White matter (WM) lesions are prevalent in individuals with increased risk for
vascular disease17. Cerebral hypoperfusion is thought to significantly impair WM connectivity affecting the speed of cognitive processing18. In healthy older adults, higher cardiorespiratory fitness is associated with preservation of age-related declined in WM
microstructure19. Associations of vascular disease to disruptions in WM structural integrity and subsequent effects on cognitive function are unclear. It is quite possible that
reports of accelerated cognitive decline in cardiovascular disease patients2 could be linked to disruptions in WM integrity that are related to vascular disease20. In the same token, improvements in cognitive performance ascribed to exercise training21 could be mediated by exercise-induced plasticity of WM structure22. Therefore, it would be interesting to elucidate WM regions vulnerable to cardiovascular disease, investigate
relationships of WM abnormalities to cognitive impairments, and explore the role of CR
in mediating disease effects on WM microstructure.
5.4
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Appendices
Appendix A:
An Overview of the Optimized VBM approach as implemented in SPM8Figure A1 : Flow chart of optimized VBM as implemented in SPM8.
In optimized VBM1 [1], the original T1 image volume in native space is segmented into GM and WM images, followed by the removal of unconnected non-brain
voxels in skin, skull and dura from the segmented images using fully automated
morphological operations (erosion and conditional dilation). [2] The resulting GM and
WM images are spatially normalized to GM/WM templates further eliminating
contributions of non-brain voxels and improving spatial normalization of GM/WM
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volume placing the images in stereotactic standard space. [4] Subsequent segmentation of
the normalized T1 images into GM, WM and CSF partitions is performed to generate a
GM concentration map. [5] Spatial smoothing is applied to the GM map by convolving
the images with an isotropic Gaussian kernel. This renders the data more normally
distributed and compensates for the inexact nature of the spatial normalization. [6]
Finally, a generalized linear model fit is applied to the smoothed GM images for group
comparisons or correlations with covariates of interest.
Alternatively, segmented GM images in spatial correspondence to the template
can be [4a] multiplied ("modulated") by the Jacobian determinant derived from the
spatial normalization step [2]. This compensates for the non-linear effect of spatial
normalization and preserves the total amount of GM in the normalized data1. Individual variations in brain sizes can be accounted for by either including the total intracranial
volume, which is the sum of GM, WM, and CSF, as covariates of no interest during the
statistical model fit (i.e. global normalization), or by modulating only the non-linear
components of the Jacobian determinant, which is recommended (dbm.neuro.uni-
jena.de/vbm/). Hence, unmodulated smoothed normalized GM images represent relative
concentration of GM while modulated smoothed normalized GM images can represent
absolute volume of GM or relative volume corrected for differences in brain sizes. In
chapter 2 and 3, absolute GM volumes corrected for differences in brain sizes were used
to investigate regional differences in brain structure.
To improve signal-to-noise ratio and the sensitivity of detecting local changes in
gray matter, the T1 images are bias corrected for image intensity non-uniformity prior to