Brainwaves Oscillations as a Potential Biomarker for Major Depression Disorder Risk

Distinguishing major depressive disorder from bipolar disorder using alpha-band activity in resting-state electroencephalogram

BACKGROUND

Bipolar disorder (BD) and major depressive disorder (MDD) share overlapping depressive symptoms, which pose challenges for achieving rapid and accurate differential diagnosis in clinical practice. This study aims to investigate whether alpha sub-band activity in electroencephalogram (EEG) can serve as a discriminative feature between MDD and BD, thereby improving diagnostic accuracy in mood disorders.

METHODS

This study recruited a total of 103 participants, comprising 37 patients diagnosed with MDD, 36 patients with BD, and 30 healthy controls (HC). All participants were matched in terms of gender and age. EEG data were acquired during both eyes-open and eyes-closed states over a 5-minute duration to examine whether different sub-oscillations in the alpha band can differentiate between MDD and BD.

RESULTS

We found that at the group level, the peak frequency of the HC group was in the low alpha band, the BD group in the medium alpha band, and the MDD group in the high alpha band. Our results indicate that the MDD and BD groups display the most pronounced differences in the high alpha band, irrespective of whether the eyes are open or closed. In contrast, the HC group exhibits some distinctions from the MDD and BD groups in the low alpha band.

CONCLUSIONS

This study provides novel insights into the differential characteristics of alpha sub-band oscillations in MDD from BD as compared to healthy controls. These observations suggest distinct neural signatures for MDD and BD, highlighting the potential value of alpha sub-band analyses in diagnostic classification.

Electroencephalography Biomarkers of α5-GABA Positive Allosteric Modulators in Rodents

Background

Reduced cortical inhibition mediated by GABA (gamma-aminobutyric acid) is reported in depression, anxiety disorders, and aging. A novel positive allosteric modulator that specifically targets the α5-GABAA receptor subunit (α5-PAM), ligand GL-II-73 shows anxiolytic, antidepressant, and procognitive effects without the common side effects associated with nonspecific modulation by benzodiazepines such as diazepam, thus suggesting novel therapeutic potential. However, it is unknown whether α5-PAM has detectable signatures in clinically relevant brain electroencephalography (EEG).

Methods

We analyzed EEG in 10 freely moving rats at baseline and following injections of α5-PAM (GL-II-73) and diazepam.

Results

We showed that α5-PAM specifically decreased theta peak power, whereas diazepam shifted peak power from high to low theta while increasing beta and gamma power. EEG decomposition showed that these effects were periodic and corresponded to changes in theta oscillation event duration.

Conclusions

Thus, our study shows that α5-PAM has robust and distinct EEG biomarkers in rodents, indicating that EEG could enable noninvasive monitoring of α5-PAM treatment efficacy.

Phase-Amplitude Coupling in Response to Transcutaneous Vagus Nerve Stimulation: Focus on Regions Implicated in Mood and Memory

OBJECTIVE

This study aimed to investigate whether transcutaneous vagus nerve stimulation (tVNS) at different frequencies affects phase-amplitude coupling among regions of the brain linked to mood and memory disorders using simultaneous magnetoencephalography (MEG) in healthy participants.

MATERIALS AND METHODS

Phase-amplitude coupling was measured among brain areas in response to different stimulation frequencies of tVNS using concurrent MEG and tVNS in 17 healthy participants. The 4 protocols were: 24 Hz cymba concha, 1 Hz cymba concha, PFM cymba concha, and 24 Hz ear lobe. A driven autoregressive method was used to estimate the coupling among brain areas in different physiological frequency bands in response to these protocols.

RESULTS

Different tVNS stimulation protocols led to alterations in phase-amplitude coupling among multiple brain regions linked to mood and memory, notably the prefrontal cortex, hippocampus, and temporal pole. Stimulation delivered at 24 Hz was observed to decrease delta-gamma coupling within the temporal pole and cingulate cortex when contrasted with 24-Hz sham stimulation. Increased alpha-gamma coupling was observed between the hippocampus and prefrontal cortex when contrasting 24 Hz with pulse-frequency-modulated stimulation. Finally, a comparison of 24-Hz with low-frequency 1-Hz stimulation showed an increase in theta-gamma coupling within the prefrontal cortex.

