TY - JOUR
T1 - Selecting software pipelines for change in flortaucipir SUVR
T2 - Balancing repeatability and group separation
AU - Schwarz, Christopher G.
AU - Therneau, Terry M.
AU - Weigand, Stephen D.
AU - Gunter, Jeffrey L.
AU - Lowe, Val J.
AU - Przybelski, Scott A.
AU - Senjem, Matthew L.
AU - Botha, Hugo
AU - Vemuri, Prashanthi
AU - Kantarci, Kejal
AU - Boeve, Bradley F.
AU - Whitwell, Jennifer L.
AU - Josephs, Keith A.
AU - Petersen, Ronald C.
AU - Knopman, David S.
AU - Jack, Clifford R.
N1 - Funding Information:
Val Lowe consults for Bayer Schering Pharma, Piramal Life Sciences, Eisai, Inc., and Merck Research and receives research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals and the NIH (NIA, NCI).
Funding Information:
The authors give their thanks to all the volunteers, participants, and coordinators who contributed to this research. We gratefully thank our funding sources that made this work possible: NIH grants R37 AG011378, R01 AG041851, R56 AG068206, U01 AG006786, P50 AG016574, P30 AG062677, R01 AG034676, R01 NS097495, U01 AG045390, U54 NS092089, U01 NS100620, R01 NS89757, R01 DC12519, R21 NS94684, R01 AG50603, U01 NS100620, Gerald and Henrietta Rauenhorst Foundation, Elsie and Marvin Dekelboum Family Foundation, Alexander Family Alzheimer's Disease Research Professorship of the Mayo Clinic, Liston Award, Schuler Foundation, and Mayo Foundation for Medical Education and Research. We also thank Brad Kemp for his assistance with details of the nuclear medicine acquisitions. We also thank AVID Radiopharmaceuticals, Inc. for their support in supplying AV-1451 precursor, chemistry production advice and oversight, and FDA regulatory cross-filing permission and documentation needed for this work.
Funding Information:
Ronald Petersen is a consultant for Roche, Inc., Biogen, Inc., and Eisai, Inc., served on a DSMB for Genentech, Inc.; receives royalties from publishing Mild Cognitive Impairment (Oxford University Press, 2003) and UpToDate; and receives research support from the NIH (P30 AG062677 (PI) and U01-AG006786 (PI), R01-AG011378 (Co-I), and U01–024904 (Co-I)).
Funding Information:
The authors give their thanks to all the volunteers, participants, and coordinators who contributed to this research. We gratefully thank our funding sources that made this work possible: NIH grants R37 AG011378 , R01 AG041851 , R56 AG068206 , U01 AG006786 , P50 AG016574 , P30 AG062677 , R01 AG034676 , R01 NS097495 , U01 AG045390 , U54 NS092089 , U01 NS100620 , R01 NS89757 , R01 DC12519 , R21 NS94684 , R01 AG50603 , U01 NS100620 , Gerald and Henrietta Rauenhorst Foundation, Elsie and Marvin Dekelboum Family Foundation, Alexander Family Alzheimer's Disease Research Professorship of the Mayo Clinic, Liston Award, Schuler Foundation, and Mayo Foundation for Medical Education and Research. We also thank Brad Kemp for his assistance with details of the nuclear medicine acquisitions. We also thank AVID Radiopharmaceuticals, Inc., for their support in supplying AV-1451 precursor, chemistry production advice and oversight, and FDA regulatory cross-filing permission and documentation needed for this work.
Funding Information:
Kejal Kantarci serves on the data safety monitoring board for Takeda Global Research and Development Center, Inc., receives research support from Avid Radioparmaceuticals and Eli Lilly, and receives funding from NIH and Alzheimer's Drug Discovery Foundation.
Funding Information:
David Knopman serves on a Data Safety Monitoring Board for the DIAN study; is an investigator in clinical trials sponsored by Biogen and Lilly Pharmaceuticals; and receives research support from the NIH.
