High-Gamma: A biological marker for suicide attempt in patients with depression

Neural activity during inhibitory control predicts suicidal ideation with machine learning

Suicide is a leading cause of death in the US and worldwide. Current strategies for preventing suicide are often focused on the identification and treatment of risk factors, especially suicidal ideation (SI). Hence, developing data-driven biomarkers of SI may be key for suicide prevention and intervention. Prior attempts at biomarker-based prediction models for SI have primarily used expensive neuroimaging technologies, yet clinically scalable and affordable biomarkers remain elusive. Here, we investigated the classification of SI using machine learning (ML) on a dataset of 76 subjects with and without SI(+/-) (n = 38 each), who completed a neuro-cognitive assessment session synchronized with electroencephalography (EEG). SI+/- groups were matched for age, sex, and mental health symptoms of depression and anxiety. EEG was recorded at rest and while subjects engaged in four cognitive tasks of inhibitory control, interference processing, working memory, and emotion bias. We parsed EEG signals in physiologically relevant theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequencies and performed cortical source imaging on the neural signals. These data served as SI predictors in ML models. The best ML model was obtained for beta band power during the inhibitory control (IC) task, demonstrating high sensitivity (89%), specificity (98%). Shapley explainer plots further showed top neural predictors as feedback-related power in the visual and posterior default mode networks and response-related power in the ventral attention, fronto-parietal, and sensory-motor networks. We further tested the external validity of the model in an independent clinically depressed sample (n = 35, 12 SI+) that engaged in an adaptive test version of the IC task, demonstrating 50% sensitivity and 61% specificity in this sample. Overall, the study suggests a promising, scalable EEG-based biomarker approach to predict SI that may serve as a target for risk identification and intervention.

Differentiation between suicide attempt and suicidal ideation in patients with major depressive disorder using cortical functional network

Studies exploring the neurophysiology of suicide are scarce and the neuropathology of related disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in drug-naïve depressed patients with suicide attempt (SA) and suicidal ideation (SI). EEG was recorded in 55 patients with SA and in 54 patients with SI. Particularly, all patients with SA were evaluated using EEG immediately after their SA (within 7 days). Graph-theory-based source-level weighted functional networks were assessed using strength, clustering coefficient (CC), and path length (PL) in seven frequency bands. Finally, we applied machine learning to differentiate between the two groups using source-level network features. At the global level, patients with SA showed lower strength and CC and higher PL in the high alpha band than those with SI. At the nodal level, compared with patients with SI, patients with SA showed lower high alpha band nodal CCs in most brain regions. The best classification performances for SA and SI showed an accuracy of 73.39%, a sensitivity of 76.36%, and a specificity of 70.37% based on high alpha band network features. Our findings suggest that abnormal high alpha band functional network may reflect the pathophysiological characteristics of suicide and serve as a clinical biomarker for suicide.

Clinical, behavioral, and electrophysiological profiles along a continuum of suicide risk: evidence from an implicit association task

BACKGROUND

An urgent need exists to identify neural correlates associated with differing levels of suicide risk and develop novel, rapid-acting therapeutics to modulate activity within these neural networks.

METHODS

Electrophysiological correlates of suicide were evaluated using magnetoencephalography (MEG) in 75 adults with differing levels of suicide risk. During MEG scanning, participants completed a modified Life-Death Implicit Association Task. MEG data were source-localized in the gamma (30-58 Hz) frequency, a proxy measure of excitation-inhibition balance. Dynamic causal modeling was used to evaluate differences in connectivity estimates between risk groups. A proof-of-concept, open-label, pilot study of five high risk participants examined changes in gamma power after administration of ketamine (0.5 mg/kg), an NMDAR antagonist with rapid anti-suicide ideation effects.

RESULTS

Implicit self-associations with death were stronger in the highest suicide risk group relative to all other groups, which did not differ from each other. Higher gamma power for self-death compared to self-life associations was found in the orbitofrontal cortex for the highest risk group and the insula and posterior cingulate cortex for the lowest risk group. Connectivity estimates between these regions differentiated the highest risk group from the full sample. Implicit associations with death were not affected by ketamine, but enhanced gamma power was found for self-death associations in the left insula post-ketamine compared to baseline.

