Right hemisphere abnormalities in major depression: quantitative electroencephalographic findings before and after treatment

Transcranial alternating current stimulation for the treatment of major depressive disorder: from basic mechanisms toward clinical applications

Non-pharmacological treatment is essential for patients with major depressive disorder (MDD) that is medication resistant or who are unable to take medications. Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation method that manipulates neural oscillations. In recent years, tACS has attracted substantial attention for its potential as an MDD treatment. This review summarizes the latest advances in tACS treatment for MDD and outlines future directions for promoting its clinical application. We first introduce the neurophysiological mechanism of tACS and its novel developments. In particular, two well-validated tACS techniques have high application potential: high-definition tACS targeting local brain oscillations and bifocal tACS modulating interarea functional connectivity. Accordingly, we summarize the underlying mechanisms of tACS modulation for MDD. We sort out the local oscillation abnormalities within the reward network and the interarea oscillatory synchronizations among multiple MDD-related networks in MDD patients, which provide potential modulation targets of tACS interventions. Furthermore, we review the latest clinical studies on tACS treatment for MDD, which were based on different modulation mechanisms and reported alleviations in MDD symptoms. Finally, we discuss the main challenges of current tACS treatments for MDD and outline future directions to improve intervention target selection, tACS implementation, and clinical validations.

Sub-Scalp Implantable Telemetric EEG (SITE) for the Management of Neurological and Behavioral Disorders beyond Epilepsy

Sub-scalp Implantable Telemetric EEG (SITE) devices are under development for the treatment of epilepsy. However, beyond epilepsy, continuous EEG analysis could revolutionize the management of patients suffering from all types of brain disorders. This article reviews decades of foundational EEG research, collected from short-term routine EEG studies of common neurological and behavioral disorders, that may guide future SITE management and research. Established quantitative EEG methods, like spectral EEG power density calculation combined with state-of-the-art machine learning techniques applied to SITE data, can identify new EEG biomarkers of neurological disease. From distinguishing syncopal events from seizures to predicting the risk of dementia, SITE-derived EEG biomarkers can provide clinicians with real-time information about diagnosis, treatment response, and disease progression.

Resting State EEG Correlates of Suicide Ideation and Suicide Attempt

Suicide is a global phenomenon that impacts individuals, families, and communities from all income groups and all regions worldwide. While it can be prevented if personalized interventions are implemented, more objective and reliable diagnostic methods are needed to complement interview-based risk assessments. In this context, electroencephalography (EEG) might play a key role. We systematically reviewed EEG resting state studies of adults with suicide ideation (SI) or with a history of suicide attempts (SAs). After searching for relevant studies using the PubMed and Web of Science databases, we applied the PRISMA method to exclude duplicates and studies that did not match our inclusion criteria. The selection process yielded seven studies, which suggest that imbalances in frontal and left temporal brain regions might reflect abnormal activation and correlate with psychological distress. Furthermore, asymmetrical activation in frontal and posterior cortical regions was detected in high-risk depressed persons, although the pattern in the frontal region was inverted in non-depressed persons. The literature reviewed suggests that SI and SA may be driven by separate neural circuits and that high-risk persons can be found within non-depressed populations. More research is needed to develop intelligent algorithms for the automated detection of high-risk EEG anomalies in the general population.

Electrophysiological correlates and predictors of the antidepressant response to repeated ketamine infusions in treatment-resistant depression

BACKGROUND

Sub-anesthetic ketamine doses rapidly reduce depressive symptoms, although additional investigations of the underlying neural mechanisms and the prediction of response outcomes are needed. Electroencephalographic (EEG)-derived measures have shown promise in predicting antidepressant response to a variety of treatments, and are sensitive to ketamine administration. This study examined their utility in characterizing changes in depressive symptoms following single and repeated ketamine infusions.

METHODS

Recordings were obtained from patients with treatment-resistant major depressive disorder (MDD) (N = 24) enrolled in a multi-phase clinical ketamine trial. During the randomized, double-blind, crossover phase (Phase 1), patients received intravenous ketamine (0.5 mg/kg) and midazolam (30 μg/kg), at least 1 week apart. For each medication, three resting, eyes-closed recordings were obtained per session (pre-infusion, immediately post-infusion, 2 h post-infusion), and changes in power (delta, theta1/2/total, alpha1/2/total, beta, gamma), alpha asymmetry, theta cordance, and theta source-localized anterior cingulate cortex activity were quantified. The relationships between ketamine-induced changes with early (Phase 1) and sustained (Phases 2,3: open-label repeated infusions) decreases in depressive symptoms (Montgomery-Åsberg Depression Rating Score, MADRS) and suicidal ideation (MADRS item 10) were examined.

RESULTS

Both medications decreased alpha and theta immediately post-infusion, however, only midazolam increased delta (post-infusion), and only ketamine increased gamma (immediately post- and 2 h post-infusion). Regional- and frequency-specific ketamine-induced EEG changes were related to and predictive of decreases in depressive symptoms (theta, gamma) and suicidal ideation (alpha). Early and sustained treatment responders differed at baseline in surface-level and source-localized theta.

CONCLUSIONS

Ketamine exerts frequency-specific changes on EEG-derived measures, which are related to depressive symptom decreases in treatment-resistant MDD and provide information regarding early and sustained individual response to ketamine.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov: Action of Ketamine in Treatment-Resistant Depression, NCT01945047.

Pharmaco-Electroencephalography-Based Assessment of Antidepressant Drug Efficacy-The Use of Magnesium Ions in the Treatment of Depression

Pharmaco-electroencephalography (pharmaco-EEG) is a technique used to assess the effects of psychotropic medications on the bioelectrical activity of the brain. The purpose of this study was to assess the treatment response with the use of the Hamilton Depression Rating Scale (HDRS) and via EEG. Over an 8-week period, we analyzed electroencephalographic tracings of 91 patients hospitalized for major depression at the Medical University of Warsaw. Thirty-nine of those patients received tricyclic antidepressants (TCAs), 35 received fluoxetine, and 17 received fluoxetine augmented with magnesium (Mg) ions. All patients had their serum drug levels monitored. The highest proportion of patients (88.2%) who showed adequate responses to treatment was observed in the fluoxetine+Mg group, whereas the lowest rates of treatment response were observed in the TCA group (58.3%). This difference was statistically significant (p = 0.029, Phi = 0.30). Our study demonstrated a relationship between achieving remission (HDRS ≤ 6 at week 8 of treatment) and obtaining a positive pharmaco-EEG profile 6 h after administration of the first dose in the group receiving fluoxetine augmented with Mg ions (p = 0.035, Phi = 0.63).

EEG Correlates of Cognitive Functions and Neuropsychiatric Disorders: A Review of Oscillatory Activity and Neural Synchrony Abnormalities

Background:

A large body of evidence suggested that disruption of neural rhythms and synchronization of brain oscillations are correlated with a variety of cognitive and perceptual processes. Cognitive deficits are common features of psychiatric disorders that complicate treatment of the motivational, affective and emotional symptoms.

Objective:

Electrophysiological correlates of cognitive functions will contribute to understanding of neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and developing novel targets for the treatment of cognitive impairments.

