Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Hemodynamic Response Function Modeling to Determine the Areas with High Blood Supply in Block-Design fMRI Experiments

Tue Jul 23 12:05:43 2024

(2019) Hemodynamic Response Function Modeling to Determine the Areas with High Blood Supply in Block-Design fMRI Experiments. Archives of Neuroscience. p. 7. ISSN 2322-3944

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Background: Determining the activated brain areas due to different activities is one of the most common targets in functional magnetic resonance imaging (fMRI) data analysis, which could be carried out by hemodynamic response function (HRF) evaluation. The HR functions reflect changes of cerebral blood flow (CBF) in response to neural activity. Objectives: In this study, five models of HRF estimation were evaluated based on a simulated dataset. Models with higher accuracy were used to determine HRF parameters of the block-design fMRI data. Methods: The fMRI data were acquired in a 3 Tesla scanner. For block-design fMR imaging, CO2 gas was administered using a face-mask under physiological monitoring. Three patients with brain tumors were scanned. The fMRI data analysis was performed using the SPM12 MATLAB toolbox. Akaike's information criterion (AIC), Schwarz' Bayesian (SBC), and mean square error (MSE) criteria were used to select the best HRF estimation model. Results: In simulation studies, the estimated HRFs by the canonical HRF plus its temporal derivative (TD), finite impulse response (FIR), and inverse logistic (IL) models were almost equal to the standard HRF. Mean square error, AIC, and SBC indices were ignorable for TD, FIR, and IL models (MSE/AIC/SBC magnitudes for TD, FIR, and IL models were 0.052/-1235.1/-1223.9, 0.055/-1206.4/-1194.9, and 0.068/-1091.5/-1049.2, respectively), which indicates that these models could accurately estimate HRF in block design fMRI studies. Conclusions: The HRF models could non-invasively evaluate the change of MR signal intensity under cerebrovascular reactivity (CVR) conditions and they might be helpful to investigate changes in human cerebral blood flow.

Item Type: Article
Rahimiforoushani, A.UNSPECIFIED
Keywords: Functional Magnetic Resonance Imaging Hemodynamic Response Function Inverse Logistic Model Finite Impulse Response Model Canonical HRF Plus Its Temporal Derivative Model challenge amplitude Neurosciences & Neurology
Page Range: p. 7
Journal or Publication Title: Archives of Neuroscience
Journal Index: ISI
Volume: 6
Identification Number:
ISSN: 2322-3944
Depositing User: مهندس مهدی شریفی

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