CONCLUSIONS

Differential implicit cognitive processing of life and death appears to be linked to suicide risk, highlighting the need for objective measures of suicidal states. Pharmacotherapies that modulate gamma activity, particularly in the insula, may help mitigate risk.Clinicaltrials.gov identifier: NCT02543983, NCT00397111.

Identifying unique subgroups in suicide risks among psychiatric outpatients

BACKGROUND

The presence of psychiatric disorders is widely recognized as one of the primary risk factors for suicide. A significant proportion of individuals receiving outpatient psychiatric treatment exhibit varying degrees of suicidal behaviors, which may range from mild suicidal ideations to overt suicide attempts. This study aims to elucidate the transdiagnostic symptom dimensions and associated suicidal features among psychiatric outpatients.

METHODS

The study enrolled patients who attended the psychiatry outpatient clinic at a tertiary hospital in South Korea (n = 1, 849, age range = 18-81; 61% women). A data-driven classification methodology was employed, incorporating a broad spectrum of clinical symptoms, to delineate distinctive subgroups among psychiatric outpatients exhibiting suicidality (n = 1189). A reference group of patients without suicidality (n = 660) was included for comparative purposes to ascertain cluster-specific sociodemographic, suicide-related, and psychiatric characteristics.

RESULTS

Psychiatric outpatients with suicidality (n = 1189) were subdivided into three distinctive clusters: the low-suicide risk cluster (Cluster 1), the high-suicide risk externalizing cluster (Cluster 2), and the high-suicide risk internalizing cluster (Cluster 3). Relative to the reference group (n = 660), each cluster exhibited distinct attributes pertaining to suicide-related characteristics and clinical symptoms, covering domains such as anxiety, externalizing and internalizing behaviors, and feelings of hopelessness. Cluster 1, identified as the low-suicide risk group, exhibited less frequent suicidal ideation, planning, and multiple attempts. In the high-suicide risk groups, Cluster 2 displayed pronounced externalizing symptoms, whereas Cluster 3 was primarily defined by internalizing and hopelessness symptoms. Bipolar disorders were most common in Cluster 2, while depressive disorders were predominant in Cluster 3.

DISCUSSION

Our findings suggest the possibility of differentiating psychiatric outpatients into distinct, clinically relevant subgroups predicated on their suicide risk. This research potentially paves the way for personalizing interventions and preventive strategies that address cluster-specific characteristics, thereby mitigating suicide-related mortality among psychiatric outpatients.

Serum lipids as predictive markers for death by suicide

Serum lipid levels have been associated with an increased risk of suicidal behaviors. This retrospective cohort study aimed to investigate the association between serum lipid levels and death by suicide among suicide attempters according to sex. Suicide attempters visiting emergency departments between 2007 and 2011 were followed up until the date of all-cause death or December 31, 2012. Sex-stratified Cox proportional hazards regression and competing risk models were constructed to obtain the hazard ratios (HR) of serum lipid measures and suicide. For each significant lipid variable in the final models, Kaplan-Meier survival analysis and cumulative incidence function (CIF) were employed to compare the time to suicide between the high- and low-lipid groups based on the best cutoff point from the receiver operating characteristic curve. In 408 female attempters (65.8 %), the HR in the Cox regression model and subdistribution HR in the competing risk model for increased total cholesterol (TC) were 0.968 and 0.970, respectively. In the Kaplan-Meier survival analysis and CIF, increased death by suicide was demonstrated in the low-TC group (< 165 mg/dL). Lower serum TC levels among female suicide attempters may predict suicide. More careful monitoring is warranted in women with lower TC levels who recently attempted suicide.

Non-suicidal Self-injury and Quantified Electroencephalogram in Adolescents and Young Adults with Depression

Objective

: This study investigated the association between non-suicidal self-injury (NSSI) and quantified electroencephalogram (QEEG) in patients with depression. We aimed to identify clinical features of NSSI and differences in QEEG findings.

