Effect of mGluR2 positive allosteric modulation on frontostriatal working memory activation in schizophrenia.

Negative symptoms and cognitive deficits contribute strongly to disability in schizophrenia, and are resistant to existing medications. Recent drug development has targeted enhanced NMDA function by increasing mGluR2/3 signaling. However, the clinical utility of such agents remains uncertain, and markers of brain circuit function are critical for clarifying mechanisms and understanding individual differences in efficacy. We conducted a double-blind, placebo-controlled, randomized cross-over (14 day washout) pilot study evaluating adjunctive use of the mGluR2 positive allosteric modulator AZD8529 (80 mg daily for 3 days), in chronic stable patients with schizophrenia (n = 26 analyzed). We focused on 3 T fMRI response in frontostriatal regions during an n-back working memory task, testing the hypothesis that AZD8529 produces fMRI changes that correlate with improvement in negative symptoms and cognition. We found that AZD8529 did not produce significant group-average effects on symptoms or cognitive accuracy. However, AZD8529 did increase n-back fMRI activation in striatum (p < 0.0001) and anterior cingulate/paracingulate (p = 0.002). Greater drug-versus-placebo effects on caudate activation significantly correlated with greater reductions in PANSS negative symptom scores (r = -0.42, p = 0.031), and exploratory correlations suggested broader effects across multiple symptom domains and regions of interest. These findings demonstrate that fMRI responses to mGluR2 positive modulation relate to individual differences in symptom reduction, and could be pursued for future biomarker development. Negative clinical results at the group level should not lead to premature termination of investigation of this mechanism, which may benefit an important subset of individuals with schizophrenia. Imaging biomarkers may reveal therapeutic mechanisms, and help guide treatment toward specific populations.

Lexical use in emotional autobiographical narratives of persons with schizophrenia and healthy controls.

Language dysfunction has long been described in schizophrenia and most studies have focused on characteristics of structure and form. This project focuses on the content of language based on autobiographical narratives of five basic emotions. In persons with schizophrenia and healthy controls, we employed a comprehensive automated analysis of lexical use and we identified specific words and semantically or functionally related words derived from dictionaries that occurred significantly more often in narratives of either group. Patients employed a similar number of words but differed in lower expressivity and complexity, more self-reference and more repetitions. We developed a classification method for predicting subject status and tested its accuracy in a leave-one-subject-out evaluation procedure. We identified a set of 18 features that achieved 65.7% accuracy in predicting clinical status based on single emotion narratives, and 74.4% accuracy based on all five narratives. Subject clinical status could be determined automatically more accurately based on narratives related to anger or happiness experiences and there were a larger number of lexical differences between the two groups for these emotions compared to other emotions.