Subjective and objective evaluation of alertness and sleep quality in depressed patients

BACKGROUND: The reliability of the subjective statements reports on disturbed night sleep and alertness in the daytime was assessed by their correlation to the objective indicators in patients with mild deprsssion. METHOD: Among patients with depression, altogether 28 patients with insomnia were examined. Their answers to typical questions, as they are used during a psychiatric interview, were scored. In parallel, night sleep quality and alertness level in the daytime were objectively estimated by means of polygraphic recording. RESULTS: The subjective statements on the type of insomnia, the estimated time of falling asleep, frequent awakenings and occurrence of disturbing dreams seem to be unreliable. Similarly, the results were disappointing when the patients were asked about alertness disturbances in the daytime. An unexpected finding was the lack of any significant correlation to the scores obtained by means of Epworth's scale. Among the factors possibly influencing the patients' reports, age, sex, coffee intake and also chronic administration of sedatives or hypnotics showed a low correlation with the sleep and alertness indicators. CONCLUSION: The statistical evaluation indicated rather poor agreement between the subjective and objective items. The statistical evaluation suggested that anxiety and depression significantly influence reports on sleep quality and alertness disturbances in the daytime

The way ahead for predictive EEG biomarkers in treatment of depression

The usage of EEG in the clinical diagnosis of psychiatric disorders has been a matter of debate for a long time (Williams, 1954). On one side, it has been argued that EEG recordings from psychiatric patients yield a relatively low abnormality detection rate, resulting in a neglectable value for diagnosis or impact on patient management (O’Sullivan et al., 2006). On the other side the psychiatric EEG has been described as a useful tool for monitoring psychopharmacological effects (Gallinat et al., 2016), and for the differential diagnosis of many disorders and syndromes, e.g., dementia, delirium, or sleep disorders. The EEG even has been approved by the U.S. Food and Drug Administration (FDA) as a biomarker testing device of e.g., attention deficit hyperactivity disorder (ADHD) (Gloss et al., 2016). Despite this dispute, it became clearer in recent years that the EEG could be of value not only for diagnostic purposes but might help in the decisions for finding the best treatment option for patients suffering from e.g., depressive disorders (Grzenda and Widge, 2020, Iosifescu, 2020). Not only since a meta-analysis on EEG biomarkers for prediction of treatment outcome in depression (Widge et al., 2019), many research groups focused on the identification of EEG signatures that could have a clinical value in the course of treatment (Olbrich and Arns, 2013). As a necessary new development and in response to critics that many studies were underpowered and markers were not replicated, data from several large studies (e.g. EMBARC (Trivedi et al., 2016) and i-SPOT-D (Williams et al., 2011)) has been used by different teams and in widespread international collaborations to look for new promising and reliable markers and to replicate other works, mainly in the field of antidepressants. Besides the search for predictors for psychopharmacological treatment outcome, markers for other interventions are needed to provide a benefit for marker-based therapy decisions. If biosignatures only yield information on e.g., a non-response to a single treatment option, then this renders useless for the patients since no alternatives for improved treatment outcomes are provided

Predictive value of heart rate in treatment of major depression with ketamine in two controlled trials

Objective: Ketamine has been shown to be effective in treatment of episodes of major depressive disorder (MDD). This controlled study aimed to analyse the predictive and discriminative power of heart rate (HR) and heart rate variability (HRV) for ketamine treatment in MDD. Methods: In 51 patients, HR and HRV were assessed at baseline before and during ketamine infusion and 24 hours post ketamine infusion. Montgomery-Åsberg Depression Rating Scale (MADRS) was used to assess changes of depressive symptoms. A 30% or 50% reduction of symptoms after 24 hours or within 7 days was defined as response. A linear mixed model was used for analysis. Results: Ketamine infusion increased HR and HRV power during and after infusion. Responders to ketamine showed a higher HR during the whole course of investigation, including at baseline with medium effect sizes (Cohen's d = 0.47-0.67). Furthermore, HR and HRV power discriminated between responders and non-responders, while normalized low and high frequencies did not. Conclusion: The findings show a predictive value of HR and HRV power for ketamine treatment. This further underlines the importance of the autonomous nervous system (ANS) and its possible malfunctions in MDD. Significance: The predictive power of HR and HRV markers should be studied in prospective studies. Neurophysiological markers could improve treatment for MDD via optimizing the choice of treatments