Methods

: This retrospective study used the medical records of 52 inpatients with major depressive episodes, aged from 15 to 30. The patients were categorized according to their history of NSSI. Their main diagnosis and sex were also considered. To evaluate clinical symptoms, self-reported scales were used. The absolute power and the Z-scores of various waves were included.

Results

: NSSI was associated with suicidal ideations (p = 0.001) and trauma history (p = 0.014). In the binary logistic regression analysis, the Z-score of absolute alpha power was higher on the FP2 node (p = 0.029), lower on the F4 node (p = 0.029) in the NSSI group. The absolute high beta power in the NSSI group was higher on the FP2 and the F3 node, but lower on the F7 and F8 node. Patients with NSSI showed higher Z-score of the absolute delta power at the FP2 node (p = 0.044). The absolute gamma power was higher on the FP2 (p = 0.012) and the F3 node (0.043), lower on the FP1 (p = 0.019) and the F7 node (0.018) in the NSSI group. The absolute high gamma power at the FP2 (p = 0.017) and F8 nodes (p = 0.045) were higher in the NSSI group.

Conclusion

: Patients with NSSI may have clinical features distinct from those of patients without NSSI. QEEG results have shown some differences, although it is less applicable due to some limitations.

EEG wakefulness regulation in transdiagnostic patients after a recent suicide attempt

OBJECTIVE

Decades of research have not yet produced statistically reliable predictors of preparatory behavior eventually leading to suicide attempts or deaths by suicide. As the nature of suicidal behavior is complex, it is best investigated in a transdiagnostic approach, while assessing objective markers, as proposed by the Research Domain Criteria (Cuthbert, 2013).

METHODS

A 15-min resting-state EEG was recorded in 45 healthy controls, and 49 transdiagnostic in-patients with a recent (<6 months) suicide attempt. Brain arousal regulation in eyes-closed condition was assessed with the Vigilance Algorithm Leipzig (VIGALL) (Sander et al., 2015).

RESULTS

A significant incline of median vigilance and vigilance slope was observed in patients within the first 3-min of the EEG recording. Additionally, a significant positive correlation of self-reported suicidal ideation with the vigilance slope over 15-min recording time, as well as a significant negative correlation with EEG vigilance stage A1 during the first 3-min was found.

CONCLUSIONS

Transdiagnostic patients with a recent suicide attempt show a distinct vigilance regulation pattern. Further studies including a control group consisting of patients without life-time suicide attempts are needed to increase the clinical utility of the findings.

SIGNIFICANCE

These findings might serve as potential objective markers of suicidal behavior.

Decreased beta 1 (12-15 Hertz) power modulates the transfer of suicidal ideation to suicide in major depressive disorder

BACKGROUND

Suicide prevention for major depressive disorder (MDD) is a worldwide challenge, especially for suicide attempt (SA). Viewing suicide as a state rather than a lifetime event provided new perspectives on suicide research.

OBJECTIVE

This study aimed to verify and complement SAs biomarkers of MDD with a recent SA sample.

METHODS

This study included 189 participants (60 healthy controls; 47 MDD patients with non-suicide (MDD-NSs), 40 MDD patients with suicide ideation (MDD-SIs) and 42 MDD patients with SA (MDD-SAs)). MDD patients with an acute SA time was determined to be within 1 week since the last SA. SUICIDALITY Part in MINI was applied to evaluate suicidality. Absolute powers in 14 frequency bands were extracted from subject's resting-state electroencephalography data and compared within four groups. The relationship among suicidality, the number of SA and powers in significant frequency bands were investigated.

RESULTS

MDD-SIs had increased powers in delta, theta, alpha and beta band on the right frontocentral channels compared to MDD-NSs, while MDD-SAs had decreased powers in delta, beta and gamma bands on widely the right frontocentral and parietooccipital channels compared to MDD-SIs. Beta 1 power was the lowest in MDD-SAs and was modulated by the number of SA. The correlation between suicidality and beta 1 power was negative in MDD-SAs and positive in MDD-SIs.

