Sleep Delta power, age, and sex effects in treatment-resistant depression.

Electroencephalographic (EEG) deficits in slow wave activity or Delta power (0.5-4 Hz) indicate disturbed sleep homeostasis and are hallmarks of depression. Sleep homeostasis is linked to restorative sleep and potential antidepressant response via non-rapid eye movement (NREM) slow wave sleep (SWS) during which neurons undergo essential repair and rejuvenation. Decreased Low Delta power (0.5-2 Hz) was previously reported in individuals with depression. This study investigated power levels in the Low Delta (0.5-<2 Hz), High Delta (2-4 Hz), and Total Delta (0.5-4 Hz) bands and their association with age, sex, and disrupted sleep in treatment-resistant depression (TRD). Mann-Whitney U tests were used to compare the nightly progressions of Total Delta, Low Delta, and High Delta in 100 individuals with TRD and 24 healthy volunteers (HVs). Polysomnographic parameters were also examined, including Total Sleep Time (TST), Sleep Efficiency (SE), and Wake after Sleep Onset (WASO). Individuals with TRD had lower Delta power during the first NREM episode (NREM1) than HVs. The deficiency was observed in the Low Delta band versus High Delta. Females with TRD had higher Delta power than males during the first NREM1 episode, with the most noticeable sex difference observed in Low Delta. In individuals with TRD, Low Delta power correlated with WASO and SE, and High Delta correlated with WASO. Low Delta power deficits in NREM1 were observed in older males with TRD, but not females. These results provide compelling evidence for a link between age, sex, Low Delta power, sleep homeostasis, and non-restorative sleep in TRD.

Exploring the impact of music on response to ketamine/esketamine: A scoping review.

Music and ketamine are both known to affect therapeutic outcomes, but few studies have investigated their co-administration. This scoping review describes the existing literature on the joint use of music and ketamine-or esketamine (the S(+) enantiomer of ketamine)-in humans. The review considers that extant studies have explored the intersection of ketamine/esketamine and music in healthy volunteers and in patients of various age groups, at different dosages, through different treatment processes, and have varied the sequence of playing music relative to ketamine/esketamine administration. Studies investigating the use of music during ketamine anesthesia are also included in the review because anesthesia and sedation were the early drivers of ketamine use. Studies pertaining to recreational ketamine use were omitted. The review was limited to articles published in the English language but not restricted by publication year. To the best of our knowledge, this scoping review is the first comprehensive exploration of the interplay between music and ketamine/esketamine and offers valuable insights to researchers interested in designing future studies.

The Right Hemisphere Is Responsible for the Greatest Differences in Human Brain Response to High-Arousing Emotional versus Neutral Stimuli: A MEG Study.

Studies investigating human brain response to emotional stimuli-particularly high-arousing versus neutral stimuli-have obtained inconsistent results. The present study was the first to combine magnetoencephalography (MEG) with the bootstrapping method to examine the whole brain and identify the cortical regions involved in this differential response. Seventeen healthy participants (11 females, aged 19 to 33 years; mean age, 26.9 years) were presented with high-arousing emotional (pleasant and unpleasant) and neutral pictures, and their brain responses were measured using MEG. When random resampling bootstrapping was performed for each participant, the greatest differences between high-arousing emotional and neutral stimuli during M300 (270-320 ms) were found to occur in the right temporo-parietal region. This finding was observed in response to both pleasant and unpleasant stimuli. The results, which may be more robust than previous studies because of bootstrapping and examination of the whole brain, reinforce the essential role of the right hemisphere in emotion processing.

Automated emotion classification in the early stages of cortical processing: An MEG study.

PURPOSE: Here we aimed to automatically classify human emotion earlier than is typically attempted. There is increasing evidence that the human brain differentiates emotional categories within 100-300 ms after stimulus onset. Therefore, here we evaluate the possibility of automatically classifying human emotions within the first 300 ms after the stimulus and identify the time-interval of the highest classification performance. METHODS: To address this issue, MEG signals of 17 healthy volunteers were recorded in response to three different picture stimuli (pleasant, unpleasant, and neutral pictures). Six Linear Discriminant Analysis (LDA) classifiers were used based on two binary comparisons (pleasant versus neutral and unpleasant versus neutral) and three different time-intervals (100-150 ms, 150-200 ms, and 200-300 ms post-stimulus). The selection of the feature subsets was performed by Genetic Algorithm and LDA. RESULTS: We demonstrated significant classification performances in both comparisons. The best classification performance was achieved with a median AUC of 0.83 (95 %- CI [0.71; 0.87]) classifying brain responses evoked by unpleasant and neutral stimuli within 100-150 ms, which is at least 850 ms earlier than attempted by other studies. CONCLUSION: Our results indicate that using the proposed algorithm, brain emotional responses can be significantly classified at very early stages of cortical processing (within 300 ms). Moreover, our results suggest that emotional processing in the human brain occurs within the first 100-150 ms.

The Temporal and Spatial Dynamics of Cortical Emotion Processing in Different Brain Frequencies as Assessed Using the Cluster-Based Permutation Test: An MEG Study.

The processing of emotions in the human brain is an extremely complex process that extends across a large number of brain areas and various temporal processing steps. In the case of magnetoencephalography (MEG) data, various frequency bands also contribute differently. Therefore, in most studies, the analysis of emotional processing has to be limited to specific sub-aspects. Here, we demonstrated that these problems can be overcome by using a nonparametric statistical test called the cluster-based permutation test (CBPT). To the best of our knowledge, our study is the first to apply the CBPT to MEG data of brain responses to emotional stimuli. For this purpose, different emotionally impacting (pleasant and unpleasant) and neutral pictures were presented to 17 healthy subjects. The CBPT was applied to the power spectra of five brain frequencies, comparing responses to emotional versus neutral stimuli over entire MEG channels and time intervals within 1500 ms post-stimulus. Our results showed significant clusters in different frequency bands, and agreed well with many previous emotion studies. However, the use of the CBPT allowed us to easily include large numbers of MEG channels, wide frequency, and long time-ranges in one study, which is a more reliable alternative to other studies that consider only specific sub-aspects.

Abnormal Emotional Processing and Emotional Experience in Patients with Peripheral Facial Nerve Paralysis: An MEG Study.

Abnormal emotional reactions of the brain in patients with facial nerve paralysis have not yet been reported. This study aims to investigate this issue by applying a machine-learning algorithm that discriminates brain emotional activities that belong either to patients with facial nerve paralysis or to healthy controls. Beyond this, we assess an emotion rating task to determine whether there are differences in their experience of emotions. MEG signals of 17 healthy controls and 16 patients with facial nerve paralysis were recorded in response to picture stimuli in three different emotional categories (pleasant, unpleasant, and neutral). The selected machine learning technique in this study was the logistic regression with LASSO regularization. We demonstrated significant classification performances in all three emotional categories. The best classification performance was achieved considering features based on event-related fields in response to the pleasant category, with an accuracy of 0.79 (95% CI (0.70, 0.82)). We also found that patients with facial nerve paralysis rated pleasant stimuli significantly more positively than healthy controls. Our results indicate that the inability to express facial expressions due to peripheral motor paralysis of the face might cause abnormal brain emotional processing and experience of particular emotions.