Prediction of Working Memory Performance in Schizophrenia by Plasma Ratio of Clozapine to <i>N</i>-Desmethylclozapine

Rajji, Tarek K.; Mulsant, Benoit H.; Davies, Simon; Kalache, Sawsan M.; Tsoutsoulas, Christopher; Pollock, Bruce G.; Remington, Gary

doi:10.1176/appi.ajp.2015.14050673

Cited by 63 publications

(44 citation statements)

References 42 publications

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“…Working memory performance prediction has become an interesting topic which received considerable attention in recent years ( Rajji et al, 2015 ; Johannesen et al, 2016 ; Tumari and Sudirman, 2016 ). In previous attempts, numerous approaches were proposed to extract reliable features to predict individual WM performance and distinguish the difficulty levels of cognitive tasks, such as alpha power ( Myers et al, 2014 ), absolute power ( Klimesch et al, 2006 ), and wavelet entropy ( Zarjam et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task

Tian

Zhang

et al. 2017

Front. Hum. Neurosci.

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Spectral entropy, which was generated by applying the Shannon entropy concept to the power distribution of the Fourier-transformed electroencephalograph (EEG), was utilized to measure the uniformity of power spectral density underlying EEG when subjects performed the working memory tasks twice, i.e., before and after training. According to Signed Residual Time (SRT) scores based on response speed and accuracy trade-off, 20 subjects were divided into two groups, namely high-performance and low-performance groups, to undertake working memory (WM) tasks. We found that spectral entropy derived from the retention period of WM on channel FC4 exhibited a high correlation with SRT scores. To this end, spectral entropy was used in support vector machine classifier with linear kernel to differentiate these two groups. Receiver operating characteristics analysis and leave-one out cross-validation (LOOCV) demonstrated that the averaged classification accuracy (CA) was 90.0 and 92.5% for intra-session and inter-session, respectively, indicating that spectral entropy could be used to distinguish these two different WM performance groups successfully. Furthermore, the support vector regression prediction model with radial basis function kernel and the root-mean-square error of prediction revealed that spectral entropy could be utilized to predict SRT scores on individual WM performance. After testing the changes in SRT scores and spectral entropy for each subject by short-time training, we found that 16 in 20 subjects’ SRT scores were clearly promoted after training and 15 in 20 subjects’ SRT scores showed consistent changes with spectral entropy before and after training. The findings revealed that spectral entropy could be a promising indicator to predict individual’s WM changes by training and further provide a novel application about WM for brain–computer interfaces.

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Section: Introductionmentioning

confidence: 99%

Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task

Tian

Zhang

et al. 2017

Front. Hum. Neurosci.

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“…The RBANS, and TMT were administered within one week following the administration of the PANSS. As done in previous studies ( Kalache et al, 2015 ; Rajji et al, 2015 ), seven main cognitive domains were assessed as follows: (i) immediate recall: RBANS story memory immediate recall (SM-IR); (ii) delayed recall: RBANS story memory delayed recall (SM-DR); (iii) language: RBANS picture naming test; (iv) auditory attention: RBANS digit span; (v) visual attention: TMT-A; (vi) information-processing speed: RBANS digit symbol coding; and (vii) executive functioning: TMT-B. For all measures, raw scores were used for the analysis.…”

Section: Methodsmentioning

confidence: 79%

Superior memory performance in healthy individuals with subclinical psychotic symptoms but without genetic load for schizophrenia

Gagnon

Kumar

Maltais

et al. 2018

Schizophrenia Research: Cognition

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“…In some patients with a low clozapine/NDMC ratio, however, it is still possible that M 1 receptors are partially activated by NDMC. In line with this possibility, a recent study in clozapine-treated patients demonstrated that the clozapine/NDMC ratio, but not individual clozapine and NDMC concentrations, was significantly and negatively associated with working memory performance after controlling for age, gender, education, and symptom severity (Rajji et al, 2015).…”

Section: Discussionmentioning

confidence: 84%

Electrophysiological evidence showing muscarinic agonist–antagonist activities of N-desmethylclozapine using hippocampal excitatory and inhibitory neurons

et al. 2016

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antagonist darifenacin was effective in both types of neurons. Muscarinic agonist activity of NDMC was higher than that of clozapine, higher in excitatory neurons than in inhibitory neurons, sensitive to pirenzepine, and partially masked when co-applied with clozapine. Muscarinic antagonist activity of clozapine as well as NDMC was not different between excitatory and inhibitory neurons, but clozapine was more effective than NDMC.These results demonstrate that NDMC has the ability to activate native M 1 receptors expressed in hippocampal excitatory neurons, but its agonist activity might be limited in clozapine-treated patients because of the presence of excessive clozapine with muscarinic 4 antagonist activity.

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Prediction of Working Memory Performance in Schizophrenia by Plasma Ratio of Clozapine to N-Desmethylclozapine

Cited by 63 publications

References 42 publications

Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task

Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task

Superior memory performance in healthy individuals with subclinical psychotic symptoms but without genetic load for schizophrenia

Electrophysiological evidence showing muscarinic agonist–antagonist activities of N-desmethylclozapine using hippocampal excitatory and inhibitory neurons

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