fMRI dynamická funkční konektivita u pacientů s Parkinsonovou chorobou
| Title in English | fMRI dynamic functional connectivity in patients with Parkinson's disease |
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| Authors | |
| Year of publication | 2021 |
| Type | Conference abstract |
| MU Faculty or unit | |
| Citation | |
| Description | Introduction Dynamic functional connectivity (DFC) in fMRI includes a set of methods that characterize the working dynamics of BOLD signal fluctuations. DFC makes it possible to expand the parameters of static connectivity analysis with new ones that can expand our knowledge of brain function. In this paper, we focus on the use of DFC in fMRI with Parkinson's disease (PD). Methodology 31 patients with PD were examined on a 3T Siemens Prisma MRI scanner. The data were preprocessed in the MATLAB program environment with the SPM toolbox, and spatial alignment, acquisition time correction, spatial normalization, and spatial blurring were used. This was followed by independent component analysis (ICA) to identify brain networks. A floating window was applied to the time courses of the ICA component, and Pearson correlation coefficients were calculated within the windows. Using k-means clustering, five states were identified and their dynamics were subsequently evaluated. The state represents mutual relations between brain networks that occur both over time and between the subjects of the studied set. The parameters of the number of transitions, the number of occurrences of a given state, and the probability of transitions between states were added for DFC evaluation. Finally, two-sample t-tests were calculated between the HC and PD groups, and DFC parameters were correlated with participants' behavioral parameters. Our results are a pilot DFC analysis on the mentioned data set. |
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