Prediction of treatment response in major depression: integration of concepts

Affective disorders and the loudness dependence of the auditory evoked potential: Serotonin and beyond

Identifying additional noninvasive biomarkers for affective disorders, such as unipolar major depressive disorder (MDD) and bipolar disorder (BD), could aid in the diagnosis and treatment of these prevalent and debilitating neuropsychiatric conditions. One such candidate biomarker is the loudness dependence of the auditory evoked potential (LDAEP), an event-related potential that measures responsiveness of the auditory cortex to different intensities of sound. The LDAEP has been associated with MDD and BD, including therapeutic response to particular classes of antidepressant drugs, while also correlating with several other neuropsychiatric disorders. It has been suggested that increased values of the LDAEP indicate low central serotonergic neurotransmission, further implicating this EEG measure in depression. Here, we briefly review the literature on the LDAEP in affective disorders, including its association with serotonergic signaling, as well as with that of other neurotransmitters such as dopamine. We summarize key findings on the LDAEP and the genetics of these neurotransmitters, as well as prediction of response to particular classes of antidepressants in MDD, including SSRIs versus noradrenergic agents. The possible relationship between this EEG measure and suicidality is addressed. We also briefly analyze acute pharmacologic studies of serotonin and/or dopamine precursor depletion and the LDAEP. In conclusion, the existing literature suggests that serotonin and norepinephrine may modulate the LDAEP in an opposing manner, and that this event-related marker may be of use in predicting response to chronic treatment with particular pharmacologic agents in the context of affective disorders, such as MDD and BD, including in the presence of suicidality.

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.

Association between the loudness dependence of auditory evoked potential, serotonergic neurotransmission and treatment outcome in patients with depression

Previous studies have suggested that the loudness dependence of auditory evoked potential (LDAEP) is associated with the effectiveness of antidepressant treatment in patients with major depressive disorders (MDD). Furthermore, both LDAEP and the cerebral serotonin 4 receptor (5-HT₄R) density is inversely related to brain serotonin levels. We included 84 patients with MDD and 22 healthy controls to examined the association between LDAEP and treatment response and its association with cerebral 5-HT₄R density. Participants underwent both EEG and 5-HT₄R neuroimaging with [¹¹C]SB207145 PET. Thirty-nine patients with MDD were re-examined after 8 weeks of treatment with selective serotonin reuptake inhibitors/serotonin noradrenaline reuptake inhibitor (SSRI/SNRI). We found that the cortical source of LDAEP was higher in untreated patients with MDD compared to healthy controls (p=0.03). Prior to SSRI/SNRI treatment, subsequent treatment responders had a negative association between LDAEP and depressive symptoms and a positive association between scalp LDAEP and symptom improvement at week 8. This was not found in source LDAEP. In healthy controls, we found a positive correlation between both scalp and source LDAEP and cerebral 5-HT₄R binding but that was not observed in patients with MDD. We did not see any changes in scalp and source LDAEP in response to SSRI/SNRI treatment. These results support a theoretical framework where both LDAEP and cerebral 5-HT₄R are indices of cerebral 5-HT levels in healthy individuals while this association seems to be disrupted in MDD. The combination of the two biomarkers may be useful for stratifying patients with MDD. Clinical Trials Registration:https://clinicaltrials.gov/ct2/show/NCT02869035?draw=1Registration number: NCT0286903.

Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review

Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.

Tackling the Electro-Topography of the Selves Through the Sphere Model of Consciousness

In the current hypothesis paper, we propose a novel examination of consciousness and self-awareness through the neuro-phenomenological theoretical model known as the Sphere Model of Consciousness (SMC). Our aim is to create a practical instrument to address several methodological issues in consciousness research. We present a preliminary attempt to validate the SMC via a simplified electrophysiological topographic map of the Self. This map depicts the gradual shift from faster to slower frequency bands that appears to mirror the dynamic between the various SMC states of Self. In order to explore our hypothesis that the SMC's different states of Self correspond to specific frequency bands, we present a mini-review of studies examining the electrophysiological activity that occurs within the different states of Self and in the context of specific meditation types. The theoretical argument presented here is that the SMC's hierarchical organization of three states of the Self mirrors the hierarchical organization of Focused Attention, Open Monitoring, and Non-Dual meditation types. This is followed by testable predictions and potential applications of the SMC and the hypotheses derived from it. To our knowledge, this is the first integrated electrophysiological account that combines types of Self and meditation practices. We suggest this electro-topographic framework of the Selves enables easier, clearer conceptualization of the connections between meditation types as well as increased understanding of wakefulness states and altered states of consciousness.

Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study

Several electroencephalogram (EEG) biomarkers for prediction of drug response in major depressive disorder (MDD) have been proposed, but validations in larger independent datasets are missing. In the current study, we investigated the prognostic value of previously suggested EEG biomarkers. We gathered data that matched prior studies in terms of EEG methodology, clinical criteria for MDD, and statistical approach as closely as possible. The NeuroPharm study is a non-randomized and open label prospective clinical trial. One hundred antidepressant free patients with MDD were enrolled in the study and 79 (57 female) were included in the per-protocol analysis. The biomarkers candidates for cross-validation were derived from prior studies such as iSPOT-D and EMBARC and include frontal and occipital alpha power and asymmetry and delta and theta activity at anterior cingulate cortex (ACC). The alpha asymmetry, reported in two out of six prior studies, could be partially validated. We found that in female patients, larger right than left frontal alpha power prior to drug treatment was associated with better clinical outcome 8 weeks later. Moreover, female non-responder had higher central left alpha power relative to the right. In contrast to prior reports, we found that lower theta activity at ACC was present in remitters and was associated with greater improvement at week 8. We provide evidence that in women with MDD, alpha asymmetry seems to be the most promising EEG biomarker for prediction of treatment response. Registration number: NCT02869035.

Does the Loudness Dependence of Auditory Evoked Potential Predict Response to Selective Serotonin Reuptake Inhibitors?: A Meta-analysis

Objective

Loudness of dependence of the auditory evoked potential (LDAEP) is an electroencephalogram-based measure that represents amplitude changes of auditory evoked potentials in primary auditory cortex. Several narrative reviews argued that pre-treatment LDAEP values predict responses to selective serotonin reuptake inhibitors (SSRIs). This study aims to quantify the overall relationship between baseline LDAEP values and treatment response to SSRIs in patients with depression and generalized anxiety disorders, evidenced by clinical symptoms reductions, across multiple studies.

Methods

In our meta-analysis, seven articles with a total sample of 241 patients were included.

Results

Our results showed that stronger baseline LDAEP values predicted favorable response to SSRIs for depression and anxiety, with a moderate effect size.

Conclusion

The current results support the idea that LDAEP is a promising biomarker for SSRIs treatment prediction in patients with depression and generalized anxiety disorder.

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.