Methods:

This review includes a description of brain oscillations in Alzheimer’s disease, bipolar disorder, attention-deficit/hyperactivity disorder, major depression, obsessive compulsive disorders, anxiety disorders, schizophrenia and autism.

Results:

The review clearly shows that the reviewed neuropsychiatric diseases are associated with fundamental changes in both spectral power and coherence of EEG oscillations.

Conclusion:

In this article, we examined the nature of brain oscillations, the association of brain rhythms with cognitive functions and the relationship between EEG oscillations and neuropsychiatric diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric disorders.

Preliminary Study on Changes of Sleep EEG Power and Plasma Melatonin in Male Patients With Major Depressive Disorder After 8 Weeks Treatment

Objective: To clarify the effects of escitalopram on sleep EEG power in patients with Major depressive disorder (MDD). Method: Polysomnography (PSG) was detected overnight, and blood samples were collected at 4 h intervals over 24 h from 13 male healthy controls and 13 male MDD patients before and after treatment with escitalopram for 8 weeks. The outcome measures included plasma melatonin levels, sleep architecture, and the sleep EEG power ratio. Results: Compared with healthy controls, MDD patients presented abnormalities in the diurnal rhythm of melatonin secretion, including peak phase delayed 3 h and a decrease in plasma melatonin levels at night and an increase at daytime, accompanied by sleep disturbances, a decrease in low-frequency bands and an increase in high-frequency bands, and the dominant right-side brain activity. Several of these abnormalities (abnormalities in the diurnal rhythm of melatonin secretion, partial sleep architecture parameters) persisted for at least the 8-week testing period. Conclusions: Eight weeks of treatment with escitalopram significantly improved subjective sleep perception and depressive symptoms of patients with MDD, and partially improved objective sleep parameters, while the improvement of circadian rhythm of melatonin was limited.

Functional connectivity of major depression disorder using ongoing EEG during music perception

OBJECTIVE

The functional connectivity (FC) of major depression disorder (MDD) has not been well studied under naturalistic and continuous stimuli conditions. In this study, we investigated the frequency-specific FC of MDD patients exposed to conditions of music perception using ongoing electroencephalogram (EEG).

METHODS

First, we applied the phase lag index (PLI) method to calculate the connectivity matrices and graph theory-based methods to measure the topology of brain networks across different frequency bands. Then, classification methods were adopted to identify the most discriminate frequency band for the diagnosis of MDD.

RESULTS

During music perception, MDD patients exhibited a decreased connectivity pattern in the delta band but an increased connectivity pattern in the beta band. Healthy people showed a left hemisphere-dominant phenomenon, but MDD patients did not show such a lateralized effect. Support vector machine (SVM) achieved the best classification performance in the beta frequency band with an accuracy of 89.7%, sensitivity of 89.4% and specificity of 89.9%.

CONCLUSIONS

MDD patients exhibited an altered FC in delta and beta bands, and the beta band showed a superiority in the diagnosis of MDD.

SIGNIFICANCE

Our study provided a promising reference for the diagnosis of MDD, and revealed a new perspective for understanding the topology of MDD brain networks during music perception.

Electroencephalography Symmetry in Power, Waveform and Power Spectrum in Major Depression

Depression is a harmful disease with high incidence. However, no effective method based on physiological information detection has been published to diagnose depression. Electroencephalography (EEG) has been used as a tool to detect physiological information of depressed patients and the symmetry of EEG receives much attention. This research focused on the symmetry of EEG in left and right homologous brain regions. 22 healthy volunteers and 41 volunteers of major depression were tested and three methods, average power ratio, waveform correlation and power spectral correlation, were adopted to measure the symmetry in all frequency bands and all brain regions. After t-test, homologous site pairs in particular frequency bands with significant differences between major depressed patients and controls were found out. Then sample entropy analysis was adopted, trying to figure out further connections between EEG symmetry and major depression. The accuracy tests were also taken and the average accuracy of some tests could reach 93.7%. The result of this research can hopefully serve as a theoretical basis for pattern recognition in the diagnosis of depression. The accuracy of pattern recognition based on multiple processing methods and sites will increase dramatically.

The Role of Quantitative EEG in the Diagnosis of Neuropsychiatric Disorders

Quantitative electroencephalography (QEEG) is a modern type of electroencephalography (EEG) analysis that involves recording digital EEG signals which are processed, transformed, and analyzed using complex mathematical algorithms. QEEG has brought new techniques of EEG signals feature extraction: analysis of specific frequency band and signal complexity, analysis of connectivity, and network analysis. The clinical application of QEEG is extensive, including neuropsychiatric disorders, epilepsy, stroke, dementia, traumatic brain injury, mental health disorders, and many others. In this review, we talk through existing evidence on the practical applications of this clinical tool. We conclude that to date, the role of QEEG is not necessarily to pinpoint an immediate diagnosis but to provide additional insight in conjunction with other diagnostic evaluations in order to objective information necessary for obtaining a precise diagnosis, correct disease severity assessment, and specific treatment response evaluation.

Imagery-induced negative affect, social touch and frontal EEG power band activity

Social touch seems to modulate emotions, but its brain correlates are poorly understood. Here, we investigated if frontal power band activity in the electroencephalogram (EEG) during aversive mental imagery is modulated by social touch from one's romantic partner and a stranger. We observed the highest theta and beta power when imaging alone, next so when being touched by a stranger, with lowest theta and beta activity during holding hands with the loved one. Delta power was higher when being alone than with a stranger or a partner, with no difference between the two. Gamma power was highest during the stranger condition and lower both when being alone and with the partner, while alpha power did not change as a function of social touch. Theta power displayed a positive correlation with electrodermal activity supporting its relation to emotional arousal. Attachment style modulated the effect of touch on the EEG as only secure but not insecure partner bonding was associated with theta power reductions. Because theta power was sensitive to the experimental perturbations, mapped onto peripheral physiological arousal and reflected partner attachment style we suggest that frontal theta power might serve as an EEG derived bio-marker for social touch in emotionally significant dyads.

Resting-state EEG beta band power predicts quality of life outcomes in patients with depressive disorders: A longitudinal investigation

BACKGROUND

Quality of life is severely impaired in patients with depressive disorders. Previous studies have focused on biomarkers predicting depressive symptomatology; however, studies investigating biomarkers predicting quality of life outcomes are limited. Improving quality of life is important because it is related not only to mental health but also to physical health. We need to develop a biomarker related to quality of life as a therapeutic target for patients with depressive disorders. Resting-state electroencephalography (EEG) is easy to record in clinical settings. The index of bandwidth spectral power predicts treatment response in depressive disorders and thus may be a candidate biomarker. However, no longitudinal studies have investigated whether EEG-recorded power could predict quality of life outcomes in patients with depressive disorders.

METHODS

The resting-state EEG-recorded bandwidth spectral power at baseline and the World Health Organization Quality of Life (QOL)-26 scores at 3-year follow-up were measured in 44 patients with depressive disorders.

RESULTS

The high beta band power (20-30 Hz) at baseline significantly predicted QOL at the 3-year follow-up after considering depressive symptoms and medication effects in a longitudinal investigation in patients with depressive disorders (β = 0.38, p = 0.01).

