Landmark‐guided region‐based spatial normalization for functional magnetic resonance imaging. Issue 11 (12th April 2022)

• Record Type: Journal Article
• Authors: He, Hengda; Razlighi, Qolamreza R.
• Is Part Of: Human brain mapping. Volume 43:Issue 11(2022)
• Page Start: 3524
• Page End: 3544
• Publication Date: 2022-04-12
• Subjects: functional MRI -- MRI -- registration -- spatial normalization; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25865
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: As the size of the neuroimaging cohorts being increased to address key questions in the field of cognitive neuroscience, cognitive aging, and neurodegenerative diseases, the accuracy of the spatial normalization as an essential preprocessing step becomes extremely important. Existing spatial normalization methods have poor accuracy particularly when dealing with the highly convoluted human cerebral cortex and when brain morphology is severely altered (e.g., aging populations). To address this shortcoming, we propose a novel spatial normalization technique that takes advantage of the existing surface‐based human brain parcellation to automatically identify and match regional landmarks. To simplify the nonlinear whole brain registration, the identified landmarks of each region and its counterpart are registered independently with topology‐preserving deformation. Next, the regional warping fields are combined by an inverse distance weighted interpolation technique to have a global warping field for the whole brain. To ensure that the final warping field is topology‐preserving, we used simultaneously forward and reverse maps with certain symmetric constraints to yield bijectivity. We have evaluated our proposed solution using both simulated and real (structural and functional) human brain images. Our evaluation shows that our solution can enhance structural correspondence compared to the existing methods. Such improvement also increases the sensitivity and specificity of the functional imaging studies, reducing the required number of subjects and subsequent study costs. We conclude that our proposed solution can effectively substitute existing substandard spatial normalization methods to deal with the demand of large cohorts which is now common in clinical and aging studies. Abstract : We propose a novel spatial normalization solution for neuroimaging data particularly functional magnetic resonance imaging (fMRI), which takes advantage of existing surface‐based parcellation technique to perform automatic landmark detection and matching for subsequent region‐based volumetric registration. The regional non‐linear warping fields obtained independently for each brain region are combined, to generate a single global warping field and enforce topology‐preserving properties. We showed that our solution improves the structural correspondence of brain regions in comparison to the top‐preforming existing methods, and also improves the sensitivity and specificity of the fMRI activation at the group‐level activation statistics.