Personality and psychopathology: In defense of a practical path toward integrating psychometric and biological approaches to advance a comprehensive model

Personality and psychopathology each reflect patterns of internal experience and outward behavior that differ between people and affect functioning. Drawing strict distinctions between the two concepts is not only difficult, but it may prove unnecessary for advancing an integrated model of psychological experiences associated with mental illness. We argue that developing such a model will be critical for improving treatment outcomes, and we discuss a practical path forward. Proponents of psychometric approaches to developing models of psychological experience focus on observable phenotypes and utilize statistical methods to describe patterns of covariation among a broad range of symptoms and dispositions. Advocates of biologically based approaches emphasize neuroscientific tools for identifying abnormalities in brain function that give rise to an individual's experience. There is substantial evidence that measures of personality and measures of symptoms capture nonoverlapping, clinically important information for understanding how and for whom treatments for mental illness work. In this article, we highlight the importance of combining psychometric and neurobiological approaches in order to understand which features of an individual those measures reflect, which aspects of neurobiology generate and maintain those features, how they relate to each other, and critically, how best to alter them to reduce distress and dysfunction.

The Hypothesis on the Prediction of Treatment Response with Buspirone Augmentation along with Serotonergic Antidepressant in Patients with Major Depressive Disorder Using Loudness Dependence of Auditory Evoked Potentials: Two Cases and Review of the Literature for Evidence

Some studies have shown that augmenting buspirone with antidepressant has similar efficacy as the combination with two antidepressants in patients with major depressive disorder (MDD). Some researchers assume that the antidepressant boosting effect of buspirone is revealed under a poop-out state, which means a phenomenon where some patients having an initial response to an antidepressant may worsen or not improve any more even though they continue treatment because of serotonin depletion. Loudness dependence of auditory evoked potential (LDAEP) is a reliable marker of central serotonergic activity, and is inversely correlated with central serotonergic activity. Thus LDAEP will be a biological marker for prediction of treatment response with buspirone augmentation with SSRI because it can measure central serotonergic activity such as serotonin depletion. Two cases will be introduced and the literature evidence about whether LDAEP can predict the treatment response of buspirone augmentation in patients with MDD will be reviewed.

Brainwaves Oscillations as a Potential Biomarker for Major Depression Disorder Risk

Major depressive disorder (MDD) is a multidimensional disorder that is characterized by the presence of alterations in mood, cognitive capacity, sensorimotor, and homeostatic functions. Given that about half of the patients diagnosed with MDD do not respond to the various current treatments, new techniques are being sought to predict not only the course of the disease but also the characteristics that differentiate responders from non-responders. Using the electroencephalogram, a noninvasive and inexpensive tool, most studies have proposed that patients with MDD have some lateralization in brain electrical activity, with alterations in alpha and theta rhythms being observed, which would be related to dysfunctions in emotional capacity such as the absence or presence of responses to the different existing treatments. These alterations help in the identification of subjects at high risk of suffering from depression, in the differentiation into responders and nonresponders to various therapies (pharmacological, electroconvulsive therapy, and so on), as well as to establish in which period of the disease the treatment will be more effective. Although the data are still inconclusive and more research is needed, these alpha and theta neurophysiological markers could support future clinical practice when it comes to establishing an early diagnosis and treating state disorders more successfully and accurately of mood disorders.

Predicting Treatment Outcome in Major Depressive Disorder Using Serotonin 4 Receptor PET Brain Imaging, Functional MRI, Cognitive-, EEG-Based, and Peripheral Biomarkers: A NeuroPharm Open Label Clinical Trial Protocol

BACKGROUND

Between 30 and 50% of patients with major depressive disorder (MDD) do not respond sufficiently to antidepressant regimens. The conventional pharmacological treatments predominantly target serotonergic brain signaling but better tools to predict treatment response and identify relevant subgroups of MDD are needed to support individualized and mechanistically targeted treatment strategies. The aim of this study is to investigate antidepressant-free patients with MDD using neuroimaging, electrophysiological, molecular, cognitive, and clinical examinations and evaluate their ability to predict clinical response to SSRI treatment as individual or combined predictors.

METHODS

We will include 100 untreated patients with moderate to severe depression (>17 on the Hamilton Depression Rating Scale 17) in a non-randomized open clinical trial. We will collect data from serotonin 4 receptor positron emission tomography (PET) brain scans, functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), cognitive tests, psychometry, and peripheral biomarkers, before (at baseline), during, and after 12 weeks of standard antidepressant treatment. Patients will be treated with escitalopram, and in case of non-response at week 4 or intolerable side effects, offered to switch to a second line treatment with duloxetine. Our primary outcome (treatment response) is assessed using the Hamilton depression rating subscale 6-item scores at week 8, compared to baseline. In a subset of the patients (n = ~40), we will re-assess the neurobiological response (using PET, fMRI, and EEG) 8 weeks after initiated pharmacological antidepressant treatment, to map neurobiological signatures of treatment responses. Data from matched controls will either be collected or is already available from other cohorts.

DISCUSSION

The extensive investigational program with follow-up in this large cohort of participants provides a unique possibility to (a) uncover potential biomarkers for antidepressant treatment response, (b) apply the findings for future stratification of MDD, (c) advance the understanding of pathophysiological underpinnings of MDD, and (d) uncover how putative biomarkers change in response to 8 weeks of pharmacological antidepressant treatment. Our data can pave the way for a precision medicine approach for optimized treatment of MDD and also provides a resource for future research and data sharing.

CLINICAL TRIAL REGISTRATION

The study was registered at clinicaltrials.gov prior to initiation (NCT02869035; 08.16.2016, URL: https://clinicaltrials.gov/ct2/results?cond=&term=NCT02869035&cntry=&state=&city=&dist=).

Biosignals to Detect the Imbalance of Explicit and Implicit Affect in Dementia: A Pilot Study

BACKGROUND

We assessed implicit and explicit emotion in older patients with dementia using biosignals.

METHODS

Fifty patients with dementia and 34 healthy individuals watched 3 videos that aimed to elicit various emotional responses. Electroencephalogram and heart rate variability were recorded.

RESULTS

Patients with dementia experienced less fun and more fear than controls. The high frequency (HF) from the baseline in response to funny stimulation as well as HF from neutral to fear stimulation in the dementia group increased further than in the control group. The slow wave (SW)-fast wave (FW) ratio from neutral to funny stimulation in the control group increased further than in the dementia group. The SW-FW from neutral to fear stimulation was further decreased in the dementia group than in the control group.

CONCLUSIONS

Although patients with dementia were more sensitive to implicit affect, they showed more enhanced imbalance between positive and negative affect in explicit affect assessment.

Early change of prefrontal theta cordance and occipital alpha asymmetry in the prediction of responses to antidepressants

BACKGROUND

The study evaluated the effectiveness of EEG alpha 1, alpha 2 and theta power, along with prefrontal theta cordance (PFC), frontal and occipital alpha 1, alpha 2 asymmetry (FAA1/2, OAA1/2) at baseline and their changes at week 1 in predicting response to antidepressants.