LIMITATIONS

We did not have healthy subjects as a comparison group in this study.

CONCLUSIONS

Our findings suggest that resting-state beta activity has the potential to be a useful biomarker for predicting future quality of life outcomes in patients with depressive disorders.

Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

The cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using high-density EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p < 0.001) in patients than in controls. The global efficiency was significantly higher (p < 0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{R}}}^{{\bf{2}}}{\boldsymbol{=}}{\bf{0.59}}\,{\bf{and}}\,{\bf{p}}{\boldsymbol{=}}{\bf{1.52}}{\bf{e}}{\boldsymbol{ \mbox{-} }}{\bf{05}}$$\end{document}R2=0.59andp=1.52e‐05). The amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression.

Resting State Quantitative Electroencephalogram Power Spectra in Patients with Depressive Disorder as Compared to Normal Controls: An Observational Study

INTRODUCTION

A significant number of quantitative electroencephalogram (qEEG) studies indicate that increased spectral activities distinguish patients with depressive disorder from control subjects. But they did not yield consistent findings in the delta, theta, alpha, or beta bands.

METHODS

A total of 30 drug-naïve or drug-free subjects with a depressive episode or recurrent depressive disorder were compared with 30 age, sex, education, and handedness-matched healthy controls using qEEG power spectra in six frequency bands (delta, theta, alpha, beta, slow beta, and fast beta) and total activities separately. Spectral analysis was performed on a section of 180 s of qEEG digitized at the rate of 512 samples/s/channel, and absolute powers were log-transformed before statistical analysis.

RESULTS

Statistically significant differences between the patients and normal controls were found in the delta and the total bands, while Structured Interview Guide for the Hamilton Depression Rating Scale ( SIGH-D) score predicted the fast beta spectral power at the left temporal region. In the entire region of the brain, in the theta band, lesser absolute spectral power was found in patients than normal controls, whereas in the fast beta band, it was greater. In other bands, greater powers of spectral activities were found in patients than normal controls consistently in the parietal and occipital regions.

CONCLUSION

Various findings of qEEG absolute power spectra could demonstrate a difference between the patients with depressive disorder and the normal controls independently and efficiently. However, all the differences collectively showed stronger evidence. The findings may steer future studies to differentiate the patients with depressive disorder from controls.

Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data

OBJECTIVE

Major depression disorder (MDD) is one of the most prevalent mental disorders worldwide. Diagnosing depression in the early stage is crucial to treatment process. However, due to depression's comorbid nature and the subjectivity in diagnosis, an early diagnosis could be challenging. Recently, machine learning approaches have been used to process Electroencephalography (EEG) and neuroimaging data to facilitate the diagnosis. In the present study, we used a multimodal machine learning approach involving EEG, eye tracking and galvanic skin response data as input to classify depression patients and healthy controls.

METHODS

One hundred and forty-four MDD depression patients and 204 matched healthy controls were recruited. They were required to watch a series of affective and neutral stimuli while EEG, eye tracking information and galvanic skin response were recorded via a set of low-cost, portable devices. Three machine learning algorithms including Random Forests, Logistic Regression and Support Vector Machine (SVM) were trained to build dichotomous classification model.

RESULTS

The results showed that the highest classification f1 score was obtained by Logistic Regression algorithms, with accuracy = 79.63%, precision = 76.67%, recall = 85.19% and f1 score = 80.70% LIMITATIONS: No hospitalized patients were available; only outpatients were included in the present study. The sample consisted mostly of young adult, and no elder patients were included.

CONCLUSIONS

The machine learning approach can be a useful tool for classifying MDD patients and healthy controls and may help for diagnostic processes.

EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.

Complexity Analysis of EEG Data in Persons With Depression Subjected to Transcranial Magnetic Stimulation

Aim: The aim of this work was to study the neurophysiological effect of repetitive transcranial magnetic stimulation (rTMS) applied to the left dorsolateral prefrontal cortex (DLPFC) in 8 patients with major depression disorder (MDD) and 10 patients with bipolar disorder (BP), considering separately responders and non-responders to rTMS therapy in each of both groups. Methods: The Higuchi's Fractal Dimension (FD) was analyzed from 64-channels EEG signals in five physiological frequency bands and every channel separately. Changes of FD were analyzed before and after 1st, 10th, and 20th session of rTMS. Results: Some differences in response to the rTMS therapy was found across individual groups. In MDD responders, FD decreased in all bands after longer stimulation (20th session). Whereas, in BP non-responders, FD decreased after 1st session in all bands as well as after 10th session in lower frequencies (delta and theta). In MDD non-responders and BP responders FD increased at the beginning of the therapy (1st and 10th session, respectively), but the final FD value did not changed in comparison to the initial FD value, except the FD decrease for theta band in BP responders. Comparison between groups showed a higher FD in MDD responders than in MDD non-responders in every band before as well as after stimulation. In contrast to MDD patients, FD was lower in BP responders than in BP non-responders in higher frequency bands (alpha, beta, and gamma) in both conditions as well as in lower frequency bands (delta and theta) after stimulation. Comparing both groups of responders, FD was lower in MDD than in BP in every band, except alpha. In case of non-responders, FD was higher in BP than in MDD in all bands in both conditions. Conclusion: The results showed that FD may be useful marker for evaluation of the rTMS effectiveness and the therapy progress as well as for group differentiation between MDD and BP or between responders and non-responders. The changes of FD under the influence of rTMS allow to unambiguously conclude whether the effect of stimulation is positive or negative as well as allow to evaluate an optimal time of rTMS.

The Abnormality of Topological Asymmetry in Hemispheric Brain Anatomical Networks in Bipolar Disorder

Convergent evidences have demonstrated a variety of regional abnormalities of asymmetry in bipolar disorder (BD). However, little is known about the alterations in hemispheric topological asymmetries. In this study, we used diffusion tensor imaging to construct the hemispheric brain anatomical network of 49 patients with BD and 61 matched normal controls. Graph theory was then applied to quantify topological properties of the hemispheric networks. Although small-world properties were preserved in the hemispheric networks of BD, the degrees of the asymmetry in global efficiency, characteristic path length, and small-world property were significantly decreased. More changes in topological properties of the right hemisphere than those of left hemisphere were found in patients compared with normal controls. Consistent with such changes, the nodal efficiency in patients with BD also showed less rightward asymmetry mainly in the frontal, occipital, parietal, and temporal lobes. In contrast to leftward asymmetry, significant rightward asymmetry was found in supplementary motor area of BD, and attributed to more deficits in nodal efficiency of the left hemisphere. Finally, these asymmetry score of nodal efficiency in the inferior parietal lobule and rolandic operculum were significantly associated with symptom severity of BD. Our results suggested that abnormal hemispheric asymmetries in brain anatomical networks were associated with aberrant neurodevelopment, and providing insights into the potential neural biomarkers of BD by measuring the topological asymmetry in hemispheric brain anatomical networks.

