Estimation of Hemodynamic Response Function in the Brain and Brain Tumors: Comparison of Inverse Logistic and Canonical Hemodynamic Response Function Models

The hemodynamic response function (HRF), reflecting cerebral blood flow in response to neural activity, plays a crucial role in the analysis of the brain data obtained by functional magnetic resonance imaging (fMRI). In this study, a comparison of two statistical models was performed to evaluate HRF for block design.
fMRI data from 3 patients with brain tumor were taken using a 3 Tesla scanner. Analysis of fMRI data was performed by the SPM12 toolbox in MATLAB software. The AIC, SBC and MSE indices were used to select the most convenient HRF mode.
Based on the simulation data, HRF estimated by canonical HRF model plus time derivations (TD) model was more consistent with simulated HRF. These models were evaluated on real data. The MSE, AIC and SBC indices were obtained for TD-logistic model (IL) models (for TD and logistic IL models; 0.052 /, 1235.1, 1223.9 and 0.068 / -1091.5 / - 1049.2, respectively). Based on the average values of T, W, H and model selection indicators, IL model for estimating HRF in healthy regions of the brain and brain tumor is a more appropriate approach.
The results of the present study can be helpful for the evaluation and diagnosis of HRF in high-metabolism points. Using the IL model to estimate HRF in the block design may lead to a better estimation of HRF and thus maintaining patient health and quality of life after surgical treatment and non-surgical medical procedures.