Jones, David T.

AbstractQuantitative measurement of β‐amyloid from amyloid PET scans typically relies on localizing target and reference regions by image registration to MRI. In this work, we present a series of simulations where 50 small random perturbations of starting location and orientation were applied to each subject's PET scan, and rigid registration usingspm_coregwas performed between each perturbed PET scan and its corresponding MRI. We then measured variation in the output PET‐MRI registrations and how this variation affected the resulting SUVR measurements. We performed these experiments using scans of 1196 participants, half using 18F florbetapir and half using 11C PiB. From these experiments, we measured the magnitude of the imprecision in the rigid registration steps used to localize measurement regions, and how this contributes to the overall imprecision in SUVR measurements. Unexpectedly, we found for both tracers that the imprecision in these measurements depends on the degree of amyloid tracer uptake, and thus also indirectly on Alzheimer's disease clinical status. We then examined common choices of reference regions, and we show that SUVR measurements using supratentorial white matter references are relatively resistant to this source of error. We also show that the use of partial volume correction further magnifies the effects of registration imprecision on SUVR measurements. Together, these results suggest that this rigid registration step is an attractive target for future work in improving measurement techniques.Hum Brain Mapp 38:3323–3336, 2017. ©2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

AbstractIntroductionBiomarkers for Alzheimer's disease (AD) pathophysiology have been developed that focus on various levels of brain organization. However, no robust biomarker of large‐scale network failure has been developed. Using the recently introduced cascading network failure model of AD, we developed the network failure quotient (NFQ) as a biomarker of this process.MethodsWe developed and optimized the NFQ using our recently published analyses of task‐free functional magnetic resonance imaging data in clinically normal (n = 43) and AD dementia participants (n = 28) from the Alzheimer's Disease Neuroimaging Initiative. The optimized NFQ (oNFQ) was then validated in a cohort spanning the AD spectrum from the Mayo Clinic (n = 218).ResultsThe oNFQ (d = 1.25, 95% confidence interval [1.25, 1.26]) had the highest effect size for differentiating persons with AD dementia from clinically normal participants. The oNFQ measure performed similarly well on the validation Mayo Clinic sample (d = 1.44, 95% confidence interval [1.43, 1.44]). The oNFQ was also associated with other available key biomarkers in the Mayo cohort.DiscussionThis study demonstrates a measure of functional connectivity, based on a cascading network failure model of AD, and was highly successful in identifying AD dementia. A robust biomarker of the large‐scale effects of AD pathophysiology will allow for richer descriptions of the disease process and its modifiers, but is not currently suitable for discriminating clinical diagnostic categories. The large‐scale network level may be one of the earliest manifestations of AD, making this an attractive target for continued biomarker development to be used in prevention trials.