Spatiotemporal Dependency of Age-Related Changes in Brain Signal Variability

Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18-72) and one magnetoencephalography (n = 31, ages 20-75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependenc

A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions

Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between neural sources. Interactions are often reconstructed using pairwise bivariate analyses, overlooking their multivariate nature: it is neglected that investigating the effect of one source on a target necessitates to take all other sources as potential nuisance variables into account; also combinations of sources may act jointly on a given target. Bivariate analyses produce networks that may contain spurious interactions, which reduce the interpretability of the network and its graph metrics. A truly multivariate reconstruction, however, is computationally intractable due to combinatorial explosion in the number of potential interactions. Thus, we have to resort to approximative methods to handle the intractability of multivariate interaction reconstruction, and thereby enable the use of networks in neuroscience. Here, we suggest such an approximative approach in the form of an algorithm that extends fast bivariate interaction reconstruction by identifying potentially spurious interactions post-hoc: the algorithm flags potentially spurious edges, which may then be pruned from the network. This produces a statistically conservative network approximation that is guaranteed to contain non-spurious interactions only. We describe the algorithm and present a reference implementation to test its performance. We discuss the algorithm in relation to other approximative multivariate methods and highlight suitable application scenarios. Our approach is a tractable and data-efficient way of reconstructing approximative networks of multivariate interactions. It is preferable if available data are limited or if fully multivariate approaches are computationally infeasible.Comment: 24 pages, 8 figures, published in PLOS On

Innovative Development of the Agroindustrial Complex of the Yamal-Nenets Autonomous Area

Цель данной работы – проанализировать состояние и потенциал инновационного развития агропромышленного комплекса Ямало-Ненецкого автономного округа (ЯНАО). Научная новизна исследования заключается в рассмотрении проблемы инновационного развития агропромышленного комплекса (АПК) в регионе со значительным вовлечением в ключевые отрасли представителей коренных малочисленных народов Севера, сложными природно-климатическими условиями, территориальной удаленностью, неразвитостью транспортной инфраструктуры. В исследовании применялись методы анализа литературы, статистического и сравнительного анализа. Выявлено, что в силу объективных причин для АПК ЯНАО характерны высокие затраты на производство, низкая рентабельность, высокий удельный вес убыточных организаций. Это определяет актуальность перехода АПК региона на инновационный путь развития (прежде всего в части совершенствования технологии производства и переработки продукции), а также важность широкого участия в этом процессе органов власти региона. В последние годы поддержка инновационного развития АПК со стороны органов власти ЯНАО реализуется через целевые государственные программы, в рамках которых объемы финансирования мероприятий сильно колеблются от года к году. Сопоставление показателей работы отраслей АПК ЯНАО со средними значениями по России и Уральскому федеральному округу (УрФО) позволило выявить сходства и различия в динамике их развития, определить основной «драйвер» роста. Дальнейшее развитие агропромышленного производства в округе требует совершенствования транспортной инфраструктуры, расширения и модернизации мощностей по глубокой переработке сырья. Важным ограничением при этом выступает необходимость сохранения традиционного уклада жизни и хозяйствования коренных малочисленных народов Севера.This article aims to analyze the state and potential of innovative development of the agroindustrial complex of the Yamal-Nenets Autonomous Area (YaNAA). The authors study the problem of innovative development of agroindustrial complex in the region with significant involvement of representatives of indigenous peoples of the North in key industries, as well as in complicated natural and climatic conditions, territorial remoteness, underdeveloped transport infrastructure. Using the methods of literature, statistical, and comparative analysis, the authors have revealed that due to certain objective reasons, the YaNAA agroindustrial complex can be characterized by high production costs, low profitability, and high proportion of loss-making organizations. This determines the relevance of the regional agroindustrial complex transitioning to an innovative way of development (in terms of improving the technology of production and processing of products), as well as broad participation in this process of regional authorities. Recently, the YaNAA authorities’ support for innovative agriculture development includes targeted state programs, although the funding for their activities vary greatly from year to year. Comparison of the performance of the YaNAA agroindustrial complex with the average values in Russia and the Ural Federal District has revealed similarities and differences in the dynamics of their development, determining the main growth catalyst. Further development of the YaNAA agroindustrial production requires improvement of transport infrastructure, as well as the expansion and modernization of facilities for deep processing of raw materials. An important limitation is the need to preserve the traditional way of life and economy of the indigenous peoples of the North

Spatiotemporal dependency of age-related changes in brain signal variability

Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18-72) and one magnetoencephalography (n = 31, ages 20-75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependenc