Parieto-temporal alpha EEG band power at baseline as a predictor of antidepressant treatment response with repetitive Transcranial Magnetic Stimulation: a preliminary study

Irremediability in psychiatric euthanasia: examining the objective standard

BACKGROUND

Irremediability is a key requirement for euthanasia and assisted suicide for psychiatric disorders (psychiatric EAS). Countries like the Netherlands and Belgium ask clinicians to assess irremediability in light of the patient's diagnosis and prognosis and 'according to current medical understanding'. Clarifying the relevance of a default objective standard for irremediability when applied to psychiatric EAS is crucial for solid policymaking. Yet so far, a thorough examination of this standard is lacking.

METHODS

Using treatment-resistant depression (TRD) as a test case, through a scoping review in PubMed, we analyzed the state-of-the-art evidence for whether clinicians can accurately predict individual long-term outcome and single out irremediable cases, by examining the following questions: (1) What is the definition of TRD; (2) What are group-level long-term outcomes of TRD; and (3) Can clinicians make accurate individual outcome predictions in TRD?

RESULTS

A uniform definition of TRD is lacking, with over 150 existing definitions, mostly focused on psychopharmacological research. Available yet limited studies about long-term outcomes indicate that a majority of patients with long-term TRD show significant improvement over time. Finally, evidence about individual predictions in TRD using precision medicine is growing, but methodological shortcomings and varying predictive accuracies pose important challenges for its implementation in clinical practice.

CONCLUSION

Our findings support the claim that, as per available evidence, clinicians cannot accurately predict long-term chances of recovery in a particular patient with TRD. This means that the objective standard for irremediability cannot be met, with implications for policy and practice of psychiatric EAS.

An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals

One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from electroencephalogram (EEG) signals before starting the treatment using machine learning approaches. Effective brain connectivity of 19-channel EEG data of MDD patients was calculated by the direct directed transfer function (dDTF) method. Then, using three feature selection methods, the best features were selected and patients were classified as responders or non-responders to rTMS treatment by using the support vector machine (SVM). Results on the 34 MDD patients indicated that the Fp2 region in the delta and theta frequency bands has a significant difference between the two groups and can be used as a significant brain biomarker to assess the rTMS treatment response. Also, the highest accuracy (89.6%) using the SVM classifier for the best features of the dDTF method based on the area under the receiver operating characteristic curve (AUC-ROC) criteria was obtained by combining the delta and theta frequency bands. Consequently, the proposed method can accurately detect the rTMS treatment response in MDD patients before starting treatment on the EEG signal to avoid financial and time costs to patients and medical centers.

Cold Cognition as Predictor of Treatment Response to rTMS; A Retrospective Study on Patients With Unipolar and Bipolar Depression

Background

Cognitive impairments are prevalent in patients with unipolar and bipolar depressive disorder (UDD and BDD, respectively). Considering the fact assessing cognitive functions is increasingly feasible for clinicians and researchers, targeting these problems in treatment and using them at baseline as predictors of response to treatment can be very informative.

Method

In a naturalistic, retrospective study, data from 120 patients (Mean age: 33.58) with UDD (n = 56) and BDD (n = 64) were analyzed. Patients received 20 sessions of bilateral rTMS (10 Hz over LDLPFC and 1 HZ over RDLPFC) and were assessed regarding their depressive symptoms, sustained attention, working memory, and executive functions, using the Beck Depression Inventory (BDI-II) and Neuropsychological Test Automated Battery Cambridge, at baseline and after the end of rTMS treatment course. Generalized estimating equations (GEE) and logistic regression were used as the main statistical methods to test the hypotheses.

Results

Fifty-three percentage of all patients (n = 64) responded to treatment. In particular, 53.1% of UDD patients (n = 34) and 46.9% of BDD patients (n = 30) responded to treatment. Bilateral rTMS improved all cognitive functions (attention, working memory, and executive function) except for visual memory and resulted in more modulations in the working memory of UDD compared to BDD patients. More improvements in working memory were observed in responded patients and visual memory, age, and sex were determined as treatment response predictors. Working memory, visual memory, and age were identified as treatment response predictors in BDD and UDD patients, respectively.

Conclusion

Bilateral rTMS improved cold cognition and depressive symptoms in UDD and BDD patients, possibly by altering cognitive control mechanisms (top-down), and processing negative emotional bias.