Retrospective analysis of quantitative electroencephalography changes in a dissimulating patient after dying by suicide: A single case report

We present the case of a 49-year-old man who was diagnosed with depressive disorder, with the first episode having a strong reactive factor. He was involuntarily admitted to a psychiatric hospital after a failed attempt at taking his own life, where he responded to psychotherapy and antidepressant therapy, as evidenced by a >60% reduction in his MADRS total score. He was discharged after 10 days of treatment, denied having suicidal ideations, and was motivated to follow the recommended outpatient care. The risk for suicide during hospitalization was also assessed using suicide risk assessment tools and psychological assessments, including projective tests. The patient underwent a follow-up examination with an outpatient psychiatrist on the 7th day after discharge, during which the suicide risk assessment tool was administered. The results indicated no acute suicide risk or worsening of depressive symptoms. On the 10th day after discharge, the patient took his own life by jumping out of the window of his flat. We believe that the patient had dissimulated his symptoms and possessed suicidal ideations, which were not detected despite repeated examinations specifically designed to assess suicidality and depression symptoms. We retrospectively analyzed his quantitative electroencephalography (QEEG) records to evaluate the change in prefrontal theta cordance as a potentially promising biomarker of suicidality, given the inconclusive results of studies published to date. An increase in prefrontal theta cordance value was found after the first week of antidepressant therapy and psychotherapy in contrast to the expected decrease due to the fading of depressive symptoms. As demonstrated by the provided case study, we hypothesized that prefrontal theta cordance may be an EEG indicator of a higher risk of non-responsive depression and suicidality despite therapeutic improvement

Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach

Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the scanner contain an electromagnetic field that is induced in leads as a result of gradient switching slight head movements and vibrations, and it is corrupted by changes in the measured potential because of the Hall phenomenon. The aim of this study is to design and test a methodology for inspecting hidden EEG structures with respect to artifacts. We propose a top-down strategy to obtain additional information that is not visible in a single recording. The time-domain independent component analysis algorithm was employed to obtain independent components and spatial weights. A nonlinear dimension reduction technique t-distributed stochastic neighbor embedding was used to create low-dimensional space, which was then partitioned using the density-based spatial clustering of applications with noise (DBSCAN). The relationships between the found data structure and the used criteria were investigated. As a result, we were able to extract information from the data structure regarding electrooculographic, electrocardiographic, electromyographic and gradient artifacts. This new methodology could facilitate the identification of artifacts and their residues from simultaneous EEG in fMRI

ELECTROPHYSIOLOGICAL CORRELATES OF SUICIDALITY

Suicidal risk assessment is still a major challenge not only in psychiatric practice. Clinical investigation of suicidality can be significantly improved by using standardized scales for assessing suicide risk. The choice of a method for assessing suicidality also has significant implications for the search of valid available biomarker of suicidal behavior, where a less complex suicidality assessment procedure yields inaccurate results. This article offers an overview and analyzes in detail clinical studies of suicidality by electrophysiological methods since 2005 to 5/2020, especially in connection with presumed pathophysiological mechanism of the t. Electrophysiological methods such as quantitative electroencephalography indicators, event-related potential, loudness dependence of the auditory evoked potential, polysomnography and heart rate variability offer a robust battery of easily available methods for assessing impaired emotional regulation. Nowadays it is unfortunately very difficult to point out the optimal electrophysiological examination of suicidal behaviour because of conflicting conclusion of presented studies which have been probably caused by various suicidal risk assessments, not always available data on affecting medication prior to testing and small samples of suicidal participants among studies. The most consistent and hopeful results are presented by evaluation of theta power by quantitative electroencephalography, although there are also few conflicting conclusions. The authors of this paper believe that this article could be good starting point for further research of electrophysiological methods in the field of suicidality

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO2 channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively