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.Magnitude of frontal theta (rostral ACC eLORETA source amplitude) and posterior alpha (spectral components of scalp current source density) at rest have been considered candidate EEG biomarkers of depression outcomes. Given inconsistent findings, we examined the discriminant and convergent validity of these measures in healthy adults. Unlike theta, two distinct alpha components constituted reliable, convergent, and discriminant biometrics. While results have marked implications for clinical utility, we make several recommendations for improving the psychometric properties of resting frontal theta.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153675/1/psyp13483.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153675/2/psyp13483_am.pd

Mood and the Market: Can Press Reports of Investors’ Mood Predict Stock Prices?

We examined whether press reports on the collective mood of investors can predict changes in stock prices. We collected data on the use of emotion words in newspaper reports on traders’ affect, coded these emotion words according to their location on an affective circumplex in terms of pleasantness and activation level, and created indices of collective mood for each trading day. Then, by using time series analyses, we examined whether these mood indices, depicting investors’ emotion on a given trading day, could predict the next day’s opening price of the stock market. The strongest findings showed that activated pleasant mood predicted increases in NASDAQ prices, while activated unpleasant mood predicted decreases in NASDAQ prices. We conclude that both valence and activation levels of collective mood are important in predicting trend continuation in stock prices

Examining the Effects of Slow-Wave Activity Disruption on Waking EEG Theta Activity

Introduction: The two-process model of sleep postulates that sleep propensity increases throughout the day, and decreases with sleep as a homeostatic process. Both EEG delta activity (slow-wave) during sleep, and EEG theta activity during waking have been shown to increase with extended waking, and decrease following sleep, suggesting that both of these measures are markers of sleep propensity. In individuals with major depressive disorder, however, altered patterns of slow-wave activity have been noted, suggesting that sleep homeostasis is dysregulated. This study aimed to examine EEG theta activity in a sample of healthy and depressed individuals following one night of baseline, and one night of selective slow-wave disrupted sleep. Methods: 25 participants (13 diagnosed with Major Depressive Disorder (MDD) and 12 healthy controls (HC)) were recruited. Following one night of adaptation sleep, participants underwent one night of baseline sleep, and one night of selective slow-wave disruption by auditory stimuli. In the evening, before sleep, and in the morning following sleep, waking EEG was recorded from participants in an upright position, with eyes open. Results: A repeated measures ANOVA revealed a significant threeway interaction, F(1,23)=5.33, p\u3c.05, between group (HC,MDD), time of day (AM,PM), and condition (baseline, disruption), such that AM theta activity was significantly lower following slow-wave disruption in those with MDD. There were no significant changes in theta activity in healthy controls. Conclusion: The present results revealed that following slow-wave disruption, waking EEG theta activity decreased in those with MDD, but not in healthy controls. This may suggest that SWA plays a different role in the homeostatic regulation of sleep in HC, and in MDD

When perceptions defy reality: The relationships between depression and actual and perceived Facebook social support

BACKGROUND: Although the relationship between depression and "offline" social support is well established, numerous questions surround the relationship between "online" social support and depression. We explored this issue by examining the social support dynamics that characterize the way individuals with varying levels of depression (Study 1) and SCID-diagnosed clinically depressed and non-depressed individuals (Study 2) interact with Facebook, the world's largest online social network. METHOD: Using a novel methodology, we examined how disclosing positive or negative information on Facebook influences the amount of social support depressed individuals (a) actually receive (based on actual social support transactions recorded on Facebook walls) and (b) think they receive (based on subjective assessments) from their Facebook network. RESULTS: Contrary to prior research indicating that depression correlates with less actual social support from "offline" networks, across both studies depression was positively correlated with social support from Facebook networks when participants disclosed negative information (p=.02 in Study 1 and p=.06 in Study 2). Yet, depression was negatively correlated with how much social support participants thought they received from their Facebook networks (p=.005 in Study 1 and p=.001 in Study 2). LIMITATIONS: The sample size was relatively small in Study 2, reflecting difficulties of recruiting individuals with Major Depressive Disorder. CONCLUSIONS: These results demonstrate that an asymmetry characterizes the relationship between depression and different types of Facebook social support and further identify perceptions of Facebook social support as a potential intervention target. (243 words; 250 max).status: publishe

A Computational Analysis Of Flanker Interference In Depression

Background. Depression is characterized by poor executive function, but – counterintuitively – in some studies, it has been associated with highly accurate performance on certain cognitively demanding tasks. The psychological mechanisms responsible for this paradoxical finding are unclear. To address this issue, we applied a drift diffusion model (DDM) to flanker task data from depressed and healthy adults participating in the multi-site Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study. Method. One hundred unmedicated, depressed adults and 40 healthy controls completed a flanker task. We investigated the effect of flanker interference on accuracy and response time, and used the DDM to examine group differences in three cognitive processes: prepotent response bias (tendency to respond to the distracting flankers), response inhibition (necessary to resist prepotency), and executive control (required for execution of correct response on incongruent trials). Results. Consistent with prior reports, depressed participants responded more slowly and accurately than controls on incongruent trials. The DDM indicated that although executive control was sluggish in depressed participants, this was more than offset by decreased prepotent response bias. Among the depressed participants, anhedonia was negatively correlated with a parameter indexing the speed of executive control (r = −0.28, p = 0.007). Conclusions. Executive control was delayed in depression but this was counterbalanced by reduced prepotent response bias, demonstrating how participants with executive function deficits can nevertheless perform accurately in a cognitive control task. Drawing on data from neural network simulations, we speculate that these results may reflect tonically reduced striatal dopamine in depression