Abstract
In the current study we focus on the modeling of functional processes of brain tissue using functional Magnetic Resonance Imaging (fMRI) data. A brain connectivity analysis of Restless Legs Syndrome (RLS) is presented. Thirteen un-medicated patients with late-onset RLS and six healthy subjects are studied using structural and functional brain images. We compare functional connectivity analysis methods, according to their dependency on models or data, as well as to model effective connectivity. An Independent Component Analysis (ICA) method is implemented and all spontaneously activated areas in resting-state condition in both patients and healthy subjects are recorded and be compared with previous studies. Functional connectivity correlation matrices of both RLS and control subjects are extracted and these functional connectivity measures were compared using a seed-based analysis method. We model the brain tissue, based on the influence that one region exerts over another, using a spectral Dynamic Causal Model (DCM) analysis, which has not yet been implemented for RLS data. Finally, a Bayesian Model Selection is chosen in order to compare the winning model that effectively describes the data. The benefits of each methodology are presented.
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Ntemou, A.K., Tripoliti, E.E., Margariti, P.N., Argyropoulou, M.I., Fotiadis, D.I. (2020). Modeling Functional Processes of Brain Tissue: An fMRI Study on Patients with Un-Medicated Late-Onset Restless Leg Syndrome. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_37
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DOI: https://doi.org/10.1007/978-3-030-31635-8_37
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