METHOD

Resting-state EEG data were recorded from 103 depressive patients that were treated in average for 5.1 ± 0.9 weeks with SSRIs (n = 57) and SNRIs (n = 46).

RESULTS

Fifty-five percent of patients (n = 56) responded to treatment (i.e.reduction of Montgomery-Åsberg Depression Rating Scale score ≥ 50%) and 45% (n = 47) of treated subjects did not reach positive treatment outcome. No differences in EEG baseline alpha and theta power or changes at week 1 for prefrontal, frontal, central, temporal and occipital regions were found between responders and non-responders. Both groups showed no differences at baseline PFC, FAA1/2 and OAA1/2 as well as change of FAA1/2 at week 1. The only parameters associated with treatment outcome were decrease of PFC in responders and increase of OAA1/2 at week 1 in non-responders. There was no influence of the used antidepressant classes on the results. The PFC change at week 1 (PFCC) (area under curve-AUC = 0.75) showed only a numerically higher predictive ability than OAA change in alpha 1 (OAA1C, AUC = 0.64)/alpha 2 (OAA2C, AUC = 0.63). A combined model, where OAA1C was added to PFCC (AUC = 0.79), did not significantly improve response prediction.

CONCLUSION

Besides PFCC, we found that OAA1C/OAA2C might be another candidate for EEG predictors of antidepressant response.

Pretreatment Rostral Anterior Cingulate Cortex Theta Activity in Relation to Symptom Improvement in Depression: A Randomized Clinical Trial

IMPORTANCE

Major depressive disorder (MDD) remains challenging to treat. Although several clinical and demographic variables have been found to predict poor antidepressant response, these markers have not been robustly replicated to warrant implementation in clinical care. Increased pretreatment rostral anterior cingulate cortex (rACC) theta activity has been linked to better antidepressant outcomes. However, no prior study has evaluated whether this marker has incremental predictive validity over clinical and demographic measures.

OBJECTIVE

To determine whether increased pretreatment rACC theta activity would predict symptom improvement regardless of randomization arm.

DESIGN, SETTING, AND PARTICIPANTS

A multicenter randomized clinical trial enrolled outpatients without psychosis and with chronic or recurrent MDD between July 29, 2011, and December 15, 2015 (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care [EMBARC]). Patients were consecutively recruited from 4 university hospitals: 634 patients were screened, 296 were randomized to receive sertraline hydrochloride or placebo, 266 had electroencephalographic (EEG) recordings, and 248 had usable EEG data. Resting EEG data were recorded at baseline and 1 week after trial onset, and rACC theta activity was extracted using source localization. Intent-to-treat analysis was conducted. Data analysis was performed from October 7, 2016, to January 19, 2018.

INTERVENTIONS

An 8-week course of sertraline or placebo.

MAIN OUTCOMES AND MEASURES

The 17-item Hamilton Rating Scale for Depression score (assessed at baseline and weeks 1, 2, 3, 4, 6, and 8).

RESULTS

The 248 participants (160 [64.5%] women, 88 [35.5%] men) with usable EEG data had a mean (SD) age of 36.75 (13.15) years. Higher rACC theta activity at both baseline (b = -1.05; 95% CI, -1.77 to -0.34; P = .004) and week 1 (b = -0.83; 95% CI, -1.60 to -0.06; P < .04) predicted greater depressive symptom improvement, even when controlling for clinical and demographic variables previously linked with treatment outcome. These effects were not moderated by treatment arm. The rACC theta marker, in combination with clinical and demographic variables, accounted for an estimated 39.6% of the variance in symptom change (with 8.5% of the variance uniquely attributable to the rACC theta marker).

CONCLUSIONS AND RELEVANCE

Increased pretreatment rACC theta activity represents a nonspecific prognostic marker of treatment outcome. This is the first study to date to demonstrate that rACC theta activity has incremental predictive validity.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT01407094.

A distinct biomarker of continuous transcutaneous vagus nerve stimulation treatment in major depressive disorder

BACKGROUND

Major depression is the fourth leading cause of disability worldwide and poses a socioeconomic burden worldwide. Transcutaneous vagus nerve stimulation (tVNS) is a promising noninvasive clinical device that may reduce the severity of major depression. However, the neural mechanism underlying continuous tVNS has not yet been elucidated.

OBJECTIVE

We aimed to explore the effect of hypothalamic subregion functional connectivity (FC) changes during continuous tVNS treatment on major depressive disorder (MDD) patients and to identify the potential biomarkers for treatment outcomes.

METHODS

Forty-one mild to moderate MDD patients were recruited and received either real or sham tVNS treatment for 4 weeks. We used a seed-to-whole brain approach to estimate the FC changes of hypothalamic subregions and their surrounding control areas during continuous tVNS treatment and explored their association with clinical outcome changes after 4 weeks of treatment.

RESULTS

Of the thirty-six patients that completed the study, those in the tVNS group had significantly lower scores on the 24-item Hamilton Depression (HAM-D) Rating Scale compared to the sham tVNS group after 4 weeks of treatment. The FC between the bilateral medial hypothalamus (MH) and rostral anterior cingulate cortex (rACC) was significantly decreased during tVNS but not during sham tVNS. The strength of this FC was significantly correlated with HAM-D improvements after 4 weeks of tVNS.

CONCLUSION

The FC between the bilateral MH and rACC may serve as a potential biomarker for the tVNS state and predict treatment responses. Our results provide insights into the neural modulation mechanisms of continuous tVNS and reveal a potential therapeutic target for MDD patients.

A Computational Model of Major Depression: the Role of Glutamate Dysfunction on Cingulo-Frontal Network Dynamics

Major depression disease (MDD) is associated with the dysfunction of multinode brain networks. However, converging evidence implicates the reciprocal interaction between midline limbic regions (typified by the ventral anterior cingulate cortex, vACC) and the dorso-lateral prefrontal cortex (dlPFC), reflecting interactions between emotions and cognition. Furthermore, growing evidence suggests a role for abnormal glutamate metabolism in the vACC, while serotonergic treatments (selective serotonin reuptake inhibitor, SSRI) effective for many patients implicate the serotonin system. Currently, no mechanistic framework describes how network dynamics, glutamate, and serotonin interact to explain MDD symptoms and treatments. Here, we built a biophysical computational model of 2 areas (vACC and dlPFC) that can switch between emotional and cognitive processing. MDD networks were simulated by slowing glutamate decay in vACC and demonstrated sustained vACC activation. This hyperactivity was not suppressed by concurrent dlPFC activation and interfered with expected dlPFC responses to cognitive signals, mimicking cognitive dysfunction seen in MDD. Simulation of clinical treatments (SSRI or deep brain stimulation) counteracted this aberrant vACC activity. Theta and beta/gamma oscillations correlated with network function, representing markers of switch-like operation in the network. The model shows how glutamate dysregulation can cause aberrant brain dynamics, respond to treatments, and be reflected in EEG rhythms as biomarkers of MDD.

