Demonstrating test-retest reliability of electrophysiological measures for healthy adults in a multisite study of biomarkers of antidepressant treatment response

Microstate dynamics and spectral components as markers of persistent and remittent attention-deficit/hyperactivity disorder

OBJECTIVE

We leveraged microstate characteristics and power features to examine temporal and spectral deviations underlying persistent and remittent attention-deficit/hyperactivity disorder (ADHD).

METHODS

50 young adults with childhood ADHD (28 persisters, 22 remitters) and 28 demographically similar healthy controls (HC) were compared on microstates features and frequency principal components (f-PCs) of eye-closed resting state. Support vector machine model with sequential forward selection (SVM-SFS) was utilized to discriminate three groups.

RESULTS

Four microstates and four comparable f-PCs were identified. Compared to HC, ADHD persisters showed prolonged duration in microstate C, elevated power of the delta component (D), and compromised amplitude of the two alpha components (A1 and A2). Remitters showed increased duration and coverage of microstate C, together with decreased activity of D, relatively intact amplitude of A1, and amplitude reduction in A2. The SVM-SFS algorithm achieved an accuracy of 93.59% in classifying persisters, remitters and controls. The most discriminative features selected were those exhibiting group differences.

CONCLUSIONS

We found widespread anomalies in ADHD persisters in brain dynamics and intrinsic EEG components. Meanwhile, the neural features in remitters exhibited multiple patterns.

SIGNIFICANCE

This study underlines the use of microstate dynamics and spectral components as potential markers of persistent and remittent ADHD.

Affective disorders and the loudness dependence of the auditory evoked potential: Serotonin and beyond

Identifying additional noninvasive biomarkers for affective disorders, such as unipolar major depressive disorder (MDD) and bipolar disorder (BD), could aid in the diagnosis and treatment of these prevalent and debilitating neuropsychiatric conditions. One such candidate biomarker is the loudness dependence of the auditory evoked potential (LDAEP), an event-related potential that measures responsiveness of the auditory cortex to different intensities of sound. The LDAEP has been associated with MDD and BD, including therapeutic response to particular classes of antidepressant drugs, while also correlating with several other neuropsychiatric disorders. It has been suggested that increased values of the LDAEP indicate low central serotonergic neurotransmission, further implicating this EEG measure in depression. Here, we briefly review the literature on the LDAEP in affective disorders, including its association with serotonergic signaling, as well as with that of other neurotransmitters such as dopamine. We summarize key findings on the LDAEP and the genetics of these neurotransmitters, as well as prediction of response to particular classes of antidepressants in MDD, including SSRIs versus noradrenergic agents. The possible relationship between this EEG measure and suicidality is addressed. We also briefly analyze acute pharmacologic studies of serotonin and/or dopamine precursor depletion and the LDAEP. In conclusion, the existing literature suggests that serotonin and norepinephrine may modulate the LDAEP in an opposing manner, and that this event-related marker may be of use in predicting response to chronic treatment with particular pharmacologic agents in the context of affective disorders, such as MDD and BD, including in the presence of suicidality.

Pursuing the epidemiology and familial risks of depression and developing an evidence based psychotherapy

This commentary, as requested, presents the highlights of my research career. The epidemiology of psychiatric disorders study, challenged in a small study, the notion that diagnosis for psychiatric disorders could be made in a community survey. This pilot study was the basis for the Epidemiology Catchment Area Study (ECA) with 18,000 participants and the many more updated surveys, which followed. The families at High and Low Risk for Depression study in its 40th year challenged the notion that children didn't get depressed and showed that parental depression was the major risk for depression, which began in youth and reoccurred over the lifespan. Interpersonal psychotherapy (IPT), now has been tested in over 150 clinical trials, recommended by the World Health Organization (WHO), globally in China, Germany, Ukraine, and many more countries.

The novel frontal alpha asymmetry factor and its association with depression, anxiety, and personality traits

Frontal alpha asymmetry (FAA) is widely examined in EEG research, yet a procedural consensus on its assessment is lacking. In this study, we tested a latent factorial approach to measure FAA. We assessed resting-state FAA at broad, low, and high alpha bands (8-13; 8-10.5; and 11-13 Hz) using mastoids as reference electrodes and Current Source Density (CSD) transformation (N = 139 non-clinical participants). From mastoid-referenced data, we extracted a frontal alpha asymmetry factor (FAAf) and a parietal factor (PAAf) subjecting all asymmetry indices to a varimax-rotated, principal component analysis. We explored split-half reliability and discriminant validity of the mastoid factors and the mastoid and CSD raw asymmetry indices (F3/4, F7/8, P3/4, and P7/8). Both factor and raw scores reached an excellent split-half reliability (>.99), but only the FAAf reached the maximum discriminant validity from parietal scores. Next, we explored the correlations of latent factor and raw FAA scores with symptoms of depression, anxiety, and personality traits to determine which associations were driven by FAA after variance from parietal activity was removed. After correcting for false discovery rate, only FAAf at the low alpha band was negatively associated with depression symptoms (a latent CES-D factor) and significantly diverged from PAAf's association with depression symptoms. With respect to personality traits, only CSD-transformed F7/8 was positively correlated with Conscientiousness and significantly diverged from the correlations between Conscientiousness and P3/4 and P7/8. Overall, the latent factor approach shows promise for isolating functionally distinct resting-state EEG signatures, although further research is needed to examine construct validity.

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Data-Driven EEG Theta and Alpha Components Are Associated with Subjective Experience during Resting State

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. However, the relationship between the ongoing experience of mind wandering and the individual biological signal is still unclear. We aim to estimate associations between subjective experiences measured with the Amsterdam Resting-State Questionnaire and data-driven components of an electroencephalogram extracted by frequency principal component analysis (f-PCA). Five minutes of resting multichannel EEG was recorded in 226 participants and six EEG data-driven components were extracted—three components in the alpha range (peaking at 9, 10.5, and 11.5 Hz) and one each in the delta (peaking at 0.5 Hz), theta (peaking at 5.5 Hz) and beta (peaking at 17 Hz) ranges. Bayesian Pearson’s correlation revealed a positive association between the individual loadings of the theta component and ratings for Sleepiness (r = 0.200, BF10 = 7.676), while the individual loadings on one of the alpha components correlated positively with scores for Comfort (r = 0.198, BF10 = 7.115). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.

Physiologically informed neuromodulation

The rapid evolution of neuromodulation techniques includes an increasing amount of research into stimulation paradigms that are guided by patients' neurophysiology, to increase efficacy and responder rates. Treatment personalisation and target engagement have shown to be effective in fields such as Parkinson's disease, and closed-loop paradigms have been successfully implemented in cardiac defibrillators. Promising avenues are being explored for physiologically informed neuromodulation in psychiatry. Matching the stimulation frequency to individual brain rhythms has shown some promise in transcranial magnetic stimulation (TMS). Matching the phase of those rhythms may further enhance neuroplasticity, for instance when combining TMS with electroencephalographic (EEG) recordings. Resting-state EEG and event-related potentials may be useful to demonstrate connectivity between stimulation sites and connected areas. These techniques are available today to the psychiatrist to diagnose underlying sleep disorders, epilepsy, or lesions as contributing factors to the cause of depression. These technologies may also be useful in assessing the patient's brain network status prior to deciding on treatment options. Ongoing research using invasive recordings may allow for future identification of mood biomarkers and network structure. A core limitation is that biomarker research may currently be limited by the internal heterogeneity of psychiatric disorders according to the current DSM-based classifications. New approaches are being developed and may soon be validated. Finally, care must be taken when incorporating closed-loop capabilities into neuromodulation systems, by ensuring the safe operation of the system and understanding the physiological dynamics. Neurophysiological tools are rapidly evolving and will likely define the next generation of neuromodulation therapies.

Neural signatures of heterogeneity in risk-taking and strategic consistency

People display a high degree of heterogeneity in risk-taking behaviour, but this heterogeneity remains poorly understood. Here, we use a neural trait approach to examine if task-independent, brain-based differences can help uncover the sources of heterogeneity in risky decision-making. We extend prior research in two key ways. First, we disentangled risk-taking and strategic consistency using novel measures afforded by the Balloon Analogue Risk Task. Second, we applied a personality neuroscience framework to explore why personality traits are typically only weakly related to risk-taking behaviour. We regressed participants' (N = 104) source localized resting-state electroencephalographic activity on risk-taking and strategic consistency. Results revealed that higher levels of resting-state delta-band current density (reflecting reduced cortical activation) in the left dorsal anterior cingulate cortex and the left dorsolateral prefrontal cortex were associated with increased risk-taking and decreased strategic consistency, respectively. These results suggest that heterogeneity in risk-taking behaviour is associated with neural dispositions related to sensitivity to the risk of loss, whereas heterogeneity in strategic consistency is associated with neural dispositions related to strategic decision-making. Finally, extraversion, neuroticism, openness, and self-control were broadly associated with both of the identified neural traits, which in turn mediated indirect associations between personality traits and behavioural measures. These results provide an explanation for the weak direct relationships between personality traits and risk-taking behaviour, supporting a personality neuroscience framework of traits and decision-making.

