Pretreatment frontal EEG and changes in suicidal ideation during SSRI treatment in major depressive disorder

How Society Anxiety Influences Attention Control in College Students: The Moderated Mediation Effect of Cognitive Flexibility and Resting-state Electroencephalography Activity

Social anxiety is a prevalent issue among college students, adversely affecting their overall well-being. Drawing from the cognitive model of social anxiety and attention control theory, heightened levels of social anxiety may correspond to poorer attention control ability. However, little is known about the underlying cognitive mechanisms of the relationship between social anxiety and attention control. To address this research gap, the current study recruited a sample of 156 college students (56 women) who underwent self-report measures of social anxiety, cognitive flexibility, and attention control, followed by a resting-state EEG recording. The results revealed a significant negative predictive effect of social anxiety on attention control, with cognitive flexibility partially mediating this relationship. Furthermore, resting-state theta power emerged as a significant moderator, accentuating the negative impact of social anxiety on cognitive flexibility among individuals with lower theta power. In addition, frontal alpha asymmetry (FAA) demonstrated a moderating effect, with lower FAA intensifying the predictive influence of cognitive flexibility on attention control. Taken together, these results suggested that social anxiety can predict attention control either directly or indirectly via the mediating role of cognitive flexibility, and lower theta power and FAA has a risk amplification effect, which provide novel insights into the treatment and prevention of social anxiety and its negative impact on college students.

The Link between Suicidality and Electroencephalography Asymmetry: A Systematic Review and Meta-analysis

As one of the leading causes of death globally, suicide has been researched extensively to better understand factors that confer risk or resilience for suicidality. Promising areas of the literature have focused on brain-based factors that might indicate susceptibility to suicide. Some studies have investigated the link between electroencephalography (EEG) asymmetry, referring to differences in electrical activity in the brain from the left to right hemisphere, and suicidality. The present study is a comprehensive review and meta-analysis of the literature to see if certain patterns in EEG asymmetry serve as a diathesis for suicidal thoughts and behaviors. The results of the current investigation found that EEG asymmetry was not systematically related to suicide based on the literature reviewed. While the present review does not rule out all brain-based factors, the findings suggest that EEG asymmetry may not be a biomarker for suicidality.

Resting State EEG Correlates of Suicide Ideation and Suicide Attempt

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

Retrospective analysis of quantitative electroencephalography changes in a dissimulating patient after dying by suicide: A single case report

We present the case of a 49-year-old man who was diagnosed with depressive disorder, with the first episode having a strong reactive factor. He was involuntarily admitted to a psychiatric hospital after a failed attempt at taking his own life, where he responded to psychotherapy and antidepressant therapy, as evidenced by a >60% reduction in his MADRS total score. He was discharged after 10 days of treatment, denied having suicidal ideations, and was motivated to follow the recommended outpatient care. The risk for suicide during hospitalization was also assessed using suicide risk assessment tools and psychological assessments, including projective tests. The patient underwent a follow-up examination with an outpatient psychiatrist on the 7th day after discharge, during which the suicide risk assessment tool was administered. The results indicated no acute suicide risk or worsening of depressive symptoms. On the 10th day after discharge, the patient took his own life by jumping out of the window of his flat. We believe that the patient had dissimulated his symptoms and possessed suicidal ideations, which were not detected despite repeated examinations specifically designed to assess suicidality and depression symptoms. We retrospectively analyzed his quantitative electroencephalography (QEEG) records to evaluate the change in prefrontal theta cordance as a potentially promising biomarker of suicidality, given the inconclusive results of studies published to date. An increase in prefrontal theta cordance value was found after the first week of antidepressant therapy and psychotherapy in contrast to the expected decrease due to the fading of depressive symptoms. As demonstrated by the provided case study, we hypothesized that prefrontal theta cordance may be an EEG indicator of a higher risk of non-responsive depression and suicidality despite therapeutic improvement.

Prevalence, risk factors and clinical correlates of suicidal ideation in adolescent patients with depression in a large sample of Chinese

BACKGROUND

Suicidal ideation (SI) has a high risk in adolescents and is now a significant concern due to its problematic outcome. However, few systemic studies of suicidal ideation have been conducted in adolescent patients. Therefore, the current study was aimed to assess the prevalence and its clinical correlate of suicidal ideation among adolescent patients with depression.

