Functional transcranial Doppler: Selection of methods for statistical analysis and representation of changes in flow velocity

Abstract

Introduction Transcranial Doppler (TCD) is a method used to study cerebral hemodynamics. In the majority of TCD studies, regression analysis and analysis of variance are the most frequently applied statistical methods. However, due to the dynamic and interdependent nature of flow velocity, nonparametric tests may allow for better statistical analysis and representation of results. Method The sample comprised 30 healthy participants, aged 33.87 ± 7.48 years; with 33% (n = 10) females. During a visuo‐motor task, the mean flow velocity (MFV) in the middle cerebral artery (MCA) was measured using TCD. The MFV was converted to values relative to the resting state. The results obtained were analyzed using the general linear model (GLM) and the general additional model (GAM). The fit indices of both analysis methods were compared with each other. Results Both MCAs showed a steady increase in MFV during the visuo‐motor task, smoothly returning to resting state values. During the first 20 seconds of the visuo‐motor task, the MFV increased by a factor of 1.06 ± 0.07 in the right‐MCA and by a factor of 1.08 ± 0.07 in the left‐MCA. GLM and GAM showed a statistically significant change in MFV (GLM:F(2, 3598) = 16.76, P < .001; GAM:F(2, 3598) = 21.63, P < .001); together with effects of hemispheric side and gender (GLM:F(4, 3596) = 7.83, P < .005; GAM:F(4, 3596) = 2.13, P = .001). Comparing the models using the χ2 test for goodness of fit yields a significant difference χ2 (9.9556) = 0.6836, P < .001. Conclusions Both the GLM and GAM yielded valid statistical models of MFV in the MCA in healthy subjects. However, the model using the GAM resulted in improved fit indices. The GAM's advantage becomes even clearer when the MFV curves are visualized; yielding a more realistic approach to brain hemodynamics, thus allowing for an improvement in the interpretation of the mathematical and statistical results. Our results demonstrate the utility of the GAM for the analysis and representation of hemodynamic parameters.

Tariff Analysis in Automobile Insurance: Is It Time to Switch from Generalized Linear Models to Generalized Additive Models?

“…Comparisons of the relative performance of models estimated using GLMs or GAMs can be found in [41][42][43][44][45]. GAMs have also been frequently combined or compared to GLMs in environmental sciences [46][47][48][49], climatology [50], meteorology [51], neuroscience [52,53], and genetics [54]. In the actuarial literature, some works have applied the GAM methodology to various problems in the insurance field [55][56][57][58][59][60].…”