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Updated: Oct 17, 2025
Functional Transcranial Doppler Ultrasound for Monitoring Cerebral Blood Flow
Published on: March 15, 2021
Functional transcranial Doppler: Selection of methods for statistical analysis and representation of changes in flow
Stephan T Egger1,2, Julio Bobes2, Erich Seifritz1
1Department of Psychiatry, Psychotherapy and Psychosomatics University of Zürich, Faculty of Medicine, Psychiatric University Hospital of Zurich Zurich Switzerland.
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.