CONCLUSION

Reduced beta 1 (12-15 Hz) power could be essential in promoting suicidal behaviour in MDD. Research on recent SA samples contributes to a better understanding of suicide mechanisms and preventing suicidal behaviour in MDD.

Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder

BACKGROUND

Major depressive disorder (MDD) has a high incidence and an unknown mechanism. There are no objective and sensitive indicators for clinical diagnosis.

OBJECTIVE

This study explored specific electrophysiological indicators and their role in the clinical diagnosis of MDD using machine learning.

METHODS

Forty first-episode and drug-naïve patients with MDD and forty healthy controls (HCs) were recruited. EEG data were collected from all subjects in the resting state with eyes closed for 10 min. The severity of MDD was assessed by the Hamilton Depression Rating Scale (HAMD-17). Machine learning analysis was used to identify the patients with MDD.

RESULTS

Compared to the HC group, the relative power of the low delta and theta bands was significantly higher in the right occipital region, and the relative power of the alpha band in the entire posterior occipital region was significantly lower in the MDD group. In the MDD group, the alpha band scalp functional connectivity was overall lower, while the scalp functional connectivity in the gamma band was significantly higher than that in the HC group. In the feature set of the relative power of the ROI in each band, the highest accuracy of 88.2% was achieved using the KNN classifier while using PCA feature selection. In the explanatory model using SHAP values, the top-ranking influence feature is the relative power of the alpha band in the left parietal region.

CONCLUSIONS

Our findings reveal that the abnormal EEG neural oscillations may reflect an imbalance of excitation, inhibition and hyperactivity in the cerebral cortex in first-episode and drug-naïve patients with MDD. The relative power of the alpha band in the left parietal region is expected to be an objective electrophysiological indicator of MDD.

Mechanisms of Sustained Increases in γ Power Post-Ketamine in a Computational Model of the Hippocampal CA3: Implications for Ketamine's Antidepressant Mechanism of Action

Subanaesthetic doses of ketamine increase γ oscillation power in neural activity measured using electroencephalography (EEG), and this effect lasts several hours after ketamine administration. The mechanisms underlying this effect are unknown. Using a computational model of the hippocampal cornu ammonis 3 (CA3) network, which is known to reproduce ketamine's acute effects on γ power, we simulated the plasticity of glutamatergic synapses in pyramidal cells to test which of the following hypotheses would best explain this sustained γ power: the direct inhibition hypothesis, which proposes that increased γ power post-ketamine administration may be caused by the potentiation of recurrent collateral synapses, and the disinhibition hypothesis, which proposes that potentiation affects synapses from both recurrent and external inputs. Our results suggest that the strengthening of external connections to pyramidal cells is able to account for the sustained γ power increase observed post-ketamine by increasing the overall activity of and synchrony between pyramidal cells. The strengthening of recurrent pyramidal weights, however, would cause an additional phase shifted voltage increase that ultimately reduces γ power due to partial cancellation. Our results therefore favor the disinhibition hypothesis for explaining sustained γ oscillations after ketamine administration.

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.

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

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

Neurophysiological markers of depression detection and severity prediction in first-episode major depressive disorder

OBJECTIVE

Deviant γ auditory steady-state responses (γ-ASSRs) have been documented in some psychiatric disorders. Nevertheless, the role of γ-ASSR in drug-naïve first-episode major depressive disorder (FEMD) patients remains equivocal. This study aimed to examine whether γ-ASSRs are impaired in FEMD patients and predict depression severity.

METHODS

Cortical reactivity was assessed in a cohort of 28 FEMD patients relative to 30 healthy control (HC) subjects during an ASSR paradigm randomly presented at 40 and 60 Hz. Event-related spectral perturbation and inter-trial phase coherence (ITC) were calculated to quantify dynamic changes of the γ-ASSR. Receiver operating characteristic curve combined with binary logistic regression were then employed to summarize ASSR variables that maximally differentiated groups.