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/9
Y1 - 2021/9
N2 - Since tau PET tracers were introduced, investigators have quantified them using a wide variety of automated methods. As longitudinal cohort studies acquire second and third time points of serial within-person tau PET data, determining the best pipeline to measure change has become crucial. We compared a total of 415 different quantification methods (each a combination of multiple options) according to their effects on a) differences in annual SUVR change between clinical groups, and b) longitudinal measurement repeatability as measured by the error term from a linear mixed-effects model. Our comparisons used MRI and Flortaucipir scans of 97 Mayo Clinic study participants who clinically either: a) were cognitively unimpaired, or b) had cognitive impairments that were consistent with Alzheimer's disease pathology. Tested methods included cross-sectional and longitudinal variants of two overarching pipelines (FreeSurfer 6.0, and an in-house pipeline based on SPM12), three choices of target region (entorhinal, inferior temporal, and a temporal lobe meta-ROI), five types of partial volume correction (PVC) (none, two-compartment, three-compartment, geometric transfer matrix (GTM), and a tau-specific GTM variant), seven choices of reference region (cerebellar crus, cerebellar gray matter, whole cerebellum, pons, supratentorial white matter, eroded supratentorial WM, and a composite of eroded supratentorial WM, pons, and whole cerebellum), two choices of region masking (GM or GM and WM), and two choices of statistic (voxel-wise mean vs. median). Our strongest findings were: 1) larger temporal-lobe target regions greatly outperformed entorhinal cortex (median sample size estimates based on a hypothetical clinical trial were 520–526 vs. 1740); 2) longitudinal processing pipelines outperformed cross-sectional pipelines (median sample size estimates were 483 vs. 572); and 3) reference regions including supratentorial WM outperformed traditional cerebellar and pontine options (median sample size estimates were 370 vs. 559). Altogether, our results favored longitudinally SUVR methods and a temporal-lobe meta-ROI that includes adjacent (juxtacortical) WM, a composite reference region (eroded supratentorial WM + pons + whole cerebellum), 2-class voxel-based PVC, and median statistics.
AB - Since tau PET tracers were introduced, investigators have quantified them using a wide variety of automated methods. As longitudinal cohort studies acquire second and third time points of serial within-person tau PET data, determining the best pipeline to measure change has become crucial. We compared a total of 415 different quantification methods (each a combination of multiple options) according to their effects on a) differences in annual SUVR change between clinical groups, and b) longitudinal measurement repeatability as measured by the error term from a linear mixed-effects model. Our comparisons used MRI and Flortaucipir scans of 97 Mayo Clinic study participants who clinically either: a) were cognitively unimpaired, or b) had cognitive impairments that were consistent with Alzheimer's disease pathology. Tested methods included cross-sectional and longitudinal variants of two overarching pipelines (FreeSurfer 6.0, and an in-house pipeline based on SPM12), three choices of target region (entorhinal, inferior temporal, and a temporal lobe meta-ROI), five types of partial volume correction (PVC) (none, two-compartment, three-compartment, geometric transfer matrix (GTM), and a tau-specific GTM variant), seven choices of reference region (cerebellar crus, cerebellar gray matter, whole cerebellum, pons, supratentorial white matter, eroded supratentorial WM, and a composite of eroded supratentorial WM, pons, and whole cerebellum), two choices of region masking (GM or GM and WM), and two choices of statistic (voxel-wise mean vs. median). Our strongest findings were: 1) larger temporal-lobe target regions greatly outperformed entorhinal cortex (median sample size estimates based on a hypothetical clinical trial were 520–526 vs. 1740); 2) longitudinal processing pipelines outperformed cross-sectional pipelines (median sample size estimates were 483 vs. 572); and 3) reference regions including supratentorial WM outperformed traditional cerebellar and pontine options (median sample size estimates were 370 vs. 559). Altogether, our results favored longitudinally SUVR methods and a temporal-lobe meta-ROI that includes adjacent (juxtacortical) WM, a composite reference region (eroded supratentorial WM + pons + whole cerebellum), 2-class voxel-based PVC, and median statistics.
KW - AV-1451
KW - Bias correction
KW - Change over time
KW - Flortaucipir
KW - GTM
KW - Geometric transfer matrix
KW - Inhomogeneity correction
KW - PVC
KW - Partial volume correction
KW - Precision
KW - RSF
KW - Reference region
KW - Region spread function
KW - SUVR
KW - Tau PET
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U2 - 10.1016/j.neuroimage.2021.118259
DO - 10.1016/j.neuroimage.2021.118259
M3 - Article
C2 - 34118395
AN - SCOPUS:85108726729
SN - 1053-8119
VL - 238
JO - NeuroImage
JF - NeuroImage
M1 - 118259
ER -