Personalization of Repetitive Transcranial Magnetic Stimulation for the Treatment of Major Depressive Disorder According to the Existing Psychiatric Comorbidity

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta-burst stimulation (iTBS) are evidenced-based treatments for patients with major depressive disorder (MDD) who fail to respond to standard first-line therapies. However, although various TMS protocols have been proven to be clinically effective, the response rate varies across clinical applications due to the heterogeneity of real-world psychiatric comorbidities, such as generalized anxiety disorder, posttraumatic stress disorder, panic disorder, or substance use disorder, which are often observed in patients with MDD. Therefore, individualized treatment approaches are important to increase treatment response by assigning a given patient to the most optimal TMS treatment protocol based on his or her individual profile. This literature review summarizes different rTMS or TBS protocols that have been applied in researches investigating MDD patients with certain psychiatric comorbidities and discusses biomarkers that may be used to predict rTMS treatment response. Furthermore, we highlight the need for the validation of neuroimaging and electrophysiological biomarkers associated with rTMS treatment responses. Finally, we discuss on which directions future efforts should focus for developing the personalization of the treatment of depression with rTMS or iTBS.

[EEG predictors of therapeutic response in psychiatry]

The literature review provides data on one of the types of biomarkers - EEG predictors of the therapeutic response of patients with different types of mental pathology. It has been shown that the quantitative parameters of the electroencephalogram (EEG) recorded before the start of the treatment course reflect not only the current functional state of the patient's brain, but also its adaptive resources in terms of the possibility and magnitude of response to therapy. The identified EEG predictors of the therapeutic response in patients with depression, schizophrenia and some other mental disorders have a sufficiently high prognostic ability, sensitivity and specificity in determining responders and non-responders, make it possible to carry out a quantitative prediction of the patient's condition after a course of treatment, and also to assist the clinician in choosing medications for optimal therapy.

Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder

Repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connectivity (FC). These changes may be related to the mechanism of action of rTMS and explain the variability in clinical outcome. We examined changes in electroencephalographic FC during the first rTMS treatment in 109 subjects treated with 10 Hz stimulation to left dorsolateral prefrontal cortex. All subjects subsequently received 30 treatments and clinical response was defined as ≥40% improvement in the inventory of depressive symptomatology-30 SR score at treatment 30. Connectivity change was assessed with coherence, envelope correlation, and a novel measure, alpha spectral correlation (αSC). Machine learning was used to develop predictive models of outcome for each connectivity measure, which were compared with prediction based upon early clinical improvement. Significant connectivity changes were associated with clinical outcome (P < 0.001). Machine learning models based on αSC yielded the most accurate prediction (area under the curve, AUC = 0.83), and performance improved when combined with early clinical improvement measures (AUC = 0.91). The initial rTMS treatment session produced robust changes in FC, which were significant predictors of clinical outcome of a full course of treatment for MDD.

[The effectiveness of high-frequency rhythmic transcranial magnetic stimulation in endogenous depressive disorders in youth]

AIM

To search for neurophysiological predictors of the effectiveness of rhythmic transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex in patients with depressive disorder of various nosology.

MATERIAL AND METHODS

Thirty-four young male patients with protracted treatment resistant depression were studied using psychopathological, psychometric methods and encephalography. A search for predictors of therapeutic efficacy was carried out in a wide range of neurophysiological indicators using different high-frequency rTMS protocols (10 Hz and 20 Hz)..

RESULTS AND CONCLUSION

The most significant changes were obtained using rTMS with a frequency of 20 Hz. A favorable effect of treatment was correlated with higher spectral power of the alpha- and beta 1-rhythm bands in EEG.

Predictors of Response to Repetitive Transcranial Magnetic Stimulation in Depression: A Review of Recent Updates

Transcranial magnetic stimulation (TMS) has been increasingly used in the treatment of various neuropsychiatric disorders including depression over the past two decades. The responses to treatment with TMS are variable as found in the recent studies. Evidences suggest that various factors influence the outcome of depression treated with TMS. Understanding the predictors of response to TMS treatment in depression will guide the clinician in appropriate selection of patients for TMS treatment as well as needful modification in the TMS technique and protocol to have a better clinical outcome. This article comprehensively reviews the factors that predict the outcome of TMS treatment in depression.

Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis

OBJECTIVE

Reducing unsuccessful treatment trials could improve depression treatment. Quantitative EEG (QEEG) may predict treatment response and is being commercially marketed for this purpose. The authors sought to quantify the reliability of QEEG for response prediction in depressive illness and to identify methodological limitations of the available evidence.

METHOD

The authors conducted a meta-analysis of diagnostic accuracy for QEEG in depressive illness, based on articles published between January 2000 and November 2017. The review included all articles that used QEEG to predict response during a major depressive episode, regardless of patient population, treatment, or QEEG marker. The primary meta-analytic outcome was the accuracy for predicting response to depression treatment, expressed as sensitivity, specificity, and the logarithm of the diagnostic odds ratio. Raters also judged each article on indicators of good research practice.

