No relationship between frontal alpha asymmetry and depressive disorders in a multiverse analysis of five studies

Music in the loop: a systematic review of current neurofeedback methodologies using music

Music, a universal element in human societies, possesses a profound ability to evoke emotions and influence mood. This systematic review explores the utilization of music to allow self-control of brain activity and its implications in clinical neuroscience. Focusing on music-based neurofeedback studies, it explores methodological aspects and findings to propose future directions. Three key questions are addressed: the rationale behind using music as a stimulus, its integration into the feedback loop, and the outcomes of such interventions. While studies emphasize the emotional link between music and brain activity, mechanistic explanations are lacking. Additionally, there is no consensus on the imaging or behavioral measures of neurofeedback success. The review suggests considering whole-brain neural correlates of music stimuli and their interaction with target brain networks and reward mechanisms when designing music-neurofeedback studies. Ultimately, this review aims to serve as a valuable resource for researchers, facilitating a deeper understanding of music's role in neurofeedback and guiding future investigations.

Stable construction and analysis of MDD modular networks based on multi-center EEG data

BACKGROUND

The modular structure can reflect the activity pattern of the brain, and exploring it may help us understand the pathogenesis of major depressive disorder (MDD). However, little is known about how to build a stable modular structure in MDD patients and how modules are separated and integrated.

METHOD

We used four independent resting state Electroencephalography (EEG) datasets. Different coupling methods, window lengths, and optimized community detection algorithms were used to find a reliable and robust modular structure, and the module differences of MDD were analyzed from the perspectives of global module attributes and local topology in multiple frequency bands.

RESULTS

The combination of the Phase Lag Index (PLI) and the Louvain algorithm can achieve better results and can achieve stability at smaller window lengths. Compared with Healthy Controls (HC), MDD had higher Modularity (Q) values and the number of modules in low-frequency bands. In addition, MDD showed significant structural changes in the frontal and parietal-occipital lobes, which were confirmed by further correlation analysis.

CONCLUSION

Our results provided a reliable validation of the modular structure construction method in MDD patients and contributed strong evidence for the changes in emotional cognition and visual system function in MDD patients from a new perspective. These results would afford valuable insights for further exploration of the pathogenesis of MDD.

The effect of high- and low-approach motivated sadness on frontal alpha asymmetry and other metrics

Sadness is commonly perceived as an affective state with negative valence. However, studies on the psychological and physiological effects of sadness have yielded mixed results. We proposed a systematic analysis of sadness, taking into account an additional dimension - the intensity of approach motivation, understood as an urge to move toward. We induced low and high approach motivation sadness while measuring electrical brain activity (EEG). We predicted that low approach motivation sadness and high approach sadness would evoke different patterns of frontal alpha activity. In our study, 41 participants were randomly assigned to a low or high approach motivation sadness induction. A significant interaction was observed when comparing low and high approach motivation sadness across the presented stories, as measured by the frontal alpha asymmetry index. To furtherly explore this effect, we conducted cluster-based permutation analysis on individual alpha peak-centered spectra, which revealed a more centrally diffused effect over the frontal areas in both hemispheres as well a significant activation over the occipital region. Low approach motivation sadness was associated with reduced alpha power over frontal areas, while high approach motivation sadness was associated with increased alpha power in the same region, both in comparison to neutral condition. These results might reflect Default Mode Network activation or the projection from occipital area. Based on these results, we propose a new perspective on sadness as a heterogeneous state that should be evaluated based on the intensity of approach motivation, rather than solely on its valence.

An electroencephalography connectome predictive model of craving for methamphetamine

Background

Methamphetamine use disorder (MUD) is characterized by prominent psychological craving and its relapsing nature. Previous studies have linked trait impulsivity and abstinence duration to drug use, but the neural substrates of drug cue-induced craving and its relationship with these traits remain unclear in MUD.

Methods

We acquired high-density resting-state electroencephalography (EEG) after participants watched a five-minute video demonstrating methamphetamine use. Combining precise source imaging to reconstruct brain activities with connectome predictive modeling (CPM), we built a craving-specific network within beta band activity from two independent MUD cohorts (N=144 for model development and N=47 for validation).

Results

This network reveals a unified neural signature for craving in MUD, spanning multiple brain networks including the medial prefrontal, frontal parietal, and subcortical networks. Our findings underscored the mediating role of this craving connectome profile in modulating the relationship between abstinence duration and craving intensity. Moreover, trait impulsivity mediated the relationship between the EEG-derived craving connectome and cue-induced craving.

Conclusion

This study presents a novel predictive model that utilizes sourced connectivity from high-density EEG of resting-state recording to successfully predict methamphetamine craving in abstinent individuals with MUD. These results shed light on the cognitive organization involved in craving, involving cognitive control, attention, and reward reactivity. A comprehensive analysis reveals EEG data's capacity to decipher craving's complex dynamics, facilitating improved understanding and targeted treatments for substance use disorders.

Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

Metric comparison of connectome-based lesion-symptom mapping in post-stroke aphasia

Connectome-based lesion-symptom mapping relates behavioural impairments to disruption of structural brain connectivity. Connectome-based lesion-symptom mapping can be based on different approaches (diffusion MRI versus lesion mask), network scales (whole brain versus regions of interest) and measure types (tract-based, parcel-based, or network-based metrics). We evaluated the similarity of different connectome-based lesion-symptom mapping processing choices and identified factors that influence the results using multiverse analysis-the strategy of conducting and displaying the results of all reasonable processing choices. Metrics derived from lesion masks and diffusion-weighted images were tested for association with Boston Naming Test and Token Test performance in a sample of 50 participants with aphasia following left hemispheric stroke. 'Direct' measures were derived from diffusion-weighted images. 'Indirect' measures were derived by overlaying lesion masks on a white matter atlas. Parcel-based connectomes were constructed for the whole brain and regions of interest (14 language-relevant parcels). Numerous tract-based and network-based metrics were calculated. There was a high discrepancy across processing approaches (diffusion-weighted images versus lesion masks), network scales (whole brain versus regions of interest) and metric types. Results indicate weak correlations and different connectome-based lesion-symptom mapping results across the processing choices. Substantial methodological work is needed to validate the various decision points that arise when conducting connectome-based lesion-symptom mapping analyses. Multiverse analysis is a useful strategy for evaluating the similarity across different processing choices in connectome-based lesion-symptom mapping.

Frontal Alpha Asymmetry and Its Modulation by Monoaminergic Neurotransmitters in Depression

Frontal alpha asymmetry (FAA) is an electroencephalography (EEG) measure that quantifies trait-like left versus right hemisphere lateralization in alpha power. Increased FAA indicates relatively greater left than right frontal cortex activation and is associated with enhanced reward-related approach behaviors rather than avoidance or withdrawal. Studies dating back several decades have often suggested that having greater FAA supports enhanced positive affect and protection against major depressive disorder (MDD), whereas having greater right frontal activation (i.e., reduced FAA) is associated with negative affect and risk for MDD. While this hypothesis is widely known, a number of other studies instead have found increased FAA in MDD, or evidence that either leftward or rightward bias in FAA is associated with depression. Here we briefly review the literature on leftward or rightward lateralization in FAA in MDD, and find much evidence that MDD is not always characterized by reduced FAA. We also review the limited literature on FAA and monoaminergic neurotransmitter systems, including pharmacologic agents that act on them. Studies of serotonin in particular provide genetic and pharmacologic evidence for modulation of FAA, where some of these data may suggest that serotonin reduces FAA. In a synthesis of the collective literature on FAA and the monoamines, we suggest that serotonin and norepinephrine may differentially affect FAA, with serotonin tending to promote right frontal activation and norepinephrine biased toward left frontal activation. These putative differences in frontal lateralization may influence MDD phenotypes or potential subtypes of the disorder, and suggest pharmacologic treatment strategies.

Emotion-Induced Frontal Alpha Asymmetry as a Candidate Predictor of Relapse After Discontinuation of Antidepressant Medication

BACKGROUND

One in 3 patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of major depressive disorder. However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by functional magnetic resonance imaging has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor.

METHODS

Patients (n = 56) who had remitted from a depressive episode while taking antidepressants underwent electroencephalography (EEG) recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a 6-month follow-up period. Thirty five healthy control participants were also tested. Current source density of the EEG power in the alpha band (8-13 Hz) was extracted and alpha asymmetry was computed by comparing the power across 2 hemispheres at frontal electrodes (F5 and F6).

RESULTS

Sad mood induction was robust across all groups. Reactivity of alpha asymmetry to sad mood did not distinguish healthy control participants from patients with remitted major depressive disorder on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in alpha asymmetry compared with patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy and a positive predictive value of 0.75).

