Abstract
IntroductionThe study of Alzheimer’s disease investigates topographic patterns of degeneration in the context of connected networks comprised of functionally distinct domains using increasingly sophisticated molecular techniques. Therefore, obtaining high precision and accuracy of neuropathologic tissue sampling will enhance the reliability of molecular studies and contribute to the understanding of Alzheimer’s disease pathology. Neuroimaging tools can help assess these aspects of current sampling protocols as well as contribute directly to their improvement.MethodsUsing a virtual sampling method on magnetic resonance images (MRIs) from 35 participants (21 women), we compared the precision and accuracy of traditional neuropathologic vs. neuroimaging-guided sampling. The impact of the resulting differences was assessed by evaluating the functional connectivity pattern of regions selected by each approach.ResultsVirtual sampling using the traditional neuropathologic approach had low neuroanatomical precision and accuracy for all cortical regions tested. Neuroimaging-guided strategies narrowed these gaps. Discrepancies in the location of traditional and neuroimaging-guided samples corresponded to differences in fMRI measures of functional connectivity.DiscussionIntegrating neuroimaging tools with the neuropathologic assessment will improve neuropathologic-neuroimaging correlations by helping to ensure specific functional domains are accurately sampled for quantitative molecular neuropathologic applications. Our neuroimaging-based simulation of current sampling practices provides a benchmark of precision and accuracy against which to measure improvements when using novel tissue sampling approaches. Our results suggest that relying on gross landmarks alone to select samples at autopsy leads to significant variability, even when sampled by the same neuropathologist. Further, this exercise highlights how sampling precision could be enhanced if neuroimaging were integrated with the standard neuropathologic assessment. More accurate targeting and improved biological homogeneity of sampled brain tissue will facilitate the interpretation of neuropathological analyses in AD and the downstream research applications of brain tissue from biorepositories.
Highlights
The study of Alzheimer’s disease investigates topographic patterns of degeneration in the context of connected networks comprised of functionally distinct domains using increasingly sophisticated molecular techniques
The maximum number of subjects who overlapped in any given voxel was only 12 of 35 subjects for middle frontal gyrus (MFG), 15 of 35 for middle and superior temporal gyri (MSTG), 21 of 35 for inferior parietal lobule (IPL), and 15 of 35 for V1, though in each case this overlap comprised less than one percent of the total mask volume
Overlap statistics showed that subject-specific samples from the traditional neuropathologic sampling approach had limited overlap with the reference samples regardless of the brain region being sampled [means of 5.2% (13.1%) for MFG, 24.0% (16.0%) for MSTG, 27.0% (21.1%) for IPL, and 26.5% (25.0%) for V1; see Figure 5]
Summary
The study of Alzheimer’s disease investigates topographic patterns of degeneration in the context of connected networks comprised of functionally distinct domains using increasingly sophisticated molecular techniques. Widely practiced guidelines for the neuropathologic assessment of AD recommend sampling four regions of cerebral cortex [middle frontal gyrus (MFG), inferior parietal lobule (IPL), superior/middle temporal gyri, and calcarine cortex] from coronal brain slices for histopathologic, biochemical, and molecular assessments (Montine et al, 2012). Together these samples constitute a miniscule fraction of the total brain volume. The MFG and superior/middle temporal gyri are sampled on the coronal slice containing the anterior commissure This approach provides some degree of consistency, but the degree to which the sampled regions correspond to the same structural and functional brain area across individuals is unknown
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