Electroencephalographic delta/alpha frequency activity differentiates psychotic disorders: a study of schizophrenia, bipolar disorder and methamphetamine-induced psychotic disorder

Electroencephalography (EEG) has been proposed as a neurophysiological biomarker to delineate psychotic disorders. It is known that increased delta and decreased alpha, which are apparent in psychosis, are indicative of inappropriate arousal state, which leads to reduced ability to attend to relevant information. On this premise, we investigated delta/alpha frequency activity, as this ratio of frequency activity may serve as an effective neurophysiological biomarker. The current study investigated differences in delta/alpha frequency activity, in schizophrenia (SCZ), bipolar I disorder with psychotic features and methamphetamine-induced psychosis. One hundred and nine participants, including individuals with SCZ (n = 28), bipolar I disorder with psychotic features (n = 28), methamphetamine-induced psychotic disorder (MPD) (n = 24) and healthy controls (CON, n = 29). Diagnosis was ascertained with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition disorders and current medication was recorded. EEG was undertaken in three testing conditions: resting eyes open, resting eyes closed and during completion of a simple cognitive task (visual continuous performance task). EEG delta/alpha frequency activity was investigated across these conditions. First, delta/alpha frequency activity during resting eyes closed was higher in SCZ and MPD globally, when compared to CON, then lower for bipolar disorder (BPD) than MPD for right hemisphere. Second, delta/alpha frequency activity during resting eyes open was higher in SCZ, BPD and MPD for all electrodes, except left frontal, when compared to CON. Third, delta/alpha frequency activity during the cognitive task was higher in BPD and MPD for all electrodes, except left frontal, when compared to CON. Assessment of EEG delta/alpha frequency activity supports the delineation of underlying neurophysiological mechanisms present in psychotic disorders, which are likely related to dysfunctional thalamo-cortical connectivity. Delta/alpha frequency activity may provide a useful neurophysiological biomarker to delineate psychotic disorders.

The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: A pilot study

BACKGROUND

This study aims to compare the effectiveness of EEG frequency band activity including interhemispheric asymmetry and prefrontal theta cordance in predicting response to escitalopram therapy at 8-weeks post-treatment, in a multi-site initiative.

METHODS

Resting state 64-channel EEG data were recorded from 44 patients with a diagnosis of major depressive disorder (MDD) as part of a larger, multisite discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). Clinical response was measured at 8-weeks post-treatment as change from baseline Montgomery-Asberg Depression Rating Scale (MADRS) score of 50% or more. EEG measures were analyzed at (1) pre-treatment baseline (2) 2 weeks post-treatment and (3) as an ''early change" variable defined as change in EEG from baseline to 2 weeks post-treatment.

RESULTS

At baseline, treatment responders showed elevated absolute alpha power in the left hemisphere while non-responders showed the opposite. Responders further exhibited a cortical asymmetry in the parietal region. Groups also differed in pre-treatment relative delta power with responders showing greater power in the right hemisphere over the left while non-responders showed the opposite. At 2 weeks post-treatment, responders exhibited greater absolute beta power in the left hemisphere relative to the right and the opposite was noted for non-responders. A reverse pattern was noted for absolute and relative delta power at 2 weeks post-treatment. Responders exhibited early reductions in relative alpha power and early increments in relative theta power. Non-responders showed a significant early increase in prefrontal theta cordance.

CONCLUSIONS

Hemispheric asymmetries in the alpha and delta bands at baseline and at 2 weeks post-treatment have moderately strong predictive utility in predicting response to antidepressant treatment.

Electroencephalographic Correlates of Suicidal Ideation in the Theta Band

Previous studies have indicated that the abnormalities of brain activity are associated with suicidal behavior. However, few studies have explored neurophysiologic biomarkers of suicidal ideation (SI) in healthy subjects. Here, we studied alterations of theta quantitative EEG (QEEG) in frontal regions associated with SI in young healthy subjects. Resting QEEG data were recorded from 90 young healthy subjects. The subjects were divided into two group based on Scale for Suicidal Ideation (SSI) scores: a high SSI group (n = 33) and a low SSI group (n = 57). Individual frontal electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8) and central midline electrodes (FCz, Cz) were examined for absolute and relative power in the theta band. Clinical state and social support was assessed using the Hospital Anxiety and Depression Scale and Functional Social Support Questionnaire. We found that theta absolute and relative power in all channels was positively correlated with SSI, except Fp1, F7 and F8. The high SSI group showed higher theta relative power at F3, Fz, FCz, and Cz than the low SSI group. Theta relative power in the fronto-central region was significantly increased in the high SSI group compared to that in the low SSI group. Hierarchical regression analysis showed that SSI score was the most powerful predictor of fronto-central theta power. The findings suggest that brain electrical activity at the fronto-central region may be associated with differences in SI in young healthy subjects. EEG activity in theta band has clinical potential as a biomarker for preventing suicide.

Low β2 Main Peak Frequency in the Electroencephalogram Signs Vulnerability to Depression

Objective: After an intense and repeated stress some rats become vulnerable to depression. This state is characterized by persistent low serum BDNF concentration. Our objective was to determine whether electrophysiological markers can sign vulnerability to depression. Methods: Forty-three Sprague Dawley rats were recorded with supradural electrodes above hippocampus and connected to wireless EEG transmitters. Twenty-nine animals experienced four daily social defeats (SD) followed by 1 month recovery. After SD, 14 rats had persistent low serum BDNF level and were considered as vulnerable (V) while the 15 others were considered as non-vulnerable (NV). EEG signals were analyzed during active waking before SD (Baseline), just after SD (Post-Stress) and 1 month after SD (Recovery). Results: We found that V animals are characterized by higher high θ and α spectral relative powers and lower β2 main peak frequency before SD. These differences are maintained at Post-Stress and Recovery for α spectral relative powers and β2 main peak frequency. Using ROC analysis, we show that low β2 main peak frequency assessed during Baseline is a good predictor of the future state of vulnerability to depression. Conclusion: Given the straightforwardness of EEG recordings, these results open the way to prospective studies in humans aiming to identify population at-risk for depression.

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

Personalized medicine in psychiatry is in need of biomarkers that resemble central nervous system function at the level of neuronal activity. Electroencephalography (EEG) during sleep or resting-state conditions and event-related potentials (ERPs) have not only been used to discriminate patients from healthy subjects, but also for the prediction of treatment outcome in various psychiatric diseases, yielding information about tailored therapy approaches for an individual. This review focuses on baseline EEG markers for two psychiatric conditions, namely major depressive disorder and attention deficit hyperactivity disorder. It covers potential biomarkers from EEG sleep research and vigilance regulation, paroxysmal EEG patterns and epileptiform discharges, quantitative EEG features within the EEG main frequency bands, connectivity markers and ERP components that might help to identify favourable treatment outcome. Further, the various markers are discussed in the context of their potential clinical value and as research domain criteria, before giving an outline for future studies that are needed to pave the way to an electrophysiological biomarker-based personalized medicine.