CONCLUSIONS

A simple EEG-based measure of emotional reactivity may have potential to contribute to clinical prediction models of antidepressant discontinuation. Given the very small sample size, this finding must be interpreted with caution and requires replication in a larger study.

Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback

EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional features. However, they only focused on regional brain structure or function and overlooked possible neural correlates of the brain network. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. We designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data from healthy participants before training. We assessed the learning performance for evoked EEG modulations during training (L1) and at rest (L2), and investigated performance-related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. The main findings of this study are described below. First, both real and sham groups could increase their FAA during training, but only the real group showed a significant increase in FAA at rest. Second, the predictors during training blocks and at rests were different: L1 was correlated with the graph-theoretical metrics (clustering coefficient and local efficiency) of the right hemispheric gray matter and functional networks, while L2 was correlated with the graph-theoretical metrics (local and global efficiency) of the whole-brain and left the hemispheric functional network. Therefore, the individual differences in FAA neurofeedback learning could be explained by individual variations in structural/functional architecture, and the correlated graph-theoretical metrics of learning performance indices showed different laterality of hemispheric networks. These results provided insight into the neural correlates of inter-individual differences in neurofeedback learning.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09939-x.

Frontal alpha asymmetry during emotion regulation in adults with lifetime major depression

Emotion regulation (ER) often is impaired in current or remitted major depression (MD), although the extent of the deficits is not fully understood. Recent studies suggest that frontal alpha asymmetry (FAA) could be a promising electrophysiological measure to investigate ER. The purpose of this study was to investigate ER differences between participants with lifetime major depression (lifetime MD) and healthy controls (HC) for the first time in an experimental task by using FAA. We compared lifetime MD (n = 34) and HC (n = 25) participants aged 18-24 years in (a) an active ER condition, in which participants were instructed to reappraise negative images and (b) a condition in which they attended to the images while an EEG was recorded. We also report FAA results from an independent sample of adolescents with current MD (n = 36) and HC adolescents (n = 38). In the main sample, both groups were able to decrease self-reported negative affect in response to negative images through ER, without significant group differences. We found no differences between groups or conditions in FAA, which was replicated within the independent adolescent sample. The lifetime MD group also reported less adaptive ER in daily life and higher difficulty of ER during the task. The lack of differences between in self-reported affect and FAA between lifetime MD and HC groups in the active ER task indicates that lifetime MD participants show no impairments when instructed to apply an adaptive ER strategy. Implications for interventional aspects are discussed.

Frontal alpha asymmetry is associated with chronic stress and depression, but not with somatoform disorders

Cardinal characteristics of somatoform disorders (SFDs) are worry of illness, and impaired affective processing. We used relative frontal alpha asymmetry (FAA), a method to measure functional lateralization of affective processing, to investigate psychobiological correlates of SFDs. With alpha activity being inversely related to cortical network activity, relative FAA refers to alpha activity on the right frontal lobe minus alpha activity on the left frontal lobe. Less relative left frontal activity, reflected by negative FAA scores, is associated with lower positive and greater negative affectivity, such as observed in depression. Due to its negative affective component (illness anxiety), we expected to find less relative left frontal activity pattern in SFDs, and positive associations with self-reported chronic stress and depression symptoms. We recorded resting-state EEG activity with 64 electrodes, placed in a 10-10 system in 26 patients with a primary SFD, 23 patients with a major depressive disorder and 25 healthy control participants. The groups did not differ in FAA. Nevertheless, across all participants, less relative left frontal activity was associated with chronic stress and depression symptoms. We concluded that FAA may not serve as an indicator of SFDs. As the relationship of FAA and depressive symptoms was fully mediated by chronic stress, future studies have to clarify whether the association between FAA and chronic stress may represent a shared underlying factor for the manifestation of mental health complaints, such as depression.

The future of psychophysiology, then and now

Since its founding in 1973, Biological Psychology has showcased and provided invaluable support to psychophysiology, a field that has grown and changed enormously. This article discusses some constancies that have remained fundamental to the journal and to the field as well as some important trends. Some aspects of our science have not received due consideration, affecting not only the generalizability of our findings but the way we develop and evaluate our research questions and the potential of our field to contribute to the common good. The article offers a number of predictions and recommendations for the next period of growth of psychophysiology.

