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Keith Jamison, Luca Vizioli, Ruyuan Zhang, Jinyi Tao, Jonathan Winawer, Kendrick Kay; A tool for automatic identification of cerebral sinuses and corresponding artifacts in fMRI. Journal of Vision 2017;17(10):295. doi: 10.1167/17.10.295.
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© ARVO (1962-2015); The Authors (2016-present)
Functional magnetic resonance imaging (fMRI) is a widely used method for investigating the cortical mechanisms of visual perception. Given that fMRI measures oxygenation-related changes in hemodynamics, it is critical to understand the factors governing the accuracy with which hemodynamics reflect neural activity. We conducted ultra-high-resolution fMRI in human visual cortex during a simple event-related visual localizer experiment (7T, 0.8-mm isotropic, 2.2-s TR, 84 slices, gradient-echo EPI), and also collected whole-brain anatomical T1- and T2-weighted volumes (3T, 0.8-mm isotropic). We find that major cerebral sinuses (superior sagittal sinus, straight sinus, and left and right transverse sinuses) can be clearly identified by computing the ratio of the T1- and T2-weighted volumes (Salimi-Khorshidi et al. 2014), and we show that these sinuses are nearly perfectly aligned across subjects after transformation to volumetric MNI space. We then construct a sinus atlas and develop a software tool that automatically predicts the location of the sinuses given only a T1-weighted anatomical volume obtained for a subject. We show that this tool accurately reproduces manual segmentations of the sinuses in our subjects. Importantly, we demonstrate that regions of the cortical surface located near the sinuses correspond to regions with signal dropout and unreliable fMRI responses in our functional data. These sinus-affected regions are not only located near hV4 as previously reported (Winawer et al. 2010), but are also located near many other regions in occipital, parietal, and temporal cortex. Because the atlas is accurate, automated, and easy to use, we suggest that it be routinely used to identify cortical regions that are likely to suffer from imaging artifacts, thereby avoiding the need to exclude regions based on ad hoc, subjective measures and aiding proper interpretation of fMRI data.
Meeting abstract presented at VSS 2017
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