Connectivity Estimation
7 papers with code • 0 benchmarks • 2 datasets
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Latest papers with no code
Brain Diffuser with Hierarchical Transformer for MCI Causality Analysis
It can captures both unidirectal and bidirectional interactions between brain regions, providing a comprehensive understanding of the brain's information processing mechanisms.
Generalized Power Iteration with Application to Distributed Connectivity Estimation of Asymmetric Networks
The problem of connectivity assessment in an asymmetric network represented by a weighted directed graph is investigated in this article.
Online functional connectivity analysis of large all-to-all networks
The analysis of EEG/MEG functional connectivity has become an important tool in neural research.
Granger Causality for Compressively Sensed Sparse Signals
In this work, we provide a mathematical proof that structured compressed sensing matrices, specifically Circulant and Toeplitz, preserve causal relationships in the compressed signal domain, as measured by Granger Causality.
Parameter Estimation in Ill-conditioned Low-inertia Power Systems
This ill-conditioning is because of converter-interfaced power systems generators' zero or small inertia contribution.
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
To bridge this gap, we developed dynamic effective connectivity estimation via neural network training (DECENNT), a novel model to learn an interpretable directed and dynamic graph induced by the downstream classification/prediction task.
Attend to connect: end-to-end brain functional connectivity estimation
Functional connectivity (FC) studies have demonstrated the benefits of investigating the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.
Improving J-divergence of brain connectivity states by graph Laplacian denoising
Using our novel formulation of the J-divergence, we are able to quantify the distance between the FC networks in the motor imagery and resting states, as well as to understand the contribution of each Laplacian variable to the total J-divergence between two states.
Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies
Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders.
Connectivity estimation of high dimensional data recorded from neuronal cells
Datasets were simulated in different ways and analysed in order to develop an evaluation framework.