Jolanta Masiak

Limbic brain structures and burnout—A systematic review

More profound understanding of the relationship between the burnout and the limbic system function can provide better insight into brain structures associated with the burnout syndrome. The objective of this review is to explore all evidence of limbic brain structures associated with the burnout syndrome. In total, 13 studies were selected. Four of them applied the neuroimaging technology to investigate the sizes/volumes of the limbic brain structures of burnout patients. Six other studies were to investigate the hypothalamus-pituitary-adrenal (HPA) axis of burnout patients. Based on the results of the studies on the HPA-axis and neuroimaging of the limbic brain structures, one can see great impact of the chronic occupational stress on the limbic structures in terms of HPA dysregulation, a decrease of BDNF, impaired neurogenesis and limbic structures atrophy. It can be concluded that chronic stress inhibits the feedback control pathway in the HPA axis, causes the decrease of brain-derived neurotrophic factor (BDNF), then impaired neurogenesis and eventually neuron atrophy.

Most popular signal processing methods in motor-imagery BCI: A review and meta-analysis

Brain-Computer Interfaces (BCI) constitute an alternative channel of communication between humans and environment. There are a number of different technologies which enable the recording of brain activity. One of these is electroencephalography (EEG). The most common EEG methods include interfaces whose operation is based on changes in the activity of Sensorimotor Rhythms (SMR) during imagery movement, so-called Motor Imagery BCI (MIBCI).The present article is a review of 131 articles published from 1997 to 2017 discussing various procedures of data processing in MIBCI. The experiments described in these publications have been compared in terms of the methods used for data registration and analysis. Some of the studies (76 reports) were subjected to meta-analysis which showed corrected average classification accuracy achieved in these studies at the level of 51.96%, a high degree of heterogeneity of results (Q = 1806577.61; df = 486; p < 0.001; I2 = 99.97%), as well as significant effects of number of channels, number of mental images, and method of spatial filtering. On the other hand the meta-regression failed to provide evidence that there was an increase in the effectiveness of the solutions proposed in the articles published in recent years. The authors have proposed a newly developed standard for presenting results acquired during MIBCI experiments, which is designed to facilitate communication and comparison of essential information regarding the effects observed. Also, based on the findings of descriptive analysis and meta-analysis, the authors formulated recommendations regarding practices applied in research on signal processing in MIBCIs.

Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning

Background: We sought to test the hypothesis that transcriptiome-level genes signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders. Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Informations Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Random Forests machine learning algorithm in R. Results: In pre-treatment lithium responders, the following genes were found having a greater than 0.5 fold-change, and differentially expressed indicating a male bias: RBPMS2, SIDT2, CDH23, LILRA5, and KIR2DS5; while the female-biased genes were: HLA-H, RPS23, FHL3, RPL10A, NBPF14, PSTPIP2, FAM117B, CHST7, and ABRACL. Conclusions: Using machine learning, we developed a pre-treatment gender- and gene-expression-based predictive model selective for lithium responders with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.

A pharmacodynamic modelling and simulation study identifying gender differences of daily olanzapine dose and dopamine D2-receptor occupancy

Abstract Background: Gender differences in treatment response rates for patients treated with antipsychotics are known. However, the literature lacks a pharmacodynamic model to allow for gender-based clinical trial simulations from modelling parameters for Olanzapine and dopamine D2 receptor occupancy. Thus, the primary aim of this analysis is to test and quantify the effect of gender on the pharmacodynamics of Olanzapine. Methods: Population pharmacodynamic modelling was performed using non-linear mixed effects modelling in MONOLIX, while the Clinical Trial Simulations were performed using R for statistical programming. The pharmacometric analysis is based on a pooled data approach from three clinical studies where patients were diagnosed with schizophrenia and one clinical study where the patients were diagnosed with bipolar disorder. Results: Olanzapine D2RO was modelled using an Emax model in a study population of 70 patients. Population pharmacodynamic parameters were estimated to be: Emax = 85.6% (RSE = 3%), ED50-Men = 5.15 mg/day (RSE = 14) and ED50-Women = 2.38 mg/day (RSE = 34%), with the p-value = 0.037 for the gender-stratified ED50 results. Conclusion: The pharmacometrics analysis and model-based dosing simulations suggest that, in order to achieve 70% D2RO, women require a 10 mg/day dose and men require approximately a 20 mg/day dose of Olanzapine. Further, clinical implications exist suggesting that clinicians should factor patient gender when considering both a starting dose, as well a, a maintenance dose for patients prescribed Olanzapine due to quantifiable gender-differences of striatal dopamine D2 receptor occupancy.

Adaptation of the humanoid robot to speech disfluency therapy

Abstract The paper describes an application that allows to use a humanoid robot as a stutterer’s assistant and therapist. Auditory and visual feedback has been used in the therapy with a humanoid robot. For this purpose, the common method of “echo” was modified. The modification is that the speaker hears delayed speech sounds uttered by the robot. The sounds of speech coming from an external microphone are captured and delayed by a computer and then, using User Datagram Protocol (UDP), sent to the robot’s system and played in its speakers. This system allows the elimination of negative feedback and external sound field’s noise. The effect of this therapy is enhanced by the fact that, in addition to the effect, relating to the action of the delayed feedback, the speaker has company during the difficult process of speaking. Visual feedback has been realized as changes in the robot’s hand movements according to the shape of the speech signal envelope and possibility of controlling speech with a metronome effect.