RESULTS

In 76 articles reporting 81 biomarkers, the meta-analytic estimates showed a sensitivity of 0.72 (95% CI=0.67-0.76) and a specificity of 0.68 (95% CI=0.63-0.73). The logarithm of the diagnostic odds ratio was 1.89 (95% CI=1.56-2.21), and the area under the receiver operator curve was 0.76 (95% CI=0.71-0.80). No specific QEEG biomarker or specific treatment showed greater predictive power than the all-studies estimate in a meta-regression. Funnel plot analysis suggested substantial publication bias. Most studies did not use ideal practices.

CONCLUSIONS

QEEG does not appear to be clinically reliable for predicting depression treatment response, as the literature is limited by underreporting of negative results, a lack of out-of-sample validation, and insufficient direct replication of previous findings. Until these limitations are remedied, QEEG is not recommended for guiding selection of psychiatric treatment.

Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression

Background: Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a non-invasive neurophysiological test that has promise as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel non-linear index of resting state EEG activity as a predictor of clinical outcome, and compare its predictive capacity to traditional frequency-based indices.

Methods: EEG was recorded from 62 patients with treatment resistant depression (TRD) and 25 healthy comparison (HC) subjects. TRD patients were treated with excitatory repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) for 4 to 6 weeks. EEG signals were first decomposed using the empirical mode decomposition (EMD) method into band-limited intrinsic mode functions (IMFs). Subsequently, Permutation Entropy (PE) was computed from the obtained second IMF to yield an index named PEIMF2. Receiver Operator Characteristic (ROC) curve analysis and ANOVA test were used to evaluate the efficiency of this index (PEIMF2) and were compared to frequency-band based methods.

Results: Responders (RP) to rTMS exhibited an increase in the PEIMF2 index compared to non-responders (NR) at F3, FCz and FC3 sites (p < 0.01). The area under the curve (AUC) for ROC analysis was 0.8 for PEIMF2 index for the FC3 electrode. The PEIMF2 index was superior to ordinary frequency band measures.

Conclusion: Our data show that the PEIMF2 index, yields superior outcome prediction performance compared to traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression; specifically entropy indices applied in band-limited EEG components. Registration in ClinicalTrials.Gov; identifiers NCT02800226 and NCT01887782.

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.

NEUROBIOLOGICAL PREDICTORS OF RESPONSE TO DORSOLATERAL PREFRONTAL CORTEX REPETITIVE TRANSCRANIAL MAGNETIC STIMULATION IN DEPRESSION: A SYSTEMATIC REVIEW

BACKGROUND

A significant proportion of patients with depression fail to respond to psychotherapy and standard pharmacotherapy, leading to treatment-resistant depression (TRD). Due to the significant prevalence of TRD, alternative therapies for depression have emerged as viable treatments in the armamentarium for this disorder. Repetitive transcranial magnetic stimulation (rTMS) is now being offered in clinical practice in broader numbers. Many studies have investigated various different neurobiological predictors of response of rTMS. However, a synthesis of this literature and an understanding of what biological targets predict response is lacking. This review aims to systematically synthesize the literature on the neurobiological predictors of rTMS in patients with depression.

METHODS

Medline (1996-2014), Embase (1980-2014), and PsycINFO (1806-2014) were searched under set terms. Two authors reviewed each article and came to consensus on the inclusion and exclusion criteria. All eligible studies were reviewed, duplicates were removed, and data were extracted individually.

RESULTS

The search identified 1,673 articles, 41 of which met both inclusion and exclusion criteria. Various biological factors at baseline appear to predict response to rTMS, including levels of certain molecular factors, blood flow in brain regions implicated in depression, electrophysiological findings, and specific genetic polymorphisms.

CONCLUSIONS

Significant methodological variability in rTMS treatment protocols limits the ability to generalize conclusions. However, response to treatment may be predicted by baseline frontal lobe blood flow, and presence of polymorphisms of the 5-hydroxytryptamine (5-HT) -1a gene, the LL genotype of the serotonin transporter linked polymorphic region (5-HTTLPR) gene, and Val/Val homozygotes of the brain-derived neurotrophic factor (BDNF) gene.

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

Resting electroencephalographic correlates of the clinical response to repetitive transcranial magnetic stimulation: A preliminary comparison between unipolar and bipolar depression

BACKGROUND

Major depressive disorder (MDD) and bipolar disorder (BP) are two different types of mood disorders, sometimes difficult to distinguish from their depressive symptoms, and for which repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) has been proposed to treat refractory patients. Here we studied whether the electroencephalogram (EEG) at rest could be used to predict the therapeutic response to left DLPFC 10 Hz rTMS, and to which extent BP and MDD patients show similar correlation between the clinical response and the cortical networks at rest.