Test-Retest Reliability of Frontal and Parietal Alpha Asymmetry during Presentation of Emotional Face Stimuli in Healthy Subjects

Resting-state and event-related frontal alpha asymmetry have been suggested as potential neurobiological biomarkers for depression and other psychiatric conditions. To be used as such, sufficient test-retest reliability needs to be demonstrated. However, test-retest reliability is underinvestigated for event-related alpha asymmetry. The objective of this study was to examine both short-term within-session and long-term between-session reliability of stimulus-related medial and lateral frontal as well as parietal alpha EEG asymmetry in healthy subjects during a simple emotional face processing task. Twenty-three healthy adults participated in two sessions with a test-retest interval of about 1 week. Reliability was estimated with Pearson's correlation coefficient and paired t test. Results revealed moderate to high within-session reliability of stimulus-related alpha asymmetry for all electrode sites and both conditions. Alpha asymmetry mean values did not change significantly within sessions. Between-session reliability was fair for frontomedial and moderate for frontolateral stimulus-related asymmetry. Exploratory exclusion of subjects with unstable between-session self-rating scores of emotional state and empathy toward stimuli resulted in some higher reliability values. Our results indicate that stimulus-related alpha asymmetry may serve as a useful electrophysiological tool given its adequate within-session reliability. However, long-term stability of stimulus-related frontal alpha asymmetry over 1 week was comparatively low and varied depending on electrode position. Influencing state factors during EEG recording, such as current mood or stimulus engagement, should be considered in future study designs and analyses. Further, we recommend to analyze alpha asymmetry from both frontomedial and frontolateral sites.

Resting posterior alpha power and adolescent major depressive disorder

For several decades, resting electroencephalogram (EEG) alpha oscillations have been used to characterize neurophysiological alterations related to major depressive disorder. Prior research has generally focused on frontal alpha power and asymmetry despite resting alpha being maximal over posterior electrode sites. Research in depressed adults has shown evidence of hemispheric asymmetry for posterior alpha power, however, the resting posterior alpha-depression link among adolescents remains unclear. To clarify the role of posterior alpha among depressed adolescents, the current study acquired eyes-closed 128-channel resting EEG data from 13 to 18 year-old depressed (n = 31) and healthy (n = 35) female adolescents. Results indicated a significant group by hemisphere interaction, as depressed adolescents exhibited significantly larger posterior alpha (i.e., lower brain activity) over the right versus left hemisphere, whereas healthy adolescents showed no hemispheric differences. Relatively greater alpha over the right versus left hemisphere correlated with depression symptoms, anhedonia symptoms, rumination, and self-criticism. Further, depressed adolescents had reduced overall posterior alpha compared to healthy youth; though, no associations with symptoms and related traits emerged. Resting posterior alpha may be a promising neurophysiological index of adolescent depression, and more broadly, may relate to risk factors characterized by enhanced perseveration.

Prediction of dispositional dialectical thinking from resting-state electroencephalography

This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.

Elucidating age and sex-dependent association between frontal EEG asymmetry and depression: An application of multiple imputation in functional regression

Frontal power asymmetry (FA), a measure of brain function derived from electroencephalography, is a potential biomarker for major depressive disorder (MDD). Though FA is functional in nature, it is typically reduced to a scalar value prior to analysis, possibly obscuring its relationship with MDD and leading to a number of studies that have provided contradictory results. To overcome this issue, we sought to fit a functional regression model to characterize the association between FA and MDD status, adjusting for age, sex, cognitive ability, and handedness using data from a large clinical study that included both MDD and healthy control (HC) subjects. Since nearly 40% of the observations are missing data on either FA or cognitive ability, we propose an extension of multiple imputation (MI) by chained equations that allows for the imputation of both scalar and functional data. We also propose an extension of Rubin's Rules for conducting valid inference in this setting. The proposed methods are evaluated in a simulation and applied to our FA data. For our FA data, a pooled analysis from the imputed data sets yielded similar results to those of the complete case analysis. We found that, among young females, HCs tended to have higher FA over the θ, α, and β frequency bands, but that the difference between HC and MDD subjects diminishes and ultimately reverses with age. For males, HCs tended to have higher FA in the β frequency band, regardless of age. Young male HCs had higher FA in the θ and α bands, but this difference diminishes with increasing age in the α band and ultimately reverses with increasing age in the θ band.

Major Depressive Disorder in Older Patients as an Inflammatory Disorder: Implications for the Pharmacological Management of Geriatric Depression

Depression is a common and highly disabling condition in older adults. It is a heterogenous disorder and there is emerging evidence of a link between inflammation and depression in older patients, with a possible inflammatory subtype of depression. Persistent low-level inflammation, from several sources including psychological distress and chronic disease, can disrupt monoaminergic and glutaminergic systems to create dysfunctional brain networks. Despite the evidence for the role of inflammation in depression, there is insufficient evidence to recommend use of any putative anti-inflammatory agent in the treatment of depression in older adults at this stage. Further characterisation of markers of inflammation and stratification of participants with elevated rates of inflammatory markers in treatment trials is needed.

Variability and effect sizes of intracranial current source density estimations during pain: Systematic review, experimental findings, and future perspectives

Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low-resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain-related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non-painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.

Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment

In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.

Modulation of attention and stress with arousal: The mental and physical effects of riding a motorcycle

Existing theories suggest that moderate arousal improves selective attention, as would be expected in the context of competitive sports or sensation-seeking activities. Here we investigated how riding a motorcycle, an attention-demanding physical activity, affects sensory processing. To do so, we implemented the passive auditory oddball paradigm and measured the EEG response of participants as they rode a motorcycle, drove a car, and sat at rest. Specifically, we measured the N1 and mismatch negativity to auditory tones, as well as alpha power during periods of no tones. We investigated whether riding and driving modulated non-CNS metrics including heart rate and concentrations of the hormones epinephrine, cortisol, DHEA-S, and testosterone. While participants were riding, we found a decrease in N1 amplitude, increase in mismatch negativity, and decrease in relative alpha power, together suggesting enhancement of sensory processing and visual attention. Riding increased epinephrine levels, increased heart rate, and decreased the ratio of cortisol to DHEA-S. Together, these results suggest that riding increases focus, heightens the brain's passive monitoring of changes in the sensory environment, and alters HPA axis response. More generally, our findings suggest that selective attention and sensory monitoring seem to be separable neural processes.

Resting EEG theta connectivity and alpha power to predict repetitive transcranial magnetic stimulation response in depression: A non-replication from the ICON-DB consortium

OBJECTIVE

Our previous research showed high predictive accuracy at differentiating responders from non-responders to repetitive transcranial magnetic stimulation (rTMS) for depression using resting electroencephalography (EEG) and clinical data from baseline and one-week following treatment onset using a machine learning algorithm. In particular, theta (4-8 Hz) connectivity and alpha power (8-13 Hz) significantly differed between responders and non-responders. Independent replication is a necessary step before the application of potential predictors in clinical practice. This study attempted to replicate the results in an independent dataset.

METHODS

We submitted baseline resting EEG data from an independent sample of participants who underwent rTMS treatment for depression (N = 193, 128 responders) (Krepel et al., 2018) to the same between group comparisons as our previous research (Bailey et al., 2019).

RESULTS

Our previous results were not replicated, with no difference between responders and non-responders in theta connectivity (p = 0.250, Cohen's d = 0.1786) nor alpha power (p = 0.357, η_p² = 0.005).

CONCLUSIONS

These results suggest that baseline resting EEG theta connectivity or alpha power are unlikely to be generalisable predictors of response to rTMS treatment for depression.

SIGNIFICANCE

These results highlight the importance of independent replication, data sharing and using large datasets in the prediction of response research.

The Developmental and Translational Study on Biomarkers and Clinical Characteristics-based Diagnostic and Therapeutic Identification of Major Depressive Disorder: Study Protocol for a Multicenter Randomized Controlled Trial in China

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous mental disease that encompasses different subtypes and specifiers. Clinically targeted treatments have not been identified yet, although standardized strategies are recommended by several clinical guidelines. The main aim of this study is to respectively identify the precise treatment for three different subtypes of MDD (ie, melancholic, atypical, and anxious).

METHODS

An 8-to-12-week, multicenter randomized controlled trial (RCT) with a parallel group design will be conducted to determine the most effective and appropriate treatment. A total of 750 adults diagnosed with MDD will be recruited, categorized into melancholic, atypical or anxious type based on the assessment of the Inventory of Depressive Symptomatology (IDS30) and the Hamilton Anxiety Scale (HAMA), and 1:1 randomly assigned to different intervention groups. Blood draw, EEG test, and MRI scan will be performed at baseline and endpoint. Clinical symptom and side-effects will be evaluated at critical decision points (CDP) including weeks two, four, six, eight, and 12 after treatment. The primary outcome is total score and reduction rate of the 17-Hamilton Depression Rating Scale (17-HDRS). The secondary outcomes include the scores of the Quick Inventory of Depressive Symptomatology-self-report (QIDS-SR), IDS30, HAMA and the Treatment Emergent Symptom Scale (TESS). All the data will be analyzed by SAS software.

DISCUSSION

The study commenced recruitment in August 2017 and is currently ongoing.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03219008 (July 17, 2017).

Stability of frontal alpha asymmetry in depressed patients during antidepressant treatment

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Frontal alpha asymmetry (FAA) is moderately stable over time.

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Antidepressant response prediction with FAA remains consistent, even after 8 weeks of treatment.

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FAA is a differential predictor of antidepressant response robust to state and drug effects.

Introduction

Frontal alpha asymmetry (FAA) is a proposed prognostic biomarker in major depressive disorder (MDD), conventionally acquired with electroencephalography (EEG). Although small studies attributed trait-like properties to FAA, a larger sample is needed to reliably asses this characteristic. Furthermore, to use FAA to predict treatment response, determining its stability, including the potential dependency on depressive state or medication, is essential.