SIGNIFICANCE

To our knowledge, this study represents the first attempt to quantify phase-amplitude coupling in response to tVNS and suggests that different stimulation frequencies can modulate coupling between different areas of the brain. Abnormal phase-amplitude coupling has been linked to multiple mood and memory disorders. Further investigations using different stimulation frequencies of tVNS to alter phase-amplitude coupling may lead to the development of tVNS as a therapeutic option for different medical conditions.

Abnormal beta bursts of depression in the orbitofrontal cortex and its relationship with clinical symptoms

BACKGROUND

Recent researches have reported that frequency-specific patterns of neural activity contain not only rhythmically sustained oscillations but also transient-bursts of isolated events. The aim of this study was to investigated the correlation between beta burst and depression in order to explore depressive disease and the neurological underpinnings of disease-related symptoms.

METHODS

We collected resting-state MEG recordings from 30 depressive patients and a matched 40 healthy controls. A Hidden Markov Model (HMM) was applied on source-space time courses for 78 cortical regions of the AAL atlas and the temporal characteristics of beta burst from the matched HMM states were captured. Group differences were evaluated on these beta burst characteristics after permutation tests and, for the depressive group, associations between burst characteristics and clinical symptom severity were determined using Spearman correlation coefficients.

RESULTS

At a threshold of p=0.05corrected, burst characteristics revealed significant differences between depression patients and controls at the group level, including increased burst amplitude in frontal lobe, decreased burst duration in occipital regions, increased burst rate and decreased burst interval time in some brain regions. Furthermore, burst amplitude in the orbitofrontal cortex (OFC) was positively related to the severity of sleep disturbance and burst rate in the OFC was negatively related to the severity of anxiety in depression patients.

CONCLUSIONS

The findings highlight OFC may be a targeted area responsible for the anxiety and sleep disturbance symptom by abnormal beta burst in depressive patients and beta burst characteristics of OFC might serve as a neuro-marker for the depression.

Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity

Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations. We utilized our previous detailed computational models of human depression microcircuits with reduced SST interneuron inhibition and α5-PAM effects, to simulate EEG of individual microcircuits across depression severity and α5-PAM doses. We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. This study provides dose prediction models for α5-PAM administration based on EEG biomarkers of depression severity. Given limitations in doing the above in the living human brain, the results and tools we developed will facilitate translation of α5-PAM treatment to clinical use.

Negative Emotion Differentiation Promotes Cognitive Reappraisal: Evidence From Electroencephalogram Oscillations and Phase-Amplitude Coupling

Cognitive reappraisal, an effective emotion regulation strategy, is influenced by various individual factors. Although previous studies have established a link between negative emotion differentiation (NED) and cognitive reappraisal, the underlying neural mechanisms remain largely unknown. Using electroencephalography, this study investigates the influence and neural basis of NED in cognitive reappraisal by integrating aspects of event-related potentials, neural oscillation rhythms, and cross-frequency coupling. The findings revealed that individuals with high NED demonstrated a significant decrease in parietal late positive potential amplitudes during cognitive reappraisal, suggesting enhanced cognitive reappraisal abilities. Moreover, high NED individuals displayed increased γ synchronization, parietal α-γ coupling, and frontal θ-γ coupling when reappraising negative emotions than those with low emotion differentiation ability. Machine learning analysis of these neural indicators highlighted the superior classification and predictive accuracy of multimodal indicators for NED as opposed to unimodal indicators. Overall, this multimodal evidence provides a comprehensive interpretation of the neurophysiological mechanisms through which NED influences cognitive reappraisal and provides preliminary empirical support for personalized cognitive reappraisal interventions to alleviate emotional problems.

