Klidové sítě v prodromálním stadiu demence s Lewyho tělísky

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Title in English Resting networks in the prodromal stage of dementia with Lewy bodies
Authors

VÝTVAROVÁ Eva GAJDOŠ Martin LAMOŠ Martin MORÁVKOVÁ Ivona BRABENEC Luboš REKTOROVÁ Irena

Year of publication 2023
Type Conference abstract
MU Faculty or unit

Central European Institute of Technology

Citation
Description Mild cognitive impairment with Lewy bodies (MCI-LB) is a prodromal stage of dementia with Lewy bodies (DLB) and it is clinically characterized by MCI together with differing combinations of the clinical features of parkinsonism, REM sleep behavior disorder, fluctuation of cognition or alertness, and visual hallucinations. Connectivity studies can offer insights on how the brain is affected by the disease. This work concentrates on the major cognitive brain networks. High-density resting state EEG data were acquired from 31 healthy controls (HC; 15 males, 16 females; 67.30±6.79) and 58 subjects diagnosed with MCI-LB (25 males, 33 females; 69.94±6.35). After standard preprocessing, the first seven minutes of recordings were reconstructed to source space using the AAL atlas parcellation to 90 regions covering the whole brain, excluding the cerebellum and vermis. The resulting time series were filtered to six frequency bands: delta (0.1-4 Hz), theta (4-5.5 Hz), fast theta (5.5-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-40 Hz). The whole-brain functional connectivity was computed using the phase-lag index. Five resting state brain networks (RSNs) were extracted: default mode (DMN), frontoparietal control (FPCN), sensorimotor (SMN), visual (VN), and dorsal attention (DAN) networks. The connectivity within individual RSNs and between RSNs was computed. The differences between HC and preLBD were analyzed by the Wilcoxon test. Further, resting-state fMRI were acquired from 30 MCI-LB (69.1 ± 6.3) a 37 HC (68.1 ± 5.5). Signals from the regions from RSNs were extracted, and the Pearson’s correlations from time-windows of the length of 61 scans were computed. The dynamic connectivity states were obtained by k-means clustering and mean dwell times were computed.
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