Beyond single paradigms, pipelines, and outcomes: Embracing multiverse analyses in psychophysiology

Psychophysiological research is an inherently complex undertaking due to the nature of the data, and its analysis is characterized by many decision points that shape the final dataset and a study's findings. These decisions create a "multiverse" of possible outcomes, and each decision from study conceptualization to statistical analysis can lead to different results and interpretations. This review describes the concept of multiverse analyses, a methodological approach designed to understand the impact of different decisions on the robustness of a study's findings and interpretation. The emphasis is on transparently showcasing different reasonable approaches for constructing a final dataset and on highlighting the influence of various decision points, from experimental design to data processing and outcome selection. For example, the choice of an experimental task can significantly impact event-related brain potential (ERP) scores or skin conductance responses (SCRs), and different tasks might elicit unique variances in each measure. This review underscores the importance of transparently embracing the flexibility inherent in psychophysiological research and the potential consequences of not understanding the fragility or robustness of experimental findings. By navigating the intricate terrain of the psychophysiological multiverse, this review serves as an introduction, helping researchers to make informed decisions, improve the collective understanding of psychophysiological findings, and push the boundaries of the field.

Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity

Major depressive disorder (MDD) is a leading cause of disability worldwide. One of the most efficacious treatments for treatment-resistant MDD is electroconvulsive therapy (ECT). Recently, magnetic seizure therapy (MST) was developed as an alternative to ECT due to its more favorable side effect profile. While these approaches have been very successful clinically, the neural mechanisms underlying their therapeutic effects are unknown. For example, clinical "slowing" of the electroencephalogram beginning in the postictal state and extending days to weeks post-treatment has been observed in both treatment modalities. However, a recent longitudinal study of a small cohort of ECT patients revealed that, rather than delta oscillations, clinical slowing was better explained by increases in aperiodic activity, an emerging EEG signal linked to neural inhibition. Here we investigate the role of aperiodic activity in a cohort of patients who received ECT and a cohort of patients who received MST treatment. We find that aperiodic neural activity increases significantly in patients receiving either ECT or MST. Although not directly related to clinical efficacy in this dataset, increased aperiodic activity is linked to greater amounts of neural inhibition, which is suggestive of a potential shared neural mechanism of action across ECT and MST.

Clinical EEG slowing induced by electroconvulsive therapy is better described by increased frontal aperiodic activity

Electroconvulsive therapy (ECT) is one of the most efficacious interventions for treatment-resistant depression. Despite its efficacy, ECT's neural mechanism of action remains unknown. Although ECT has been associated with "slowing" in the electroencephalogram (EEG), how this change relates to clinical improvement is unresolved. Until now, increases in slow-frequency power have been assumed to indicate increases in slow oscillations, without considering the contribution of aperiodic activity, a process with a different physiological mechanism. In this exploratory study of nine MDD patients, we show that aperiodic activity, indexed by the aperiodic exponent, increases with ECT treatment. This increase better explains EEG "slowing" when compared to power in oscillatory peaks in the delta (1-3 Hz) range and is correlated to clinical improvement. In accordance with computational models of excitation-inhibition balance, these increases in aperiodic exponent are linked to increasing levels of inhibitory activity, suggesting that ECT might ameliorate depressive symptoms by restoring healthy levels of inhibition in frontal cortices.

Excess death estimates from multiverse analysis in 2009-2021

Excess death estimates have great value in public health, but they can be sensitive to analytical choices. Here we propose a multiverse analysis approach that considers all possible different time periods for defining the reference baseline and a range of 1 to 4 years for the projected time period for which excess deaths are calculated. We used data from the Human Mortality Database on 33 countries with detailed age-stratified death information on an annual basis during the period 2009-2021. The use of different time periods for reference baseline led to large variability in the absolute magnitude of the exact excess death estimates. However, the relative ranking of different countries compared to others for specific years remained largely unaltered. The relative ranking of different years for the specific country was also largely independent of baseline. Averaging across all possible analyses, distinct time patterns were discerned across different countries. Countries had declines between 2009 and 2019, but the steepness of the decline varied markedly. There were also large differences across countries on whether the COVID-19 pandemic years 2020-2021 resulted in an increase of excess deaths and by how much. Consideration of longer projected time windows resulted in substantial shrinking of the excess deaths in many, but not all countries. Multiverse analysis of excess deaths over long periods of interest can offer an approach that better accounts for the uncertainty in estimating expected mortality patterns, comparative mortality trends across different countries, and the nature of observed mortality peaks.

Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity

Major depressive disorder (MDD) is a leading cause of disability worldwide. One of the most efficacious treatments for treatment-resistant MDD is electroconvulsive therapy (ECT). Recently, magnetic seizure therapy (MST) was developed as an alternative to ECT due to its more favorable side effect profile. While these approaches have been very successful clinically, the neural mechanisms underlying their therapeutic effects are unknown. For example, clinical "slowing" of the electroencephalogram beginning in the postictal state and extending days to weeks post-treatment has been observed in both treatment modalities. However, a recent longitudinal study of a small cohort of ECT patients revealed that, rather than delta oscillations, clinical slowing was better explained by increases in aperiodic activity, an emerging EEG signal linked to neural inhibition. Here we investigate the role of aperiodic activity in a cohort of patients who received ECT and a cohort of patients who received MST treatment. We find that aperiodic neural activity increases significantly in patients receiving either ECT or MST. Although not directly related to clinical efficacy in this dataset, increased aperiodic activity is linked to greater amounts of neural inhibition, which is suggestive of a potential shared neural mechanism of action across ECT and MST.

No evidence from a negative mood induction fMRI task for frontal functional asymmetry as a suitable neurofeedback target

Frontal functional asymmetry (FA) has been proposed as a potential target for neurofeedback (NFB) training for mental disorders but most FA NFB studies used electroencephalography while the investigations of FA NFB in functional magnetic resonance imaging (fMRI) are rather limited. In this study, we aimed at identifying functional asymmetry effects in fMRI and exploring its potential as a target for fMRI NFB studies by re-analyzing an existing data set containing a resting state measurement and a sad mood induction task of n = 30 participants with remitted major depressive disorder and n = 30 matched healthy controls. We applied low-frequency fluctuations (ALFF), fractional ALFF, and regional homogeneity and estimated functional asymmetry in both a voxel-wise and regional manner. We assessed functional asymmetry during rest and negative mood induction as well as functional asymmetry changes between the phases, and associated the induced mood change with the change in functional asymmetry. Analyses were conducted within as well as between groups. Despite extensive analyses, we identified only very limited effects. While some tests showed nominal significance, our results did not contain any clear identifiable patterns of effects that would be expected if a true underlying effect would be present. In conclusion, we do not find evidence for FA effects related to negative mood in fMRI, which questions the usefulness of FA measures for real-time fMRI neurofeedback as a treatment approach for affective disorders.

Altered beta band spatial-temporal interactions during negative emotional processing in major depressive disorder: An MEG study

BACKGROUND

The mood-concordance bias is a key feature of major depressive disorder (MDD), but the spatiotemporal neural activity associated with emotional processing in MDD remains unclear. Understanding the dysregulated connectivity patterns during emotional processing and their relationship with clinical symptoms could provide insights into MDD neuropathology.

METHODS

We enrolled 108 MDD patients and 64 healthy controls (HCs) who performed an emotion recognition task during magnetoencephalography recording. Network-based statistics (NBS) was used to analyze whole-brain functional connectivity (FC) across different frequency ranges during distinct temporal periods. The relationship between the aberrant FC and affective symptoms was explored.

RESULTS

MDD patients exhibited decreased FC strength in the beta band (13-30 Hz) compared to HCs. During the early stage of emotional processing (0-100 ms), reduced FC was observed between the left parahippocampal gyrus and the left cuneus. In the late stage (250-400 ms), aberrant FC was primarily found in the cortex-limbic-striatum systems. Moreover, the FC strength between the right fusiform gyrus and left thalamus, and between the left calcarine fissure and left inferior temporal gyrus were negatively associated with Hamilton Depression Rating Scale (HAMD) scores.

LIMITATIONS

Medication information was not involved.

CONCLUSION

MDD patients exhibited abnormal temporal-spatial neural interactions in the beta band, ranging from early sensory to later cognitive processing stages. These aberrant interactions involve the cortex-limbic-striatum circuit. Notably, aberrant FC in may serve as a potential biomarker for assessing depression severity.

