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. 2012 Jul 4:4:17.
doi: 10.3389/fnagi.2012.00017. eCollection 2012.

The relationship of topographical memory performance to regional neurodegeneration in Alzheimer's disease

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The relationship of topographical memory performance to regional neurodegeneration in Alzheimer's disease

George Pengas et al. Front Aging Neurosci. .

Abstract

The network activated during normal route learning shares considerable homology with the network of degeneration in the earliest symptomatic stages of Alzheimer's disease (AD). This inspired the virtual route learning test (VRLT) in which patients learn routes in a virtual reality environment. This study investigated the neural basis of VRLT performance in AD to test whether impairment was underpinned by a network or by the widely held explanation of hippocampal degeneration. VRLT score in a mild AD cohort was regressed against gray matter (GM) density and diffusion tensor metrics of white matter (WM) (n = 30), and, cerebral glucose metabolism (n = 26), using a mass univariate approach. GM density and cerebral metabolism were then submitted to a multivariate analysis [support vector regression (SVR)] to examine whether there was a network associated with task performance. Univariate analyses of GM density, metabolism and WM axial diffusion converged on the vicinity of the retrosplenial/posterior cingulate cortex, isthmus and, possibly, hippocampal tail. The multivariate analysis revealed a significant, right hemisphere-predominant, network level correlation with cerebral metabolism; this comprised areas common to both activation in normal route learning and early degeneration in AD (retrosplenial and lateral parietal cortices). It also identified right medio-dorsal thalamus (part of the limbic-diencephalic hypometabolic network of early AD) and right caudate nucleus (activated during normal route learning). These results offer strong evidence that topographical memory impairment in AD relates to damage across a network, in turn offering complimentary lesion evidence to previous studies in healthy volunteers for the neural basis of topographical memory. The results also emphasize that structures beyond the mesial temporal lobe (MTL) contribute to memory impairment in AD-it is too simplistic to view memory impairment in AD as a synonym for hippocampal degeneration.

Keywords: Alzheimer's; MRI; PET; multivariate; retrosplenial cortex; support vector; topographical memory.

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Figures

Figure 1
Figure 1
Results of the univariate linear regression of VRLT error score with GM density. Above panel: glass brain; below panel: selected slices projected onto a single subject template. The slice labeled “y = −39” best illustrates the bilateral isthmus/retrosplenial lesion.
Figure 2
Figure 2
Results of the univariate linear regression of VRLT error score with FDG metabolism. Above panel: glass brain; below panel: selected slices projected onto a single subject template.
Figure 3
Figure 3
Areas of correlation between increased axial diffusion (λ1, red) and VRLT error score. The TBSS “skeleton” (i.e., the white matter tract centers on which the statistics are computed) is green.
Figure 4
Figure 4
Multivariate support vector regression of VRLT performance with normalized FDG metabolism.
Figure 5
Figure 5
Plots of the Support Vector Regression (predicted versus observed VRLT score). (A) Shows the full cohort: note that one data point (circled) appeared to be a potential outlier. Removing this data point (B) diminished the strength of the correlation though it remained significant (p = 0.05).
Figure 6
Figure 6
Convergence of results across different imaging modalities and analysis techniques. Note that the retrosplenial/isthmus correlation is common to all analyses (see rows y = −38 and z = 0).

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References

    1. Acosta-Cabronero J., Williams G. B., Pengas G., Nestor P. J. (2010). Absolute diffusivities define the landscape of white matter degeneration in Alzheimer's disease. Brain 133(Pt 2), 529–539 10.1093/brain/awp257 - DOI - PubMed
    1. Acosta-Cabronero J., Williams G. B., Pereira J. M., Pengas G., Nestor P. J. (2008). The impact of skull-stripping and radio-frequency bias correction on grey-matter segmentation for voxel-based morphometry. Neuroimage 39, 1654–1665 10.1016/j.neuroimage.2007.10.051 - DOI - PubMed
    1. Ashburner J., Friston K. J. (2000). Voxel-based morphometry-the methods. Neuroimage 11(6 Pt 1), 805–821 10.1016/j.neuroimage.2008.01.003 - DOI - PubMed
    1. Baron J. C., Chetelat G., Desgranges B., Perchey G., Landeau B., de la Sayette V., Eustache F. (2001). In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. Neuroimage 14, 298–309 10.1006/nimg.2001.0848 - DOI - PubMed
    1. Braak H., Braak E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 - PubMed

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