RESULTS

FEMD patients exhibited significantly inferior 40 Hz-ASSR-ITC in the right hemisphere versus HC subjects (p = 0.007), along with attenuated θ-ITC that reflected underlying impairments in θ responses during 60 Hz clicks (p < 0.05). Moreover, the 40 Hz-ASSR-ITC and θ-ITC in the right hemisphere can be used as a combinational marker to detect FEMD patients with 84.0 % sensitivity and 81.5 % specificity (area under the curve was 0.868, 95 % CI: 0.768-0.968). Pearson's correlations between the depression severity and ASSR variables were further conducted. The symptom severity of FEMD patients was negatively correlated with 60 Hz-ASSR-ITC in the midline and right hemisphere, possibly indicating that depression severity mediated high γ neural synchrony.

CONCLUSIONS

Our findings provide critical insight into the pathological mechanism of FEMD, suggesting first that 40 Hz-ASSR-ITC and θ-ITC in right hemisphere constitute potential neurophysiological markers for early depression detection, and second, that high γ entrainment deficits may contribute to underlying symptom severity in FEMD patients.

Hampered gamma oscillations induced by sad emotion underlying suicide attempt in major depressive disorder

AIM

Major depressive disorder (MDD) is associated with high suicidality, especially for those with suicide attempt (SA). Although impaired oscillatory activity has been previously reported in patients with SA, little is known about precise temporal-spatial variability of its neural dynamics. To solve this, the current study probed the spectral power and network interactions underlying SA in MDD.

METHODS

The present study recruited 104 subjects including 56 subjects with MDD (30 with SA and 26 without SA) and 48 healthy controls, who performed sad expressions recognition task during magnetoencephalography (MEG) recording. By investigating source-reconstructed MEG-data, brain states representing different task stages were estimated from a Hidden Markov model. Spectrum power and network connectivity were compared via Gaussian Mixture Models, and fractional occupancy (FO) of states were compared via an independent F-test.

RESULTS

Brain states were corresponding to various frequencies (theta/beta/low gamma/ high gamma). In low gamma band (35-45 Hz), the early visual state exhibited increased activation and hyper inter-network connectivity between visual regions and the limbic system, while the middle fronto-parietal state exhibited attenuated activation and decreased intra-network connectivity within fronto-parietal regions in SA group. Crucially, FO values of these two states were significantly correlated with the suicide risks.

CONCLUSIONS

Suicide behavior of patients with MDD was significantly associated with aberrant oscillations in low gamma band. Elevated oscillations in occipital cortices and attenuated oscillations in fronto-parietal cortices were significantly associated with SA. Manifesting sadness indulging and reckless decision-making, the hampered temporal characteristics could help explain the neural-electric basis of SA.

Slow Wave Sleep Deficits in the Flinders Sensitive Line Rodent Model of Depression: Effects of Medial Forebrain Bundle Deep-Brain Stimulation

Major Depressive Disorder (MDD) is an affective disorder typically accompanied by sleep disturbances. Deep brain stimulation (DBS) of the medial forebrain bundle (MFB) is an emerging intervention for treatment-resistant depression, but its effect on sleep has not been closely examined. Here we aimed to characterise sleep deficits in the Flinders sensitive line, an established rodent model of depression, and investigate the consequences of MFB stimulation on sleep-related phenotypes. Rats were implanted with bilateral stimulation electrodes in the MFB, surface electrodes to record electrocorticography and electromyography for sleep scoring and electrodes within the prelimbic cortex, nucleus accumbens (NAc) and dorsal hippocampus. Recordings of sleep and oscillatory activity were conducted prior to and following twenty-four hours of MFB stimulation. Behavioural anti-depressant effects were monitored using the forced swim test. Previously unreported abnormalities in the Flinders sensitive line rats were observed during slow wave sleep, including decreased circadian amplitude of its rhythm, a reduction in slow wave activity and elevated gamma band oscillations. Previously established rapid eye movement sleep deficits were replicated. MFB stimulation had anti-depressant effects on behavioural phenotype, but did not significantly impact sleep architecture; it suppressed elevated gamma activity during slow wave sleep in the electrocorticogram and prelimbic cortex signals. Diverse abnormalities in Flinders sensitive line rats emphasise slow wave sleep as a state of dysfunction in affective disorders. MFB stimulation is able to affect behaviour and sleep physiology without influencing sleep architecture. Gamma modulation may represent a component of antidepressant mechanism.

