Association between Eeg-Based Functional Connectivity at Rest and Verbal Intelligence

This study is devoted to the investigation of the relationship between different aspects of verbal abilities and global characteristics of brain resting-state functional connectivity (the characteristic path length and the clustering coefficient). Verbal abilities were evaluated using three verbal scales of the Universal Intellectual Test: “Missing words”, “Comprehension” and “Analogies”. Data for the assessment of resting-state functional connectivity were obtained using the electroencephalography method, which allows recording changes of neuronal activity in millisecond resolution. The characteristics of the brain functional connectivity are calculated based on graph theory. Statistically significant correlation coefficients between some scales of verbal intelligence and the clustering coefficient are obtained.Исследование посвящено изучению взаимосвязи различных аспектов вербальных способностей и глобальных характеристик функциональной связанности мозга в состоянии спокойного бодрствования (характеристической длины пути и кластерного коэффициента). Вербальные способности оценены с помощью трех вербальных шкал Универсального интеллектуального теста: «Пропущенные слова», «Понятливость» и «Аналогии». Данные для оценки функциональной связанности мозга получены с помощью метода электроэнцефалографии, который позволяет регистрировать изменения нейрональной активности в миллисекундном разрешении. Характеристики функциональной связанности мозга рассчитаны на основе теории графов. Получены статистически значимые коэффициенты корреляции между некоторыми шкалами вербального интеллекта и кластерным коэффициентом.Исследование выполнено при финансовой поддержке гранта РФФИ, проект № 18-013-00944 «Нейрофизиологические механизмы индивидуальных различий интеллекта»

Towards a generic test of the strong field dynamics of general relativity using compact binary coalescence

Coalescences of binary neutron stars and/or black holes are amongst the most likely gravitational-wave signals to be observed in ground based interferometric detectors. Apart from the astrophysical importance of their detection, they will also provide us with our very first empirical access to the genuinely strong-field dynamics of General Relativity (GR). We present a new framework based on Bayesian model selection aimed at detecting deviations from GR, subject to the constraints of the Advanced Virgo and LIGO detectors. The method tests the consistency of coefficients appearing in the waveform with the predictions made by GR, without relying on any specific alternative theory of gravity. The framework is suitable for low signal-to-noise ratio events through the construction of multiple subtests, most of which involve only a limited number of coefficients. It also naturally allows for the combination of information from multiple sources to increase one's confidence in GR or a violation thereof. We expect it to be capable of finding a wide range of possible deviations from GR, including ones which in principle cannot be accommodated by the model waveforms, on condition that the induced change in phase at frequencies where the detectors are the most sensitive is comparable to the effect of a few percent change in one or more of the low-order post-Newtonian phase coefficients. In principle the framework can be used with any GR waveform approximant, with arbitrary parameterized deformations, to serve as model waveforms. In order to illustrate the workings of the method, we perform a range of numerical experiments in which simulated gravitational waves modeled in the restricted post-Newtonian, stationary phase approximation are added to Gaussian and stationary noise that follows the expected Advanced LIGO/Virgo noise curves.Comment: 26 pages, 23 figures, Accepted by PR