Hypoglycemia-associated changes in the electroencephalogram in patients with type 1 diabetes and normal hypoglycemia awareness or unawareness

Hypoglycemia is associated with increased activity in the low-frequency bands in the electroencephalogram (EEG). We investigated whether hypoglycemia awareness and unawareness are associated with different hypoglycemia-associated EEG changes in patients with type 1 diabetes. Twenty-four patients participated in the study: 10 with normal hypoglycemia awareness and 14 with hypoglycemia unawareness. The patients were studied at normoglycemia (5-6 mmol/L) and hypoglycemia (2.0-2.5 mmol/L), and during recovery (5-6 mmol/L) by hyperinsulinemic glucose clamp. During each 1-h period, EEG, cognitive function, and hypoglycemia symptom scores were recorded, and the counterregulatory hormonal response was measured. Quantitative EEG analysis showed that the absolute amplitude of the θ band and α-θ band up to doubled during hypoglycemia with no difference between the two groups. In the recovery period, the θ amplitude remained increased. Cognitive function declined equally during hypoglycemia in both groups and during recovery reaction time was still prolonged in a subset of tests. The aware group reported higher hypoglycemia symptom scores and had higher epinephrine and cortisol responses compared with the unaware group. In patients with type 1 diabetes, EEG changes and cognitive performance during hypoglycemia are not affected by awareness status during a single insulin-induced episode with hypoglycemia.

Different patterns of local field potentials from limbic DBS targets in patients with major depressive and obsessive compulsive disorder

The role of distinct limbic areas in emotion regulation has been largely inferred from neuroimaging studies. Recently, the opportunity for intracranial recordings from limbic areas has arisen in patients undergoing deep brain stimulation (DBS) for neuropsychiatric disorders including major depressive disorder (MDD) and obsessive compulsive disorder (OCD). Here we test the hypothesis that distinct temporal patterns of local field potential (LFP) activity in the human limbic system reflect disease state and symptom severity in MDD and OCD patients. To this end, we recorded LFPs via implanted DBS electrodes from the bed nucleus of stria terminalis (BNST area) in 12 patients (5 OCD, 7 MDD) and from the subgenual cingulate cortex in 7 MDD patients (CG25 area). We found a distinct pattern of oscillatory activity with significantly higher α-power in MDD compared with OCD in the BNST area (broad α-band 8-14 Hz; P<0.01) and a similar level of α-activity in the CG25 area as in the BNST area in MDD patients. The mean α-power correlated with severity of depressive symptoms as assessed by the Beck depression inventory in MDD (n=14, r=0.55, P=0.042) but not with severity of obsessive compulsive symptoms in OCD. Here we show larger α-band activity in MDD patients compared with OCD recorded from intracranial DBS targets. Our results suggest that α-activity in the limbic system may be a signature of symptom severity in MDD and may serve as a potential state biomarker for closed loop DBS in MDD.

EEG power asymmetry and functional connectivity as a marker of treatment effectiveness in DBS surgery for depression

Recently, deep brain stimulation (DBS) has been evaluated as an experimental therapy for treatment-resistant depression. Although there have been encouraging results in open-label trials, about half of the patients fail to achieve meaningful benefit. Although progress has been made in understanding the neurobiology of MDD, the ability to characterize differences in brain dynamics between those who do and do not benefit from DBS is lacking. In this study, we investigated EEG resting-state data recorded from 12 patients that have undergone DBS surgery. Of those, six patients were classified as responders to DBS, defined as an improvement of 50% or more on the 17-item Hamilton Rating Scale for Depression (HAMD-17). We compared hemispheric frontal theta and parietal alpha power asymmetry and synchronization asymmetry between responders and non-responders. Hemispheric power asymmetry showed statistically significant differences between responders and non-responders with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry was characterized by an increase in frontal theta in the right hemisphere relative to the left combined with an increase in parietal alpha in the left hemisphere relative to the right in non-responders compared with responders. Hemispheric mean synchronization asymmetry showed a statistically significant difference between responders and non-responders in the theta band, with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry resulted from an increase in frontal synchronization in the right hemisphere relative to the left combined with an increase in parietal synchronization in the left hemisphere relative to the right in non-responders compared with responders. Connectivity diagrams revealed long-range differences in frontal/central-parietal connectivity between the two groups in the theta band. This pattern was observed irrespective of whether EEG data were collected with active DBS or with the DBS stimulation turned off, suggesting stable functional and possibly structural modifications that may be attributed to plasticity.

EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response

Major depressive disorder (MDD) has high population prevalence and is associated with substantial impact on quality of life, not least due to an unsatisfactory time span of sometimes several weeks from initiation of treatment to clinical response. Therefore extensive research focused on the identification of cost-effective and widely available electroencephalogram (EEG)-based biomarkers that not only allow distinguishing between patients and healthy controls but also have predictive value for treatment response for a variety of treatments. In this comprehensive overview on EEG research on MDD, biomarkers that are either assessed at baseline or during the early course of treatment and are helpful in discriminating patients from healthy controls and assist in predicting treatment outcome are reviewed, covering recent decades up to now. Reviewed markers include quantitative EEG (QEEG) measures, connectivity measures, EEG vigilance-based measures, sleep-EEG-related measures and event-related potentials (ERPs). Further, the value and limitations of these different markers are discussed. Finally, the need for integrated models of brain function and the necessity for standardized procedures in EEG biomarker research are highlighted to enhance future research in this field.

The moderating influence of nicotine and smoking on resting-state mood and EEG changes in remitted depressed patients during tryptophan depletion

Comorbidity between depression and tobacco use may reflect self-medication of serotonergically mediated mood dysregulation, which has been associated with aberrant cortical activation and hemispheric asymmetry in patients with major depressive disorders (MDD). This randomized, double-blind study in 28 remitted MDD patients examined the moderating effects of acute nicotine and smoker vs. nonsmoker status on mood and EEG changes accompanying transient reductions in serotonin induced by acute tryptophan depletion (ATD). In smokers, who exhibited greater posterior high alpha power and increased left frontal low alpha power (signs of deactivation) compared to nonsmokers, ATD increased self-ratings of depressed mood and elevated left frontal and right parietal high alpha power (i.e. further cortical deactivation). Smokers were not affected by nicotine administration. In nonsmokers, ATD did not influence depression ratings, but it reduced vigor ratings and increased frontal and posterior theta power; both of which were blocked by acute nicotine. These findings indicate a role for nicotinic receptors in disordered mood.

Is EEG a useful test in adult psychiatry?

BACKGROUND

We present a brief overview of the use of EEG in psychiatry, with particular emphasis on differential diagnosis examination in case of acute psychiatric conditions.

METHOD

The article is based on a literature search in PubMed and the authors' own collections of articles and personal experience.

RESULTS

Onset of epilepsy, encephalitis or other cerebral diseases may be accompanied by psychiatric or cognitive symptoms. Slow EEG activity may be an unspecific sign of cerebral disease. Psychiatric patients are also at an increased risk of epilepsy. In case of seizure symptoms such as convulsions or conditions with rapid changes in affective states, epileptiform activity during EEG is a specific sign of epileptic aetiology or comorbidity. Quantitative frequency analysis (QEEG) is useful for research purposes and in exceptional cases also in a clinical context. No QEEG method has as yet become accepted as providing reliable, independent markers for psychiatric disease or treatment response.

INTERPRETATION

EEG should be undertaken in case of newly occurring psychoses and for conditions with a fluctuating or progradiating loss of cognitive function. Adult psychiatric patients with seizure symptoms or rapid changes in affective states should also be referred to EEG.

Electrocortical features of depression and their clinical utility in assessing antidepressant treatment outcome

Major depressive disorder (MDD) is primarily characterized by decreased affect and accompanying behavioural consequences, but it is also associated with cognitive dysfunction. Assessment of electroencephalographic (EEG) activity and associated event-related potentials (ERPs; derived from averaged EEG activity in response to a stimulus) in the context of MDD has provided insights into the electrocortical abnormalities associated with the disorder. Importantly, EEG and ERPs also have emerged as candidates for predicting and optimizing antidepressant (AD) treatment outcome. This is critical in light of relatively low remission rates or a limited response to initial AD interventions. In contrast to other neuroimaging approaches, EEG and ERPs may be superior for predicting and monitoring AD response, as electrocortical measures are relatively inexpensive, easy to use, and have excellent temporal (that is, millisecond) resolution, enabling fine-grained assessment of basic cognitive and emotive processes. This review aims to highlight the most consistently noted EEG and ERP features in MDD, which may one day assist with diagnostic confirmation, as well as the potential clinical utility of specific electrocortical measures in aiding with response prediction.

Fluoxetine versus other types of pharmacotherapy for depression

BACKGROUND

Depression is common in primary care and is associated with marked personal, social and economic morbidity, thus creating significant demands on service providers. The antidepressant fluoxetine has been studied in many randomised controlled trials (RCTs) in comparison with other conventional and unconventional antidepressants. However, these studies have produced conflicting findings.Other systematic reviews have considered selective serotonin reuptake inhibitor (SSRIs) as a group which limits the applicability of the indings for fluoxetine alone. Therefore, this review intends to provide specific and clinically useful information regarding the effects of fluoxetine for depression compared with tricyclics (TCAs), SSRIs, serotonin-noradrenaline reuptake inhibitors (SNRIs), monoamineoxidase inhibitors (MAOIs) and newer agents, and other conventional and unconventional agents.

OBJECTIVES

To assess the effects of fluoxetine in comparison with all other antidepressive agents for depression in adult individuals with unipolar major depressive disorder.

SEARCH METHODS

We searched the Cochrane Collaboration Depression, Anxiety and Neurosis Review Group Controlled Trials Register (CCDANCTR)to 11May 2012. This register includes relevant RCTs from the Cochrane Central Register of Controlled Trials (CENTRAL) (all years),MEDLINE (1950 to date), EMBASE (1974 to date) and PsycINFO (1967 to date). No language restriction was applied. Reference lists of relevant papers and previous systematic reviews were handsearched. The pharmaceutical company marketing fluoxetine and experts in this field were contacted for supplemental data.

SELECTION CRITERIA

All RCTs comparing fluoxetine with any other AD (including non-conventional agents such as hypericum) for patients with unipolar major depressive disorder (regardless of the diagnostic criteria used) were included. For trials that had a cross-over design only results from the first randomisation period were considered.

DATA COLLECTION AND ANALYSIS

Data were independently extracted by two review authors using a standard form. Responders to treatment were calculated on an intention-to-treat basis: dropouts were always included in this analysis. When data on dropouts were carried forward and included in the efficacy evaluation, they were analysed according to the primary studies; when dropouts were excluded from any assessment in the primary studies, they were considered as treatment failures. Scores from continuous outcomes were analysed by including patients with a final assessment or with the last observation carried forward. Tolerability data were analysed by calculating the proportion of patients who failed to complete the study due to any causes and due to side effects or inefficacy. For dichotomous data, odds ratios (ORs) were calculated with 95% confidence intervals (CI) using the random-effects model. Continuous data were analysed using standardised mean differences (SMD) with 95% CI.

MAIN RESULTS

A total of 171 studies were included in the analysis (24,868 participants). The included studies were undertaken between 1984 and 2012. Studies had homogenous characteristics in terms of design, intervention and outcome measures. The assessment of quality with the risk of bias tool revealed that the great majority of them failed to report methodological details, like the method of random sequence generation, the allocation concealment and blinding. Moreover, most of the included studies were sponsored by drug companies, so the potential for overestimation of treatment effect due to sponsorship bias should be considered in interpreting the results. Fluoxetine was as effective as the TCAs when considered as a group both on a dichotomous outcome (reduction of at least 50% on the Hamilton Depression Scale) (OR 0.97, 95% CI 0.77 to 1.22, 24 RCTs, 2124 participants) and a continuous outcome (mean scores at the end of the trial or change score on depression measures) (SMD 0.03, 95% CI -0.07 to 0.14, 50 RCTs, 3393 participants). On a dichotomousoutcome, fluoxetine was less effective than dothiepin or dosulepin (OR 2.13, 95% CI 1.08 to 4.20; number needed to treat (NNT) =6, 95% CI 3 to 50, 2 RCTs, 144 participants), sertraline (OR 1.37, 95% CI 1.08 to 1.74; NNT = 13, 95% CI 7 to 58, 6 RCTs, 1188 participants), mirtazapine (OR 1.46, 95% CI 1.04 to 2.04; NNT = 12, 95% CI 6 to 134, 4 RCTs, 600 participants) and venlafaxine(OR 1.29, 95% CI 1.10 to 1.51; NNT = 11, 95% CI 8 to 16, 12 RCTs, 3387 participants). On a continuous outcome, fluoxetine was more effective than ABT-200 (SMD -1.85, 95% CI -2.25 to -1.45, 1 RCT, 141 participants) and milnacipran (SMD -0.36, 95% CI-0.63 to -0.08, 2 RCTs, 213 participants); conversely, it was less effective than venlafaxine (SMD 0.10, 95% CI 0 to 0.19, 13 RCTs,3097 participants). Fluoxetine was better tolerated than TCAs considered as a group (total dropout OR 0.79, 95% CI 0.65 to 0.96;NNT = 20, 95% CI 13 to 48, 49 RCTs, 4194 participants) and was better tolerated in comparison with individual ADs, in particular amitriptyline (total dropout OR 0.62, 95% CI 0.46 to 0.85; NNT = 13, 95% CI 8 to 39, 18 RCTs, 1089 participants), and among the newer ADs ABT-200 (total dropout OR 0.18, 95% CI 0.08 to 0.39; NNT = 3, 95% CI 2 to 5, 1 RCT, 144 participants), pramipexole(total dropout OR 0.12, 95% CI 0.03 to 0.42, NNT = 3, 95% CI 2 to 5, 1 RCT, 105 participants), and reboxetine (total dropout OR0.60, 95% CI 0.44 to 0.82, NNT = 9, 95% CI 6 to 24, 4 RCTs, 764 participants).

AUTHORS' CONCLUSIONS

The present study detected differences in terms of efficacy and tolerability between fluoxetine and certain ADs, but the clinical meaning of these differences is uncertain.Moreover, the assessment of quality with the risk of bias tool showed that the great majority of included studies failed to report details on methodological procedures. Of consequence, no definitive implications can be drawn from the studies' results. The better efficacy profile of sertraline and venlafaxine (and possibly other ADs) over fluoxetine may be clinically meaningful,as already suggested by other systematic reviews. In addition to efficacy data, treatment decisions should also be based on considerations of drug toxicity, patient acceptability and cost.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

OBJECTIVE

The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD).

METHODS

A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure.

RESULTS

A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%].

CONCLUSIONS

These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG.

SIGNIFICANCE

The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.

A review of electroencephalographic changes in diabetes mellitus in relation to major depressive disorder

A bidirectional relationship exists between diabetes mellitus (DM) and major depressive disorder (MDD), with depression commonly reported in both type 1 DM (T1DM) and type 2 DM (T2DM), and depressive symptoms associated with a higher incidence of diabetes. However, how the two conditions are pathologically connected is not completely understood. Similar neurophysiological abnormalities have been reported in both DM and MDD, including elevated electroencephalographic (EEG) activity in low-frequency slow waves and increased latency and/or reduced amplitude of event-related potentials. It is possible that this association reflects some common underlying pathology, and it has been proposed that diabetes may place patients at risk for depression through a biological mechanism linking the metabolic changes of DM to changes in the central nervous system. In this review we will discuss EEG abnormalities in DM, as well as the biological mechanisms underlying various EEG parameters, in order to evaluate whether or not a common EEG biosignature exists between DM and MDD. Identifying such commonalities could significantly inform the current understanding of the mechanisms that subserve the development of the two conditions. Moreover, this new insight may provide the basis for informing new drug discovery capable of mitigating and possibly even preventing both conditions.

α Power, α asymmetry and anterior cingulate cortex activity in depressed males and females

Left fronto-cortical hypoactivity, thought to reflect reduced activity in approach-related systems, and right parietal hypoactivity, associated with emotional under-arousal, have been noted in major depressive disorder (MDD). Altered theta activity in the anterior cingulate cortex (ACC) has also been associated with the disorder. We assessed resting frontal and parietal alpha asymmetry and power in non-medicated MDD (N = 53; 29 females) and control (N = 43; 23 females) individuals. Theta activity was examined using standardized low-resolution electromagnetic tomography (sLORETA) in the ACC [BA24ab and BA32 comprising the rostral ACC and BA25/subgenual (sg) ACC]. The MDD group, and particularly depressed males, displayed increased overall frontal and parietal alpha power and left midfrontal hypoactivity (alpha(2)-indexed). They also exhibited increased sgACC theta(2) activity. MDD females had increased right parietal activity, suggesting increased emotive arousal. Thus, unmedicated depressed adults were characterized by lower activity in regions implicated in approach/positive affective tendencies as well as diffuse cortical hypoarousal, though sex specific modulations emerged. Altered theta in the sgACC may reflect emotion regulation abnormalities in MDD.

CLASSIFICATION OF EEG SIGNALS IN NORMAL AND DEPRESSION CONDITIONS BY ANN USING RWE AND SIGNAL ENTROPY

EEG is useful for the analysis of the functional activity of the brain and a detailed assessment of this non-stationary waveform can provide crucial parameters indicative of the mental state of patients. The complex nature of EEG signals calls for automated analysis using various signal processing methods. This paper attempts to classify the EEG signals of normal and depression patients using well-established signal processing techniques involving relative wavelet energy (RWE) and artificial feedForward neural network. High frequency noise present in the recorded signal is removed using total variation filtering (TVF). Classification of the frequency bands of EEG signals into appropriate detail levels and approximation level is carried out using an eight-level multiresolution decomposition method of discrete wavelet transform (DWT). Parseval's theorem is used for calculating the energy at different resolution levels. RWE analysis gives information about the signal energy distribution at different decomposition levels. Both RWE and feedforward Network are used to classify the signals from normal controls and depression patients. The performance of the artificial neural network was evaluated using the classification accuracy and its value of 98.11% indicates a great potential for classifying normal and depression signals.

Neural expression of nicotine's antidepressant properties during tryptophan depletion: an EEG study in healthy volunteers at risk for depression

Nicotine amelioration of serotonergically mediated mood dysregulation may contribute to the comorbidity between cigarette smoking and depression, a disorder which is associated with aberrant activation and hemispheric asymmetry in frontal and posterior cortical regions. This randomized, double-blind study in 20 healthy volunteers with a positive family history of depression examined the effects of transdermal nicotine on mood and EEG changes accompanying transient reductions in serotonin induced by acute tryptophan depletion (ATD). Increased self-ratings of depressed mood and elevation in left frontal high alpha power (decreased activation) were evidenced with ATD (vs. balanced mixture) in participants treated with the placebo but not the nicotine treated group. Nicotine alone increased vigor and posterior high alpha bilaterally, and during ATD it prevented the reduction in left frontal high alpha that was evident in the placebo patch group. These findings indicate that in depression prone individuals, nicotine acts to stabilize the mood lowering and associated frontal functional asymmetry elicited by an acute decrease in brain serotonin.

The use of the EEG in measuring therapeutic drug action: focus on depression and antidepressants

A major issue in proof of concept studies and early clinical trials of novel therapeutic agents is that the active drugs can often have a relatively small additional effect compared with placebo. This is especially the case in psychiatry when we usually have no direct method of measuring the pathology underlying the disorder being studied but, rather, have to rely on the subjective assessment of psychiatric symptoms. The use of the electroencephalogram (EEG) offers two potential major means of addressing this problem. First it is able to provide direct data relating to neural activity that may be abnormal in certain disorders. As such there are opportunities for utilizing the EEG in a variety of ways as an objective outcome measure. Second there is growing evidence that in certain circumstances the EEG can be used to predict which patients are likely to respond to treatment, thus potentially increasing the power of studies by decreasing non-response rates and increasing mean changes in outcome measure. Both of these uses of the EEG are illustrated in reference to the study of mood disorders and in particular depression and its treatment with antidepressants.

Effects of nicotine on electroencephalography and affect in adolescent females with major depressive disorder: a pilot study

BACKGROUND

Given that smoking is typically initiated during adolescence, and that this period in brain development seems to be uniquely sensitive to nicotine, depressed youth may be most susceptible to the neuromodulatory and mood-altering effects of nicotine. Electroencephalographic (EEG) studies suggest that individuals with major depressive disorder (MDD) exhibit left frontal lobe hypoactivation (indexed by increased EEG alpha), a region implicated in positive affect regulation, as well as right parietal hypoactivation. Smoking/nicotine abstinence has been associated with increased left frontal and right parietal alpha activity (reduced activation), which has been correlated with increased depression ratings; nicotine administration seems to normalize this depression-associated asymmetry.

OBJECTIVES

This pilot study investigated whether acute nicotine administration in adolescent female smokers with MDD would alter resting EEG activity and affect.

METHODS

Subjective mood ratings and EEG recordings were acquired before and 2 hours after administering a transdermal placebo or nicotine (21 mg) patch to 8 adolescent female smokers with MDD.

RESULTS

Nicotine induced a modest increase in alpha1 amplitude in the right hemisphere and simultaneously decreased left-favoring alpha1 amplitude asymmetry. It also attenuated left alpha1 and alpha2 amplitude in the central region. Consistent with nicotine's stimulatory action, nicotine decreased theta amplitude in the right parietal region. No accompanying mood alterations were found, although smoking withdrawal and craving as well as physical symptom scores were reduced with nicotine.

CONCLUSIONS

The results of this pilot study, the first to examine the electrocortical effects of nicotine in depressed adolescents, indicate that nicotine modulates EEG asymmetry measures, laying the stage for further research regarding the role of nicotine on affective neurocircuitry in this population.

Clinical neurofeedback: case studies, proposed mechanism, and implications for pediatric neurology practice

Trends in alternative medicine use by American health care consumers are rising substantially. Extensive literature exists reporting on the effectiveness of neurofeedback in the treatment of autism, closed head injury, insomnia, migraine, depression, attention deficit hyperactivity disorder, epilepsy, and posttraumatic stress disorder. We speculated that neurofeedback might serve as a therapeutic modality for patients with medically refractory neurological disorders and have begun referring patients to train with clinical neurofeedback practitioners. The modality is not always covered by insurance. Confident their child's medical and neurological needs would continue to be met, the parents of 3 children with epilepsy spectrum disorder decided to have their child train in the modality. The children's individual progress following neurofeedback are each presented here. A proposed mechanism and practice implications are discussed.

EEG frontal asymmetry in the depressed and remitted elderly: is it related to the trait or to the state of depression?

BACKGROUND

Over the last 30 years, frontal EEG asymmetry has been investigated with regards to the study of emotion, motivation, and psychopathology.

METHOD

We analyzed the frontal alpha asymmetry, depressive symptoms with a Beck Depression Inventory (BDI) and quality of life with a Short Form Health Survey-36® (SF-36®) in depressed (n=12), remitted (n=8) and non-depressed (n=7) elderly subjects. We also evaluated the correlation between the frontal EEG asymmetry and physical and mental aspects of SF-36®.

RESULTS

The groups showed no difference regarding the frontal alpha asymmetry (F=0.37; p=0.69). Moreover, there was no significant correlation between frontal asymmetry and quality of life (mental and physical aspects).

CONCLUSION

The results showed no evidence of a relationship between frontal asymmetry, quality of life and depression in the elderly. Future studies on frontal asymmetry should carefully consider the effects of age.

Individualized alpha activity and frontal asymmetry in major depression

Lateralized differences in frontal alpha power in individuals with major depressive disorder (MDD) are thought to reflect an aberrant affective processing style. However research into anterior alpha asymmetry and MDD has often produced conflicting results. The current study aimed to investigate whether individualized alpha bandwidths provide a more sensitive measure of anterior alpha asymmetry in MDD than the traditional fixed 8-13 Hz alpha band. Resting EEG was recorded from 34 right-handed female participants (18 controls, 16 MDD). Each participant's Individual Alpha Frequency was used to delineate a broad individualized alpha band and three individualized narrow alpha sub-bands: lower alpha1, lower alpha 2 and upper alpha. Activity within the broad and narrow individualized bandwidths and within the traditional fixed alpha band were used to compare a) controls and acutely depressed individuals and b) medicated and unmedicated MDD participants. Individualizing and subdividing the alpha bandwidth did not add appreciably to the sensitivity of anterior alpha asymmetry in MDD as no significant differences in lateralized alpha power between controls and MDD participants were observed in any alpha bandwidth. This finding was consistent under two reference schemes and across multiple scalp locations. Within the MDD group, antidepressant use was associated with significantly greater right than left hemispheric power in the lower alpha 1 band. The relevance of this finding is discussed in relation to the electrophysiological correlates of antidepressant medication use, lateralized differences in affective processing and treatment resistant MDD.

Frontal EEG predictors of treatment outcome in major depressive disorder

OBJECTIVE

To investigate the role of frontal EEG as predictor of clinical response to SSRIs or venlafaxine in major depressive disorder (MDD).

METHOD

82 subjects (age 35.9+/-13.0; 47.6% female) meeting DSM-IV criteria for MDD entered an 8-week prospective treatment with SSRIs or venlafaxine. At baseline and week 1 we recorded serial, 4-channel EEGs (F7-Fpz, F8-Fpz, A1-Fpz, A2-Fpz). We evaluated prospectively the relative theta power as predictor of treatment outcome. We also developed an Antidepressant Treatment Response (ATR) index using EEG parameters assessed at baseline and week 1.

RESULTS

45 subjects (54.9%) responded to treatment (HAM-D-17 reduction>or=50%). At baseline, frontal relative theta power (i.e., 4-8 Hz power/2-20 Hz power) was significantly (p=0.017) lower (21%) in treatment responders than in non-responders (24%). Baseline relative theta power predicted treatment response with 63% accuracy [64% sensitivity, 62% specificity, 66% area under the receiver operator curve (AUROC) (p=0.014)]. Relative theta power at week 1 predicted treatment response with 60% accuracy [62% sensitivity, 57% specificity, 61% AUROC (p=0.089)]. ATR predicted response with 70% accuracy [82% sensitivity, 54% specificity, 72% AUROC (p=0.001)].

CONCLUSION

Using automated analysis of frontal EEG collected during the first week of antidepressant treatment it may be possible to facilitate prediction of SSRI or venlafaxine efficacy in MDD.

The clinical use of quantitative EEG in cognitive disorders

The primary diagnosis of most cognitive disorders is clinically based, but the EEG plays a role in evaluating, classifying and following some of these disorders. There is an ongoing debate over routine use of qEEG. Although many findings regarding the clinical use of quantitative EEG are awaiting validation by independent investigators while confirmatory clinical follow-up studies are also needed, qEEG can be cautiously used by a skilled neurophysiologist in cognitive dysfunctions to improve the analysis of background activity, slow/fast focal activity, subtle asymmetries, spikes and waves, as well as in longitudinal follow-ups.

Quantitative EEG findings in patients with acute, brief depression combined with other fluctuating psychiatric symptoms: a controlled study from an acute psychiatric department

BACKGROUND

Patients with brief depressive episodes and concurrent rapidly fluctuating psychiatric symptoms do not fit current diagnostic criteria and they can be difficult to diagnose and treat in an acute psychiatric setting. We wanted to study whether these patients had signs of more epileptic or organic brain dysfunction than patients with depression without additional symptomatology.

METHODS

Sixteen acutely admitted patients diagnosed with a brief depressive episode as well as another concurrent psychiatric diagnosis were included. Sixteen patients with major depression served as controls. Three electroencephalographic studies (EEG) were visually interpreted and the background activity was also analysed with quantitative electroencephalography (QEEG).

RESULTS

The group with brief depression and concurrent symptoms had multiple abnormal features in their standard EEG compared to patients with major depression, but they did not show significantly more epileptiform activity. They also had significantly higher temporal QEEG delta amplitude and interhemispheric temporal delta asymmetry.

CONCLUSION

Organic brain dysfunction may be involved in the pathogenesis of patients with brief depressive episodes mixed with rapidly fluctuating psychiatric symptoms. This subgroup of depressed patients should be investigated further in order to clarify the pathophysiology and to establish the optimal evaluation scheme and treatment in an acute psychiatric setting.