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Grey matter network disruptions are related to amyloid-beta in cognitively healthy elderly

      Background

      Grey matter networks are disrupted in Alzheimer's disease. It is unclear when these disruptions start during the development of Alzheimer's disease. Amyloid deposition is among the earliest changes. A recent study demonstrated that abeta 42 affects grey matter networks. But since this network was derived at a group level these results could not be associated with risk of individual patients. Here we studied the effects of abeta 42 on single- subject grey matter networks in cognitively healthy elderly.

      Methods

      Study participants were 193 cognitively healthy middle aged adults enrolled in the Gipuzkoa Alzheimer Project (GAP), a longitudinal study on pre-clinical AD recruiting subjects from the general population. Inclusion criteria were a MMSE > 25 and a clinical dementia rating = 0. CSF was obtained by lumbar puncture following international consensus recommendations. Levels of abeta 42 were determined with ELISA kits (InnotestTM β Amyloid1 42, Fujirebio Innogenetics). T1 weighted structural MRI scans were obtained at 3T. Native space grey matter segmentations (obtained with SPM8) were used to extract single subject grey matter networks. Normalized clustering coefficient γ and normalized path length λ were computed. Non parametric testing (based on 10.000 random permutations) was used to determine the significance of relationships between abeta42 deposition (dependent variable) and network property values (independent variable), including gender, whole brain volume and age as covariates. Multiple hypotheses testing was corrected for with false discovery rate (FDR).

      Results

      All subjects had an age range between 39 and 79 years old (mean age = 57 years), and 58% were female. Lower abeta42 CSF levels, indicative of a higher plaque load in the brain, were associated with lower connectivity density (β = 0.20; SE .07); p < .05), lower clustering values (β = .18; SE =.08; p < .05) and higher path length values (β = .21, SE =.08, p < .01). Figure 1 shows the anatomical areas where lower clustering coefficient was related to decreased abeta42 CSF levels, and 6 areas in which higher path length values were associated with decreased abeta42 CSF levels.

      Conclusions

      These results suggest that grey matter networks might have use as an early marker for AD pathology.
      Figure thumbnail fx1
      Figure 1Surface plot of the standardised β values of the relationship between abeta42 with clustering and path length that were significant (pFDR < .05). a) Lower clustering values were associated with low Aβ42 values in the bilateral precentral gyri, left precuneus, supplementary motor area, middle frontal gyrus and right lingual gyrus. b) Higher path length values in the left supplementary motor area, inferior parietal gyrus, middle temporal gyrus, right precuneus, lingual gyrus, precentral gyrus and cuneus were associated with low abeta42 CSF values.