Gender Specific Disruptions in Emotion Processing in Younger Adults with Depression

Background: One of the principal theories regarding the biological basis of major depressive disorder (MDD) implicates a dysregulation of emotion-processing circuitry. Gender differences in how emotions are processed and relative experience with emotion processing might help to explain some of the disparities in the prevalence of MDD between women and men. This study sought to explore how gender and depression status relate to emotion processing. Methods: This study employed a 2 (MDD status) × 2 (gender) factorial design to explore differences in classifications of posed facial emotional expressions (N=151). Results: For errors, there was an interaction between gender and depression status. Women with MDD made more errors than did nondepressed women and men with MDD, particularly for fearful and sad stimuli (Ps Ps P=.01). Men with MDD, conversely, performed similarly to control men (P=.61). Conclusions: These results provide novel and intriguing evidence that depression in younger adults (years) differentially disrupts emotion processing in women as compared to men. This interaction could be driven by neurobiological and social learning mechanisms, or interactions between them, and may underlie differences in the prevalence of depression in women and men. Depression and Anxiety, 2009. Published 2008 Wiley-Liss, Inc

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

The Functional Connectivity Landscape of the Human Brain

Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dynamic basis of these networks is inter-regional communication involving local and distal regions. It is assumed that inter-regional distances play a pivotal role in modulating network dynamics. Using three different neuroimaging modalities, 6 datasets were evaluated to determine whether experimental manipulations asymmetrically affect functional relationships based on the distance between brain regions in human participants. Contrary to previous assumptions, here we show that short- and long-range connections are equally likely to strengthen or weaken in response to task demands. Additionally, connections between homotopic areas are the most stable and less likely to change compared to any other type of connection. Our results point to a functional connectivity landscape characterized by fluid transitions between local specialization and global integration. This ability to mediate functional properties irrespective of spatial distance may engender a diverse repertoire of cognitive processes when faced with a dynamic environment.</p

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

Growing evidence suggests that loudness dependency of auditory evoked potentials (LDAEP) and resting EEG alpha and theta may be biological markers for predicting response to antidepressants. In spite of this promise, little is known about the joint reliability of these markers, and thus their clinical applicability. New standardized procedures were developed to improve the compatibility of data acquired with different EEG platforms, and used to examine test‐retest reliability for the three electrophysiological measures selected for a multisite project—Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC). Thirty‐nine healthy controls across four clinical research sites were tested in two sessions separated by about 1 week. Resting EEG (eyes‐open and eyes‐closed conditions) was recorded and LDAEP measured using binaural tones (1000 Hz, 40 ms) at five intensities (60–100 dB SPL). Principal components analysis of current source density waveforms reduced volume conduction and provided reference‐free measures of resting EEG alpha and N1 dipole activity to tones from auditory cortex. Low‐resolution electromagnetic tomography (LORETA) extracted resting theta current density measures corresponding to rostral anterior cingulate (rACC), which has been implicated in treatment response. There were no significant differences in posterior alpha, N1 dipole, or rACC theta across sessions. Test‐retest reliability was .84 for alpha, .87 for N1 dipole, and .70 for theta rACC current density. The demonstration of good‐to‐excellent reliability for these measures provides a template for future EEG/ERP studies from multiple testing sites, and an important step for evaluating them as biomarkers for predicting treatment response.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135271/1/psyp12758_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135271/2/psyp12758.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135271/3/psyp12758-sup-0001-suppinfo1.pd

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

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

Effects of slow-wave activity on mood disturbance in major depressive disorder

BACKGROUND: Studies have demonstrated that decreases in slow-wave activity (SWA) predict decreases in depressive symptoms in those with major depressive disorder (MDD), suggesting that there may be a link between SWA and mood. The aim of the present study was to determine if the consequent change in SWA regulation following a mild homeostatic sleep challenge would predict mood disturbance. METHODS: Thirty-seven depressed and fifty-nine healthy adults spent three consecutive nights in the sleep laboratory. On the third night, bedtime was delayed by 3 h, as this procedure has been shown to provoke SWA. The Profile of Mood States questionnaire was administered on the morning following the baseline and sleep delay nights to measure mood disturbance. RESULTS: Results revealed that following sleep delay, a lower delta sleep ratio, indicative of inadequate dissipation of SWA from the first to the second non-rapid eye movement period, predicted increased mood disturbance in only those with MDD. CONCLUSIONS: These data demonstrate that in the first half of the night, individuals with MDD who have less SWA dissipation as a consequence of impaired SWA regulation have greater mood disturbance, and may suggest that appropriate homeostatic regulation of sleep is an important factor in the disorder