METHODS

A total of 1635 adolescent patients (748 males/ 887 females) with depression were recruited in this study. The clinical and demographic data were collected by a self-administered questionnaire. Suicidal ideation was assessed by interview. Children's Depression Inventory (CDI) was used to evaluate depressive symptoms, Adolescent Self-Rating Life Events Check List (ASLEC) was used to assess the stressful life events.

RESULTS

The study showed that the prevalence of suicidal ideation in adolescent depression patients was 38.2% (625/1635). Compared to the non-SI patients, SI patients had greater scores on CDI and ASLEC, had inadequate sleeping time, and were more likely to be females. Further logistic regression analysis indicated that suicidal ideation in adolescent patients with depression was significantly associated with females, inadequate sleeping time, the severity of depression, and higher learning pressure.

LIMITATIONS

No causal relationship could be drawn due to the cross-sectional design.

CONCLUSIONS

Our results suggest a high prevalence of suicidal ideation in adolescents with depression. Moreover, the severity of depression, sex, sleep time, and learning pressure are all related to suicidal ideation. Early recognition and treatment of suicidal ideation can effectively prevent the occurrence of suicide among adolescent patients.

Correlation between executive function and quantitative EEG in patients with anxiety by the Research Domain Criteria (RDoC) framework

The Research Domain Criteria (RDoC) project was proposed by the National Institute of Mental Health in 2010 to create a new diagnostic system including symptoms and data from genetics, neuroscience, physiology, and self-reports. The purpose of this study was to determine the link between anxiety and executive functions through quantitative electroencephalography (qEEG) based on the RDoC system. Nineteen-channel EEGs were recorded at the psychiatric clinic from 41 patients with symptoms of anxiety. The EEG power spectra were analysed. The Executive Intelligence Test (EXIT) including the K-WAIS-IV, Stroop, controlled oral word association, and the design fluency tests were performed. A partial, inversed, and significant association was observed between executive intelligence quotient (EIQ) and the absolute delta power in the central region. Similarly, a partial, inversed, and significant association was observed between design fluency and the absolute delta power in the left parietal area. Our findings suggest that the increase in delta power in the central region and left P3 was negatively correlated with the decrease in executive function. It is expected that the absolute delta power plays a specific role in the task-negative default mode network in the relationship between anxiety and executive function.

Multilayer MEG functional connectivity as a potential marker for suicidal thoughts in major depressive disorder

Major depressive disorder (MDD) is highly heterogeneous in its clinical presentation. The present exploratory study used magnetoencephalography (MEG) to investigate electrophysiological intrinsic connectivity differences between healthy volunteers and unmedicated participants with treatment-resistant MDD. The study examined canonical frequency bands from delta through gamma. In addition to group comparisons, correlational studies were conducted to determine whether connectivity was related to five symptom factors: depressed mood, tension, negative cognition, suicidal thoughts, and amotivation. The MDD and healthy volunteer groups did not differ significantly at baseline when corrected across all frequencies and clusters, although evidence of generalized slowing in MDD was observed. Notably, however, electrophysiological connectivity was strongly related to suicidal thoughts, particularly as coupling of low frequency power fluctuations (delta and theta) with alpha and beta power. This analysis revealed hub areas underlying this symptom cluster, including left hippocampus, left anterior insula, and bilateral dorsolateral prefrontal cortex. No other symptom cluster demonstrated a relationship with neurophysiological connectivity, suggesting a specificity to these results as markers of suicidal ideation.

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.

OBJECTIVE

To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019.

EXPOSURES

Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180).

MAIN OUTCOMES AND MEASURES

The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index.

RESULTS

The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]).

CONCLUSIONS AND RELEVANCE

This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00693849.

Frontal Alpha Asymmetry Moderated by Suicidal Ideation in Patients with Major Depressive Disorder: A Comparison with Healthy Individuals

Objective

Frontal alpha asymmetry (FAA) of electroencephalography (EEG) has been studied to differentiate patients with major depressive disorder (MDD) from healthy controls (HC). However, inconsistent results have been obtained thus far. Suicidal ideation (SI) has been known to alter frontal lobe activity, and could be an important covariate in FAA studies. This study aimed to explore the influence of FAA on the relationship among MDD patients with SI and without SI, and HC.

Methods

Sixty-seven patients with MDD (44 without and 23 with SI) and 60 HCs were recruited. Resting state EEG was recorded with their eyes open, and FAA as a lateralized index of alpha power was calculated in the frontal brain region. Hamilton Rating Scale for Anxiety and Depression scores were estimated.