Theta oscillations: A rhythm difference comparison between major depressive disorder and anxiety disorder

BACKGROUND

Due to substantial comorbidities of major depressive disorder (MDD) and anxiety disorder (AN), these two disorders must be distinguished. Accurate identification and diagnosis facilitate effective and prompt treatment. EEG biomarkers are a potential research hotspot for neuropsychiatric diseases. The purpose of this study was to investigate the differences in EEG power spectrum at theta oscillations between patients with MDD and patients with AN.

METHODS

Spectral analysis was used to study 66 patients with MDD and 43 patients with AN. Participants wore 16-lead EEG caps to measure resting EEG signals. The EEG power spectrum was measured using the fast Fourier transform. Independent samples t-test was used to analyze the EEG power values of the two groups, and p < 0.05 was statistically significant.

RESULTS

EEG power spectrum of the MDD group significantly differed from the AN group in the theta oscillation on 4-7 Hz at eight electrode points at F3, O2, T3, P3, P4, FP1, FP2, and F8.

CONCLUSION

Participants with anxiety demonstrated reduced power in the prefrontal cortex, left temporal lobe, and right occipital regions. Confirmed by further studies, theta oscillations could be another biomarker that distinguishes MDD from AN.

Quantitative Electroencephalogram Standardization: A Sex- and Age-Differentiated Normative Database

We describe the utility of a standardized index (Z-score) in quantitative EEG (QEEG) capable of when referenced to a resting-state, sex- and age-differentiated QEEG normative database (ISB-NormDB). Our ISB-NormDB comprises data for 1,289 subjects (553 males, 736 females) ages 4.5 to 81 years that met strict normative data criteria. A de-noising process allowed stratification based on QEEG variability between normal healthy men and women at various age ranges. The ISB-NormDB data set that is stratified by sex provides a unique, highly accurate ISB-NormDB model (ISB-NormDB: ISB-NormDB-Male, ISB-NormDB-Female). To evaluate the trends and accuracy of the ISB-NormDB, we used actual data to compare Z-scores obtained through the ISB-NormDB with those obtained through a traditional QEEG normative database to confirm that basic trends are maintained in most bands and are sensitive to abnormal test data. Finally, we demonstrate the value of our standardized index of QEEG, and highlight it's capacity to minimize the confounding variables of sex and age in any analysis.

Differences of EEG Frequency and Spatial Parameters in Depressive Female Adolescents With Suicidal Attempts and Non-suicidal Self-injuries

Background. Both non-suicidal self-injuries (NSSIs) and suicidal attempts (SAs) in adolescence represent significant risk factors for consequent suicide, but neurophysiological markers and predictors of these two forms of auto-aggressive behavior have been studied insufficiently. Objective. The aim of the study was to identify the differences of electroencephalographic (EEG) frequency and spatial parameters between depressive female adolescents with solely NSSI, and with combined NSSI + SA behavior in their history. Methods. The study included 45 female depressive in-patients aged 16-25 years. Baseline resting EEG spectral power, asymmetry, and coherence were analyzed in 8 narrow frequency sub-bands. Results. In the NSSI + SA subgroup (n = 24), the spectral power of parietal-occipital alpha-2 (9-11 Hz) was higher than in the NSSI subgroup, its focus was localized in the right hemisphere, and alpha-3 (11-13 Hz) spectral power was higher than alpha-1 (8-9 Hz). In the NSSI subgroup (n = 21) alpha-1 spectral power was higher than alpha-3, and foci of alpha-2 and alpha-3 were localized in the left hemisphere. EEG coherence was also higher in the NSSI + SA subgroup than in the NSSI subgroup, especially in frontal-central-parietal regions. Conclusions. The spatial distribution of the EEG frequency components in the NSSI + SA subgroup reflects the greater activation of the left hemisphere that is more typical for the EEG of individuals with an increased risk for suicide. In the NSSI subgroup, the right hemisphere is relatively more activated, and EEG coherence is lower, which is more typical for EEG in depressive disorders. The results obtained suggested the use of EEG to clarify the degree of suicidal risk in depressive female adolescents with NSSI.

