Quantification of Inter-subject Variability in Human Brain and Its Impact on Analysis of fMRI Data
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In functional magnetic resonance imaging (fMRI) studies, inter-subject anatomical variability of the human brain has been a major challenge in finding reliable functional/anatomical correspondences. Assessment of brain-behavior relations involves a series of geometrical/statistical operations on brain images to minimize such inter-subject variability, so that group maps of brain activity relative to brain anatomy can be developed. Various methods of image registration, segmentation, and analysis have been proposed for mapping functional activity on to anatomical atlases of the brain. The two most common techniques that have been widely accepted and used by neuroimaging scientists are volume-based (VB) analysis using group registration methods and region-of-interest (ROI)-based methods using automated segmentation algorithms or macro/microanatomical probabilistic atlases for labeling. Nevertheless, the analysis results based on these techniques are significantly affected by the accuracy of the selected segmentation and/or registration methods. Furthermore, conventional fMRI data analysis techniques (VB, and ROI-based methods) mainly rely on the assumption that brain processes are common and universal among individual humans; however, besides anatomical differences, there also exist cognitive and behavioral variability among individuals due to differential engagement of brain networks even when performing an identical cognitive task. In this thesis, I have assessed the impact of anatomy-based alignment techniques (VB, and ROI-based methods) on sensitivity of fMRI data group analysis. I evaluated the effect of the type of inter-subject registration used and related factors on sensitivity of group-level fMRI data analysis. Furthermore, I have also assessed the goodness of fit of probabilistic maps by proposing an evidence-based framework for evaluation of probabilistic maps. As a test model, I have selected the human auditory cortex. Auditory cortex is an interesting yet challenging case with substantial inter-individual functional/anatomical variability. For the sake of ROI-based method of analysis, I have proposed a novel approach for automatic segmentation of Heschl's gyrus, which is the landmark for primary auditory cortex. Finally, in order to assess the impact of inter-subject variability in anatomy on functional organization, I analyze data from an fMRI study, which demonstrates that the degree to which anatomical registration compensates for functional variability depends on the brain region activated.