Methods

In the international Study to Predict Optimized Treatment in Depression (iSPOT-D), a multi-center, randomized, prospective open-label trial, 1008 MDD participants were randomized to treatment with escitalopram, sertraline or venlafaxine-extended release. Treatment response was established eight weeks after treatment initiation and resting state EEG was measured both at baseline and after eight weeks (n = 453).

Results

FAA did not change significantly after eight weeks of treatment (n = 453, p = .234), nor did we find associations with age, sex, depression severity, or change in depression severity. After randomizing females to escitalopram or sertraline, for whom treatment response could be predicted in an earlier study, FAA after eight weeks resulted in equivalent response prediction as baseline FAA (one tailed p = .028).

Conclusion

We demonstrate that FAA is a stable trait, robust to time, state and pharmacological status. This confirms FAA stability. Furthermore, as prediction of treatment response is irrespective of moment of measurement and use of medication, FAA can be used as a state-invariant prognostic biomarker with promise to optimize MDD treatments.

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low-variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.

Data-driven derivation of natural EEG frequency components: An optimised example assessing resting EEG in healthy ageing

BACKGROUND

The majority of electroencephalographic (EEG) investigations in normal ageing have determined EEG spectra from epochs recorded in the eyes-closed (EC) and/or eyes-open (EO) resting states, and summed amplitudes or power estimates within somewhat-arbitrary and/or inconsistently defined traditional frequency band limits.

NEW METHOD

Natural frequency components were sought using a data-driven frequency Principal Components Analysis (f-PCA) approach, optimised to reduce between-condition and between-group misallocation of variance. Frequency component correspondence was screened using the Congruence Coefficient and topographic correlations for potential matches on Condition and/or Group. The amplitudes of corresponding natural components were then explored as a function of these independent variables.

RESULTS

Separate f-PCAs with Young and Older adults' EC and EO data each yielded between six and nine components that peaked across the traditional delta to beta band ranges. Across these, two components were matched on Group and Condition, while a further six were matched on Condition (within-groups), and four on Group (within-conditions).

COMPARISON WITH EXISTING METHODS

Multiple frequency components were found within the traditional bands, and provided a wider perspective in terms of additional natural component details. In addition to novel insights, the well-documented age-related spectral reductions were seen in the common delta component, and in one EC (but no EO) alpha component.

CONCLUSIONS

This combination of optimised f-PCA approach and component screening procedure has wide potential in the EEG field beyond the ageing topic explored here, being applicable across an extensive range of studies using EEG oscillations to explore aspects of cognitive processing and individual differences.

Pretreatment Rostral Anterior Cingulate Cortex Connectivity With Salience Network Predicts Depression Recovery: Findings From the EMBARC Randomized Clinical Trial

BACKGROUND

Baseline rostral anterior cingulate cortex (rACC) activity is a well-replicated nonspecific predictor of depression improvement. The rACC is a key hub of the default mode network, which prior studies indicate is hyperactive in major depressive disorder. Because default mode network downregulation is reliant on input from the salience network and frontoparietal network, an important question is whether rACC connectivity with these systems contributes to depression improvement.

METHODS

Our study evaluated this hypothesis in outpatients (N = 238; 151 female) enrolled in the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) 8-week randomized clinical trial of sertraline versus placebo for major depressive disorder. Depression severity was measured using the Hamilton Rating Scale for Depression, and electroencephalography was recorded at baseline and week 1. Exact low-resolution electromagnetic tomography was used to compute activity from the rACC, and key regions within the default mode network (posterior cingulate cortex), frontoparietal network (left dorsolateral prefrontal cortex), and salience network (right anterior insula [rAI]). Connectivity in the theta band (4.5-7 Hz) and beta band (12.5-21 Hz) was computed using lagged phase synchronization.

RESULTS

Stronger baseline theta-band rACC-rAI (salience network hub) connectivity predicted greater depression improvement across 8 weeks of treatment for both treatment arms (B = -0.57, 95% confidence interval = -1.07, -0.08, p = .03). Early increases in theta-band rACC-rAI connectivity predicted greater likelihood of achieving remission at week 8 (odds ratio = 2.90, p = .03).

CONCLUSIONS

Among patients undergoing treatment, theta-band rACC-rAI connectivity is a prognostic, albeit treatment-nonspecific, indicator of depression improvement, and early connectivity changes may predict clinically meaningful outcomes.

