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.

The interview is still the main and most important tool in psychiatrist's work. The neuroimaging methods such as CT or MRI are widely used in other fields of medicine, for instance neurology. However, psychiatry lacks effective quantitative methods to support of diagnosis. A novel neuroinformatic approach to help clinical patients by means of electroencephalographic technology in order to build foundations for finding neurophysiological biomarkers of psychiatric disorders is proposed. A cohort of 30 right-handed patients (21 males, 9 females) with psychiatric disorders (mainly with panic and anxiety disorder, Asperger syndrome as well as with phobic anxiety disorders, schizophrenia, bipolar affective disorder, obsessive-compulsive disorder, nonorganic hypersomnia, and moderate depressive episode) were examined using the dense array EEG amplifier in the P300 experiment. The results were compared with the control group of 30 healthy, right-handed male volunteers. The quantitative analysis of cortical activity was conducted using the sLORETA source localization algorithm. The most active Brodmann Areas were pointed out and a new quantitative observable of electrical charge flowing through the selected Brodmann Area is proposed. The precise methodology and research protocol for collecting EEG data as well as the roadmap of future investigations in this area are presented. The essential result of this study is the idea proven by the initial results of our experiments that it is possible to determine quantitatively biomarkers of particular psychiatric disorders in order to support the process of diagnosis and hopefully choose most appropriate medical treatment later.

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Introduction 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 modeling 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 modeling was performed using nonlinear mixed effects modeling in MONOLIX while the Clinical Trial Simulations were performed using R for statistical programming. The pharmacometric analysis is based from 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 modeled using an Emax model in a study population of seventy patients. Population pharmacodynamic parameters were estimated to be: Emax=85.6% (R.S.E.=3%), ED50-Men=5.15 mg/day (R.S.E.=14) and ED50-Women= 2.38 mg/day (R.S.E.=34%) with the p-value=0.037 for the gender-stratified ED50 results. Conclusion Based on the pharmacometrics analysis and model-based dosing simulations suggests that in order to achieve 70% D2RO women require a 10mg/day dose and men require approximately a 20mg/day dose of Olanzapine. Further, clinical implications exist suggesting that clinicians should factor patient gender when considering both a starting dose, as well as, a maintenance dose for patients prescribed Olanzapine due to quantifiable gender-differences of striatal dopamine D2 receptor occupancy.

Coraz więcej dowodów wskazuje, że neurotropowy czynnik pochodzenia mózgowego BDNF (ang. brain derived neurotrophic factor) to najbardziej rozpowszechniona neurotrofina w układzie nerwowym, która odgrywa ważną rolę jako wskaźnik skutecznie realizowanej rehabilitacji wobec osób z rozpoznaniem schizofrenii. Obecnie, w oparciu o nowoczesną diagnostykę laboratoryjną i aparaturową możliwe jest diagnozowanie deficytów, które wpływają na poziom funkcjonowania chorych, a na ich podstawie ustalanie indywidualnego programu readaptacyjnego, uwzględniającego różne formy terapii, w różnych środowiskach. W oparciu o przegląd dostępnego piśmiennictwa w pracy zaprezentowano dotychczasowe wyniki badań analizujących związek pomiędzy wybranymi oddziaływaniami rehabilitacyjnymi stosowanymi u pacjentów z rozpoznaniem schizofrenii, a zmianami stężenia BDNF - związku pomiędzy poziomem BDNF z aktywnością fizyczną oraz z terapią EEG Biofeedback. Badania dotyczące zastosowania prezentowanej metody wydają się wskazywać na użyteczność czynnika BDNF w ocenie skuteczności realizowanych oddziaływań rehabilitacyjnych w tej grupie chorych. Zmiany w stężeniu czynnika neurotropowego mogą być wskaźnikiem synergizmu ośrodkowego i obwodowego układu nerwowego, a wysokie stężenia BDNF uwarunkowane aktywnością fizyczną oraz neuromodulacyjnym efektem terapii EEG Biofeedback mogą wskazywać na ich skuteczność. Wykorzystanie różnych metod neurorehabilitacyjnych może poprawić funkcjonowanie społeczne osób chorych na schizofrenię. Traktowanie BDNF jako biologicznego wskaźnika tych procesów może być interesującą hipotezą.

