Intracranial current density (LORETA) differences in QEEG frequency bands between depressed and non-depressed alcoholic patients

Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.

Functional brain network features specify DBS outcome for patients with treatment resistant depression

Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1-3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS.

A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis

BACKGROUND

Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed through questionnaire-based approaches; however, these methods may not lead to an accurate diagnosis. In this regard, many studies have focused on using electroencephalogram (EEG) signals and machine learning techniques to diagnose MDD.

NEW METHOD

This paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework.

RESULTS

The proposed method is validated with a public EEG dataset, including the EEG data of 34 MDD patients and 30 healthy subjects. The evaluation of the proposed framework is conducted using 10-fold cross-validation, providing the metrics such as accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR). The best performance of the proposed method has provided an average AC of 99%, SE of 98.4%, SP of 99.6%, F1 of 98.9%, and FDR of 0.4% using the support vector machine with RBF kernel (RBFSVM) classifier.

COMPARISON WITH EXISTING METHODS

The obtained results demonstrate that the proposed method outperforms other approaches for MDD classification based on EEG signals.

CONCLUSIONS

According to the obtained results, a highly accurate MDD diagnosis would be provided using the proposed method, while it can be utilized to develop a computer-aided diagnosis (CAD) tool for clinical purposes.

Brainwaves Oscillations as a Potential Biomarker for Major Depression Disorder Risk

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

Multivariate Analyses Reveal Biological Components Related to Neuronal Signaling and Immunity Mediating Electroencephalograms Abnormalities in Alcohol-Dependent Individuals from the Collaborative Study on the Genetics of Alcoholism Cohort

BACKGROUND

The underlying molecular mechanisms associated with alcohol use disorder (AUD) risk have only been partially revealed using traditional approaches such as univariate genomewide association and linkage-based analyses. We therefore aimed to identify gene clusters related to Electroencephalograms (EEG) neurobiological phenotypes distinctive to individuals with AUD using a multivariate approach.

METHODS

The current project adopted a bimultivariate data-driven approach, parallel independent component analysis (para-ICA), to derive and explore significant genotype-phenotype associations in a case-control subset of the Collaborative Study on the Genetics of Alcoholism (COGA) dataset. Para-ICA subjects comprised N = 799 self-reported European Americans (367 controls and 432 AUD cases), recruited from COGA, who had undergone resting EEG and genotyping. Both EEG and genomewide single nucleotide polymorphism (SNP) data were preprocessed prior to being subjected to para-ICA in order to derive genotype-phenotype relationships.

RESULTS

From the data, 4 EEG frequency and 4 SNP components were estimated, with 2 significantly correlated EEG-genetic relationship pairs. The first such pair primarily represented theta activity, negatively correlated with a genetic cluster enriched for (but not limited to) ontologies/disease processes representing cell signaling, neurogenesis, transmembrane drug transportation, alcoholism, and lipid/cholesterol metabolism. The second component pair represented mainly alpha activity, positively correlated with a genetic cluster with ontologies similarly enriched as the first component. Disease-related enrichments for this component revealed heart and autoimmune disorders as top hits. Loading coefficients for both the alpha and theta components were significantly reduced in cases compared to controls.

CONCLUSIONS

Our data suggest plausible multifactorial genetic components, primarily enriched for neuronal/synaptic signaling/transmission, immunity, and neurogenesis, mediating low-frequency alpha and theta abnormalities in alcohol addiction.

Intracranial source activity (eLORETA) related to scalp-level asymmetry scores and depression status

Frontal EEG alpha asymmetry provides a promising index of depression risk, yet very little is known about the neural sources of alpha asymmetry. To identify these sources, this study examined alpha asymmetry using a distributed inverse solution: exact low resolution brain electromagnetic tomography (eLORETA). Findings implicated a generator in lateral midfrontal regions that contributed to both surface asymmetry and depression risk. Participants with any lifetime history of depressive episodes were characterized by less left than right activity in the precentral gyrus and midfrontal gyrus. Anhedonia accounted for a significant portion of the relationship between alpha asymmetry and lifetime major depressive disorder. Results are suggestive of convergence between motivational and capability models of asymmetry and replicate results from experimental studies in a large resting-state data set. The capability model of frontal alpha asymmetry is contextualized in terms of motor preparedness following emotional mobilization.

The Brain of Binge Drinkers at Rest: Alterations in Theta and Beta Oscillations in First-Year College Students with a Binge Drinking Pattern

Background: Previous studies have reported anomalous resting brain activity in the electroencephalogram (EEG) of alcoholics, often reflected as increased power in the beta and theta frequency bands. The effects of binge drinking, the most common pattern of excessive alcohol consumption during adolescence and youth, on brain activity at rest is still poorly known. In this study, we sought to assess the pattern of resting-state EEG oscillations in college-aged binge drinkers (BDs).

Methods: Resting-state brain activity during eyes-open and eyes-closed conditions was recorded from 60 channels in 80 first-year undergraduate students (40 controls and 40 BDs). Cortical sources activity of EEG rhythms was estimated using exact Low-Resolution Electromagnetic Tomography (eLORETA) analysis.