Resting EEG Measures of Brain Arousal in a Multisite Study of Major Depression

Several studies have found upregulated brain arousal during 15-minute EEG recordings at rest in depressed patients. However, studies based on shorter EEG recording intervals are lacking. Here we aimed to compare measures of brain arousal obtained from 2-minute EEGs at rest under eyes-closed condition in depressed patients and healthy controls in a multisite project-Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC). We expected that depressed patients would show stable and elevated brain arousal relative to controls. Eighty-seven depressed patients and 36 healthy controls from four research sites in the United States were included in the analyses. The Vigilance Algorithm Leipzig (VIGALL) was used for the fully automatic classification of EEG-vigilance stages (indicating arousal states) of 1-second EEG segments; VIGALL-derived measures of brain arousal were calculated. We found that depressed patients scored higher on arousal stability ( Z = -2.163, P = .015) and A stages (dominant alpha activity; P = .027) but lower on B1 stages (low-voltage non-alpha activity, P = .008) compared with healthy controls. No significant group differences were observed in Stage B2/3. In summary, we were able to demonstrate stable and elevated brain arousal during brief 2-minute recordings at rest in depressed patients. Results set the stage for examining the value of these measures for predicting clinical response to antidepressants in the entire EMBARC sample and evaluating whether an upregulated brain arousal is particularly characteristic for responders to antidepressants.

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

OBJECTIVE

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

Variation in reward- and error-related neural measures attributable to age, gender, race, and ethnicity

Event-related potentials (ERPs) have been widely applied to the study of individual differences in reward and error processing, including recent proposals of several ERPs as possible biomarkers of mental illness. A criterion for all biomarkers, however, is that they be generalizable across the relevant populations, something which has yet to be demonstrated for many commonly studied reward- and error-related ERPs. The aim of this study was to examine variation in reward and error-related ERPs across core demographic variables: age, gender, race, and ethnicity. Data was drawn from three studies with relatively large samples (N range 207-527). Results demonstrated that ERPs varied across the demographic variables of interest. Several examples include attenuated reward-related ERPs with increasing age, larger error-related ERPs for men than women, and larger ERPs to feedback after losses for individuals who identified as Hispanic/Latino. Overall, these analyses suggest systematic variation in ERPs that is attributable to core demographic variables, which could give rise to seemingly inconsistent results across studies to the extent that these sample characteristics differ. Future psychophysiological studies should include these analyses as standard practice and assess how these differences might exacerbate, mask, or confound relationships of interest.

Using Mismatch Negativity to Investigate the Pathophysiology of Substance Use Disorders and Comorbid Psychosis

Substance use disorders (SUDs) have a devastating impact on society and place a heavy burden on health care systems. Given that alcohol, tobacco, and cannabis use have the highest prevalence, further understanding of the underlying pathophysiology of these SUDs is crucial. Electroencephalography is an inexpensive, temporally superior, and translatable technique which enables investigation of the pathobiology of SUDs through the evaluation of various event-related potential components, including mismatch negativity (MMN). The goals of this review were to investigate the effects of acute and chronic alcohol, tobacco, and cannabis use on MMN among nonpsychiatric populations and patients with comorbid psychosis. A literature search was performed using the database PubMed, and 36 articles met our inclusion and exclusion criteria. We found a pattern of attenuation of MMN amplitude among patients with alcoholism across acute and chronic alcohol use, and this dysregulation was not heritable. Reports were limited, and results were mixed on the effects of acute and chronic tobacco and cannabis use on MMN. Reports on comorbid SUDs and psychosis were even fewer, and also presented mixed findings. These preliminary results suggest that MMN deficits may be associated with SUDs, specifically alcohol use disorder, and serve as a possible biomarker for treating these common disorders.

Association between Cortical GABA and Loudness Dependence of Auditory Evoked Potentials (LDAEP) in Humans

Loudness dependence of auditory evoked potentials (LDAEP) is a widely used EEG-based biomarker for central serotonergic activity. Serotonin has been shown to be associated with different psychiatric disorders such as depression and schizophrenia. Despite its clinical significance, the underlying neurochemical mechanism of this promising marker is not fully understood, and further research is needed to improve its validity. Other neurotransmitters might have a significant impact on this measure. Thus, we assessed the inhibitory action through individual GABA/H20 concentrations and GABA/glutamate ratios by means of magnetic resonance spectroscopy at 3T in healthy subjects. The measurements were assessed in the primary auditory cortex to investigate the association with the LDAEP, whose generators are mainly in the primary auditory cortex. For the first time, this study examines the link between GABAergic neurotransmission and LDAEP, and the data preliminary show that GABA may not contribute to the generation of EEG-based LDAEP.

Pretreatment Rostral Anterior Cingulate Cortex Theta Activity in Relation to Symptom Improvement in Depression: A Randomized Clinical Trial

IMPORTANCE

Major depressive disorder (MDD) remains challenging to treat. Although several clinical and demographic variables have been found to predict poor antidepressant response, these markers have not been robustly replicated to warrant implementation in clinical care. Increased pretreatment rostral anterior cingulate cortex (rACC) theta activity has been linked to better antidepressant outcomes. However, no prior study has evaluated whether this marker has incremental predictive validity over clinical and demographic measures.

OBJECTIVE

To determine whether increased pretreatment rACC theta activity would predict symptom improvement regardless of randomization arm.

