Research ArticleNeurology

Spatiotemporal Distribution of β-Amyloid in Alzheimer Disease Is the Result of Heterogeneous Regional Carrying Capacities

Alex Whittington, David J. Sharp and Roger N. Gunn; for the Alzheimer’s Disease Neuroimaging Initiative
Journal of Nuclear Medicine May 2018, 59 (5) 822-827; DOI: https://doi.org/10.2967/jnumed.117.194720

Article Figures & Data

Figures

  • FIGURE 1.
    FIGURE 1.

    Logistic growth model describing Aβ PET imaging signal over time as function of PET NS, K, T50, and r.

  • FIGURE 2.
    FIGURE 2.

    Sixteen logistic growth models of Aβ accumulation with example curves from 3 distinct brain regions. Models in gray have regionally different T50s and are consistent with spreading from seed regions, whereas models in white are consistent with local tissue properties driving Aβ accumulation process.

  • FIGURE 3.
    FIGURE 3.

    Model fitting of most parsimonious logistic growth model (model 11) to chronological 18F-AV-45 Aβ PET data in 9 regions. Model accurately describes data for regions of high (top row), medium (middle row), and low (bottom row) accumulation. A = anterior; D = dorsal; inf = inferior.

  • FIGURE 4.
    FIGURE 4.

    Parametric images displayed as orthographic projections for K and NS obtained from fitting model 11 at voxel level. Gray matter (GM) and white matter (WM) probability maps are displayed for reference. Highest carrying capacities were in frontal lobe, and lowest were in cerebellum, occipital lobe, and brain stem. NS image is consistent with known NS of 18F-AV-45 to white matter.

Tables

  • TABLE 1

    Subject Characteristics

    CharacteristicJack et al. (26)ADNI
    Number260769
    Median age (y)79 (range, 70–94)73 (range, 55–91)
    Male patients (n)162 (62%)438 (57%)
    MCI/AD patients (n)55 (21%)558 (73%)
    APOE*E4–positive patients (n)87 (33%)342 (44%)
    Median MMSE score28 (range, 23–30)28 (range, 19–30)

    • MCI = mild cognitive impairment; MMSE = Mini Mental State Examination.

    • Data from Jack et al. were combined with data from ADNI to create cross-sectional dataset.

  • TABLE 2

    Sixteen Parameterizations of Logistic Growth Model of Aβ Accumulation Used to Analyze Chronological 18F-AV-45 SUVr PET Data at Regional Level

    ModelK (SUVr)r (y−1)T50 (y)NSParametersSSQΔBICi
    1GlobalGlobalGlobalGlobal43,073.781,500
    2GlobalLocalGlobalGlobal932,273.761,600
    3LocalGlobalGlobalGlobal931,324.024,200
    4GlobalGlobalLocalGlobal931,245.719,900
    5GlobalGlobalGlobalLocal931,147.214,200
    6LocalLocalGlobalGlobal1821,131.414,300
    7LocalGlobalLocalGlobal1821,079.311,000
    8GlobalLocalLocalGlobal1821,070.210,400
    9GlobalGlobalLocalLocal1821,002.65,910
    10GlobalLocalGlobalLocal182977.04,120
    11LocalGlobalGlobalLocal182920.60
    12LocalLocalLocalGlobal2711,046.99,890
    13LocalGlobalLocalLocal271918.9865
    14GlobalLocalLocalLocal271918.8861
    15LocalLocalGlobalLocal271911.0267
    16LocalLocalLocalLocal360908.71,090

    • SSQ = sum of squared residuals; ΔBICi = difference in BIC between model 11 and all other models.

    • Ninety cortical and subcortical regions were included, and parameters were either restricted to single value across all regions (global) or fitted individually for each region (local). ΔBIC gives measure of parsimony of each model in relation to smallest BIC value. Model 11 (local K, global r, global T50 and local NS) gives most parsimonious fit to data.

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