The electroencephalographic activity of particular brain areas during the decision making process is still little known. This paper presents results of experiments on the group of 30 patients with a wide range of psychiatric disorders and 41 members of the control group. All subjects were performing the Iowa Gambling Task that is often used for decision process investigations. The electroencephalographical activity of participants was recorded using the dense array amplifier. The most frequently active Brodmann Areas were estimated by means of the photogrammetry techniques and source localization algorithms. The analysis was conducted in the full frequency as well as in alpha, beta, gamma, delta, and theta bands. Next the mean electric charge flowing through each of the most frequently active areas and for each frequency band was calculated. The comparison of the results obtained for the subjects and the control groups is presented. The difference in activity of the selected Brodmann Areas can be observed in all variants of the task. The hyperactivity of amygdala is found in both the patients and the control group. It is noted that the somatosensory association cortex, dorsolateral prefrontal cortex, and primary visual cortex play an important role in the decision-making process as well. Some of our results confirm the previous findings in the fMRI experiments. In addition, the results of the electroencephalographic analysis in the broadband as well as in specific frequency bands were used as inputs to several machine learning classifiers built in Azure Machine Learning environment. Comparison of classifiers' efficiency is presented to some extent and finding the most effective classifier may be important for planning research strategy toward finding decision-making biomarkers in cortical activity for both healthy people and those suffering from psychiatric disorders.

There are still no good quantitative methods to be applied in psychiatric diagnosis. The interview is still the main and most important tool in the psychiatrist work. This paper presents the results of electroencephalographic research with the subjects of a group of 30 patients with psychiatric disorders compared to the control group of healthy volunteers. All subjects were solving working memory task. The digit-span working memory task test was chosen as one of the most popular tasks given to subjects with cognitive dysfunctions, especially for the patients with panic disorders, depression (including the depressive phase of bipolar disorder), phobias, and schizophrenia. Having such cohort of patients some results for the subjects with insomnia and Asperger syndrome are also presented. The cortical activity of their brains was registered by the dense array EEG amplifier. Source localization using the photogrammetry station and the sLORETA algorithm was then performed in five EEG frequency bands. The most active Brodmann Areas are indicated. Methodology for mapping the brain and research protocol are presented. The first results indicate that the presented technique can be useful in finding psychiatric disorder neurophysiological biomarkers. The first attempts were made to associate hyperactivity of selected Brodmann Areas with particular disorders.

Objective: Antipsychotic compounds are known to induce sedation somnolence and have expanded clinical indications beyond schizophrenia to regulatory approval in bipolar disorder, treatment-resistant depression, and is being repurposed in infectious diseases and oncology. However, the medical sciences literature lacks a comprehensive association between sedation and somnolence among a wide-range of antipsychotic compounds. The objective of this study is to assess the disproportionality of sedation and somnolence among thirty-seven typical and atypical antipsychotics.Materials and Methods: Patient adverse drug reactions (ADR) cases were obtained from the United States Food and Drug Administration Adverse Events Reporting System (FAERS) between January 01, 2004 and September 30, 2020 for a wide-array of clinical indications and off-label use of antipsychotics. An assessment of disproportionality were based on cases of sedation and somnolence and calculated using the case/non-case methodology. Statistical analysis resulting in the reporting odds-ratio (ROR) with corresponding 95% confidence intervals (95% CI) were conducted using the R statistical programming language.Results: Throughout the reporting period, there were a total of 9,373,236 cases with 99,251 specific ADRs reporting sedation and somnolence. Zuclopenthixol (n = 224) ROR = 13.3 (95% CI, 11.6–15.3) was most strongly associated of sedation and somnolence and haloperidol decanoate long-acting injection (LAI) was not statistically associated sedation and somnolence. Further, among atypical antipsychotic compounds, tiapride and asenapine were the top two compounds most strongly associated with sedation and somnolence. Comprehensively, the typical antipsychotics ROR = 5.05 (95%CI, 4.97–5.12) had a stronger association with sedation and somnolence when compared to atypical antipsychotics ROR = 4.65 (95%CI, 4.47–4.84).Conclusion: We conducted a head-to-head comparison of thirty-seven antipsychotics and ranked the compounds based on the association of sedation and somnolence from ADR data collected throughout 16 years from the FAERS. The results are informative and with recent interests in repurposing phenothiazine antipsychotics in infectious disease and oncology provides an informative assessment of the compounds during repurposing and in psychopharmacology.