DESIGN, SETTING, AND PARTICIPANTS

A multicenter randomized clinical trial enrolled outpatients without psychosis and with chronic or recurrent MDD between July 29, 2011, and December 15, 2015 (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care [EMBARC]). Patients were consecutively recruited from 4 university hospitals: 634 patients were screened, 296 were randomized to receive sertraline hydrochloride or placebo, 266 had electroencephalographic (EEG) recordings, and 248 had usable EEG data. Resting EEG data were recorded at baseline and 1 week after trial onset, and rACC theta activity was extracted using source localization. Intent-to-treat analysis was conducted. Data analysis was performed from October 7, 2016, to January 19, 2018.

INTERVENTIONS

An 8-week course of sertraline or placebo.

MAIN OUTCOMES AND MEASURES

The 17-item Hamilton Rating Scale for Depression score (assessed at baseline and weeks 1, 2, 3, 4, 6, and 8).

RESULTS

The 248 participants (160 [64.5%] women, 88 [35.5%] men) with usable EEG data had a mean (SD) age of 36.75 (13.15) years. Higher rACC theta activity at both baseline (b = -1.05; 95% CI, -1.77 to -0.34; P = .004) and week 1 (b = -0.83; 95% CI, -1.60 to -0.06; P < .04) predicted greater depressive symptom improvement, even when controlling for clinical and demographic variables previously linked with treatment outcome. These effects were not moderated by treatment arm. The rACC theta marker, in combination with clinical and demographic variables, accounted for an estimated 39.6% of the variance in symptom change (with 8.5% of the variance uniquely attributable to the rACC theta marker).

CONCLUSIONS AND RELEVANCE

Increased pretreatment rACC theta activity represents a nonspecific prognostic marker of treatment outcome. This is the first study to date to demonstrate that rACC theta activity has incremental predictive validity.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT01407094.

Temporal stability of posterior EEG alpha over twelve years

OBJECTIVE

We previously identified posterior EEG alpha as a potential biomarker for antidepressant treatment response. To meet the definition of a trait biomarker or endophenotype, it should be independent of the course of depression. Accordingly, this report evaluated the temporal stability of posterior EEG alpha at rest.

METHODS

Resting EEG was recorded from 70 participants (29 male; 46 adults), during testing sessions separated by 12 ± 1.1 years. EEG alpha was identified, separated and quantified using reference-free methods that combine current source density (CSD) with principal components analysis (PCA). Measures of overall (eyes closed-plus-open) and net (eyes closed-minus-open) posterior alpha amplitude and asymmetry were compared across testing sessions.

RESULTS

Overall alpha was stable for the full sample (Spearman-Brown [r_SB] = .834, Pearson's r = .718), and showed excellent reliability for adults (r_SB = .918; r = 0.848). Net alpha showed acceptable reliability for adults (r_SB = .750; r = .600). Hemispheric asymmetries (right-minus-left hemisphere) of posterior overall alpha showed significant correlations, but revealed acceptable reliability only for adults (r_SB = .728; r = .573). Findings were highly comparable between 29 male and 41 female participants.

CONCLUSIONS

Overall posterior EEG alpha amplitude is reliable over long time intervals in adults.

SIGNIFICANCE

The temporal stability of posterior EEG alpha oscillations at rest over long time intervals is indicative of an individual trait.

Using Event-Related Potentials and Startle to Evaluate Time Course in Anxiety and Depression

The National Institute of Mental Health's Research Domain Criteria initiative is a research framework designed toward understanding psychopathology as abnormalities of dimensional neurobehavioral constructs rather than in terms of DSM-defined categories. Research Domain Criteria constructs within the negative valence domain are particularly relevant for understanding anxiety and depressive disorders, which are pervasive, debilitating, and characterized by negative processing bias. One important direction for Research Domain Criteria research is investigating processes and parameters related to the time course (or chronometry) of negative valenced constructs. Two reliable methods for assessing chronometry are event-related potentials (ERPs) and startle blink. In this qualitative review, we examine ERP and startle studies of individuals with anxiety or depression or individuals vulnerable to affective disorders. The aim of the review is to highlight how these methods can inform the role of chronometry in the spectrum of anxiety and depression. ERP studies examining different chronometry facets of negative valenced responses have shown that transdiagnostic groups of individuals with internalizing psychopathologies exhibit abnormalities at early stages of processing. Startle reactivity studies have robustly differentiated fear-based disorders (e.g., panic disorder, social phobia) from other anxiety disorders (e.g., generalized anxiety disorder) and have also shown that different internalizing phenotypes exhibit different patterns of habituation. Findings lend support to the value of ERP and startle measures in identifying groups that cut across conventional classification systems. We also highlight methodological issues that can aid in the validity and reproducibility of ERP and startle findings and, ultimately, in the goal of developing more precise models of anxiety and depression.

A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial

BACKGROUND

One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials.

METHODS

Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission.

RESULTS

Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level.

CONCLUSION

Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.

EEG frequency PCA in EEG-ERP dynamics

Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG.

Aberrant Modulation of Brain Oscillatory Activity and Attentional Impairment in Attention-Deficit/Hyperactivity Disorder

Electroencephalography and magnetoencephalography are noninvasive neuroimaging techniques that have been used extensively to study various resting-state and cognitive processes in the brain. The purpose of this review is to highlight a number of recent studies that have investigated the alpha band (8-12 Hz) oscillatory activity present in magnetoencephalography and electroencephalography, to provide new insights into the maladaptive network activity underlying attentional impairments in attention-deficit/hyperactivity disorder (ADHD). Studies reviewed demonstrate that event-related decrease in alpha is attenuated during visual selective attention, primarily in ADHD inattentive type, and is often significantly associated with accuracy and reaction time during task performance. Furthermore, aberrant modulation of alpha activity has been reported across development and may have abnormal or atypical lateralization patterns in ADHD. Modulations in the alpha band thus represent a robust, relatively unexplored putative biomarker of attentional impairment and a strong prospect for future studies aimed at examining underlying neural mechanisms and treatment response among individuals with ADHD. Potential limitations of its use as a diagnostic biomarker and directions for future research are discussed.

Brain-Based Biomarkers for the Treatment of Depression: Evolution of an Idea

An ambition of depression biomarker research is to augment psychometric and cognitive assessment of clinically relevant phenomena with neural measures. Although such applications have been slow to arrive, we observe a steady evolution of the idea and anticipate emerging technologies with some optimism. To highlight critical themes and innovations in depression biomarker research, we take as our point of reference a specific research narrative. We begin with an early model of frontal-limbic dysfunction, which represents a conceptual shift from localized pathology to understanding symptoms as an emergent property of distributed networks. Over the decades, this model accommodates perspectives from neurology, psychiatry, clinical, and cognitive neuroscience, and preserves past insight as more complex methods become available. We also track the expanding mission of brain biomarker research: from the development of diagnostic tools to treatment selection algorithms, measures of neurocognitive functioning and novel targets for neuromodulation. To conclude, we draw from this particular research narrative future directions for biomarker research. We emphasize integration of measurement modalities to describe dynamic change in domain-general networks, and we speculate that a brain-based framework for psychiatric problems may dissolve classical diagnostic and disciplinary boundaries. (JINS, 2017, 23, 870-880).

Emotion processing in female youth: Testing the stability of the late positive potential

The Emotional Interrupt Task (EIT) has been used to probe emotion processing in healthy and clinical samples; however, research exploring the stability and reliability of behavioral measures and ERPs elicited from this task is limited. Establishing the psychometric properties of the EIT is critical, particularly as phenotypes and biological indicators may represent traitlike characteristics that underlie psychiatric illness. To address this gap, test-retest stability and internal consistency of behavioral indices and ERPs resulting from the EIT in healthy, female youth (n = 28) were examined. At baseline, participants were administered the EIT while high-density 128-channel EEG data were recorded to probe the late positive potential (LPP). One month later, participants were readministered the EIT. Four principal findings emerged. First, there is evidence of an interference effect at baseline, as participants showed a slower reaction time for unpleasant and pleasant images relative to neutral images, and test-retest of behavioral measures was relatively stable over time. Second, participants showed a potentiated LPP to unpleasant and pleasant images compared to neutral images, and these effects were stable over time. Moreover, in a test of the difference waves (unpleasant-neutral vs. pleasant-neutral), there was sustained positivity for unpleasant images. Third, behavioral measures and LPP demonstrated excellent internal consistency (odd/even correlations) across conditions. Fourth, highlighting important age-related differences in LPP activity, younger age was associated with larger LPP amplitudes across conditions. Overall, these findings suggest that the LPP following emotional images is a stable and reliable marker of emotion processing in healthy youth.

Introduction to the special issue on recentering science: Replication, robustness, and reproducibility in psychophysiology

In recent years, the psychological and behavioral sciences have increased efforts to strengthen methodological practices and publication standards, with the ultimate goal of enhancing the value and reproducibility of published reports. These issues are especially important in the multidisciplinary field of psychophysiology, which yields rich and complex data sets with a large number of observations. In addition, the technological tools and analysis methods available in the field of psychophysiology are continually evolving, widening the array of techniques and approaches available to researchers. This special issue presents articles detailing rigorous and systematic evaluations of tasks, measures, materials, analysis approaches, and statistical practices in a variety of subdisciplines of psychophysiology. These articles highlight challenges in conducting and interpreting psychophysiological research and provide data-driven, evidence-based recommendations for overcoming those challenges to produce robust, reproducible results in the field of psychophysiology.