EEG in psychiatric practice: to do or not to do?

Electroencephalography (EEG) is a non-invasive investigation that can aid the diagnosis of psychiatric and neuropsychiatric disorders. A good predictor of an abnormal EEG recording is the presence of an organic factor identified during the clinical assessment. The non-invasiveness and low cost of the procedure and its ability to measure spontaneous brain activity appear to attract clinicians to utilise this investigative tool. However, studies have reported that EEGs arising from psychiatric referrals have the lowest abnormality detection rate. The focus of this article is to improve this by highlighting the current pitfalls and providing recommendations for appropriate utilisation of EEG. We describe specific EEG changes associated with major psychiatric disorders. We conclude by offering pragmatic considerations when referring a patient for EEG, emphasising the fact that the information provided to the neurophysiologist plays a crucial role in interpreting the EEG recording in a diagnostically meaningful way.

A Circadian Genomic Signature Common to Ketamine and Sleep Deprivation in the Anterior Cingulate Cortex

BACKGROUND

Conventional antidepressants usually require several weeks to achieve a full clinical response in patients with major depressive disorder, an illness associated with dysregulated circadian rhythms and a high incidence of suicidality. Two rapid-acting antidepressant strategies, low-dose ketamine (KT) and sleep deprivation (SD) therapies, dramatically reduce depressive symptoms within 24 hours in a subset of major depressive disorder patients. However, it is unknown whether they exert their actions through shared regulatory mechanisms. To address this question, we performed comparative transcriptomics analyses to identify candidate genes and relevant pathways common to KT and SD.

METHODS

We used the forced swim test, a standardized behavioral approach to measure antidepressant-like activity of KT and SD. We investigated gene expression changes using high-density microarrays and pathway analyses (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Gene Set Enrichment Analysis) in KT- and SD-treated mice compared with saline-treated control male mice.

RESULTS

We show that KT and SD elicit common transcriptional responses implicating distinct elements of the circadian clock and processes involved in neuronal plasticity. There is an overlap of 64 genes whose expression is common in KT and SD. Specifically, there is downregulation of clock genes including Ciart, Per2, Npas4, Dbp, and Rorb in both KT- and SD-treated mice.

CONCLUSIONS

We demonstrate a potential involvement of the circadian clock in rapid antidepressant responses. These findings could open new research avenues to help design chronopharmacological strategies to treat major depressive disorder.

The Comparison of Effectiveness of Various Potential Predictors of Response to Treatment With SSRIs in Patients With Depressive Disorder

The substantial non-response rate in depressive patients indicates a continuing need to identify predictors of treatment outcome. The aim of this 6-week, open-label study was (1) to compare the efficacy of a priori defined predictors: ≥20% reduction in MADRS score at week 1, ≥20% reduction in MADRS score at week 2 (RM ≥ 20% W2), decrease of cordance (RC), and increase of serum and plasma level of brain-derived neurotrophic factor at week 1; and (2) to assess whether their combination yields higher efficacy in the prediction of response to selective serotonin re-uptake inhibitors (SSRIs) than when used singly. Twenty-one patients (55%) achieved a response to SSRIs. The RM ≥20% W2 (areas under curve-AUC = 0.83) showed better predictive efficacy compared to all other predictors with the exception of RC. The identified combined model (RM ≥ 20% W2 + RC), which predicted response with an 84% accuracy (AUC = 0.92), may be a useful tool in the prediction of response to SSRIs.

Recent advances in predicting responses to antidepressant treatment

Major depressive disorder is one of the leading causes of disability in the world since depression is highly frequent and causes a strong burden. In order to reduce the duration of depressive episodes, clinicians would need to choose the most effective therapy for each individual right away. A prerequisite for this would be to have biomarkers at hand that would predict which individual would benefit from which kind of therapy (for example, pharmacotherapy or psychotherapy) or even from which kind of antidepressant class. In the past, neuroimaging, electroencephalogram, genetic, proteomic, and inflammation markers have been under investigation for their utility to predict targeted therapies. The present overview demonstrates recent advances in all of these different methodological areas and concludes that these approaches are promising but also that the aim to have such a marker available has not yet been reached. For example, the integration of markers from different systems needs to be achieved. With ongoing advances in the accuracy of sensing techniques and improvement of modelling approaches, this challenge might be achievable.

The behavioral approach system and augmenting/reducing in auditory event-related potentials during emotional visual stimulation

In the recent Reinforcement Sensitivity Theory Personality Questionnaire (RST-PQ, Corr and Cooper, 2016) the behavioral approach system (BAS) has been conceptualized as multidimensional in which facets of reward interest and reactivity, and goal-drive persistence, are separate from impulsivity. Aim of the present work was to highlight the predictive power of BAS and its facets in differentiating electrocortical responses by using an auditory augmenting/reducing event-related potential (ERP) paradigm during emotional visual stimulation. ERPs were recorded for 5 levels of intensity in 39 women. The RST-PQ was used to measure the total BAS (T-BAS) and its four facets of Goal-Drive Persistence (GDP), Reward Interest (RI), Reward Reactivity (RR), and Impulsivity (IMP). T-BAS and RI, and to a less extent GDP and RR, were significantly associated with higher N1/P2 amplitudes at central sites (C3, Cz, C4) across neutral, positive and negative slides. Similar, but less pronounced relations were found for GDP and RR, but this relation was lacking for Imp facet. In addition, N1/P2 slope at central sites was positively correlated with T-BAS, GDP, RI, RR, but not Imp. Indeed, T-BAS facets failed to maintain a significant correlation with N1/P2 slope, after controlling for T-BAS residual scores, indicating that T-BAS drives these significant correlations. LORETA analysis at 219ms (P2 wave) from tone onset revealed a significant activation of the right inferior parietal lobule (IPL, BA40) and left anterior cingulate gyrus (BA32) in high T-BAS compared to low T-BAS participants. Results are discussed within a revised RST framework differentiating reward components from impulsivity.

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.

Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design

Remission rates for Major Depressive Disorder (MDD) are low and unpredictable for any given antidepressant. No biological or clinical marker has demonstrated sufficient ability to match individuals to efficacious treatment. Biosignatures developed from the systematic exploration of multiple biological markers, which optimize treatment selection for individuals (moderators) and provide early indication of ultimate treatment response (mediators) are needed. The rationale and design of a multi-site, placebo-controlled randomized clinical trial of sertraline examining moderators and mediators of treatment response is described. The target sample is 300 participants with early onset (≤30 years) recurrent MDD. Non-responders to an 8-week trial are switched double blind to either bupropion (for sertraline non-responders) or sertraline (for placebo non-responders) for an additional 8 weeks. Clinical moderators include anxious depression, early trauma, gender, melancholic and atypical depression, anger attacks, Axis II disorder, hypersomnia/fatigue, and chronicity of depression. Biological moderator and mediators include cerebral cortical thickness, task-based fMRI (reward and emotion conflict), resting connectivity, diffusion tensor imaging (DTI), arterial spin labeling (ASL), electroencephalograpy (EEG), cortical evoked potentials, and behavioral/cognitive tasks evaluated at baseline and week 1, except DTI, assessed only at baseline. The study is designed to standardize assessment of biomarkers across multiple sites as well as institute replicable quality control methods, and to use advanced data analytic methods to integrate these markers. A Differential Depression Treatment Response Index (DTRI) will be developed. The data, including biological samples (DNA, RNA, and plasma collected before and during treatment), will become available in a public scientific repository.

CLINICAL TRIAL REGISTRATION

Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC). Identifier: NCT01407094. URL: http://clinicaltrials.gov/show/NCT01407094.

Using Electroencephalography for Treatment Guidance in Major Depressive Disorder

Given the high prevalence of treatment-resistant depression and the long delays in finding effective treatments via trial and error, valid biomarkers of treatment outcome with the ability to guide treatment selection represent one of the most important unmet needs in mood disorders. A large body of research has investigated, for this purpose, biomarkers derived from electroencephalography (EEG), using resting state EEG or evoked potentials. Most studies have focused on specific EEG features (or combinations thereof), whereas more recently machine-learning approaches have been used to define the EEG features with the best predictive abilities without a priori hypotheses. While reviewing these different approaches, we have focused on the predictor characteristics and the quality of the supporting evidence.

Tobacco use is associated with reduced amplitude and intensity dependence of the cortical auditory evoked N1-P2 component

RATIONALE

Tobacco use is linked to cerebral atrophy and reduced cognitive performance in later life. However, smoking-related long-term effects on brain function remain largely uncertain. Previous studies suggest that nicotine affects serotonergic signaling, and the intensity dependence (alias loudness dependence) of the auditory evoked N1-P2 potential has been proposed as a marker of serotonergic neurotransmission.

OBJECTIVE

In the present study, we assesed the effects of chronic smoking on amplitude and intensity dependence of the auditory evoked N1-P2 potential.

METHODS

Subjects underwent a 15-min intensity dependence of auditory evoked potentials (IAEP) paradigm. From N = 1739 eligible subjects (40-79 years), we systematically matched current smokers, ex-smokers, and never-smokers by sex, age, alcohol and caffeine consumption, and socioeconomic status. Between-group differences and potential dose-dependencies were evaluated.

RESULTS

Analyses revealed higher N1-P2 amplitudes and intensity dependencies in never-smokers relative to ex- and current smokers, with ex-smokers exhibiting intermediate intensity dependencies. Moreover, we observed pack years and number of cigarettes consumed per day to be inversely correlated with amplitudes in current smokers.

CONCLUSIONS

According to the IAEP serotonin hypothesis, our results suggest serotonin activity to be highest in current smokers, intermediate in ex-smokers, and lowest in never-smokers. To our knowledge, the present study is the first providing evidence for a dose-dependent reduction in N1-P2 amplitudes. Further, we extend prior research by showing reduced amplitudes and intensity dependencies in ex-smokers even 25 years, on average, after cessation. While we can rule out several smoking-related confounders to bias observed associations, causal inferences remain to be established by future longitudinal studies.

Interhemispheric Asymmetries and Theta Activity in the Rostral Anterior Cingulate Cortex as EEG Signature of HIV-Related Depression: Gender Matters

Resting EEGs of 40 people living with HIV (PLWH) on long-term antiretroviral treatment were examined for z-scored deviations from a healthy control (normative database) to examine the main and interaction effects of depression and gender. Regions of interest were frontal (alpha) and central (all bands) for interhemispheric asymmetries in quantitative EEGs and theta in the rostral anterior cingulate cortex (rACC) in low-resolution electromagnetic tomography (LORETA). Z-scored normed deviations of depressed PLWH, compared with nondepressed, showed right-dominant interhemispheric asymmetries in all regions. However, after adjusting for multiple testing, significance remained only central for theta, alpha, and beta. Reversed (left-dominant) frontal alpha asymmetry is a potential EEG marker of depression in the HIV negative population that was not reversed in depressive PLWH; however, corresponding with extant literature, gender had an effect on the size of frontal alpha asymmetry. The LORETA analysis revealed a trending interactional effect of depression and gender on theta activity in the rACC in Brodmann area 32. We found that compared to men, women had greater right-dominant frontal alpha-asymmetry and elevated theta activity in voxels of the rACC, which may indicate less likelihood of depression and a higher likelihood of response to antidepressants. In conclusion, subtle EEG deviations, such as right-dominant central theta, alpha, and beta asymmetries and theta activity in the rACC may mark HIV-related depressive symptoms and may predict the likelihood of response to antidepressants but gender effects need to be taken into account. Although this study introduced the use of LORETA to examine the neurophysiological correlates of negative affect in PLWH, further research is needed to assess the utility of this tool in diagnostics and treatment monitoring of depression in PLWH.

Salience Network Functional Connectivity Predicts Placebo Effects in Major Depression

BACKGROUND

Recent neuroimaging studies have demonstrated resting-state functional connectivity (rsFC) abnormalities among intrinsic brain networks in Major Depressive Disorder (MDD); however, their role as predictors of treatment response has not yet been explored. Here, we investigate whether network-based rsFC predicts antidepressant and placebo effects in MDD.

METHODS

We performed a randomized controlled trial of two weeklong, identical placebos (described as having either "active" fast-acting, antidepressant effects or as "inactive") followed by a ten-week open-label antidepressant medication treatment. Twenty-nine participants underwent a rsFC fMRI scan at the completion of each placebo condition. Networks were isolated from resting-state blood-oxygen-level-dependent signal fluctuations using independent component analysis. Baseline and placebo-induced changes in rsFC within the default-mode, salience, and executive networks were examined for associations with placebo and antidepressant treatment response.

RESULTS

Increased baseline rsFC in the rostral anterior cingulate (rACC) within the salience network, a region classically implicated in the formation of placebo analgesia and the prediction of treatment response in MDD, was associated with greater response to one week of active placebo and ten weeks of antidepressant treatment. Machine learning further demonstrated that increased salience network rsFC, mainly within the rACC, significantly predicts individual responses to placebo administration.

CONCLUSIONS

These data demonstrate that baseline rsFC within the salience network is linked to clinical placebo responses. This information could be employed to identify patients who would benefit from lower doses of antidepressant medication or non-pharmacological approaches, or to develop biomarkers of placebo effects in clinical trials.