Results

FAA was higher (increased alpha power in the left frontal region) in the MDD group than in the HC group. The FAA was lower (reduced alpha power in the left frontal region) in MDD patients with SI than in MDD patients without SI. The severity of depression and anxiety symptoms were significantly correlated with FAA only in MDD patients with SI. SI moderated the effects of depressive symptom on FAA in the MDD group.

Conclusion

Our results suggest that SI is a clinically important moderator of frontal alpha asymmetry in patients with MDD.

Age-specific urinary metabolite signatures and functions in patients with major depressive disorder

Major depressive disorder (MDD) patients in different age ranges might have different urinary metabolic phenotypes, because age could significantly affect the physiological and psychological status of person. Therefore, it was very important to take age into consideration when studying MDD. Here, a dual platform metabolomic approach was performed to profile urine samples from young and middle-aged MDD patients. In total, 18 and 15 differential metabolites that separately discriminated young and middle-aged MDD patients, respectively, from their respective HC were identified. Only ten metabolites were significantly disturbed in both young and middle-aged MDD patients. Meanwhile, two different biomarker panels for diagnosing young and middle-aged MDD patients, respectively, were identified. Additionally, the TCA cycle was significantly affected in both young and middle-aged MDD patients, but the Glyoxylate and dicarboxylate metabolism and phenylalanine metabolism were only significantly affected in young and middle-aged MDD patients, respectively. Our results would be helpful for developing age-specific diagnostic method for MDD and further investigating the pathogenesis of this disease.

Association between cognitive deficits and suicidal ideation in patients with major depressive disorder

The role of cognitive function in suicidal ideation in patients with major depressive disorder (MDD) has not been adequately explored. This research sought to measure the relationship between suicidal ideation and cognitive function. Therefore, in this study, the association between cognitive function and suicidal ideation in patients with MDD was assessed. Cognitive function was evaluated in 233 patients with MDD using the Japanese version of the Brief Assessment of Cognition in Schizophrenia (BACS). Suicidal ideation was assessed using item 3 of the Hamilton Depression Rating Scale. Approximately 59.2% of the patients (138/233) expressed suicidal ideation. Among the BACS subtests, only the executive function scores were significantly lower in patients with MDD with than in those without (p < 0.005). In addition, the executive function, motor speed function, and composite scores correlated negatively with the severity of suicidal ideation in these patients. These results suggest that executive function, motor speed function, and global neuropsychological function are associated with suicidal ideation in patients with MDD and that the BACS neuropsychological battery is an efficient instrument for monitoring these characteristics. Moreover, specific BACS scores can potentially serve as cognitive biomarkers of suicide risk in patients with MDD.

Electroencephalographic Correlates of Suicidal Ideation in the Theta Band

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

Neurophysiological correlates of suicidal ideation in major depressive disorder: Hyperarousal during sleep

BACKGROUND

Suicide is a major public health concern, and a barrier to reducing the suicide rate is the lack of objective predictors of risk. The present study considers whether quantitative sleep electroencephalography (EEG) may be a neurobiological correlate of suicidal ideation.

METHODS

Participants included 84 (45 female, mean age=26.6) adults diagnosed with major depressive disorder (MDD). The item that measures thoughts of death or suicide on the Quick Inventory of Depressive Symptomatology (QIDS) was used to classify 47 participants as low suicidal ideation (24 females, mean age=26.1) and 37 as high suicidal ideation (21 females, mean age=27.3). Data were obtained from archival samples collected at the University of Michigan and University of Texas Southwestern Medical Center between 2004 and 2012. Sleep EEG was quantified using power spectral analysis, and focused on alpha, beta, and delta frequencies.

RESULTS

Results indicated that participants with high compared to low suicidal ideation experienced 1) increased fast frequency activity, 2) decreased delta activity, and 3) increased alpha-delta sleep after adjusting for age, sex, depression, and insomnia symptoms.

LIMITATIONS

Limitations include the exclusion of imminent suicidal intent, a single suicidal ideation item, and cross-sectional archival data.

CONCLUSIONS

This is one of the first studies to provide preliminary support that electrophysiological brain activity during sleep is associated with increased suicidal ideation in MDD, and may point toward central nervous system (CNS) hyperarousal during sleep as a neurobiological correlate of suicidal ideation.

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.

Does suicidal desire moderate the association between frontal delta power and psychological pain?

