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Peter Fransson
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Journal Articles
Publisher: Journals Gateway
Network Neuroscience (2019) 3 (4): 1094–1120.
Published: 01 September 2019
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Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal coactivation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in coactivation patterns of distributed brain regions have an inherent dynamic nature with regard to the frequency spectrum of intrinsic brain oscillations. In this modeling study, we introduced frequency dynamics into a system of coupled oscillators, where each oscillator represents the local mean-field model of a brain region. We first showed that the collective behavior of interacting oscillators reproduces previously shown features of brain dynamics. Second, we examined the effect of simulated lesions in gray matter by applying an in silico perturbation protocol to the brain model. We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space. Author Summary Computational modeling of the brain enables us to test different hypotheses without any experimental complication, and it provides us with a platform for improving our understanding of different brain mechanisms. In this study, we proposed a new macroscopic computational model of the brain oscillations for resting-state fMRI. Optimizing model parameters using empirical data was performed based on several measures of functional connectivity and instantaneous coherence. We simulated the effect of malfunction in a brain region by changing that region’s dynamics to evoke noisy behavior. Together with presenting a new paradigm for local vulnerability mapping in the brain connectome, we evaluated the hazard rate induced after perturbing a brain region by measuring divergence of the perturbed model from the original model in feature space. The analysis of hazard rates induced by primary failures of individual brain regions provides relevant insights not only into the size of the damage inflicted on the connectome by a particular failure, but also into the potential origins of disease. Furthermore, we proposed a spatial brain map that is associated with the regional hazardousness rates, which is in good agreement with the known pathophysiologic roles of malfunction in different functional systems in the brain.
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Journal Articles
Publisher: Journals Gateway
Network Neuroscience (2017) 1 (2): 69–99.
Published: 01 June 2017
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Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto. Author Summary Temporal network theory is a subfield of network theory that has had limited application to date within network neuroscience. The aims of this work are to introduce temporal network theory, define the metrics relevant to the context of network neuroscience, and illustrate their potential by analyzing a resting-state fMRI dataset. We found both between-subjects and between-task differences that illustrate the potential for these tools to be applied in a wider context. Our tools for analyzing temporal networks have been released in a Python package called Teneto.