. Patient-centered connectivity-based prediction of tau pathology spread in Alzheimer's disease. Sci Adv. 2020 Nov;6(48) Print 2020 Nov PubMed.


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  1. This is really excellent work, highlighting the heterogeneity in tau accumulation patterns. Especially interesting is the subject-specific modeling of tau spread, using each subject’s individual tau PET to identify the regions that were most likely to accumulate tau. This individualized sampling of target regions of interest (ROIs) increased sensitivity to detect tau changes and reduced the sample size needed in simulated trials. The fact that this method is fully automated is very encouraging and ought to make it broadly applicable across tau PET studies.

    For widespread use, some questions remain and details would need to be worked out. For example, how does each subject’s target ROI, being unique, affect interpretation of a significant result (or lack thereof)? How does this individualized sampling perform at different points along the AD continuum (i.e., in cognitively normal versus mild cognitive impairment; in those with little tau versus those with a lot)? How does tau positivity probability (TPP) compare to SUVr as an outcome measure? Since TPP is sample-dependent, which sample should be used to define this for a clinical trial?

    Overall, this is exciting work and a big step forward in the direction of precision medicine.

    View all comments by Justin Sanchez

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