Alpha wave asymmetry is associated with only one component of melancholia, and in different directions across brain regions

Alpha wave asymmetry inconsistently correlates with Major Depressive Disorder (MDD). One possible reason for this inconsistency is the heterogeneity of MDD, leading to study of depressive 'subtypes', one of which is Melancholia. To investigate the correlation between Melancholia and alpha-wave asymmetry, 100 community participants (44 males, 56 females; aged at least 18 yr) completed the Zung self-rated Depression Scale, and underwent 3 min of eyes closed EEG recording from 24 scalp sites. There was no significant correlation between EEG data and Melancholia total score for the entire sample, but there was for those participants who had clinically significant depression (n = 33). When examined at the level of individual Melancholia scale items, significant EEG data correlations were found for some of the items but not for others. Factor analysis revealed a two-factor structure for the Melancholia scale, only one of which exhibited significant correlations with EEG AA data. Further exploration of those data identified two subcomponents of that Melancholia factor, one which was inversely correlated with frontal alpha asymmetry, and another which was directly correlated with parietal-occipital alpha wave asymmetry. These findings suggest that Melancholia may itself be heterogeneous, similarly to MDD, and rely upon different aspects of cognitive function.

Network analysis of frontal lobe alpha asymmetry confirms the neurophysiological basis of four subtypes of depressive behavior

Introduction

Although depression is widespread carries a major disease burden, current treatments remain non-universally effective, arguably due to the heterogeneity of depression, and leading to the consideration of depressive "subtypes" or "depressive behavior subtypes." One such model of depressive behavior (DB) subtypes was investigated for its associations with frontal lobe asymmetry (FLA), using a different data analytic procedure than in previous research in this field.

Methods

100 community volunteers (54 males, 46 females) aged between 18 yr. and 75 years (M = 32.53 yr., SD = 14.13 yr) completed the Zung Self-rating Depression Scale (SDS) and underwent 15 min of eyes closed EEG resting data collection across 10 frontal lobe sites. DB subtypes were defined on the basis of previous research using the SDS, and alpha-wave (8-13 Hz) data produced an index of FLA. Data were examined via network analysis.

Results

Several network analyses were conducted, producing two models of the association between DB subtypes and FLA, confirming unique neurophysiological profiles for each of the four DB subtypes.

Discussion

As well as providing a firm basis for using these DB subtypes in clinical settings, these findings provide a reasonable explanation for the inconsistency in previous FLA-depression research.

EEG frontal lobe asymmetry as a function of sex, depression severity, and depression subtype

To investigate possible contributors to the inconsistent association between frontal lobe asymmetry (FLA) and depression, EEG data were collected across five frontal sites, and examined for their associations with four subtypes of depression (Depressed mood, Anhedonia, Cognitive depression, Somatic depression). One hundred community volunteers (54 males, 46 females) aged at least 18 yr completed standardized scales for depression and anxiety, and gave EEG data under Eyes Open and Eyes Closed conditions. Results indicated that, although there was no significant correlation between the differences in EEG power across each of the five pairs of frontal sites and total depression scores, there were several meaningful correlations (accounting for at least 10% of the variance) between specific EEG site differences data and each of the four depression subtypes. There were also different patterns of association between FLA and the depression subtypes according to sex, and total depression severity. These findings help to explain the apparent inconsistency in previous FLA-depression results, and argue for a more nuanced approach to this hypothesis.

Excess death estimates from multiverse analysis in 2009-2021

Excess death estimates have great value in public health, but they can be sensitive to analytical choices. Here we propose a multiverse analysis approach that considers all possible different time periods for defining the reference baseline and a range of 1 to 4 years for the projected time period for which excess deaths are calculated. We used data from the Human Mortality Database on 33 countries with detailed age-stratified death information on an annual basis during the period 2009-2021. The use of different time periods for reference baseline led to large variability in the absolute magnitude of the exact excess death estimates. However, the relative ranking of different countries compared to others for specific years remained largely unaltered. The relative ranking of different years for the specific country was also largely independent of baseline. Averaging across all possible analyses, distinct time patterns were discerned across different countries. Countries had declines between 2009 and 2019, but the steepness of the decline varied markedly. There were also large differences across countries on whether the COVID-19 pandemic years 2020-2021 resulted in an increase of excess deaths and by how much. Consideration of longer projected time windows resulted in substantial shrinking of the excess deaths in many, but not all countries. Multiverse analysis of excess deaths over long periods of interest can offer a more unbiased approach to understand comparative mortality trends across different countries, the range of uncertainty around estimates, and the nature of observed mortality peaks.

