Cross-frequency Phase Coupling of Brain Oscillations and Relevance Attribution as Saliency Detection in Abstract Reasoning.
Abstract reasoning is associated with the ability to detect relations among objects, ideas, events. It underlies the understanding of other individuals’ thoughts and intentions. In natural settings, individuals have to infer relevant associations that have proven to be reliable or precise predictors. Salience theory suggests that the attribution of meaning to stimulus depends on their contingency, saliency, and relevance to adaptation. So far, subjective estimates of relevance have mostly been explored in motivation and implicit learning. Mechanisms underlying formation of associations in abstract thinking with regard to their subjective relevance, or salience, are not clear. Applying novel computational methods, we investigated relevance detection in categorization tasks in 17 healthy individuals. Two models of relevance detection were developed: a conventional one with nouns from the same semantic category, an aberrant one based on an insignificant common feature. Control condition introduced non-related words. The participants were to detect either a relevant principle or an insignificant feature to group presented words. In control condition they inferred that the stimuli were irrelevant to any grouping idea. Cross-frequency phase coupling analysis revealed statistically distinct patterns of synchronization representing search and decision in the models of normal and aberrant relevance detection. Significantly distinct frontotemporal functional networks with central and parietal components in the theta and alpha frequency bands may reflect differences in relevance detection.
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.
In this research we compare the performance of different data mining techniques in the analysis of electroencephalogram (EEG) data. We study the question od predicting post-comatose neuro-developmental scores based mainly on statistical features of the EEG recordings. We compare results from applying different data mining techniques, such as the Elastic Net, Lasso, Gaussian Support Vector Regression and Random Forest Regression. We also compare the results produced with different matrix completion methods.
Our sentences about the world are organized to properly convey the constantly changing visual environment. This skill develops early in life. When fully developed, it entails constant, regular, and automatic mappings from elements of a visual scene onto sentence constituents and the grammatical relations between them. The visual system contributes initially to this process by providing perceptual information for conceptual and linguistic analysis but the perceptual information that enters the language production system is not organized indiscriminately. The attentional system filters information for processing based on its noticeability, importance, or relevance. This process allows representing salience parameters in linguistic output. Individual languages’ grammars have specific devices responsible for this representation.
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 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.
This book presents the results of analysis of human capital in Murmansk and Archangelsk regions, republics of Komi and Karelia, and Nenets Autonomous Region. The authors considered migration processes and their trends; some of these were analyzed at municipal level. Having taken in account the importance of life expectancy as a complex indicator of sustainable development, the authors identified the periods of its growth and decline. Age-specific differences were also scrutinized. The relative contributions of major causes of mortality in life expectancy at birth were estimated. The authors described the dynamics of population of small indigenous peoples of the North (Vepsians, Nenets, Komi), the problems associated with their self-identification, census administration, migration, childbirth and life expectancy. The authors analyzed climate change as the new health risk factor, which affects safety of food and drinking water, accessibility of medical services and specific practices of deer-herding. A separate chapter of the book is devoted to current and future trends in working-age population until 2002. Each territory of Barents Sea Region displayed its own peculiar behavior of this indicator. The authors compared selected social, economic and demographic indicators in European part of Russian Arctic with those in foreign countries which belong to Barents Sea Region. This monograph was a product of collaborative efforts of the researchers from Economic Forecasting Institute and Institute of Demography of Higher School of Economics. B. A. Revich, Doctor of Medicine, and B. N. Porfiryev, Corresponding Member of Russian Academy of Sciences, edited this book.
This prototype development explains the challenges encountered during the ISO/IEEE 11073 standard implementation process. The complexity of the standard and the consequent heavy requirements, which have not encouraged software engineers to adopt the standard. The developing complexity evaluation drives us to propose two possible implementation strategies that cover almost all possible use cases and eases handling the standard by non-expert users. The first one is focused on medical devices (MD) and proposes a low-memory and low-processor usage technique. It is based on message patterns that allow simple functions to generate ISO/IEEE 11073 messages and to process them easily. MD act as X73 agent. Second one is focused on more powerful device X73 manager, which do not have the MDs' memory and processor usage constraints. The protocol between Agent and Manager is point-to-point and we can distribute the functionality between devices.
Developed both implementation X73 Agent and Manager will cut developing time for applications based on ISO/EEE 11073.
In the internal medicine wide spectrum the gastroenterology is one of the chapters, less enlightened by the scientific evidence. It does not mean that the practice of the grasntroenterology may ot be improved by the systematic use of the approaches of the evidence based medicine