Psychological pain frequently underlies thoughts of suicide. We investigated if recent suicidal desire moderated the association between potential neurophysiological markers and psychological pain assessed on the Psychache Scale (PS) and the Orbach & Mikulincer Mental Pain Questionnaire (OMMP). The OMMP specifically assesses current psychological pain that may more readily capture emotions present during recent suicidal desire. In contrast, the PS leaves the timeframe undefined. A secondary analysis was conducted of resting-state EEG data and heart rate obtained in adults with a history of depression. In simultaneous multiple regression models, while controlling for depressive symptoms, recent suicidal desire moderated associations with right-frontal EEG delta power (ΔR² = .07, p < .01) and low-frequency heart rate variability (nonsignificantly) for pain assessed on the PS. No indication for moderation was found for pain on the OMMP. The relationship between the two measures of psychological pain was stronger for individuals with recent suicidal desire (r = .75, p < .01 vs. r = .50, p < .05). The findings suggest that, unless a respondent’s psychological pain is recent and substantial, the PS may not capture the intensity of current psychological pain as effectively as the OMMP.

Suicidal ideation is associated with reduced prefrontal activation during a verbal fluency task in patients with major depressive disorder

BACKGROUND

Despite the known relationship between prefrontal function and increased suicidality during major depressive episodes, the links between prefrontal function and suicidality remain unclear in major depressive disorder (MDD). Suicidal ideation usually precedes a suicide attempt. If prefrontal cortex (PFC) activity is a biomarker for suicidal ideation in depression, monitoring it could be useful for suicide prevention. Therefore, in this study, we assessed the association between prefrontal function and suicidal ideation in MDD.

METHODS

Prefrontal function in 67 patients with MDD (31 with suicidal ideation and 36 without) and 67 age-, gender-, and intelligence quotient-matched healthy controls (HCs) was evaluated using near-infrared spectroscopy (NIRS) during a verbal fluency task (VFT). Suicidal ideation was assessed using item 3 of the Hamilton Depression Rating Scale (HAMD).

RESULTS

Regional hemodynamic changes were significantly smaller in patients with MDD than in HCs in prefrontal and temporal regions. Hemodynamic changes in the right dorsolateral PFC (DLPFC), orbitofrontal cortex (OFC), and right frontopolar cortex (FPC) regions in patients with MDD with suicidal ideation were significantly smaller than in those without suicidal ideation. In addition, hemodynamic changes correlated negatively with the severity of suicidal ideation in the DLPFC, OFC, and FPC in patients with MDD.

LIMITATIONS

Further studies with a larger sample size are required to verify our findings.

CONCLUSIONS

These results suggest that the DLPFC, OFC, and FPC are brain substrates of suicidal ideation in depressive states in patients with MDD, and that NIRS data can be employed as a clinically useful biomarker for the assessment of suicide risk.

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

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

Measuring severe adverse events and medication selection using a "PEER Report" for nonpsychotic patients: a retrospective chart review

We previously reported on an objective new tool that uses quantitative electroencephalography (QEEG) normative- and referenced-electroencephalography sampling databases (currently called Psychiatric EEG Evaluation Registry [PEER]), which may assist physicians in determining medication selection for optimal efficacy to overcome trial-and-error prescribing. The PEER test compares drug-free QEEG features for individual patients to a database of patients with similar EEG patterns and known outcomes after pharmacological interventions. Based on specific EEG data elements and historical outcomes, the PEER Report may also serve as a marker of future severe adverse events (eg, agitation, hostility, aggressiveness, suicidality, homicidality, mania, hypomania) with specific medications. We used a retrospective chart review to investigate the clinical utility of such a registry in a naturalistic environment.

RESULTS

This chart review demonstrated significant improvement on the global assessment scales Clinical Global Impression - Improvement and Quality of Life Enjoyment and Satisfaction - Short Form as well as time to maximum medical improvement and decreased suicidality occurrences. The review also showed that 54.5% of previous medications causing a severe adverse event would have been raised as a caution had the PEER Report been available at the time the drug was prescribed. Finally, due to the significant amount of off-label prescribing of psychotropic medications, additional, objective, evidence-based data aided the prescriber toward better choices.

CONCLUSION

The PEER Report may be useful, particularly in treatment-resistant patients, in helping to guide medication selection. Based on the preliminary data obtained from this chart review, additional studies are warranted to establish the safety and efficacy of adding PEER data when making medication decisions.