Real-time TMS-EEG for brain state-controlled research and precision treatment: a narrative review and guide

Transcranial magnetic stimulation (TMS) modulates neuronal activity, but the efficacy of an open-loop approach is limited due to the brain state's dynamic nature. Real-time integration with electroencephalography (EEG) increases experimental reliability and offers personalized neuromodulation therapy by using immediate brain states as biomarkers. Here, we review brain state-controlled TMS-EEG studies since the first publication several years ago. A summary of experiments on the sensorimotor mu rhythm (8-13 Hz) shows increased cortical excitability due to TMS pulse at the trough and decreased excitability at the peak of the oscillation. Pre-TMS pulse mu power also affects excitability. Further, there is emerging evidence that the oscillation phase in theta and beta frequency bands modulates neural excitability. Here, we provide a guide for real-time TMS-EEG application and discuss experimental and technical considerations. We consider the effects of hardware choice, signal quality, spatial and temporal filtering, and neural characteristics of the targeted brain oscillation. Finally, we speculate on how closed-loop TMS-EEG potentially could improve the treatment of neurological and mental disorders such as depression, Alzheimer's, Parkinson's, schizophrenia, and stroke.

Support vector machine classification of patients with depression based on resting-state electroencephalography

Background

Depression is one of the most common mental disorders. Although depression is typically diagnosed by identifying specific symptoms and through history, no recognized standard for depression diagnosis exists. This assures the development of objective diagnostic tools for depression.

Objectives

We investigated the differences in the resting-state electroencephalograms (EEGs) of patients with depression and healthy controls (HCs) to distinguish patients from HCs by using a support vector machine (SVM) classifier with the following two feature selection approaches: t test and receiver operating characteristic analysis.

Methods

We used the EEG data from the Patient Repository of EEG Data + Computational Tools; this study included 21 patients with depressive disorder (MDD) and 21 HCs. The relative frequency power, alpha interhemispheric asymmetry, left-right coherence, strength, clustering coefficient (CC), shortest path length, sample entropy (SampEn), multiscale entropy (MSE), and detrended fluctuation analysis (DFA) data were extracted to determine candidate EEG features associated with depression.

Results

With the t-test selection, the SVM classifier demonstrated the highest performance with the accuracy, sensitivity, and specificity of 96.66%, 95.93%, and 97.550% for the eye-open condition and 91.33%, 90.59%, and 91.81% for the eye-closed condition, respectively. For comparisons of features in the 2 selection approaches, the most influential features were relative frequency power and left-right coherence.

Conclusion

Using this information to distinguish patients with MDD from HC subjects with the SVM classifier resulted in a mean accuracy over 90%. Although this result may not be robust enough for clinical applications, further exploration is necessary given the simplicity, objectivity, and efficiency of the classifier.

Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB)

BACKGROUND

The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear.

METHODS

In this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB).

RESULTS

We found that the MDD group showed significantly higher power in the high alpha range (10.5-11.5 Hz) and lower power in the low alpha range (7-8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone.

CONCLUSIONS

Our results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

Rationale and design of a double-blinded, randomized placebo-controlled trial of 40 Hz light neurostimulation therapy for depression (FELIX)

BACKGROUND

Major depressive disorder (MDD) is a debilitating condition that affects more than 300 million people worldwide. Current treatments are based on a trial-and-error approach, and reliable biomarkers are needed for more informed and personalized treatment solutions. One of the potential biomarkers, gamma-frequency (30-80 Hz) brainwaves, are hypothesized to originate from the excitatory-inhibitory interaction between the pyramidal cells and interneurons. The imbalance between this interaction is described as a crucial pathological mechanism in neuropsychiatric conditions, including MDD, and the modulation of this pathological interaction has been investigated as a potential target. Previous studies attempted to induce gamma activity in the brain using rhythmic light and sound stimuli (GENUS - Gamma Entrainment Using Sensory stimuli) that resulted in neuroprotective effects in Alzheimer's disease (AD) patients and animal models. Here, we investigate the antidepressant, cognitive, and electrophysiological effects of the novel light therapy approach using 40 Hz masked flickering light for patients diagnosed with MDD.

METHODS AND DESIGN

Sixty patients with a current diagnosis of a major depressive episode will be enrolled in a randomized, double-blinded, placebo-controlled trial. The active treatment group will receive 40 Hz masked flickering light stimulation while the control group will receive continuous light matched in color temperature and brightness. Patients in both groups will get daily light treatment in their own homes and will attend four follow-up visits to assess the symptoms of depression, including depression severity measured by Hamilton Depression Rating Scale (HAM-D17), cognitive function, quality of life and sleep, and electroencephalographic changes. The primary endpoint is the mean change from baseline to week 6 in depression severity (HAM-D6 subscale) between the groups.

Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture

Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for the direct quantification of rhythmicity. We applied pACF to human intracerebral stereoelectroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.

Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder

Background. Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method. The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction.

In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression

Reduced inhibition by somatostatin-expressing interneurons is associated with depression. Administration of positive allosteric modulators of α5 subunit-containing GABAA receptor (α5-PAM) that selectively target this lost inhibition exhibit antidepressant and pro-cognitive effects in rodent models of chronic stress. However, the functional effects of α5-PAM on the human brain in vivo are unknown, and currently cannot be assessed experimentally. We modeled the effects of α5-PAM on tonic inhibition as measured in human neurons, and tested in silico α5-PAM effects on detailed models of human cortical microcircuits in health and depression. We found that α5-PAM effectively recovered impaired cortical processing as quantified by stimulus detection metrics, and also recovered the power spectral density profile of the microcircuit EEG signals. We performed an α5-PAM dose-response and identified simulated EEG biomarker candidates. Our results serve to de-risk and facilitate α5-PAM translation and provide biomarkers in non-invasive brain signals for monitoring target engagement and drug efficacy.

High-frequency repetitive transcranial magnetic stimulation regulates neural oscillations of the hippocampus and prefrontal cortex in mice by modulating endocannabinoid signalling

BACKGROUND

Neural oscillations play a role in the antidepressant effects of repetitive transcranial magnetic stimulation (rTMS). However, the effects of high-frequency rTMS on the neural oscillations of the medial prefrontal cortex (mPFC) and hippocampus (HPC) and its molecular mechanism have not been fully clarified.

METHODS

The depressive-like behaviours, local field potentials (LFPs) of the ventral HPC (vHPC)-mPFC, and alternations of endocannabinoid system (ECS) in the HPC and mPFC were observed after rTMS treatment. Meanwhile, depressive-like behaviours and LFPs were also observed after cannabinoid type-1 receptor (CB1R) antagonist AM281 or monoacylglycerol lipase inhibitor JZL184 injection. Moreover, the antidepressant effect of rTMS was further assessed in glutamatergic-CB1R and gamma-amino butyric acid (GABA)-ergic -CB1R knockout mice.

RESULTS

Alternations of endocannabinoids and energy value and synchronisation of mPFC-vHPC, especially the decrease of theta oscillation induced by CUMS, were alleviated by rTMS. JZL184 has similar effects to rTMS and AM281 blocked the effects of rTMS. GABAergic-CB1R deletion inhibited CUMS-induced depressive-like behaviours whereas Glutaminergic-CB1R deletion dampened the antidepressant effects of rTMS.

LIMITATIONS

The immediate effect of rTMS on field-potential regulation was not observed. Moreover, the role of region-specific regulation of the ECS in the antidepressant effect of rTMS was unclear and the effects of cell-specific CB1R knockout on neuronal oscillations of the mPFC and vHPC should be further investigated.

CONCLUSION

Endocannabinoid system mediated the antidepressant effects and was involved in the regulation of LFP in the vHPC-mPFC of high-frequency rTMS.

A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS) have been proven effective non-invasive treatments for patients with drug-resistant major depressive disorder (MDD). However, some depressed patients do not respond to these treatments. Therefore, the investigation of reliable and valid brain oscillations as potential indices for facilitating the precision of diagnosis and treatment protocols has become a critical issue. The current review focuses on brain oscillations that, mostly based on EEG power analysis and connectivity, distinguish between MDD and controls, responders and non-responders, and potential depression severity indices, prognostic indicators, and potential biomarkers for rTMS or iTBS treatment. The possible roles of each biomarker and the potential reasons for heterogeneous results are discussed, and the directions of future studies are proposed.