Resting-state frontal, frontlateral, and parietal alpha asymmetry:A pilot study examining relations with depressive disorder type and severity

Introduction

The search for biomarkers has been central to efforts of improving clinical diagnosis and prognosis in psychopathology in the last decades. The main approach has been to validate biomarkers that could accurately discriminate between clinical diagnoses of very prevalent forms of psychopathology. One of the most popular electrophysiological markers proposed for discrimination in depressive disorders is the electroencephalography (EEG)-derived frontal alpha asymmetry. However, the validity, reliability and predictive value of this biomarker have been questioned in recent years, mainly due to conceptual and methodological heterogeneity.

Methods

In the current non-experimental, correlational study we investigated relationship of resting-state EEG alpha asymmetry from multiple sites (frontal, frontolateral, and parietal) with different forms of depressive disorders (varying in type or severity), in a clinical sample.

Results

Results showed that alpha asymmetry in the parietal (P3-P4) was significantly higher than in the frontal (F3-F4) and frontolateral sites (F7-F8). However, we did not find significant relations between alpha asymmetry indices and our depressive disorder measures, except for a moderate positive association between frontolateral alpha asymmetry (eyes-closed only) and depressive disorder severity (determined through clinical structured interview). We also found no significant differences in alpha asymmetry between participants, depending on their depression type.

Discussion

Based on results, we propose the parietal and frontolateral asymmetry indices to form hypotheses that should not be abandoned in the depression markers research, but worth for further experimental research. Methodological and clinical implications of the current findings are discussed.

Frontal asymmetry assessed in infancy using functional near-infrared spectroscopy is associated with emotional and behavioral problems in early childhood

Frontal asymmetry (FA), the difference in brain activity between the left versus right frontal areas, is thought to reflect approach versus avoidance motivation. This study (2012-2021) used functional near-infrared spectroscopy to investigate if infant (Mage  = 7.63 months; N = 90; n = 48 male; n = 75 White) FA in the dorsolateral prefrontal cortex relates to psychopathology in later childhood (Mage  = 62.05 months). Greater right FA to happy faces was associated with increased internalizing (η2  = .09) and externalizing (η2  = .06) problems at age 5 years. Greater right FA to both happy and fearful faces was associated with an increased likelihood of a lifetime anxiety diagnosis (R2  > .13). FA may be an informative and early-emerging marker for psychopathology.

Pre-Stimulus Power but Not Phase Predicts Prefrontal Cortical Excitability in TMS-EEG

The cortical response to transcranial magnetic stimulation (TMS) has notable inter-trial variability. One source of this variability can be the influence of the phase and power of pre-stimulus neuronal oscillations on single-trial TMS responses. Here, we investigate the effect of brain oscillatory activity on TMS response in 49 distinct healthy participants (64 datasets) who had received single-pulse TMS over the left dorsolateral prefrontal cortex. Across all frequency bands of theta (4-7 Hz), alpha (8-13 Hz), and beta (14-30 Hz), there was no significant effect of pre-TMS phase on single-trial cortical evoked activity. After high-powered oscillations, whether followed by a TMS pulse or not, the subsequent activity was larger than after low-powered oscillations. We further defined a measure, corrected_effect, to enable us to investigate brain responses to the TMS pulse disentangled from the power of ongoing (spontaneous) oscillations. The corrected_effect was significantly different from zero (meaningful added effect of TMS) only in theta and beta bands. Our results suggest that brain state prior to stimulation might play some role in shaping the subsequent TMS-EEG response. Specifically, our findings indicate that the power of ongoing oscillatory activity, but not phase, can influence brain responses to TMS. Aligning the TMS pulse with specific power thresholds of an EEG signal might therefore reduce variability in neurophysiological measurements and also has the potential to facilitate more robust therapeutic effects of stimulation.

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.

Exposure to Depression Memes on Social Media Increases Depressive Mood and It Is Moderated by Self-Regulation: Evidence From Self-Report and Resting EEG Assessments

This study aimed to investigate the effects of depression memes, spread mainly on social media, on depressive mood, and the moderating role of self-regulation based on self-report and electrophysiological (resting EEG frontal alpha asymmetry) assessments. We conducted a semi-online crossover study; first, we collected brain activity data from healthy young adults (n = 32) who were subsequently provided a link to the online experiment. Each participant participated in both the neutral and meme conditions. We also evaluated their level of depressive mood immediately before and after exposure to the stimuli. We further conducted a series of linear mixed effects model analyses and found that depression memes contributed to an increase in depressive symptoms. Specifically, lack of emotional clarity, difficulties in goal-directed behaviors in emotional distress, and impulse control difficulties were linked to greater depressive mood in the case of exposure to depression memes compared with neutral images. However, time interactions were insignificant. These results mainly indicate the centrality of behavioral problems during times of emotional distress caused by depression memes. Lastly, although frontal alpha asymmetry did not predict a change in depressive mood or significantly differ across conditions, lower inhibitory control may result in increased processing of depression memes as negative stimuli. This result is consistent with our self-report results (e.g., impulsivity) as well as other related studies in the literature. However, further research is needed to verify these frontal alpha asymmetry results.

