| Autor: |
Yu C; Department of Computer Science, Vanderbilt University, Nashville, TN, USA., Liu Y; College of Information Science and Engineering, Northeastern University, Shenyang, China.; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA., Cai LY; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA., Kerley CI; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA., Xu K; Department of Computer Science, Vanderbilt University, Nashville, TN, USA., Taylor WD; Psychiatry & Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA., Kang H; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA., Shafer AT; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA., Beason-Held LL; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA., Resnick SM; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA., Landman BA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA.; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.; Psychiatry & Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA., Lyu I; Department of Computer Science, Vanderbilt University, Nashville, TN, USA.; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA. |
| Abstrakt: |
Resting-state functional MRI (rsfMRI) provides important information for studying and mapping the activities and functions of the brain. Conventionally, rsfMRIs are often registered to structural images in the Euclidean space without considering cortical surface geometry. Meanwhile, a surface-based representation offers a relaxed coordinate chart, but this still requires surface registration for group-wise data analysis. In this work, we investigate the performance of two existing surface registration methods in a surface-based rsfMRI analysis framework: FreeSurfer and Hierarchical Spherical Deformation (HSD). To minimize registration bias, we establish shape correspondence using both methods in a group-wise manner that estimates the unbiased average of a given cohort. To evaluate their performance, we focus on neuroanatomical alignment as well as the amount of distortion that can potentially bias surface tessellation for secondary level rsfMRI data analyses. In the pilot analysis, we examine a single timepoint of imaging data from 100 subjects out of an aging cohort. Overall, HSD establishes improved shape correspondence with reduced mean curvature deviation (10.94% less on average per subject, paired t-test: p <10 -10 ) and reduced registration distortion (FreeSurfer: average 41.91% distortion per subject, HSD: 18.63%, paired t-test: p <10 -10 ). Furthermore, HSD introduces less distortion than FreeSurfer in the areas identified in the individual components that were extracted by surface-based independent component analysis (ICA) after spatial smoothing and time series normalization. Consequently, we show that FreeSurfer capture individual components with globally similar but locally different patterns in ICA in visual inspection. |