Sex differences in amygdalohippocampal oscillations and neuronal activation in a rodent anxiety model and in response to infralimbic deep brain stimulation

Introduction

Depression and anxiety are highly comorbid mental disorders with marked sex differences. Both disorders show altered activity in the amygdala, hippocampus, and prefrontal cortex. Infralimbic deep brain stimulation (DBS-IL) has anxiolytic and antidepressant effects, but the underlying mechanisms remain unclear. We aimed to contribute to understanding sex differences in the neurobiology of these disorders.

Methods

In male and female rats, we recorded neural oscillations along the dorsoventral axis of the hippocampus and the amygdala in response to an anxiogenic drug, FG-7142. Following this, we applied DBS-IL.

Results

Surprisingly, in females, the anxiogenic drug failed to induce most of the changes observed in males. We found sex differences in slow, delta, theta, and beta oscillations, and the amygdalo-hippocampal communication in response to FG-7142, with modest changes in females. Females had a more prominent basal gamma, and the drug altered this band only in males. We also analyzed c-Fos expression in both sexes in stress-related structures in response to FG-7142, DBS-IL, and combined interventions. With the anxiogenic drug, females showed reduced expression in the nucleus incertus, amygdala, septohippocampal network, and neocortical levels. In both experiments, the DBS-IL reversed FG-7142-induced effects, with a more substantial effect in males than females.

Discussion

Here, we show a reduced response in female rats which contrasts with the higher prevalence of anxiety in women but is consistent with other studies in rodents. Our results open compelling questions about sex differences in the neurobiology of anxiety and depression and their study in animal models.

Neurostimulation as a treatment for mood disorders in patients: recent findings

PURPOSE OF REVIEW

The use of neurostimulation to treat mood disorders dates back to the 1930s. Recent studies have explored various neurostimulation methods aimed at both restoring a healthy brain and reducing adverse effects in patients. The purpose of this review is to explore the most recent hypotheses and clinical studies investigating the effects of stimulating the brain on mood disorders.

RECENT FINDINGS

Recent work on brain stimulation and mood disorders has focused mainly on three aspects: enhancing efficacy and safety by developing new approaches and protocols, reducing treatment duration and chances of relapse, and investigating the physiological and pathological mechanisms behind treatment outcomes and possible adverse effects.This review includes some of the latest studies on both noninvasive techniques, such as transcranial magnetic stimulation, magnetic seizure therapy, transcranial direct current stimulation, transcranial alternating current stimulation, electroconvulsive treatment, and invasive techniques, such as deep brain stimulation and vagus nerve stimulation.

SUMMARY

Brain stimulation is widely used in clinical settings; however, there is a lack of understanding about its neurobiological mechanism. Further studies are needed to understand the neurobiology of brain stimulation and how it can be used to treat mood disorders in their diversity, including comorbidities with other illnesses.

Alpha peak activity in resting-state EEG is associated with depressive score

Introduction

Depression is a serious psychiatric disorder characterized by prolonged sadness, loss of interest or pleasure. The dominant alpha peak activity in resting-state EEG is suggested to be an intrinsic neural marker for diagnosis of mental disorders.

Methods

To investigate an association between alpha peak activity and depression severity, the present study recorded resting-state EEG (EGI 128 channels, off-line average reference, source reconstruction by a distributed inverse method with the sLORETA normalization, parcellation of 68 Desikan-Killiany regions) from 155 patients with depression (42 males, mean age 35 years) and acquired patients' scores of Self-Rating Depression Scales. We measured both the alpha peak amplitude that is more related to synchronous neural discharging and the alpha peak frequency that is more associated with brain metabolism.

Results

The results showed that over widely distributed brain regions, individual patients' alpha peak amplitudes were negatively correlated with their depressive scores, and individual patients' alpha peak frequencies were positively correlated with their depressive scores.

Discussion

These results reveal that alpha peak amplitude and frequency are associated with self-rating depressive score in different manners, and the finding suggests the potential of alpha peak activity in resting-state EEG acting as an important neural factor in evaluation of depression severity in supplement to diagnosis.

Vagusnervstimulation bei schwer zu behandelnden Depressionen

Einführung

Seit 20 Jahren ist die Vagusnervstimulation (VNS) eine europaweit zugelassene invasive Therapieoption für therapieresistente Depressionen (TRD). Im Gegensatz zu geläufigeren Behandlungen wie EKT sind Kenntnisse über VNS sowohl in der Allgemeinbevölkerung als auch in Fachkreisen gering.