Navigating the manyverse of skin conductance response quantification approaches - A direct comparison of trough-to-peak, baseline correction, and model-based approaches in Ledalab and PsPM

Raw data are typically required to be processed to be ready for statistical analyses, and processing pipelines are often characterized by substantial heterogeneity. Here, we applied seven different approaches (trough-to-peak scoring by two different raters, script-based baseline correction, Ledalab as well as four different models implemented in the software PsPM) to two fear conditioning data sets. Selection of the approaches included was guided by a systematic literature search by using fear conditioning research as a case example. Our approach can be viewed as a set of robustness analyses (i.e., same data subjected to different processing pipelines) aiming to investigate if and to what extent these different quantification approaches yield comparable results given the same data. To our knowledge, no formal framework for the evaluation of robustness analyses exists to date, but we may borrow some criteria from a framework suggested for the evaluation of "replicability" in general. Our results from seven different SCR quantification approaches applied to two data sets with different paradigms suggest that there may be no single approach that consistently yields larger effect sizes and could be universally considered "best." Yet, at least some of the approaches employed show consistent effect sizes within each data set indicating comparability. Finally, we highlight substantial heterogeneity also within most quantification approaches and discuss implications and potential remedies.

Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG/MEG rhythms

Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.

Revisiting Hemispheric Asymmetry in Mood Regulation: Implications for rTMS for Major Depressive Disorder

Hemispheric differences in emotional processing have been observed for over half a century, leading to multiple theories classifying differing roles for the right and left hemisphere in emotional processing. Conventional acceptance of these theories has had lasting clinical implications for the treatment of mood disorders. The theory that the left hemisphere is broadly associated with positively valenced emotions, while the right hemisphere is broadly associated with negatively valenced emotions, drove the initial application of repetitive transcranial magnetic stimulation (rTMS) for the treatment of major depressive disorder (MDD). Subsequent rTMS research has led to improved response rates while adhering to the same initial paradigm of administering excitatory rTMS to the left prefrontal cortex (PFC) and inhibitory rTMS to the right PFC. However, accumulating evidence points to greater similarities in emotional regulation between the hemispheres than previously theorized, with potential implications for how rTMS for MDD may be delivered and optimized in the near future. This review will catalog the range of measurement modalities that have been used to explore and describe hemispheric differences, and highlight evidence that updates and advances knowledge of TMS targeting and parameter selection. Future directions for research are proposed that may advance precision medicine and improve efficacy of TMS for MDD.

fMRI Neurofeedback-Enhanced Cognitive Reappraisal Training in Depression: A Double-Blind Comparison of Left and Right vlPFC Regulation

Affective disorders are associated with maladaptive emotion regulation strategies. In particular, the left more than the right ventrolateral prefrontal cortex (vlPFC) may insufficiently regulate emotion processing, e.g., in the amygdala. A double-blind cross-over study investigated NF-supported cognitive reappraisal training in major depression (n = 42) and age- and gender-matched controls (n = 39). In a randomized order, participants trained to upregulate either the left or the right vlPFC during cognitive reappraisal of negative images on two separate days. We wanted to confirm regional specific NF effects with improved learning for left compared to right vlPFC (ClinicalTrials.gov NCT03183947). Brain responses and connectivity were studied with respect to training progress, gender, and clinical outcomes in a 4-week follow-up. Increase of vlPFC activity was stronger after NF training from the left- than the right-hemispheric ROI. This regional-specific NF effect during cognitive reappraisal was present across patients with depression and controls and supports a central role of the left vlPFC for cognitive reappraisal. Further, the activity in the left target region was associated with increased use of cognitive reappraisal strategies (r = 0.48). In the 4-week follow-up, 75% of patients with depression reported a successful application of learned strategies in everyday life and 55% a clinically meaningful symptom improvement suggesting clinical usability.