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21 December 2018 A study of single subject VBM and DARTEL on healthy subjects
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Proceedings Volume 10975, 14th International Symposium on Medical Information Processing and Analysis; 109750Q (2018) https://doi.org/10.1117/12.2511457
Event: 14th International Symposium on Medical Information Processing and Analysis, 2018, Mazatlán, Mexico
Abstract
Voxel Based Morphometry (VBM) is a methodology for medical image analysis that can be used to detect GM decrease in brain images at a group level. The performance of VBM with DARTEL registration method has already been studied, but not in a single subject. Classic VBM is not expected to detect GM decrease in healthy patients, but, at single subject level some differences could be detected, due to the complexity of sulcal patterns and inter and intra-individual anatomical variability. Thus, this study assesses the effects of DARTEL method in single subject VBM for healthy subjects. We applied a 2 sample T-test with 2 covariates, age and gender. The p-values were corrected using False Discovery Rate (FDR) (p<0.05), using a cluster extent threshold of k>15. We used 3 group sizes, 20, 50 and 100, and four different smoothing kernel of 3, 5, 8 and 10 mm. Each subject was compared against the rest of its group. We observed that the single subject VBM detected GM decrease in healthy patients for all the group sizes and smoothing. In spite of that, detected voxels were different for almost all the patients. For most of the cases, the maximum percentage of subjects with the same voxel detected was less than 5% and of all the detections, less than 5% were presented in more than 1 subject.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hernan Claudio Kulsgaard, Delfina Braggio, Mariana Bendersky, Lucia Alba Ferrara, and Ignacio Larrabide "A study of single subject VBM and DARTEL on healthy subjects", Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750Q (21 December 2018); https://doi.org/10.1117/12.2511457
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