and perphenazine was associated with more discontinuation for extrapyramidal effects. The majority of patients in each group discontinued their assigned treatment owing to inefficacy or intolerable side effects or for other reasons. Olanzapine was the most effective in terms of the rates of discontinuation, and the efficacy of the conventional antipsychotic agent perphenazine appeared similar to that of quetiapine, risperidone, and ziprasidone. Olanzapine was associated with greater weight gain and increases in measures of glucose and lipid metabolism. Copyright 2005 Massachusetts Medical Society.
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              Estimation of metabolite concentrations from localized in vivo proton NMR spectra.

              The LCModel method analyzes an in vivo spectrum as a Linear Combination of Model spectra of metabolite solutions in vitro. By using complete model spectra, rather than just individual resonances, maximum information and uniqueness are incorporated into the analysis. A constrained regularization method accounts for differences in phase, baseline, and lineshapes between the in vitro and in vivo spectra, and estimates the metabolite concentrations and their uncertainties. LCModel is fully automatic in that the only input is the time-domain in vivo data. The lack of subjective interaction should help the exchange and comparison of results. More than 3000 human brain STEAM spectra from patients and healthy volunteers have been analyzed with LCModel. N-acetylaspartate, cholines, creatines, myo-inositol, and glutamate can be reliably determined, and abnormal levels of these or elevated levels of lactate, alanine, scyllo-inositol, glutamine, or glucose clearly indicate numerous pathologies. A computer program will be available.

                Author and article information

                Schizophr Bull
                Schizophr Bull
                Schizophrenia Bulletin
                Oxford University Press (US )
                March 2021
                10 September 2020
                10 September 2020
                : 47
                : 2
                : 505-516
                [1 ] Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London , London, UK
                [2 ] NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust , London, UK
                [3 ] Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester , Manchester, UK
                [4 ] Academic Unit of Radiology, Medical School, Faculty of Medicine, Dentistry & Health, University of Sheffield , Sheffield, UK
                [5 ] Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London , London, UK
                [6 ] Division of Psychology and Mental Health, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester , Manchester, UK
                [7 ] Division of Psychiatry, University of Edinburgh , Edinburgh, UK
                [8 ] Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London , London, UK
                [9 ] Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne , Parkville, Victoria, Australia
                [10 ] CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University , Cardiff, UK
                [11 ] Centre for Cardiovascular Science, University of Edinburgh , Edinburgh, UK
                [12 ] MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University , Cardiff, UK
                [13 ] Division of Informatics, Imaging and Data Sciences, University of Manchester , Manchester, UK
                [14 ] Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital , London, UK
                Author notes
                To whom correspondence should be addressed; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AF, UK; tel: (+44)-207-848-0721, fax: +44 207 848 0976, e-mail: Alice.Egerton@ 123456kcl.ac.uk

                J.H.M. and O.D.H. share senior authorship.

                Author information
                © The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 12
                Funded by: Medical Research Council, DOI 10.13039/501100000265;
                Funded by: National Institute for Health Research, DOI 10.13039/501100000272;
                Funded by: Specialist Biomedical Research Center for Mental Health;
                Funded by: Institute of Psychiatry, Psychology and Neuroscience, DOI 10.13039/100013376;
                Funded by: NHS;
                Funded by: King’s College London, DOI 10.13039/100009360;
                Funded by: Department of Health, DOI 10.13039/501100003921;
                Regular Articles

                1h-mrs,pet,antipsychotic response, treatment resistance,schizophrenia
                1h-mrs, pet, antipsychotic response, treatment resistance, schizophrenia


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