Exploring brain networks is an essential step towards understanding functional organization of the brain, which needs characterization of linear and nonlinear connections based on measurements like EEG or MEG. Conventional measures of connectivity are mostly linear and bivariate. This paper proposes an effective connectivity measure called Adaptive Neuro-Fuzzy Inference System Granger Causality (ANFISGC). The proposed measure is based on the symplectic geometry embedding dimension, Adaptive Neuro-Fuzzy Inference System (ANFIS) predictor, and Granger Causality (GC). It is a powerful predictor that detects both linear and nonlinear causal information flow. It is not bivariate and thus can distinguish between direct and indirect connections. The performance of the proposed method is evaluated and compared with those of the Linear Granger Causality (LGC), Kernel Granger Causality (KGC), combination of Pairwise Granger Causality and Conditional Granger Causality (PwGC + CGC), Transfer Entropy (TE), and Phase Transfer Entropy (PTE) methods using simulated and experimental MEG data. Simulation results show that ANFISGC outperforms the other methods in detecting both linear and nonlinear connections and, by increasing the coupling strength between nodes, the value of ANFISGC increases. In the analysis of the time series of the brain sources of epilepsy patients obtained from the MEG inverse problem, the regions found by ANFISGC were more similar to the clinical findings than those found by the other methods.
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http://dx.doi.org/10.1016/j.neuroimage.2018.07.024 | DOI Listing |
Cogn Neurodyn
December 2024
Machine Learning Group, Luleå University of Technology, Luleå, Sweden.
Finding the synchronization between Electroencephalography (EEG) and human cognition is an essential aspect of cognitive neuroscience. Adaptive Control of Thought-Rational (ACT-R) is a widely used cognitive architecture that defines the cognitive and perceptual operations of the human mind. This study combines the ACT-R and EEG-based cortex-level connectivity to highlight the relationship between ACT-R modules during the EEG-based -back task (for validating working memory performance).
View Article and Find Full Text PDFHeliyon
December 2024
Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia.
While there is extensive research on the environmental impacts of renewable energy sources, a notable gap remains in understanding the specific environmental effects of geothermal energy. This knowledge shortfall is particularly critical for Indonesia, which holds the world's second-largest geothermal potential but has yet to effectively harness these valuable resources. This study aims to address this gap by providing a preliminary evaluation of the dynamic impact of geothermal energy consumption and various macroeconomic variables on carbon dioxide (CO) emissions in Indonesia from 1995 to 2020.
View Article and Find Full Text PDFBMC Glob Public Health
September 2024
Department of Public Health, North Dakota State University, Fargo, ND, USA.
Background: Off-label use of semaglutide for non-diabetic weight loss (which regulators have linked to social media promotion) created worldwide supply shortages. We evaluated worldwide semaglutide interest measured by online search behavior to gauge social media and conventional print media reporting's effect on search interest.
Methods: Using Google Trends Extended for Health (GTEH) multiple sampling, we retrieved regional online interest (ROI) for all countries and extracted timelines and top search queries for January 2021-August 2023 for countries with median ROI ≥ 20 using the "semaglutide" topic.
The study explains the time-quantile-frequency adjustments of green growth to energy vulnerability, energy uncertainties, and geopolitical risks (GPR) in the United States (US). Novel insights with notable policy implications emerged following the empirical analysis of monthly data spanning 2000 m1-2022 m12. The study implemented the Wavelet Quantile Correlation (WQC), Wavelet Quantile Granger Causality, and the Rolling Windows Wavelet Quantile Granger Causality to understand the dynamics among the variables.
View Article and Find Full Text PDFHeliyon
December 2024
School of Economics, College of Business and Economics, University of Johannesburg, South Africa.
As artificial intelligence (AI) continues to advance, its impact on employment is a topic of concern. In South Africa, where low-skilled labor forms a significant portion of the workforce, the integration of AI technologies raises questions about the future of employment opportunities and economic stability. This manuscript explores the relationship between AI adoption, low-skilled employment dynamics, and its implications using key economic indicators such as inflation, interest rates, and foreign direct investment (FDI).
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