Methoden

In diesem narrativen Review geben wir eine klinisch und wissenschaftlich fundierte Übersicht über die VNS. Hypothesen zum Wirkmechanismus sowie die aktuelle Evidenzlage zur Wirksamkeit werden dargestellt. Das perioperative Management, das Nebenwirkungsprofil und die Nachbetreuung einschließlich Dosistitration werden beschrieben. Ein Vergleich über internationale Leitlinienempfehlungen zur VNS findet sich ebenfalls. Ferner formulieren wir Kriterien, die bei der Auswahl geeigneter Patienten hilfreich sind.

Ergebnisse

Die elektrischen Impulse werden über den N. vagus afferent weitergeleitet und stimulieren über verschiedene Wege ein neuromodulatorisches zerebrales Netzwerk. Viele Studien und Fallserien zeigten die Wirksamkeit von VNS als adjuvantes Verfahren bei TRD. Der Effekt tritt mit einer Latenz von 3 bis 12 Monaten ein und steigt möglicherweise mit der Dauer der VNS. Unter der Beachtung der Stimulationsempfehlungen sind die Nebenwirkungen für die meisten Patienten tolerabel.

Fazit

Die VNS ist eine zugelassene, wirksame und gut verträgliche Langzeittherapie für chronische und therapieresistente Depressionen. Weitere Sham-kontrollierte Studien über einen längeren Beobachtungszeitraum sind zur Verbesserung der Evidenz wünschenswert.

Zusatzmaterial online

Die Online-Version dieses Beitrags (10.1007/s00115-022-01282-6) enthält eine weitere Infobox. Beitrag und Zusatzmaterial stehen Ihnen auf www.springermedizin.de zur Verfügung. Bitte geben Sie dort den Beitragstitel in die Suche ein, das Zusatzmaterial finden Sie beim Beitrag unter „Ergänzende Inhalte“.

Evaluating the Color Preferences for Elderly Depression in the United Arab Emirates

The elderly are more prone to develop depression from physical, psychological, and economic changes, and 25.7% of the United Arab Emirates’ (UAE) elderly population suffer from depression. Color therapy is a widely accepted treatment to solve the depressive symptoms of the elderly. The color preference of the Seniors’ Happiness Centre—in Ajman UAE—a residential space for the elderly, could improve the quality of life, including depression symptoms. This paper explored the relationship between the color preference of the resident bedroom space and the depressive symptoms. As a methodology, using color images as stimuli, the physiological and psychological responses of the 86 elderly participants to the proposed color preference of the resident bedroom interiors—observed through a viewing box to simulate 3D space perception—were compared and analyzed to investigate the relationship between the color preference and depression by a survey with the Geriatric Depression Scale (GDS) and Electroencephalogram (EEG) measurement. The results showed that the elderly’s preference for warm colors is higher than that of cold colors, and each room needs a different color scheme because the elderly, 65 and above, have different visual characteristics. There was no significant difference between the left and right alpha wave values of the prefrontal cortex of the participant group. The main reason is that the brain waves are minute electrical signals and appear different from person to person. The color scheme on one side of the wall with increased saturation seemed to improve depressive symptoms effectively. It was found that psychologically, healthy elderly reacted positively to the single-color scheme of the Blue cool color, but elderly with depression reacted well to the contrast color scheme of the Blue-Yellow/Red cool color. This study will serve as critical data to propose more color preferences for the Seniors’ Happiness Center suitable for the elderly by studying the response to more diverse colors in the UAE.

Acute effect of vagus nerve stimulation (VNS) on brain function

INTRODUCTION

VNS is a non-pharmalogical neuromodulatory treatment option for difficult-to-treat depression. A pulse generator implanted in the left chest area is connected to an electrode that is wrapped around the left vagal nerve. It is presumed, that the vagal afferent network modulates neuronal activity in key monoaminergic structures.

METHODS

We performed MEG recording during active stimulation of the left vagal nerve. Our patient was a 60 years old female treated with VNS since December 2019 due to unipolar major depression.

