Statistical power of fMRI group-studies is significantly hampered by high inter-subject-variance, arising from differences in baseline physiology (i.e. blood volume-CBV). We recently presented a Vascular-Autocalibration method (VasA) [1] to account for vascularization differences between subjects, thus improving the sensitivity in group-studies. VasA is based on the observation that global slow-respiration induced BOLD-signal-changes within an fMRI-experiment can be taken as an indicator for vascular reactivity and baseline venous CBV. VasA calibration values can be obtained from any fMRI-time-series, by estimating the low-frequency components of the residuals in the task-GLM. These residuals resemble those fMRI-signal variations that do not match up with the task-paradigm but are dominated from variations in breathing patterns. Here, we investigate the mechanism and the physiological basis underlying VasA. We compare it to the Davis’ model calibration-parameter M [2]. M is a function (among others) of the baseline-CBV and venous-deoxyhemoglobin concentration of the blood [3]. To make the VasA method available widely, we developed a SPM-toolbox that readily integrates VasA into standard fMRI-processing. To compare VasA and the quantitative calibration parameter M, we conducted two experiments in five subjects after receiving their consent. The first experiment consisted of a hypercapnia task of breathing air/5%CO2/air for 2min/5min/5min, respectively to estimate M. The heart-rate and respiratory gas composition were recorded during the gas challenge. In the second experiment, a 10-min flashing checkerboard paradigm (30s-rest vs. 30s-stimulation) was used to activate the visual-cortex. During both experiments, time-series of CBV-changes and BOLD-signal-changes were captured with the SS-SI-VASO sequence [4]. The acquisition parameters were: 7T-Siemens-MRI scanner, 7-slices, TE/TI1/TI2/TR=19/765/2265/3000ms, adjusted inversion efficiency=75% with tr-FOCI-pulse. To minimize and assess the influence of partial-voluming with WM and CSF, 1.5mm was used. The M-value was estimated with the Davis’ model [2] on a voxel-wise-basis assuming: CBV_rest=5.5%, α_total =0.38, α_veins=0.2, β=1 at 7T [5]. To estimate the VasA maps, the subjects’ time-series was fitted voxel-wise using the respective GLM describing the experimental paradigm in SPM [6]. The maps were extracted from the residuals of the model fit as described in [1]. For direct comparison of VasA and M-values, the MRI-volume data were coregistered to each other after the application of 3mm smoothing in spm. Fig.1B shows the correlation of a VasA with an M-value map in one representative participant. Fig.1A shows the corresponding scatter density plot across all voxels. The correlation coefficient was 0.83±0.15 (mean±SD) including all GM-voxels. The stability of this high correlation between regional VasA and M-values across participants can be seen in Fig.2. Example maps in Fig.1B-C indicate that regional contrast-to-noise-ratio (CNR) in VasA maps is higher compared to M-value maps. The vascular reactivity shown in VasA maps was relatively homogeneously distributed across GM and not only confined to the visual cortex, where most of task response was located (Fig.1D). Even though VasA captured vascular reactivity throughout large portions of GM, there was a small tendency in VasA to overestimate vascular reactivity in regions of significant CSF partial-voluming (defined in EPI space, based on multi-TI-T1-maps, Fig.1E). This resulted in a non-linear trend for high M-values in the scatter plots (curved arrows-Fig.2) which mainly reflected voxels containing pial-veins close to CSF. Fig.3 depicts screenshots of the novel SPM-toolbox for VasA analysis, to facilitate the use of VasA in group-studies.The strong correlation between VasA and M-values suggests that both measures have similar physiological origins. The deviations in areas of large-partial-voluming with WM might be coming from the difficulty to estimate M-value with low-SNR CBV-data in WM. The deviations in areas of large-partial-voluming with CSF might arise from VasA overestimations due to increased cardiac/respiratory noise contributions in CSF. The higher CNR of VasA compared to conventional M-value maps is likely due to a higher-CNR of the gradient-EPI to the BOLD effect compared to the low-CNR CBF/CBV imaging methods. VasA activation maps generally do not show increased bias in areas of large task-related activation. This suggests that VasA measures truthfully reflect vascular-reactivity rather than residuals due to imperfect modelling of task demands or residual non modelled neuronal activity.The data show a strong correlation between vascular calibration measures obtained with VasA and the more established vascular reactivity value M. This suggests that VasA-calibration maps reflect vascular reactivity, particularly baseline venous CBV distribution. Since potential VasA contaminations from inaccurate task modelling could not be detected, VasA calibration is a reliable tool for fMRI calibration with enhanced CNR compared to conventional M-value calibration. A SPM-Toolbox (Fig.3) was developed and will be made available widely to help in analysing existing large datasets in an efficient fashion and provide significantly increased statistical-power.