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Research ArticleNeurology

TauIQ: A Canonical Image Based Algorithm to Quantify Tau PET Scans

Alex Whittington, Roger N. Gunn and for the Alzheimer’s Disease Neuroimaging Initiative
Journal of Nuclear Medicine September 2021, 62 (9) 1292-1300; DOI: https://doi.org/10.2967/jnumed.120.258962
Alex Whittington
1Invicro LLC, London, United Kingdom; and
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Roger N. Gunn
1Invicro LLC, London, United Kingdom; and
2Department of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
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  • FIGURE 1.
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    FIGURE 1.

    Methodology for creating 18F-flortaucipir canonical images. (A) Creation of chronological 18F-flortaucipir dataset using associated 18F-florbetapir data. (B) Generation of 18F-flortaucipir K and NS canonical images from voxelwise modeling of chronological dataset.

  • FIGURE 2.
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    FIGURE 2.

    Canonical images for 18F-flortaucipir PET derived from spatiotemporal modeling of chronological AD data in MNI152 space: tau canonical image (K) (A), nonspecific canonical image (NS) (B), and reference structural T1-weighted MR image (C).

  • FIGURE 3.
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    FIGURE 3.

    TauIQ algorithm that estimates global and local tau outcome measures.

  • FIGURE 4.
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    FIGURE 4.

    Examples of TauIQ decomposition of 18F-flortaucipir data into nonspecific, global, and local tau signals.

  • FIGURE 5.
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    FIGURE 5.

    Cross-sectional analysis shown as box plots of distributions of 18F-flortaucipir TauL and SUVR outcome measures by clinical group.

  • FIGURE 6.
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    FIGURE 6.

    Longitudinal analysis shown as box plots of distributions of change in 18F-flortaucipir TauL and SUVR outcome measures.

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    FIGURE 7.

    Correlations between clinical scores (CDR-SB, MMSE, and ADAS-Cog) and tau imaging outcome measures.

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    TABLE 1

    Cross-Sectional Analysis*

    Outcome measureCN− vs. CN+CN− vs. MCI+CN− vs. dementia+
    TauIQ TauL1.00 (0.69–1.35), P < 0.101.53 (1.21–1.88)2.70 (2.28–3.26), P < 0.44
    SUVR Jack meta-ROI0.69 (0.39–1.03)1.58 (1.21–2.01)2.64 (2.16–3.27)
    SUVR Braak I/II0.55 (0.26–0.85)1.62 (1.24–2.04), P < 0.642.46 (1.98–3.03)
    SUVR Braak III/IV0.57 (0.28–0.88)1.34 (0.99–1.72)2.37 (1.93–2.92)
    SUVR Braak V/VI0.49 (0.19–0.80)0.97 (0.63–1.32)1.90 (1.48–2.40)
    • ↵* Effect sizes with CIs for 18F-flortaucipir TauL and SUVR outcome measures derived from group comparisons. Best-performing method in terms of effect size is highlighted in bold along with probability it is best method.

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    TABLE 2

    Longitudinal Analysis*

    ParameterTauIQ TauLSUVR Jack meta-ROISUVR Braak I/IISUVR Braak III/IVSUVR Braak V/VI
    CN−
     Mean and SD1.20 (2.31)0.003 (0.037)0.001 (0.037)0.003 (0.036)−0.001 (0.037)
     Effect size and CI0.52 (0.28 to 0.79), P < 0.0160.09 (−0.18 to 0.37)0.02 (−0.25 to 0.30)0.08 (−0.19 to 0.37)−0.02 (−0.30 to 0.24)
     n94033,812557,74537,422847,562
    CN+
     Mean and SD2.01 (2.97)0.009 (0.044)0.007 (0.039)0.007 (0.041)0.007 (0.039)
     Effect size and CI0.68 (0.43 to 0.98), P < 0.0220.21 (−0.12 to 0.55)0.19 (−0.15 to 0.52)0.18 (−0.16 to 0.55)0.17 (−0.16 to 0.55)
     n5465,7266,6998,1709,019
    MCI−
     Mean and SD1.87 (2.28)0.008 (0.032)−0.002 (0.040)0.004 (0.031)0.010 (0.025)
     Effect size and CI0.82 (0.57 to 1.58), P < 0.130.24 (−0.22 to 0.82)−0.04 (−0.49 to 0.53)0.13 (−0.31 to 0.79)0.40 (−0.05 to 0.94)
     n3774,458190,97014,5121,610
    MCI+
     Mean and SD3.61 (3.31)0.023 (0.049)0.008 (0.052)0.023 (0.047)0.017 (0.044)
     Effect size and CI1.09 (0.79 to 1.54), P < 0.0200.47 (0.12 to 0.85)0.16 (−0.24 to 0.57)0.49 (0.13 to 0.93)0.39 (0.00 to 0.89)
     n2131,13010,2291,0631,655
    Dementia−
     Mean and SD1.53 (2.43)−0.001 (0.032)−0.007 (0.019)0.002 (0.024)0.002 (0.024)
     Effect size and CI0.63 (−0.13 to 2.14), P < 0.28−0.04 (−1.55 to 0.76)−0.34 (−1.32 to 0.76)0.09 (−1.18 to 1.01)0.06 (−1.52 to 1.01)
     n640147,8602,08029,59563,426
    Dementia+
     Mean and SD2.52 (6.64)0.004 (0.085)−0.011 (0.079)0.002 (0.080)−0.005 (0.078)
     Effect size and CI0.38 (−0.17 to 1.42), P < 0.270.05 (−0.44 to 0.83)−0.13 (−0.58 to 0.71)0.02 (−0.56 to 0.76)−0.06 (−0.64 to 0.71)
     n1,74199,38513,98532,260767,106
    • ↵* Change in 18F-flortaucipir TauL and SUVR outcome measures per year for each clinical group. Best-performing method in terms of effect size (mean/SD) is highlighted in bold along with probability that it is the best method. n is number of subjects, in both active and placebo arms of simulated clinical trial, required to show 25% reduction in tau accumulation in active arm over period of 1 y (power = 80%, α = 0.05).

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Journal of Nuclear Medicine: 62 (9)
Journal of Nuclear Medicine
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September 1, 2021
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TauIQ: A Canonical Image Based Algorithm to Quantify Tau PET Scans
Alex Whittington, Roger N. Gunn, for the Alzheimer’s Disease Neuroimaging Initiative
Journal of Nuclear Medicine Sep 2021, 62 (9) 1292-1300; DOI: 10.2967/jnumed.120.258962

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TauIQ: A Canonical Image Based Algorithm to Quantify Tau PET Scans
Alex Whittington, Roger N. Gunn, for the Alzheimer’s Disease Neuroimaging Initiative
Journal of Nuclear Medicine Sep 2021, 62 (9) 1292-1300; DOI: 10.2967/jnumed.120.258962
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Keywords

  • Statistical Analysis
  • canonical images
  • flortaucipir
  • IQ platform
  • power in clinical trials
  • tau PET
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