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Characterize the Effect of Regional Variations in Venule Vasculature Related to Temporal Variability of Hemodynamic Responses Latency at the Human Primary Visual Cortex1Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, 2Department of Medical Imaging, Taipei Medical University - Shuang Ho Hospital, New Taipei, Taiwan, 3Aim for the Top University Project Office, National Taiwan Normal University, Taipei, Taiwan, 4Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
Synopsis
We correlated between the temporal chacteristics of the BOLD signals and venule structure at human primary visual cortex (V1). Functional MRI was measured by the high temporal resolution (100ms) simultaneous-multi-slice inverse imaging. Venule probability map was estimated from high spatial resolution (0.85mm) susceptibility-weighted imaging (SWI). Siginficant correlation was found between venule density and intra-/inter-subject temporal variability of the BOLD signal at V1. This correlation suggests that the temporal instability of BOLD signal is likely attributed to vascular structure or reactivity.
Purpose
The blood oxygenation level-dependent (BOLD) signal varies significantly across subjects, brain regions, and repetitive measurements1-4. The BOLD signal is highly weighted by venous oxygenation changes5, 6. The latency in the BOLD dynamics is closely related to the transit time of the oxygenated blood5 and the expansion of venous drainage system7, 8. Based on previous studies, here we hypothesize that the local venule vasculature differences underlie the variability of the BOLD latency. To test ths hypothesis, we used high temporal resolusion simultaneous-multi-slice inverse imaging (SMS-InI)9 to record BOLD signal latency and variabilily at the human visual cortex. Meanwhile, we also estimated a venule probability (VP) map. Via the correlation between these two measures, we aimed at revealing how the regional variations of venule vasculature is related to the temporal variability of the BOLD signal.Methods
Data were collected on a 3T MRI scanner (Skyra, Siemens) from 11 healthy subjects with written informed consents approved by local Institute Reviewing Board. SMS-InI measured the BOLD signal with 100 ms temporal precision, 5mm isotropic spatial resolution, and whole-brain coverage9. A local template of the BOLD dynamics elicted by checkerboard flashing (500 ms duration; 8 Hz reveral) at the human primary visual cortices (V1) were estimated by General Linear Model (GLM) with finite impulse response bases (30-s duration with 6-s pre-stimulus baseline) for each subject. Four runs of data were collected from each subject and each run included 60 trials of visual stimuli. The V1 regions-of-interest (ROIs) was identified between the intersection of fMRI and probabilistic labels provided by the FreeSurfer10, 11. The relative latency and the variability of the BOLD response at each cortical location were estimated from an ensemble of responses estimated by using a bootstrap approach, each iteration of which randomly partitioned all trials into two groups and GLM was used to estimate the hemodynamics for each group. The bootstrap was repeated 100 times. The time instant maximizing the correlation coefficient between the BOLD reponse in each bootstrap and the local template (shifting +/- 4 s) was marked. The relative latency was the average of this timing across 100 bootstrap iterations and subjects. The intra-subject variability was the standard deviation across bootstrap iterations and then the average across subjects. The inter-subject variability was the average across bootstrap iterations and then the standard deviation across subjects.
Vasculature was measured by the vesselness method12-14 based on susceptibility-weighted images (SWI; TR/TE = 27/20 ms, flip angle = 15°, Voxel size = 0.86x1.86x1.5 mm3, FOV = 22x20x12 cm3). The vesselness method allows us to visualize the venous vascular tree across the brain (Figure 1, right). Structural images for each subject were also acquired using a 3D T1-weighted sequence (MP-RAGE). The location of the gray-white matter boundary for each subject was estimated to yield a cortical model15-17, which was used to register individual’s fMRI and SWI data to their own cortical surface space. Between-subject averaging was done by morphing individual data through a spherical coordinate system18 implemented in Freesurfer10, 11. The VP map (Figure 2, bottom) were generated by estimating the probability of venules at each location in all subjects. Finally, we calculated the Pearson’s correlation coefficient between VP and intra-/inter- subject temporal variability of hemodynamic responses as well as the BOLD latency at V1.
Results
V1 locations sensitive to peripheral vision (large eccentricity) and vertical meridian shows more extreme values of relative latency and intra-/inter-subject variability. No clear trends of VP distribution was found by inspection (Figure 2). However, we found that VP was significantly correlated to intra- (r2 = 0.07; p = 0.002; Figure 3, middle) and inter- (r2 = 0.05; p = 0.013; Figure 3, right) subject temporal variability of hemodynamic responses within V1. However, no significant correlation between the BOLD latency and VP (r2 = 0.00; p = 0.59; Figure 3, left).Discussion & Conclusion
The correlation between VP and the intra-/inter-subject variability corroborated with the previous study showing stronger BOLD signal has larger amplitude variability because more venous contribution leads to larger BOLD signal. In fact, this correlation was found in resting-state and task-related fMRI19. Significant correlations between the response variability and the VP also suggest the major source of instable BOLD responses from the underlying vascular structure and/or reactivity, rather than the associated neuronal response fluctuations. Taken together, better control over vascular responses is expected to improve the stability of BOLD signal.Acknowledgements
This work was partially supported by Ministry of Science and Technology, Taiwan (103-2628-B-002-002-MY3, 105-2221-E-002-104), and the Academy of Finland (No. 298131).References
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Figures
Fig. 1
Segmented venules from a single subject.
Left: T1 weighted anatomical image.
Middle: Susceptibility weighted imaging (SWI).
Right: The segmented venule overlaied on SWI. (The red color indicated segmented
venules)
Fig. 2
Relative latency, temporal intra-/inter-subject variability, and venule probability
(VP) map.
Morph
the group average binary venule mask through the spherical coordinate system
implemented by Freesurfer to generate the VP map.
Fig. 3 Correlation betwen venule probability and relative
latency as well as inter-/intra-subject variability of the BOLD dynamics
Left: No signfignificant correlations between venule probability (VP) and the
relative latency of hemodynamic responses within V1.
Middle: Signficant correlation between venule probability (VP) and intra-subject
temporal variability of hemodynamic responses within V1.
Right: Signficant correlation between venule probability (VP) and inter-subject
temporal variability of hemodynamic responses within V1.