A COMPARISON OF THE EPILEPTIC DISCHARGES DRIVEN BOLD RESPONSE FUNCTIONS IN EEG‑FMRI DATA

• Published in: Acta neurobiologiae experimentalis Vol. 82; p. XLV
• Main Authors: Hryniewicz, Nikodem, Sińczuk, Marcin, Rola, Rafał, Janko, Ewa‑Piątkowska, Ryglewicz, Danuta, Bogorodzki, Piotr
• Format: Journal Article
• Language: English
• Published: Warsaw Polish Academy of Sciences 01-01-2022
• Subjects: EEG; Electroencephalography; Epilepsy; Functional magnetic resonance imaging; Hemodynamic responses; Neurological diseases; Response functions; Statistical analysis; Statistics; Thalamus
• ISSN: 0065-1400; 1689-0035

Epilepsy is one of the most common neurological diseases. Using the discharge onset times derived from the EEG signal, one can compute statistical parametric maps (SPM) from fMRI data. It is necessary to prepare GLM regressors by convolving the driving neuronal activity function with the hemodynamic response function (HRF). This work aimed to prepare a MATLAB application that allows EEG-fMRI analysis with different either HRFs or driving functions. Additionally, we compared the results obtained from one patient's data. We proposed 4 different driving functions and 4 models of the HRF. Standard statistical analysis in SPM12 showed activation cluster in thalamus, the voxel showing the maximum statistical value was therefore chosen as the voxel of interest. The BOLD signal from the voxel was extracted and the beta and mean square error values (MSE) were determined for each HRF model using different driving functions. Prepared toolbox enabled efficient processing and analysis of EEG-fMRI data. The calculated beta values and MSE showed differences in the analysis with the use of various regressors. It has also been shown that changing the parameters of the HRF can improve the fit of estimation to the actual BOLD response, which can improve the result of the analysis.