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GREY MATTER CONNECTIVITY IS ASSOCIATED WITH CLINICAL PROGRESSION IN NON-DEMENTED, AMYLOID POSITIVE PATIENTS

      Background

      Accumulation of amyloid in the brain is among the first changes leading to Alzheimer’s disease (AD), yet its prognostic value is limited. Grey matter connectivity is disrupted in AD, and these disruptions are associated with worse cognitive functioning. We studied whether grey matter connectivity has prognostic value, by comparing amyloid positive patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) and analyzing its association with clinical progression.

      Methods

      CODA (COnnectivity in DementiA) includes 222 non-demented patients (62 (28%) SCD; 160 (78%) MCI;109 (49%) female; 68 ± 8 years; 28 ± 2.4 MMSE) with abnormal amyloid CSF (<640 pg/ml), T1-weighted structural MRI and ≥1 year annual follow up data available selected from the Amsterdam Dementia Cohort. The main outcome parameter was clinical progression (CDR change ≥ 0.5). Single-subject networks were based on grey matter segmentations. We calculated the degree, connectivity density, path length, clustering, and small world parameters. All measures were Z transformed and inverted. ANCOVAs were used for cross-sectional comparisons of disease outcome and baseline diagnosis. Separate Cox proportional hazard models were fitted for each connectivity predictor for time to dementia onset and corrected for age, gender, whole brain volume and scanner.

      Results

      After 2.2 (IQR 1.3–3.1) years 122 (55%) people showed clinical progression (N=23 SCD; N=99 MCI). Normalized clustering and small world property showed main effects of diagnosis and clinical progression, which is suggestive of a linear trend (Figure 1). Cox analyses indicated that lower values of 5 grey matter network parameters were related to clinical progression: degree (HR = 1.48; 95%CI = 1.09–2.02), connectivity density (HR = 1.49; 95%CI = 1.09-1.81), clustering (HR = 2.92; 95%CI = 1.27–6.69; Figure 2), normalized clustering (HR = 1.47; 95%CI = 1.13–1.91), and small world value (HR = 1.45; 95%CI = 1.13–1.87). No interaction effects of baseline diagnosis and network properties on time to dementia onset were found (all pia >.05).

      Conclusions

      In non-dementia phases of AD, grey matter networks disruptions suggestive of a change towards a more random network organization were associated with time to clinical progression. Our findings suggest that connectivity based markers have prognostic value in amyloid positive individuals.
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      Figure 1Linear trend analysis of normalized clustering coefficient (gamma) and the small world parameter. Both these network parameters had highest values in patients with subjective cognitive decline who remained stable over time, and the lowest in MCI patients who progressed to dementia.
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      Figure 2Survival curves for the time to dementia onset in subjects with subjective cognitive impairment or mild cognitive impairment due to Alzheimer's disease with separate lines for clustering coefficient tertiles: blue represents subjects with the most lowest values, green represents intermediate values and red line represents subjects with the highest values.