Antinociceptive activity of the new triple reuptake inhibitor NS18283 in a mouse model of chemotherapy-induced neuropathic pain

BACKGROUND

Chronic neuropathic pain can lead to anxiety and depression. Drugs that block reuptake of serotonin, norepinephrine and/or dopamine are widely used to treat depression, and have emerged as useful drugs in the treatment of neuropathic pain. This study compared the acute antinociceptive effects of NS18283, a novel triple monoamine reuptake inhibitor (MRI) with indatraline, venlafaxine and escitalopram in a mouse model of neuropathic pain.

METHOD

Neuropathic pain-like behaviours were induced in mice by repeated injections of oxaliplatin (OXA), and assessed using the von Frey hair test, the cold plate test and the thermal preference plate test. Anxio/depressive phenotype and antidepressant-like properties of compounds were assessed by the novelty suppressed feeding test and the tail suspension test, respectively.

RESULTS

In vivo microdialysis experiments showed that each MRI increased extracellular serotonin, norepinephrine and/or dopamine levels in the cingulate cortex, in agreement with their in vitro reuptake inhibitory properties. Indatraline (3 mg/kg) reversed the full repertoire of OXA-induced neuropathic hypersensitivity. NS18283 (10 mg/kg) reversed OXA-induced mechano-hypersensitivity and cold allodynia. Venlafaxine (16 mg/kg) and escitalopram (4 mg/kg) only reversed cold allodynia and mechano-hypersensitivity, respectively. All MRIs produced antidepressant-like activity in anxio/depressive phenotype of OXA mice.

CONCLUSIONS

Acute administration of drugs that enhance the activity of serotonin, norepinephrine and dopamine neurotransmission within nociceptive pathways may provide a broader spectrum of antinociception than dual or selective reuptake inhibitors in animal models of neuropathic pain. Whether similar observations would occur after repeated administration of such compounds in an attempt to simulate dosing in humans, or be compromised by dopaminergic-mediated adverse effects warrants further investigation.

Utility of event-related potentials in predicting antidepressant treatment response: An iSPOT-D report

It is essential to improve antidepressant treatment of major depressive disorder (MDD) and one way this could be achieved is by reducing the number of treatment steps by employing biomarkers that can predict treatment outcome. This study investigated differences between MDD patients and healthy controls in the P3 and N1 component from the event-related potential (ERP) generated in a standard two-tone oddball paradigm. Furthermore, the P3 and N1 are investigated as predictors for treatment outcome to three different antidepressants. In the international Study to Predict Optimized Treatment in Depression (iSPOT-D)--a multi-center, international, randomized, prospective practical trial--1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-XR. The study also recruited 336 healthy controls. Treatment response and remission were established after eight weeks using the 17-item Hamilton Rating Scale for Depression. P3 and N1 latencies and amplitudes were analyzed using a peak-picking approach and further replicated by using exact low resolution tomography (eLORETA). A reduced P3 was found in MDD patients compared to controls by a peak-picking analysis. This was validated in a temporal global field power analysis. Source density analysis revealed that the difference in cortical activity originated from the posterior cingulate and parahippocampal gyrus. Male non-responders to venlafaxine-XR had significantly smaller N1 amplitudes than responders. This was demonstrated by both analytical methods. Male non-responders to venlafaxine-XR had less activity originating from the left insular cortex. The observed results are discussed from a neural network viewpoint.

The Antidepressant Treatment Response Index as a Predictor of Reboxetine Treatment Outcome in Major Depressive Disorder

Biomarkers to predict clinical outcomes early during the treatment of major depressive disorder (MDD) could reduce suffering and improve outcomes. A quantitative electroencephalogram (qEEG) biomarker, the Antidepressant Treatment Response (ATR) index, has been associated with outcomes of treatment with selective serotonin reuptake inhibitor antidepressants in patients with MDD. Here, we report the results of a post hoc analysis initiated to evaluate whether the ATR index may also be associated with reboxetine treatment outcome, given that its putative mechanism of action is via norepinephrine reuptake inhibition (NRI). Twenty-five adults with MDD underwent qEEG studies during open-label treatment with reboxetine at doses of 8 to 10 mg daily for 8 weeks. The ATR index calculated after 1 week of reboxetine treatment was significantly associated with overall Hamilton Depression Rating Scale (HAM-D) improvement at week 8 (r=0.605, P=.001), even after controlling for baseline depression severity (P=.002). The ATR index predicted response (≥50% reduction in HAM-D) with 70.6% sensitivity and 87.5% specificity, and remission (final HAM-D≤7) with 87.5% sensitivity and 64.7% specificity. These results suggest that the ATR index may be a useful biomarker of clinical response during NRI treatment of adults with MDD. Future studies are warranted to investigate further the potential utility of the ATR index as a predictor of noradrenergic antidepressant treatment response.

Current source density analysis of resting state EEG in depression: a review

Electroencephalography (EEG) has been widely used in the neurophysiological investigation of major depressive disorder (MDD) during past decades. An approach that has attracted particular interest over the past 20 years is current source density (CSD) that assesses current source in extracellular spaces, which are the local generators of the field potentials caused by the activation of neurones. Our aim was to review the current literature regarding resting state CSD analysis in MDD patients. To date, the most prominent aspects in such studies comprise the identification of clinical endophenotypes on the basis of resting state CSD, and the investigation of CSD with respect to treatment outcome prediction. Increased alpha band resting state CSD in frontal regions is typical for MDD, while increased theta band activity in the rostral anterior cingulate gyrus (rACC) has been found to be a good predictor of better antidepressant response. However, differences in the methods used in different studies could be responsible for some contradictions in reported findings. Further research is needed for better distinction of depressive patients from patients with other psychiatric disorders, as well as from healthy subjects.

Frontal and rostral anterior cingulate (rACC) theta EEG in depression: implications for treatment outcome?

In major depressive disorder (MDD), elevated theta current density in the rostral anterior cingulate (rACC), as estimated by source localization of scalp-recorded electroencenphalogram (EEG), has been associated with response to antidepressant treatments, whereas elevated frontal theta has been linked to non-response. This study used source localization to attempt to integrate these apparently opposite results and test, whether antidepressant response is associated with elevated rACC theta and non-response with elevated frontal theta and whether theta activity is a differential predictor of response to different types of commonly used antidepressants. In the international Study to Predict Optimized Treatment in Depression (iSPOT-D), a multi-center, international, randomized, prospective practical trial, 1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-XR. The study also recruited 336 healthy controls. Treatment response and remission were established after eight weeks using the 17-item Hamilton Rating Scale for Depression (HRSD17). The resting-state EEG was assessed at baseline with eyes closed and source localization (eLORETA) was employed to extract theta from the rACC and frontal cortex. Patients with MDD had elevated theta in both frontal cortex and rACC, with small effect sizes. High frontal and rACC theta were associated with treatment non-response, but not with non-remission, and this effect was most pronounced in a subgroup with previous treatment failures. Low theta in frontal cortex and rACC are found in responders to antidepressant treatments with a small effect size. Future studies should investigate in more detail the role of previous treatment (failure) in the association between theta and treatment outcome.

