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Found 6 papers, 0 papers with code
Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
Finally, we demonstrate the applicability of lsNGC to estimating causality in large, real-world systems by inferring directional nonlinear, causal relationships among a large number of relatively short time series acquired from functional Magnetic Resonance Imaging (fMRI) data of the human brain.
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality
As a reference method, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity.
Tracking Results and Utilization of Artificial Intelligence (tru-AI) in Radiology: Early-Stage COVID-19 Pandemic Observations
Objective: To introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) in radiology.
Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data
Finally, we demonstrate the applicability of lsNGC to estimating causality in large, real-world systems by inferring directional nonlinear, multivariate causal relationships among a large number of relatively short time-series acquired from functional Magnetic Resonance Imaging (fMRI) data of the human brain.
Automated Identification of Thoracic Pathology from Chest Radiographs with Enhanced Training Pipeline
Our results suggest that, in addition to using sophisticated network architectures, a good learning rate, scheduler and a robust optimizer can boost performance.
A Framework for Exploring Non-Linear Functional Connectivity and Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric Clustering
We present a computational framework for analysis and visualization of non-linear functional connectivity in the human brain from resting state functional MRI (fMRI) data for purposes of recovering the underlying network community structure and exploring causality between network components.
