Repetitive transcranial magnetic stimulation (rTMS) for schizophrenia patients treated with clozapine

Objectives: Biological strategies to improve treatment efficacy in clozapine-treated patients are urgently needed. Repetitive transcranial magnetic stimulation (rTMS) merits consideration as intervention for patients with persistent auditory hallucinations (AH) or negative symptoms (NS) not responding sufficiently to clozapine treatment. Methods: Data from 10 international RCTs of rTMS for patients being treated with clozapine were pooled. Two levels of symptomatic response were defined: improvement of ≥20% and ≥50% on study-specific primary endpoint scales. Changes in the positive and negative syndrome scale (PANSS) from baseline to endpoint assessment were also analysed. Results: Analyses of 131 patients did not reveal a significant difference for ≥20% and ≥50% response thresholds for improvement of AH, negative or total symptoms between active and sham rTMS groups. The number needed to treat (NNT) for an improvement in persistent AH was nine following active rTMS. PANSS scores did not improve significantly from baseline to endpoint between active and sham groups in studies investigating NS and AH. Conclusions: rTMS as a treatment for persistent symptoms in clozapine-treated patients did not show a beneficial effect of active compared to sham treatment. For AH, the size of the NNTs indicates a possible beneficial effect of rTMS

Innovative approaches to hallucinations in psychosis and affective disorders: A focus on noninvasive brain stimulation interventions

Auditory verbal hallucinations (AVHs) are defined as verbal perceptions without an objective provoking external stimulus. AVHs are core symptoms of schizophrenia and psychotic spectrum disorders and have a wide prevalence in other severe psychiatric disorders including affective disorders and substance-use disorders. Despite adequate pharmacological treatment, AVHs can persist over the long-term course of these disorders in a significant percentage of patients, causing significant individual impairment. Noninvasive brain stimulation interventions represent a new frontier in the investigation and development of novel treatment options for both schizophrenia and psychotic spectrum disorders. In particular, transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) have been used in the treatment of AVHs in the last two decades. These techniques have the common feature of delivering electrical energy to the brain from an external source, as happens with tDCS, or through the induction of magnetic fields, as the case of repetitive TMS. The electrical stimulation is aimed to produce an excitation or inhibition of specific functional neuro-circuits that are involved in the pathogenesis of AVHs. In this chapter, we summarized main evidence in relation to the therapeutic use of the above-mentioned approaches in patients with AVHs, with specific attention to the recommendations of available international guidelines. Current major limitations and possible future perspectives are discussed as well

Emotional processing in Parkinson's disease and anxiety: an EEG study of visual affective word processing

A general problem in the design of an EEG-BCI system is the poor quality and low robustness of the extracted features, affecting overall performance. However, BCI systems that are applicable in real-time and outside clinical settings require high performance. Therefore, we have to improve the current methods for feature extraction. In this work, we investigated EEG source reconstruction techniques to enhance the extracted features based on a linearly constrained minimum variance (LCMV) beamformer. Beamformers allow for easy incorporation of anatomical data and are applicable in real-time. A 32-channel EEG-BCI system was designed for a two-class motor imagery (MI) paradigm. We optimized a synchronous system for two untrained subjects and investigated two aspects. First, we investigated the effect of using beamformers calculated on the basis of three different head models: a template 3-layered boundary element method (BEM) head model, a 3-layered personalized BEM head model and a personalized 5-layered finite difference method (FDM) head model including white and gray matter, CSF, scalp and skull tissue. Second, we investigated the influence of how the regions of interest, areas of expected MI activity, were constructed. On the one hand, they were chosen around electrodes C3 and C4, as hand MI activity theoretically is expected here. On the other hand, they were constructed based on the actual activated regions identified by an fMRI scan. Subsequently, an asynchronous system was derived for one of the subjects and an optimal balance between speed and accuracy was found. Lastly, a real-time application was made. These systems were evaluated by their accuracy, defined as the percentage of correct left and right classifications. From the real-time application, the information transfer rate (ITR) was also determined. An accuracy of 86.60 ± 4.40% was achieved for subject 1 and 78.71 ± 0.73% for subject 2. This gives an average accuracy of 82.66 ± 2.57%. We found that the use of a personalized FDM model improved the accuracy of the system, on average 24.22% with respect to the template BEM model and on average 5.15% with respect to the personalized BEM model. Including fMRI spatial priors did not improve accuracy. Personal fine- tuning largely resolved the robustness problems arising due to the differences in head geometry and neurophysiology between subjects. A real-time average accuracy of 64.26% was reached and the maximum ITR was 6.71 bits/min. We conclude that beamformers calculated with a personalized FDM model have great potential to ameliorate feature extraction and, as a consequence, to improve the performance of real-time BCI systems