METHODS

Eight MDD (6 females) and 10 BP patients (6 females) were included. The rTMS therapy consisted of 10 to 20 neuronavigated sessions, with 2000 pulses continuously applied at 120% motor threshold for each session. RTMS sessions at the beginning, middle and end of the therapy were performed while recording EEG signals. EEG spectral power was partitioned using the common physiological frequency bands and was statistically analysed at the scalp level and after cortical source reconstruction.

RESULTS

We found significantly higher power in theta and beta bands in BP patients than in MDD patients, mainly localised in the prefrontal cortex. In addition, responders showed higher power in delta and theta bands in parietal regions and weaker frontal alpha power, when compared to non-responders.

DISCUSSION

These preliminary findings on a small cohort suggest that pre-treatment EEG oscillatory patterns may have some predictive value regarding rTMS therapy, both for MDD and BP disorders.

EEG alpha asymmetry as a gender-specific predictor of outcome to acute treatment with different antidepressant medications in the randomized iSPOT-D study

OBJECTIVE

To determine whether EEG occipital alpha and frontal alpha asymmetry (FAA) distinguishes outpatients with major depression (MDD) from controls, predicts antidepressant treatment outcome, and to explore the role of gender.

METHODS

In the international Study to Predict Optimized Treatment in Depression (iSPOT-D), a multi-center, randomized, prospective open-label trial, 1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-extended release. The study also recruited 336 healthy controls. Treatment response was established after eight weeks and resting EEG was measured at baseline (two minutes eyes open and eyes closed).

RESULTS

No differences in EEG alpha for occipital and frontal cortex, or for FAA, were found in MDD participants compared to controls. Alpha in the occipital and frontal cortex was not associated with treatment outcome. However, a gender and drug-class interaction effect was found for FAA. Relatively greater right frontal alpha (less cortical activity) in women only was associated with a favorable response to the Selective Serotonin Reuptake Inhibitors escitalopram and sertraline. No such effect was found for venlafaxine-extended release.

CONCLUSIONS

FAA does not differentiate between MDD and controls, but is associated with antidepressant treatment response and remission in a gender and drug-class specific manner.

SIGNIFICANCE

Future studies investigating EEG alpha measures in depression should a-priori stratify by gender.

Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance

OBJECTIVE

The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN).

METHODS

The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation.

RESULTS

The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively.

CONCLUSION

Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.

Analysis of Brain Functional Changes in High-Frequency Repetitive Transcranial Magnetic Stimulation in Treatment-Resistant Depression

Repetitive transcranial magnetic stimulation (rTMS) is a treatment procedure that uses magnetic fields to stimulate nerve cells in the brain, and is associated with significant improvements in clinical symptoms of major depressive disorder (MDD). The effect of rTMS treatment on the brain can be evaluated by cordance, a quantitative electroencephalography (QEEG) method that extracts information from absolute and relative power of EEG spectra. In this study, to analyze brain functional changes, pre- and post-rTMS, QEEG data were collected from 6 frontal electrodes (Fp1, Fp2, F3, F4, F7, and F8) in 2 slow bands (delta and theta) for 55 MDD subjects. To examine brain changes, cordance scores were determined, using repeated-measures analysis of variance (ANOVA). High-frequency rTMS was associated with cordance decrease in left frontal and right prefrontal regions in both delta and theta for nonresponders; it was associated with cordance increase in all right and left frontal electrodes, except F8, for responders.

Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

OBJECTIVE

Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection.

METHODS

Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects.

RESULTS

BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset.

CONCLUSION

ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination.

Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS)

A group of European experts was commissioned to establish guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS) from evidence published up until March 2014, regarding pain, movement disorders, stroke, amyotrophic lateral sclerosis, multiple sclerosis, epilepsy, consciousness disorders, tinnitus, depression, anxiety disorders, obsessive-compulsive disorder, schizophrenia, craving/addiction, and conversion. Despite unavoidable inhomogeneities, there is a sufficient body of evidence to accept with level A (definite efficacy) the analgesic effect of high-frequency (HF) rTMS of the primary motor cortex (M1) contralateral to the pain and the antidepressant effect of HF-rTMS of the left dorsolateral prefrontal cortex (DLPFC). A Level B recommendation (probable efficacy) is proposed for the antidepressant effect of low-frequency (LF) rTMS of the right DLPFC, HF-rTMS of the left DLPFC for the negative symptoms of schizophrenia, and LF-rTMS of contralesional M1 in chronic motor stroke. The effects of rTMS in a number of indications reach level C (possible efficacy), including LF-rTMS of the left temporoparietal cortex in tinnitus and auditory hallucinations. It remains to determine how to optimize rTMS protocols and techniques to give them relevance in routine clinical practice. In addition, professionals carrying out rTMS protocols should undergo rigorous training to ensure the quality of the technical realization, guarantee the proper care of patients, and maximize the chances of success. Under these conditions, the therapeutic use of rTMS should be able to develop in the coming years.

