Abstract
Interest in the studying of functional connections in the brain has grown considerably in the last decades, as many studies have pointed out that alterations in the interaction among brain areas can play a role as markers of neurological diseases. Most studies in this field treat the brain network as a system of connections stationary in time, but dynamic features of brain connectivity can provide useful information, both on physiology and pathological conditions of the brain. In this paper, we propose the application of a computational methodology, named Particle Filter (PF), to study non-stationarities in brain connectivity in functional Magnetic Resonance Imaging (fMRI). The PF algorithm estimates time-varying hidden parameters of a first-order linear time-varying Vector Autoregressive model (VAR) through a Sequential Monte Carlo strategy. On simulated time series, the PF approach effectively detected and enabled to follow time-varying hidden parameters and it captured causal relationships among signals. The method was also applied to real fMRI data, acquired in presence of periodic tactile or visual stimulations, in different sessions. On these data, the PF estimates were consistent with current knowledge on brain functioning. Most importantly, the approach enabled to detect statistically significant modulations in the cause-effect relationship between brain areas, which correlated with the underlying visual stimulation pattern presented during the acquisition.
Highlights
The understanding of brain functioning is linked to the study of the dynamic interaction among anatomically segregated brain areas
3.2 Real functional Magnetic Resonance Imaging (fMRI) data 3.2.1 Motor network Red lines in Fig. 5 represent average values of the aij coefficients obtained on fMRI time series and the blue histogram represents the corresponding distribution of mean values of causal interactions for the permuted time series
Part of the mismatch between the proposed method and delayed correlation could be explained by the fact that the Particle Filter (PF) algorithm studies the network as a whole and produces estimates of aij coefficients that update at every time instant, while delayed correlation is a measure of pairwise causality that does not take into account possible non-stationarities and spurious cause-effect relationships mediated by other nodes of the network
Summary
The understanding of brain functioning is linked to the study of the dynamic interaction among anatomically segregated brain areas. These interactions are labeled functional and effective connectivity and refer to distinct ways of considering connections among brain region. Functional Magnetic Resonance Imaging (fMRI) is frequently employed in brain connectivity studies, given its non-invasiveness and satisfactory spatiotemporal resolution, both in physiology and pathology (e.g. Alzheimer’s disease [3,4,5], schizophrenia [6] and Major Depression Disorder [7]). From brain connectivity studies it emerged that brain dynamics, in particular effective connectivity, may provide a biological marker for specific brain disease and a tool for monitoring responses to treatments of these pathologies [8,9,10,11].
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