Aberrant Long-Range Temporal Correlations in Depression Are Attenuated after Psychological Treatment
The spontaneous oscillatory activity in the human brain shows long-range temporal correlations (LRTC) that extend over time scales of seconds to minutes. Previous research has demonstrated aberrant LRTC in depressed patients; however, it is unknown whether the neuronal dynamics normalize after psychological treatment. In this study, we recorded EEG during eyes-closed rest in depressed patients (N = 71) and healthy controls (N = 25), and investigated the temporal dynamics in depressed patients at baseline, and after attending either a brief mindfulness training or a stress reduction training. Compared to the healthy controls, depressed patients showed stronger LRTC in theta oscillations (4–7 Hz) at baseline. Following the psychological interventions both groups of patients demonstrated reduced LRTC in the theta band. The reduction of theta LRTC differed marginally between the groups, and explorative analyses of separate groups revealed noteworthy topographic differences. A positive relationship between the changes in LRTC, and changes in depressive symptoms was observed in the mindfulness group. In summary, our data show that aberrant temporal dynamics of ongoing oscillations in depressive patients are attenuated after treatment, and thus may help uncover the mechanisms with which psychotherapeutic interventions affect the brain.
In online social networks, high level features of user behavior such as character traits can be predicted with data from user profiles and their connections. Recent publications use data from online social networks to detect people with depression propensity and diagnosis. In this study, we investigate the capabilities of previously published methods and metrics applied to the Russian online social network VKontakte. We gathered user profile data from most popular communities about suicide and depression on VK.com and performed comparative analysis between them and randomly sampled users. We have used not only standard user attributes like age, gender, or number of friends but also structural properties of their egocentric networks, with results similar to the study of suicide propensity in the Japanese social network Mixi.com. Our goal is to test the approach and models in this new setting and propose enhancements to the research design and analysis. We investigate the resulting classifiers to identify profile features that can indicate depression propensity of the users in order to provide tools for early depression detection. Finally, we discuss further work that might improve our analysis and transfer the results to practical applications.
The article is dedicated to neural basis of verb processing. Three verb groups were analysed: abstract, tool action and hand action verbs. We found that imageability of a verb might influence both the time of its processing and the amount of cerebral activation it is related to.
Increasing evidence suggests that neuronal communication is a defining property of functionally specialized brain networks and that it is implemented through synchronization between population activities of distinct brain areas. The detection of long-range coupling in electroencephalography (EEG) and magnetoencephalography (MEG) data using conventional metrics (such as coherence or phase-locking value) is by definition contaminated by spatial leakage. Methods such as imaginary coherence, phase-lag index or orthogonalized amplitude correlations tackle spatial leakage by ignoring zero-phase interactions. Although useful, these metrics will by construction lead to false negatives in cases where true zero-phase coupling exists in the data and will underestimate interactions with phase lags in the vicinity of zero. Yet, empirically observed neuronal synchrony in invasive recordings indicates that it is not uncommon to find zero or close-to-zero phase lag between the activity profiles of coupled neuronal assemblies. Here, we introduce a novel method that allows us to mitigate the undesired spatial leakage effects and detect zero and near zero phase interactions. To this end, we propose a projection operation that operates on sensor-space cross-spectrum and suppresses the spatial leakage contribution but retains the true zero-phase interaction component. We then solve the network estimation task as a source estimation problem defined in the product space of interacting source topographies. We show how this framework provides reliable interaction detection for all phase-lag values and we thus refer to the method as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS). Realistic simulations demonstrate that PSIICOS has better detector characteristics than existing interaction metrics. Finally, we illustrate the performance of PSIICOS by applying it to real MEG dataset recorded during a standard mental rotation task. Taken together, using analytical derivations, data simulations and real brain data, this study presents a novel source-space MEG/EEG connectivity method that overcomes previous limitations and for the first time allows for the estimation of true zero-phase coupling via non-invasive electrophysiological recordings.
The Abstract book contains the abstracts of the posters presentations of the participants of the Methodological school: Methods of data processing in EEg and MEG, Moscow, 16-30th of April, 2013. The School was devoted to the theoretical and practical aspects of the contemporary methods of the dynamic mapping of brain activity by analysis of multichannel MEG and EEG.
The authors discuss the differences between time management programs and time perspective coaching and explore the possibilities of applying Zimbardo Time Perspective Inventory in the coaching process. Based on the analysis of an individual time perspective profile, the coach may choose from a range of interventions suggested in this chapter. Specific interventions focused on overcoming the drawbacks of past-negative and present-fatalistic outlooks on time, as well as fostering the advantages of future, present-hedonistic, and past-positive time perspectives are presented together with questions aimed to help clients in gaining a better understanding and new insights in each of the time perspective domains. A separate section discusses the potential of mindfulness interventions in fostering balanced time perspective.
The Abstract book contains the abstracts of the posters presentations of the participants of the Methodological school: Methods of data processing in EEG and MEG, Moscow, 16-30th of April, 2013. The School was devoted to the theoretical and practical aspects of the contemporary methods of the dynamic mapping of brain activity by analysis of multichannel MEG and EEG.