Abnormal brain activity in fronto-central regions in mental disorders with suicide: An EEG Study

Suicide is a global health problem, and early and accurate identification of suicide attempt individuals has very important clinical significance. Thus the exploration of neurobiological mechanisms underlying suicidal behavior is crucial for systematically preventing suicide. However, the neurophysiological biomarkers for identifying affective disorders with suicidal attempt are remain unknown. Here, we recruited 28 patients with mental disorders from Tianjin Anding Hospital, and the subjects were divided into suicide attempt group (SA=14) and non suicide attempt group (NSA=14) according to whether they had attempted suicide. We also recruited 14 healthy subjects matched with age and sex ratio as healthy control group (HC=14). By recording the electroencephalogram(EEG) data of 60 electrodes in resting state for eight minutes (four minutes with open eyes and four minutes with close eyes), the absolute power of five frequency bands( delta(0.5-4Hz), theta(4-8Hz), alpha(8-13Hz), beta (13-30Hz), gamma(30-65Hz)) were analyzed to explore the changes of brain rhythm. And then the Modulation index (MI) was calculated to quantify the intensity of phase amplitude coupling (PAC) between different frequency bands in different brain regions, so as to observe the mechanism of neuronal synchronization in different frequency bands. We found that the absolute power of SA group was significantly higher than NSA group and HC group in delta (P<0.05), beta (P<0.05) and gamma (P<0.05) bands. The PAC strength between beta and gamma was calculated and it showed that the PAC strength of SA group was significantly weaker than NSA group in fronto-central regions, indicating that decreased synchronization between neurons could bring about brain function impairment. These findings suggest that the brain electrical activity in the fronto-central regions of the SA group may be damaged, which may lead to an increased suicidal risk in mental disoders. The EEG activity in delta, beta, gamma band and PAC in fronto-central regions may be used as a potential clinical biomarker for preventing suicide.

Prefrontal Lobe and Posterior Cingulate Cortex Activations in Patients with Major Depressive Disorder by Using Standardized Weighted Low-Resolution Electromagnetic Tomography

BACKGROUND

The differences in brain activity between patients with major depressive disorder (MDD) and healthy adults have been confirmed by functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG). The prefrontal lobe and posterior cingulate cortex (PCC) are related to emotional regulation in patients with MDD. However, the high cost and poor time resolution of fMRI and PET limit their clinical application. Recently, researchers have used high time resolution of standardized weighted low-resolution electromagnetic tomography (swLORETA) to investigate deep brain activity. This study aimed to convert raw EEG signals into swLORETA images and explore deep brain activity in patients with MDD and healthy adults.

METHODS

BrainMaster EEG equipment with a 19-channel EEG cap was used to collect resting EEG data with eyes closed for 5 min. NeuroGuide software was used to remove the EEG artifacts, and the swLORETA software was used to analyze 12,700 voxels of current source density (CSD) for 139 patients with MDD and co-morbid anxiety symptoms (mean age = 43.08, SD = 13.76; 28.78% were male) and 134 healthy adults (mean age = 40.60, SD = 13.52; 34.33% were male). Deep brain activity in the frontal lobe and PCC at different frequency bands was analyzed, including delta (1-4 Hz), theta (5-7 Hz), alpha (8-11 Hz), beta (12-24 Hz), beta1 (12-14 Hz), beta2 (15-17 Hz), beta3 (18-24 Hz), and high beta (25-29 Hz).

RESULTS

There was lower delta and theta and higher beta, beta1, beta2, beta3, and high-beta activity at the prefrontal lobe (dorsal medial prefrontal cortex [dmPFC], ventral medial prefrontal cortex [vmPFC], and dorsal lateral prefrontal cortex [dlPFC], ventral lateral prefrontal cortex [vlPFC], orbital frontal cortex [OFC]) and PCC in MDD patients compared with healthy adults. There was no significant difference in alpha activity between the two groups.

CONCLUSION

This study indicates brain hyperactivity in the right prefrontal lobe (dlPFC and vmPFC) and PCC in patients with MDD with co-morbid anxiety symptoms, and the dlPFC and PCC were also related to emotion regulation in MDD. Inhibiting high-beta activity or restoring delta and theta activity to the normal range in the right frontal lobe and PCC may be possible in z-score neurofeedback protocols for patients with MDD in future studies.

An EEG signature of suicidal behavior in female patients with major depressive disorder? A non-replication

INTRODUCTION

A recent study showed hypoactivity in the beta/gamma band in female suicide ideators and suicide attempters diagnosed with depression, relative to a low-risk group. The current study aimed to conceptually replicate these results.

METHODS

In the iSPOT-D sub-sample (n = 402), suicide ideators and low-risk individuals were identified. Confining analyses to females only, differences between low-risk individuals and suicide ideators were tested for using the electroencephalogram (EEG) frequency bands SMR (Sensori-Motor-Rhythm; 12-15 Hz), beta (14.5-30 Hz), beta I (14.5-20 Hz), beta II (20-25 Hz), beta III (25-30 Hz), gamma I (31-49 Hz) using LORETA-software.

RESULTS

None of the tested frequency bands showed to be significantly different between suicide ideators and low-risk individuals.

CONCLUSIONS

The current study could not conceptually replicate the earlier published results. Several reasons could explain this non-replication, among which possible electromyographic (EMG) contamination in the beta/gamma band in the original study.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT00693849. URL: http://clinicaltrials.gov/ct2/show/NCT00693849.

Abnormalities in Electroencephalographic Microstates Among Adolescents With First Episode Major Depressive Disorder

Background: Recent studies have reported changes in the electroencephalograms (EEG) of patients with major depressive disorder (MDD). However, little research has explored EEG differences between adolescents with MDD and healthy controls, particularly EEG microstates differences. The aim of the current study was to characterize EEG microstate activity in adolescents with MDD and healthy controls (HCs). Methods: A total of 35 adolescents with MDD and 35 HCs were recruited in this study. The depressive symptoms were assessed by Hamilton Depression Scale (HAMD) and Children's Depression Inventory (CDI), and the anxiety symptoms were assessed by Chinese version of DSM-5 Level 2-Anxiety-Child scale. A 64-channel EEG was recorded for 5 min (eye closed, resting-state) and analyzed using microstate analysis. Microstate properties were compared between groups and correlated with patients' depression scores. Results: We found increased occurrence and contribution of microstate B in MDD patients compared to HCs, and decreased occurrence and contribution of microstate D in MDD patients compared to HCs. While no significant correlation between depression severity (HAMD score) and the microstate metrics (occurrence and contribution of microstate B and D) differing between MDD adolescents and HCs was found. Conclusions: Adolescents with MDD showed microstate B and microstate D changes. The obtained results may deepen our understanding of dynamic EEG changes among adolescents with MDD and provide some evidence of changes in brain development in adolescents with MDD.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Psychopharmacology of Suicide

Despite the psychotherapeutic and pharmacotherapeutic advances in the treatment of depressive, anxiety, and psychotic disorders over the past several decades, the suicide rate in the United States has increased over the past 18 years. Medications are safer and more difficult to use in overdosing, but not necessarily more efficacious in bringing about complete remission of symptoms. The current article focuses on the theories and psychosocial and neurobiological correlates of suicide with the intent of formulating intervention strategies to prevent suicide. Particular attention is on medications that may contribute to suicidal ideation and behaviors and those that show evidence of reducing the risk of suicide. [Journal of Psychosocial Nursing and Mental Health Services, 57(12), 9-14.].