Přínos rozšíření analýzy funkční konektivity o dynamické parametry u prodromálního stádia demence s Lewyho tělísky
| Title in English | The benefit of extending functional connectivity analysis to include dynamic parameters in the prodromal stage of dementia with Lewy bodies |
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| Authors | |
| Year of publication | 2024 |
| Type | Conference abstract |
| MU Faculty or unit | |
| Citation | |
| Description | The analysis of resting-state fMRI data often relies on statistical metrics that assess relationships between signals as a whole. In this study, we aim to explore the contribution that dynamic functional connectivity (dFC) parameters can bring to such analyses. We demonstrate this by evaluating their ability to classify the prodromal stage of dementia with Lewy bodies (MCI-LB) from resting-state fMRI data. We used a dataset of 26 participants with MCI-LB and 26 age-matched healthy controls. Each study participant underwent neuropsychological testing and MRI scanning on a 3T MRI scanner at the Laboratory of Multimodal and Functional Neuroimaging in Brno. Resting-state fMRI data were acquired using a multi-echo multiband BOLD fMRI sequence (multiband factor 5; TR 980 ms; TE [14.00, 34.63, 55.26] ms; 580 scans). Classification analyses showed that combining static and dynamic parameters increased classification accuracy by 7.7% in LDA. Therefore, when planning classification analyses, we consider it beneficial to extend the static analysis of resting-state fMRI data to include dynamic parameters. To further evaluate their contribution, we plan to conduct cross-validation of classification models and testing with additional types of classifiers. |
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