Prediction of long-term treatment response to selective serotonin reuptake inhibitors (SSRIs) using scalp and source loudness dependence of auditory evoked potentials (LDAEP) analysis in patients with major depressive disorder

BACKGROUND

Animal and clinical studies have demonstrated that the loudness dependence of auditory evoked potentials (LDAEP) is inversely related to central serotonergic activity, with a high LDAEP reflecting weak serotonergic neurotransmission and vice versa, though the findings in humans have been less consistent. In addition, a high pretreatment LDAEP appears to predict a favorable response to antidepressant treatments that augment the actions of serotonin. The aim of this study was to test whether the baseline LDAEP is correlated with response to long-term maintenance treatment in patients with major depressive disorder (MDD).

METHODS

Scalp N1, P2 and N1/P2 LDAEP and standardized low resolution brain electromagnetic tomography-localized N1, P2, and N1/P2 LDAEP were evaluated in 41 MDD patients before and after they received antidepressant treatment (escitalopram (n = 32, 10.0 ± 4.0 mg/day), sertraline (n = 7, 78.6 ± 26.7 mg/day), and paroxetine controlled-release formulation (n = 2, 18.8 ± 8.8 mg/day)) for more than 12 weeks. A treatment response was defined as a reduction in the Beck Depression Inventory (BDI) score of >50% between baseline and follow-up.

RESULTS

The responders had higher baseline scalp P2 and N1/P2 LDAEP than nonresponders (p = 0.017; p = 0.036). In addition, changes in total BDI score between baseline and follow-up were larger in subjects with a high baseline N1/P2 LDAEP than those with a low baseline N1/P2 LDAEP (p = 0.009). There were significantly more responders in the high-LDAEP group than in the low-LDAEP group (p = 0.041).

CONCLUSIONS

The findings of this study reveal that a high baseline LDAEP is associated with a clinical response to long-term antidepressant treatment.

The effectiveness of prefrontal theta cordance and early reduction of depressive symptoms in the prediction of antidepressant treatment outcome in patients with resistant depression: analysis of naturalistic data

Current studies suggest that an early improvement of depressive symptoms and the reduction of prefrontal theta cordance value predict the subsequent response to antidepressants. The aim of our study was (1) to compare the predictive abilities of early clinical improvement defined as ≥ 20 % reduction in Montgomery and Åsberg Depression Rating Scale (MADRS) total score at week 1 and 2, and the decrease of prefrontal theta cordance at week 1 in resistant depressive patients and (2) to assess whether the combination of individual predictors yields more robust predictive power than either predictor alone. Eighty-seven subjects were treated (≥ 4 weeks) with various antidepressants chosen according to the judgment of attending psychiatrists. Areas under curve (AUC) were calculated to compare predictive effect of defined single predictors (≥ 20 % reduction in MADRS total score at week 1 and 2, and the decrease of cordance at week 1) and combined prediction models. AUCs of all three predictors were not statistically different (pair-wise comparison). The model combining all predictors yielded an AUC value 0.91 that was significantly higher than AUCs of each individual predictor. The results indicate that the combined predictor model may be a useful and clinically meaningful tool for the prediction of antidepressant response in patients with resistant depression.

Modulation of induced frontocentral theta (Fm-θ) event-related (de-)synchronisation dynamics following mindfulness-based cognitive therapy in Major Depressive Disorder

Depressive severity has been associated with attenuated neocortical frontal midline theta (Fm-θ) power/evoked activity. Mindfulness-Based Cognitive Therapy (MBCT) has shown to be a successful novel intervention for Major Depressive Disorder (MDD), albeit precise working mechanisms remain elusive. We examined the hypothesis that MBCT would have modulating effects upon evoked Fm-θ power, in addition to investigating possible mediation of induced event-related de/synchronisation (ERD/ERS) dynamics. Fifty one patients with a primary diagnosis of MDD (26 exposed to MBCT vs. 25 wait-list/WL controls) undertook a Go/NoGo task consisting of positive, negative and neutral words, further stratified into abstract versus trait adjective matrices. Depressive symptom severity and rumination were also examined. A pattern of enhanced induced Fm-θ synchronisation during the latter 400-800 ms temporal-window pre-to-post MBCT was observed; the contrary in the WL. Modulated ERD/ERS dynamics correlated to amelioration in depressive and rumination symptoms in the MBCT group. We propose the primary action pathway alluded to a neural disengagement mechanism enacting upon tonic neuronal assemblies implicated in emotional and self-related processing. Due to the complexity and presently undiscovered complete unified scientific understanding of neuro-oscillatory-dynamics, and associated clinical interplays; we hypothesise that the electro-cortical and connected clinical working pathways of MBCT in depression are multi-levelled constituting nonlinear and interdependent mechanisms, represented by mediated EEG synchronisation dynamics.

Do prefrontal midline electrodes provide unique neurophysiologic information in Major Depressive Disorder?

Brain oscillatory activity from the midline prefrontal region has been shown to reflect brain dysfunction in subjects with Major Depressive Disorder (MDD). It is not known, however, whether electrodes from this area provide unique information about brain function in MDD. We examined a set of midline sites and two other prefrontal locations for detecting cerebral activity differences between subjects with MDD and healthy controls. Resting awake quantitative EEG (qEEG) data were recorded from 168 subjects: 47 never-depressed adults and 121 with a current major depressive episode. Individual midline electrodes (Fpz, Fz, Cz, Pz, and Oz) and prefrontal electrodes outside the hairline (Fp1, Fp2) were examined with absolute and relative power and cordance in the theta band. We found that MDD subjects exhibited higher values of cordance (p = 0.0066) at Fpz than controls; no significant differences were found at other locations, and power measures showed trend-level differences. Depressed adults showed higher midline cordance than did never-depressed subjects at the most-anterior midline channel. Salient abnormalities in MDD may be detectable by focusing on the prefrontal midline region, and EEG metrics from focused electrode arrays may offer clinical practicality for clinical monitoring.

