Neural markers of negative symptom outcomes in distributed working memory brain activity of antipsychotic-naive schizophrenia patients

Abstract

Since working memory deficits in schizophrenia have been linked to negative symptoms, we tested whether features of the one could predict the treatment outcome in the other. Specifically, we hypothesized that working memory-related functional connectivity at pre-treatment can predict improvement of negative symptoms in antipsychotic-treated patients. Fourteen antipsychotic-naive patients with first-episode schizophrenia were clinically assessed before and after 7 months of quetiapine monotherapy. At baseline, patients underwent functional magnetic resonance imaging while performing a verbal n-back task. Spatial independent component analysis identified task-modulated brain networks. A linear support vector machine was trained with these components to discriminate six patients who showed improvement in negative symptoms from eight non-improvers. Classification accuracy and significance was estimated by leave-one-out cross-validation and permutation tests, respectively. Two frontoparietal and one default mode network components predicted negative symptom improvement with a classification accuracy of 79% (p = 0.003). Discriminating features were found in the frontoparietal networks but not the default mode network. These preliminary data suggest that functional patterns at baseline can predict negative symptom treatment-response in schizophrenia. This information may be used to stratify patients into subgroups thereby facilitating personalized treatment.

Abnormal effective connectivity and psychopathological symptoms in the psychosis high-risk state

“…In this regard, working memory-related frontoparietal connectivity patterns at pretreatment baseline predicted the improvement in negative symptoms in antipsychotic-naive patients with schizophrenia. 58 Thus, further studies are needed to establish the specific relation between frontoparietal connectivity during working memory processing and symptom expression.…”

The acute brain response to levodopa heralds dyskinesias in Parkinson disease

“…We entered the parameters of each region (n = 3) and patient (n = 26) as predictors in a binary classifier to see whether the parameters could classify a given patient as dyskinetic or nondyskinetic. For classification analysis, we applied a linear support vector machine (SVM; c‐value = 1)25 implemented in LIBSVM v3.17 as described previously 26. We used leave‐one‐out cross‐validation to assess classification accuracy, the true‐positive rate (sensitivity), and false‐positive rate (1 − specificity) and permutation tests (10,000 permutations) to derive the corresponding probability value.…”

Computational neuroimaging strategies for single patient predictions

“…This has shown some promising recent results, indicating that it may become possible to predict individual trajectories of patients with schizophrenia (Anticevic et al, 2015) or mood disorders (Lythe et al, 2015;Schmaal et al, 2015) from neuroimaging data, or forecast individual treatment responses to psychotherapy (Mansson et al, 2015), antidepressants (DeBattista et al, 2011;McGrath et al, 2013;Miller et al, 2013) and antipsychotics (Hadley et al, 2014;Nejad et al, 2013).…”