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Journal Article

Physiological basis of vascular autocalibration (VasA): Comparison to hypercapnia calibration methods


Weiskopf,  Nikolaus
Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom;
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Kazan, S. M., Huber, L., Flandin, G., Ivanov, D., Bandettini, P., & Weiskopf, N. (2017). Physiological basis of vascular autocalibration (VasA): Comparison to hypercapnia calibration methods. Magnetic Resonance in Medicine, 78(3), 1168-1173. doi:10.1002/mrm.26494.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002B-BACE-2
The statistical power of functional MRI (fMRI) group
studies is significantly hampered by high intersubject spatial
and magnitude variance. We recently presented a vascular
autocalibration method (VasA) to account for vascularization
differences between subjects and hence improve the sensitivi-
ty in group studies. Here, we validate the novel calibration
method by means of direct comparisons of VasA with more
established measures of baseline venous blood volume (and
indirectly vascular reactivity), the M-value.
Seven healthy volunteers participated in two 7T (T)
fMRI experiments to compare M-values with VasA estimates:
(i) a hypercapnia experiment to estimate voxelwise M-value
maps, and (ii) an fMRI experiment using visual stimulation to
estimate voxelwise VasA maps.
We show that VasA and M-value calibration maps
show the same spatial profile, providing strong evidence that
VasA is driven by local variations in vascular reactivity as
reflected in the M-value.
The agreement of vascular reactivity maps
obtained with VasA when compared with M-value maps con-
firms empirically the hypothesis that the VasA method is an
adequate tool to account for variations in fMRI response
amplitudes caused by vascular reactivity differences in healthy
volunteers. VasA can therefore directly account for them and
increase the statistical power of group studies. The VasA tool-
box is available as a statistical parametric mapping (SPM)
toolbox, facilitating its general application.