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- W2616767121 abstract "by Stochastic Oscillator Models and Kalman Filtering Arno Solin, Enrico Glerean, Simo Sarkka 1 Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland 2 Advanced Magnetic Imaging Centre, Aalto University, Espoo, Finland INTRODUCTION A growing interest in brain connectivity has emerged with advances in non-invasive imaging techniques and development of methods to estimate connectivity (see [5] for a review). With functional magnetic resonance imaging (fMRI), functional connectivity is often estimated as the average pairwise similarity between the temporal dynamics of two regions of interests (ROIs) producing a static similarity index usually between −1 and 1. However there has been growing interest in estimating time-varying functional connectivity during rest [1] and during complex stimulation [2], where the stimulus is continuously changing, modulating the functional connectivity in time. Here, we study the functional connectivity between pairs of voxels by considering the time–frequency dynamics between them. Each pair of time series is decomposed into oscillatory components described by a stochastic oscillator model [4], and coherence between the different frequency components is converted into network edges time series, i.e. yielding a time-varying functional network." @default.
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- W2616767121 date "2013-01-01" @default.
- W2616767121 modified "2023-09-27" @default.
- W2616767121 title "Timefrequency dynamics of brain connectivity by stochastic oscillator models and Kalman filtering" @default.
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