Results: EEG-source localization analysis revealed that BDs showed, in comparison with controls, significantly higher intracranial current density in the beta frequency band over the right temporal lobe (parahippocampal and fusiform gyri) during eyes-open resting state as well as higher intracranial current density in the theta band over the bilateral occipital cortex (cuneus and lingual gyrus) during eyes-closed resting condition.

Conclusions: These findings are in line with previous results observing increased beta and/or theta power following chronic or heavy alcohol drinking in alcohol-dependent subjects and BDs. Increased tonic beta and theta oscillations are suggestive of an augmented cortical excitability and of potential difficulties in the information processing capacity in young BDs. Furthermore, enhanced EEG power in these frequency bands may respond to a neuromaturational delay as a result of excessive alcohol consumption during this critical brain developmental period.

Understanding alcohol use disorders with neuroelectrophysiology

Neurocognitive deficits associated with impairments in various brain regions and neural circuitries, particularly involving frontal lobes, have been associated with chronic alcoholism, as well as with a predisposition to develop alcohol use and related disorders (AUDs). AUD is a multifactorial disorder caused by complex interactions between behavioral, genetic, and environmental liabilities. Neuroelectrophysiologic techniques are instrumental in understanding brain and behavior relationships and have also proved very useful in evaluating the genetic diathesis of alcoholism. This chapter describes findings from neuroelectrophysiologic measures (electroencephalogram, event-related potentials, and event-related oscillations) related to acute and chronic effects of alcohol on the brain and those that reflect underlying deficits related to a predisposition to develop AUDs and related disorders. The utility of these measures as effective endophenotypes to identify and understand genes associated with brain electrophysiology, cognitive networks, and AUDs has also been discussed.

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

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

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

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

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

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

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

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

Event-Related Oscillations in Alcoholism Research: A Review

Alcohol dependence is characterized as a multi-factorial disorder caused by a complex interaction between genetic and environmental liabilities across development. A variety of neurocognitive deficits/dysfunctions involving impairments in different brain regions and/or neural circuitries have been associated with chronic alcoholism, as well as with a predisposition to develop alcoholism. Several neurobiological and neurobehavioral approaches and methods of analyses have been used to understand the nature of these neurocognitive impairments/deficits in alcoholism. In the present review, we have examined relatively novel methods of analyses of the brain signals that are collectively referred to as event-related oscillations (EROs) and show promise to further our understanding of human brain dynamics while performing various tasks. These new measures of dynamic brain processes have exquisite temporal resolution and allow the study of neural networks underlying responses to sensory and cognitive events, thus providing a closer link to the physiology underlying them. Here, we have reviewed EROs in the study of alcoholism, their usefulness in understanding dynamical brain functions/dysfunctions associated with alcoholism as well as their utility as effective endophenotypes to identify and understand genes associated with both brain oscillations and alcoholism.

EEG delta oscillations as a correlate of basic homeostatic and motivational processes

Functional significance of delta oscillations is not fully understood. One way to approach this question would be from an evolutionary perspective. Delta oscillations dominate the EEG of waking reptiles. In humans, they are prominent only in early developmental stages and during slow-wave sleep. Increase of delta power has been documented in a wide array of developmental disorders and pathological conditions. Considerable evidence on the association between delta waves and autonomic and metabolic processes hints that they may be involved in integration of cerebral activity with homeostatic processes. Much evidence suggests the involvement of delta oscillations in motivation. They increase during hunger, sexual arousal, and in substance users. They also increase during panic attacks and sustained pain. In cognitive domain, they are implicated in attention, salience detection, and subliminal perception. This evidence shows that delta oscillations are associated with evolutionary old basic processes, which in waking adults are overshadowed by more advanced processes associated with higher frequency oscillations. The former processes rise in activity, however, when the latter are dysfunctional.

LORETA analysis of three-dimensional distribution of δ band activity in schizophrenia: relation to negative symptoms

We sought to determine if altered electroencephalography (EEG) activities, such as delta band activity, in specific brain regions are associated with psychotic symptoms. Data were obtained from 17 neuroleptic-naive patients with schizophrenia and age- and sex-matched 17 healthy control subjects. Low Resolution Brain Electromagnetic Tomography (LORETA) was used to generate current source density images of delta, theta, alpha, and beta activities. Localization of the difference in EEG activity between the two groups was assessed by voxel-by-voxel non-paired t-test of the LORETA images. Spearman's correlation coefficient was obtained to relate LORETA values of EEG current density in brain regions showing a significant between-group difference and psychopathology scores. Delta band activity, represented by LORETA current density, was greater for patients in the following areas; the left inferior temporal gyrus, right middle frontal gyrus, right superior frontal gyrus, right inferior frontal gyrus, and right parahippocampal gyrus. LORETA values for delta band activity in the above five brain regions were negatively correlated with negative, but not positive symptoms. The results of this study suggest the role for electrophysiological changes in some of the brain regions, e.g. prefrontal cortex, in the manifestation of negative symptoms.