While all three cortical models have a significant correlation with cortical folding (as measured by gyrification index), only the cortical ribbon has a strong correlation with cortical thickness measurements. Hence, known changes that occur in cortical thickness in Alzheimer’s disease would be missed by the pial and grey/white cortical models. This likely accounts for much of the improved ability to discriminate between clinical groups used in this paper. It also may explain why the cortical ribbon was the only model to have a significant correlation with the ADAS-cog. All three cortical measures we have analyzed (cortical thickness, gyrification index and FD of the cortical ribbon) provided a significant difference between normal subjects and patients, even though the greatest effect size was obtained using the FD of the cortical ribbon. In terms of separating controls from mild AD patients, the area under the ROC curve analysis suggests that cortical ribbon f3D is a “good” test, cortical thickness and gyrification index are “fair” tests, grey/white surface f3D is a “poor” test, and pial surface f3D is a “worthless” test.
Atrophic changes that occur on the pial surface could either increase or decrease the complexity, depending on how the atrophy occurs. For example, a change in the pial surface that decreased the folding area would decrease complexity; conversely, if the change increased sulcal depth, then the complexity would increase. Both types of changes are noted on the brains used in this study. By using the cortical ribbon, the conflicting effects on the pial surface are overcome by adding the complementary effects of the cortical thickness changes while also incorporating the structural changes occurring at the grey/white junction.
Our results also corroborated the well established observation that there are significant differences in the average cortical thickness of control subjects compared to patients with mild Alzheimer’s disease. We also found that the gyrification index is also significantly different between control and mild AD patients. To the best of our knowledge, this effect has not been clearly documented in Alzheimer’s disease.
The effect size using the cortical ribbon f3D was larger than either using cortical thickness or using the gyrification index. Moreover, the fractal analysis technique using the cortical ribbon is able to account for more of the variance in the ADAS-cog scores than either the cortical thickness or gyrification index measures. This improved discrimination will likely be needed to correctly categorize less clinically distinct cases (i.e. normal vs. mild cognitive impairment).
There are many other structural factors that likely influence the cortical ribbon f3D. Atrophic changes that occur at the grey/white junction are likely to be affected by volume change occurring in the sub-cortical white matter, basal ganglia, and lateral ventricles. These changes could be an important source of cortical fractal dimensionality change, and thus should not be removed in the context of this paper (e.g. transforming images into a Talairach space, covariance). Further exploration of the specific effects of changes in these volumetric factors, along with other measures including normalized brain volume, age, or normalized cortical surface area, on cortical f3D is needed.
The methods used in this paper take advantage of high-contrast magnetic resonance imaging to generate high-resolution three-dimensional continuous models of the cerebral cortex. This approach has been used in several other recent studies of high resolution models of the pial surface (Blanton et al., 2001
; Im et al., 2006
; Jiang et al., 2008
; Luders et al., 2004
; Narr et al., 2004
; Sandu et al., 2008
; Thompson et al., 2005
) and grey/white junction surfaces (Sandu et al., 2008
). These surface based methods provide higher resolution data than voxel-based masking methods. Consequently, using intermediate surfaces to generate fractal data from the entire cortical ribbon generates a continuous 3D volume model that is more topologically accurate than a grey matter voxel mask. Note that this limitation in using the grey matter voxel-mask may eventually be overcome using very high field (i.e.
> 7 Tesla) high resolution images.
While this whole-brain fractal measure is quite promising, there are several limitations to this analysis technique. First, the whole-brain approach is generating an aggregate measure across the entire cerebral cortex. However, the atrophic changes that occur in Alzheimer’s disease do not occur in all regions of the brain equally. There are also significant regional variations in cortical f3D
values (Jiang et al., 2008
; King et al., 2009
). This technique could be improved by performing a more localized analysis. This would be beneficial for several reasons. By focusing on regions of interest, the discriminative power could be significantly increased. Furthermore, different neurodegenerative diseases, such as Frontotemporal dementia and Dementia with Lewy Bodies, have very different asymmetric patterns of cortical involvement. Obtaining statistically normalized spatial maps will likely be needed to perform a prospective categorization. Moreover, the significant global atrophic changes associated with normal aging are not accounted for. In this paper, age was averaged within the two groups. A better method may utilize regression models to generate a map showing Z-scaled significant deviations comparing subjects to age-matched controls. These two methodological improvements are likely to greatly increase the sensitivity and specificity of the fractal analysis technique.
Finally, it is likely that no single imaging biomarker will have enough specificity and sensitivity for prospective diagnosis. Therefore, having as many complementary biomarkers as possible will aid in prospective categorization. Cortical f3D could serve as an important adjunct to currently used imaging markers such as volumetric assessments (i.e. hippocampal volume, lateral ventricle volume), functional measures (i.e. Fluoro-deoxyglucose Positron emission tomography, functional magnetic resonance imaging), and direct amyloid binding agents (i.e. Pittsburg Compound B, AV45).