RESULTS

MEG recording and analysis were possible despite stimulation signals and the metal stimulation systems. We saw a reproducible reduction of the 10-Hz-alpha amplitude after the end of the 30 s stimulation period in wide-spread areas including parieto-occipital cortex where alpha oscillations are prominently generated. During stimulation, however, alpha oscillations remained unaffected. These findings could be reproduced in a second measurement.

CONCLUSION

Increased alpha power was linked to depressive states and alterations of cortical activity. A reduction may indicate cortical activation by stimulation of the vagal nerve as a possible mechanism of action of VNS in depression.

Minocycline inhibits sleep deprivation-induced aberrant microglial activation and Keap1-Nrf2 expression in mouse hippocampus

Sleep deprivation (SD) is a hallmark of modern society and associated with many neuropsychiatric disorders, including depression and anxiety. However, the cellular and molecular mechanisms underlying SD-associated depression and anxiety remain elusive. Does the neuroinflammation play a role in mediating the effects of SD? In this study, we investigated SD-induced cellular and molecular alterations in the hippocampus and asked whether treatment with an anti-inflammatory drug, minocycline, could attenuate these alterations. We found that SD animals exhibit activated microglia and decreased levels of Keap1 and Nrf2 (antioxidant and anti-inflammatory factors) in the hippocampus. In vivo local field potential recordings show decreased theta and beta oscillations, but increased high gamma oscillations, as a result of SD. Behavioral analysis revealed increased immobility time in the forced swim and tail suspension tests, and decreased sucrose intake in SD mice, all indicative of depressive-like behavior. Moreover, open field test and elevated plus maze test results indicated that SD increases anxiety-like behavior. Interestingly, treatment with the microglial modulator minocycline prevented SD-induced microglial activation, restored Keap1 and Nrf2 levels, normalized neuronal oscillations, and alleviated depressive-like and anxiety-like behavior. The present study reveals that microglial activation and Keap1-Nrf2 signaling play a crucial role in SD-induced behavioral alteration, and that minocycline treatment has a protective effect on these alterations.

Insights into the Pathophysiology of Psychiatric Symptoms in Central Nervous System Disorders: Implications for Early and Differential Diagnosis

Different psychopathological manifestations, such as affective, psychotic, obsessive-compulsive symptoms, and impulse control disturbances, may occur in most central nervous system (CNS) disorders including neurodegenerative and neuroinflammatory diseases. Psychiatric symptoms often represent the clinical onset of such disorders, thus potentially leading to misdiagnosis, delay in treatment, and a worse outcome. In this review, psychiatric symptoms observed along the course of several neurological diseases, namely Alzheimer’s disease, fronto-temporal dementia, Parkinson’s disease, Huntington’s disease, and multiple sclerosis, are discussed, as well as the involved brain circuits and molecular/synaptic alterations. Special attention has been paid to the emerging role of fluid biomarkers in early detection of these neurodegenerative diseases. The frequent occurrence of psychiatric symptoms in neurological diseases, even as the first clinical manifestations, should prompt neurologists and psychiatrists to share a common clinico-biological background and a coordinated diagnostic approach.

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.

Electroencephalographic Parameters Differentiating Melancholic Depression, Non-melancholic Depression, and Healthy Controls. A Systematic Review

Introduction: The objective of this systematic review was to investigate whether electroencephalographic parameters can serve as a tool to distinguish between melancholic depression, non-melancholic depression, and healthy controls in adults. Methods: A systematic review comprising an extensive literature search conducted in PubMed, Embase, Google Scholar, and PsycINFO in August 2020 with monthly updates until November 1st, 2020. In addition, we performed a citation search and scanned reference lists. Clinical trials that performed an EEG-based examination on an adult patient group diagnosed with melancholic unipolar depression and compared with a control group of non-melancholic unipolar depression and/or healthy controls were eligible. Risk of bias was assessed by the Strengthening of Reporting of Observational Studies in Epidemiology (STROBE) checklist. Results: A total of 24 studies, all case-control design, met the inclusion criteria and could be divided into three subgroups: Resting state studies (n = 5), sleep EEG studies (n = 10), and event-related potentials (ERP) studies (n = 9). Within each subgroup, studies were characterized by marked variability on almost all levels, preventing pooling of data, and many studies were subject to weighty methodological problems. However, the main part of the studies identified one or several EEG parameters that differentiated the groups. Conclusions: Multiple EEG modalities showed an ability to distinguish melancholic patients from non-melancholic patients and/or healthy controls. The considerable heterogeneity across studies and the frequent methodological difficulties at the individual study level were the main limitations to this work. Also, the underlying premise of shifting diagnostic paradigms may have resulted in an inhomogeneous patient population. Systematic Review Registration: Registered in the PROSPERO registry on August 8th, 2020, registration number CRD42020197472.