Examining relations between alpha power as well as anterior cingulate cortex-localized theta activity and response to single or dual antidepressant pharmacotherapies

Electrocortical indices may be useful in predicting antidepressant response. Greater pretreatment alpha power and high rostral anterior cingulate cortex (rACC) theta activity tend to index a favorable outcome. The predictive utility of alpha power asymmetry has been under-explored. Baseline alpha2 (10.5-13.0 Hz) power/asymmetry, rACC theta2 (6.0-8.0 Hz) activity and early (one week) changes in these measures were assessed in relation to antidepressant response by week 12 to three treatment regimens (escitalopram (ESC) + bupropion (BUP), ESC or BUP) in patients with major depressive disorder (N=51). No treatment differences in response existed at week 12. Overall, treatment responders exhibited high, and non-responders low, frontal baseline alpha2 power. Frontal alpha2 power weakly discriminated responders/non-responders overall while posterior alpha2 power and BA25-localized theta2 activity strongly discriminated ESC responders/non-responders. No associations with alpha2 asymmetry and response emerged. BUP responders exhibited high, and BUP non-responders low, baseline rACC theta2 activity. Greater early decreases in rACC theta2 activity existed in ESC+BUP non-responders versus ESC+BUP responders. BUP responders exhibited greater rACC theta2 activity decreases than ESC responders. These preliminary results indicate that baseline and early changes in alpha2 and rACC theta2 activity associate with response and have implications for tailoring antidepressant treatments.

Pretreatment anterior cingulate activity predicts antidepressant treatment response in major depressive episodes

Major depressive disorder leads to substantial individual and socioeconomic costs. Despite the ongoing efforts to improve the treatment for this condition, a trial-and-error approach until an individually effective treatment is established still dominates clinical practice. Searching for clinically useful treatment response predictors is one of the most promising strategies to change this quandary therapeutic situation. This study evaluated the predictive value of a biological and a clinical predictor, as well as a combination of both. Pretreatment EEGs of 31 patients with a major depressive episode were analyzed with neuroelectric brain imaging technique to assess cerebral oscillations related to treatment response. Early improvement of symptoms served as a clinical predictor. Treatment response was assessed after 4 weeks of antidepressant treatment. Responders showed more slow-frequency power in the right anterior cingulate cortex compared to non-responders. Slow-frequency power in this region was found to predict response with good sensitivity (82 %) and specificity (100 %), while early improvement showed lower accuracy (73 % sensitivity and 65 % specificity). Combining both parameters did not further improve predictive accuracy. Pretreatment activity within the anterior cingulate cortex is related to antidepressive treatment response. Our results support the search for biological treatment response predictors using electric brain activity. This technique is advantageous due to its low individual and socioeconomic burden. The benefits of combining both, a clinically and a biologically based predictor, should be further evaluated using larger sample sizes.

Serum BDNF levels in relation to illness severity, suicide attempts, and central serotonin activity in patients with major depressive disorder: a pilot study

The aim of this study was to test the hypothesis that serum levels of brain-derived neurotrophic factor (BDNF) are correlated with the loudness dependence of auditory evoked potentials (LDAEP). The question of whether there is a difference in BDNF levels between depressive patients according to their illness severity, history of suicide attempts, and central serotonin activity was also addressed. A sample of 51 patients who met the criteria for major depressive disorder following diagnosis using axis I of the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders – text revision comprised the study subjects. The patients were stratified into two subgroups based on their illness severity, history of suicide attempts, and their LDAEP values. The LDAEP was evaluated by measuring the auditory event-related potentials, and serum BDNF was measured using blood sampling before beginning medication with serotonergic agents. There was no difference in serum BDNF levels between the two patient subgroups. The subgroup with moderate-to-severe depression (n = 16) was reanalyzed after stratifying it into two subgroups according to LDAEP and BDNF values (dichotomized at the medians into low and high). The high-LDAEP subgroup had higher serum BDNF levels and total Barratt Impulsiveness Scale score than the low-LDAEP subgroup (p = 0.03 and 0.036, respectively). Serum BDNF levels were positively correlated with LDAEP and total Beck Hopelessness Scale (BHS) score (r = 0.56, p = 0.025, and r = 0.59, p = 0.016, respectively). The high-BDNF subgroup had a higher LDAEP and total BHS score than the low-BDNF subgroup (p = 0.046 and p = 0.011, respectively). This is the first study to demonstrate a relationship between the BDNF level and LDAEP in Asian depressive patients. Intriguingly, the high-BDNF subgroup (divided according to illness severity) exhibited a more severe psychopathology on some psychometric rating scales, a finding that conflicts with previous results.

Personalized Medicine in ADHD and Depression: Use of Pharmaco-EEG

This chapter summarises recent developments on personalised medicine in psychiatry with a focus on ADHD and depression and their associated biomarkers and phenotypes. Several neurophysiological subtypes in ADHD and depression and their relation to treatment outcome are reviewed. The first important subgroup consists of the 'impaired vigilance' subgroup with often-reported excess frontal theta or alpha activity. This EEG subtype explains ADHD symptoms well based on the EEG Vigilance model, and these ADHD patients responds well to stimulant medication. In depression this subtype might be unresponsive to antidepressant treatments, and some studies suggest these depressive patients might respond better to stimulant medication. Further research should investigate whether sleep problems underlie this impaired vigilance subgroup, thereby perhaps providing a route to more specific treatments for this subgroup. Finally, a slow individual alpha peak frequency is an endophenotype associated with treatment resistance in ADHD and depression. Future studies should incorporate this endophenotype in clinical trials to investigate further the efficacy of new treatments in this substantial subgroup of patients.

Mechanisms of antidepressant resistance

Depression is one of the most frequent and severe mental disorder. Since the discovery of antidepressant (AD) properties of the imipramine and then after of other tricyclic compounds, several classes of psychotropic drugs have shown be effective in treating major depressive disorder (MDD). However, there is a wide range of variability in response to ADs that might lead to non response or partial response or in increased rate of relapse or recurrence. The mechanisms of response to AD therapy are poorly understood, and few biomarkers are available than can predict response to pharmacotherapy. Here, we will first review markers that can be used to predict response to pharmacotherapy, such as markers of drug metabolism or blood-brain barrier (BBB) function, the activity of specific brain areas or neurotransmitter systems, hormonal dysregulations or plasticity, and related molecular targets. We will describe both clinical and preclinical studies and describe factors that might affect the expression of these markers, including environmental or genetic factors and comorbidities. This information will permit us to suggest practical recommendations and innovative treatment strategies to improve therapeutic outcomes.

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.

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.

Neuronal generators of posterior EEG alpha reflect individual differences in prioritizing personal spirituality

Prominent posterior EEG alpha is associated with depression and clinical response to antidepressants. Given that religious belief was protective against depression in a longitudinal study of familial risk, we hypothesized that individuals who differed by strength of spiritual beliefs might also differ in EEG alpha. Clinical evaluations and self-reports of the importance of religion or spirituality (R/S) were obtained from 52 participants, and again at 10-y followup when EEG was measured. EEG alpha was quantified using frequency PCA of current source densities (CSD-fPCA). Participants who rated R/S as highly important at initial assessment showed greater alpha compared to those who did not. Those who rated R/S important in both sessions showed greater alpha than those who changed their ratings. EEG differences were particularly well-defined for participants with lifetime depression. Findings extend the view of alpha as a marker for affective processes, suggesting an association with the ontogenesis of spirituality.