The EEG as an index of neuromodulator balance in memory and mental illness

There is a strong correlation between signature EEG frequency patterns and the relative levels of distinct neuromodulators. These associations become particularly evident during the sleep-wake cycle. The monoamine-acetylcholine balance hypothesis is a theory of neurophysiological markers of the EEG and a detailed description of the findings that support this proposal are presented in this paper. According to this model alpha rhythm reflects the relative predominance of cholinergic muscarinic signals and delta rhythm that of monoaminergic receptor effects. Both high voltage synchronized rhythms are likely mediated by inhibitory Gαi/o-mediated transduction of inhibitory interneurons. Cognitively, alpha and delta EEG measures are proposed to indicate automatic and flexible strategies, respectively. Sleep is associated with marked changes in relative neuromodulator levels corresponding to EEG markers of distinct stages. Sleep studies on memory consolidation present some of the strongest evidence yet for the respective roles of monoaminergic and cholinergic projections in declarative and non-declarative memory processes, a key theoretical premise for understanding the data. Affective dysregulation is reflected in altered EEG patterns during sleep.

Biological markers in noninvasive brain stimulation trials in major depressive disorder: a systematic review

OBJECTIVES

The therapeutic effects of transcranial magnetic stimulation (TMS) and transcranial direct current stimulation in patients with major depression have shown promising results; however, there is a lack of mechanistic studies using biological markers (BMs) as an outcome. Therefore, our aim was to review noninvasive brain stimulation trials in depression using BMs.

METHODS

The following databases were used for our systematic review: MEDLINE, Web of Science, Cochrane, and SCIELO. We examined articles published before November 2012 that used TMS and transcranial direct current stimulation as an intervention for depression and had BM as an outcome measure. The search was limited to human studies written in English.

RESULTS

Of 1234 potential articles, 52 articles were included. Only studies using TMS were found. Biological markers included immune and endocrine serum markers, neuroimaging techniques, and electrophysiological outcomes. In 12 articles (21.4%), end point BM measurements were not significantly associated with clinical outcomes. All studies reached significant results in the main clinical rating scales. Biological marker outcomes were used as predictors of response, to understand mechanisms of TMS, and as a surrogate of safety.

CONCLUSIONS

Functional magnetic resonance imaging, single-photon emission computed tomography, positron emission tomography, magnetic resonance spectroscopy, cortical excitability, and brain-derived neurotrophic factor consistently showed positive results. Brain-derived neurotrophic factor was the best predictor of patients' likeliness to respond. These initial results are promising; however, all studies investigating BMs are small, used heterogeneous samples, and did not take into account confounders such as age, sex, or family history. Based on our findings, we recommend further studies to validate BMs in noninvasive brain stimulation trials in MDD.

Baseline and treatment-emergent EEG biomarkers of antidepressant medication response do not predict response to repetitive transcranial magnetic stimulation

There has been a surge of interest in biomarkers that can rapidly predict or assess response to psychiatric treatment, as the current standard practice of extended therapeutic trials is often dissatisfying to both clinicians and patients. Electroencephalographic (EEG) biomarkers in particular have been proposed as an inexpensive yet rapid way of determining whether a patient is responding to an intervention, usually before subjective mood improvement occurs. However, even the most well-reported EEG algorithms have not been subjected to independent replication, limiting their clinical generalizability. It is also unclear whether those biomarkers can generalize beyond their original study population, e.g. to patients undergoing somatic treatments for depression. We report here analysis of EEG data from the pivotal OPT-TMS study of transcranial magnetic stimulation (rTMS) for major depressive disorder. In this dataset, previously reported biomarkers of medication response showed no significant correlation with eventual response to rTMS treatment. Furthermore, EEG power in multiple bands measured at baseline and throughout the treatment course did not correlate with or predict either binary (response/nonresponse) or continuous (Hamilton Rating Scale for Depression) outcome measures. While somewhat limited by technical difficulties in data collection, these analyses are adequately powered to detect clinically relevant biomarkers. We believe this highlights a need for wider-scale independent replication of previous EEG biomarkers, both in pharmacotherapy and neuromodulation.