Face Recognition Brain Functional Connectivity in Patients With Major Depression: A Brain Source Localization Study by ERP

Objective: Patients with major depressive disorder (MDD) presents with face recognition defects. These defects negatively affect their social interactions. However, the cause of these defects is not clear. This study sought to explore whether MDD patients develop facial perceptual processing disorders with characteristics of brain functional connectivity (FC). Methods: Event-related potential (ERP) was used to explore differences between 20 MDD patients and 20 healthy participants with face and non-face recognition tasks based on 64 EEG parameters. After pre-processing of EEG data and source reconstruction using the minimum-norm estimate (MNE), data were converted to AAL90 template to obtain a time series of 90 brain regions. EEG power spectra were determined using Fieldtrip incorporating a Fast Fourier transform. FC was determined for all pairs of brain signals for theta band using debiased estimate of weighted phase-lag index (wPLI) in Fieldtrip. To explore group differences in wPLI, independent t-tests were performed with p < 0.05 to indicate statistical significance. False discovery rate (FDR) correction was used to adjust p-values. Results: The findings showed that amplitude induction by face pictures was higher compared with that of non-face pictures both in MDD and healthy control (HC) groups. Face recognition amplitude in MDD group was lower compared with that in the HC group. Two time periods with significant differences were then selected for further analysis. Analysis showed that FC was stronger in the MDD group compared with that in the HC group in most brain regions in both periods. However, only one FC between two brain regions in HC group was stronger compared with that in the MDD group. Conclusion: Dysfunction in brain FC among MDD patients is a relatively complex phenomenon, exhibiting stronger and multiple connectivity with several brain regions of emotions. The findings of the current study indicate that the brain FC of MDD patients is more complex and less efficient in the initial stage of face recognition.

Theta and gamma connectivity is linked with affective and cognitive symptoms in Parkinson's disease

BACKGROUND

The progression of Parkinson's disease (PD) can often exacerbate symptoms of depression, anxiety, and/or cognitive impairment. In this study, we explore the possibility that multiple brain network responses are associated with symptoms of depression, anxiety and cognitive impairment in PD. This association is likely to provide insights into a single multivariate relationship, where common affective symptoms occurring in PD cohorts are related with alterations to electrophysiological response.

METHODS

70 PD patients and 21 healthy age-matched controls (HC) participated in a high-density electroencephalography (EEG) study. Functional connectivity differences between PD and HC groups of oscillatory activity at rest and during completion of an emotion-cognition task were examined to identify key brain oscillatory activities. A canonical correlation analysis (CCA) was applied to identify a putative multivariate relationship between connectivity patterns and affective symptoms in PD groups.

RESULTS

A CCA analysis identified a single mode of co-variation linking theta and gamma connectivity with affective symptoms in PD groups. Increases in frontotemporal gamma, frontal and parietal theta connectivity were related with increased anxiety and cognitive impairment. Decreases in temporal region theta and frontoparietal gamma connectivity were associated with higher depression ratings and PD patient age.

LIMITATIONS

This study only reports on optimal dosage of dopaminergic treatment ('on' state) in PD and did not investigate at "off" medication".

CONCLUSIONS

Theta and gamma connectivity during rest and task-states are linked to affective and cognitive symptoms within fronto-temporo-parietal networks, suggesting a potential assessment avenue for understanding brain-behaviour associations in PD with electrophysiological task paradigms.