Electroencephalography-derived biomarkers of antidepressant response

Recent meta-analyses point to the relatively low efficacy of commonly used antidepressant medications. Selecting the most effective medications for depressed subjects having failed previous treatments is especially difficult. There is a clear need for objective biomarkers that could assist and optimize such treatment selection. We will review here a growing body of evidence suggesting that several electroencephalography (EEG)-based methods may be useful for predicting antidepressant response and eventually for guiding clinical treatment decisions. While most of these methods are based on resting-state EEGs (e.g., alpha- and theta-band EEG abnormalities, the combined Antidepressant Response Index (ATR), cordance, referenced EEG), others include EEG source localization and evoked potentials. The limitations of these technologies and the potential clinical uses will also be outlined.

Brain functional changes (QEEG cordance) and worsening suicidal ideation and mood symptoms during antidepressant treatment

OBJECTIVE

Antidepressant medications are efficacious overall; however, some individuals experience worsening mood symptoms and increased suicidal ideation (SI) during treatment. We examined the quantitative electroencephalographic (QEEG) cordance biomarker of brain function biomarker in relation to treatment-emergent symptom worsening.

METHOD

Seventy-two major depressive disorder (MDD) subjects were treated with fluoxetine 20 mg (n = 13), venlafaxine 150 mg (n = 24), or placebo (n = 35) under double-blind conditions. Behavioral ratings determined whether each subject demonstrated worsening of depressed mood, anxiety, or SI during treatment. QEEG cordance data were analyzed to determine whether symptom worsening was associated with neurophysiological changes.

RESULTS

Antidepressant treatment-emergent SI (13.5%) was associated with a large transient decrease in midline-and-right-frontal (MRF) cordance 48 h after start of medication.

CONCLUSION

Hypothesis-generating results suggest a pattern of functional changes in midline and right frontal brain regions associated with antidepressant treatment-emergent SI in MDD.

Antidepressant-induced suicidality: an update

Evidence suggests that antidepressant treatment may in some cases result in worsening depression and increased risk of suicidality in pediatric and adolescent patients. The United States Food and Drug Administration requires that antidepressants carry a black box warning regarding such a risk in patients up to age 24. Many studies of antidepressant-induced suicidality among adults have also reported an increased risk, while several other investigations involving that population have not supported such a finding. This article provides an update of the controversy surrounding antidepressants as a potential cause of suicidal ideations or behavior. Antidepressant-induced suicidality appears to be an uncommon occurrence but also a legitimate phenomenon. Close monitoring and a follow-up care should be provided for patients after initiation of a new antidepressant.

Quantitative electroencephalographic abnormalities in fibromyalgia patients

There is increasing acceptance that pain in fibromyalgia (FM) is a result of dysfunctional sensory processing in the spinal cord and brain, and a number of recent imaging studies have demonstrated abnormal central mechanisms. The objective of this report is to statistically compare quantitative electroencephalogram (qEEG) measures in 85 FM patients with age and gender matched controls in a normative database. A statistically significant sample (minimum 60 seconds from each subject) of artifact-free EEG data exhibiting a minimum split-half reliability ratio of 0.95 and test-retest reliability ratio of 0.90 was used as the threshold for acceptable data inclusion. FM subject EEG data was compared to EEGs of age and gender matched healthy subjects in the Lifespan Normative Database and analyzed using NeuroGuide 2.0 software. Analyses were based on spectral absolute power, relative power and coherence. Clinical evaluations included the Fibromyalgia Impact Questionnaire (FIQ), Beck Depression Inventory and Fischer dolorimetry for pain pressure thresholds. Based on Z-statistic findings, the EEGs from FM subjects differed from matched controls in the normative database in three features: (1) reduced EEG spectral absolute power in the frontal International 10-20 EEG measurement sites, particularly in the low- to mid-frequency EEG spectral segments; (2) elevated spectral relative power of high frequency components in frontal/central EEG measurement sites; and (3) widespread hypocoherence, particularly in low- to mid-frequency EEG spectral segments, in the frontal EEG measurement sites. A consistent and significant negative correlation was found between pain severity and the magnitude of the EEG abnormalities. No relationship between EEG findings and medicine use was found. It is concluded that qEEG analysis reveals significant differences between FM patients compared to age and gender matched healthy controls in a normative database, and has the potential to be a clinically useful tool for assessing brain function in FM patients.

Prediction of response to antidepressants: is quantitative EEG (QEEG) an alternative?

Selecting the most effective antidepressant for depressed subjects having failed previous treatments is difficult; the rates of success are relatively low. There is a clear need for objective biomarkers which could assist and optimize such treatment selection. We review here the current literature and recent developments on the role of quantitative EEG (QEEG) predictors of treatment outcome in major depressive disorder.