Tengfei Guo1, Matthias Brendel2, Timo Grimmer3, Axel Rominger2, Igor Yakushev. 1. Department of Nuclear Medicine, Technische Universität München, Munich, Germany. 2. Department of Nuclear Medicine, University of Munich, Munich, Germany. 3. Department of Psychiatry and Psychotherapy, Technische Universität München, Munich, Germany; and.
Abstract
Knowledge about spatial and temporal patterns of β-amyloid (Aβ) accumulation is essential for understanding Alzheimer disease (AD) and for design of antiamyloid drug trials. Here, we tested whether the regional pattern of longitudinal Aβ accumulation can be predicted by baseline amyloid PET. Methods: Baseline and 2-y follow-up 18F-florbetapir PET data from 58 patients with incipient and manifest dementia due to AD were analyzed. With the determination of how fast amyloid deposits in a given region relative to the whole-brain gray matter, a pseudotemporal accumulation rate for each region was calculated. The actual accumulation rate of 18F-florbetapir was calculated from follow-up data. Results: Pseudotemporal measurements from baseline PET data explained 87% (P < 0.001) of the variance in longitudinal accumulation rate across 62 regions. The method accurately predicted the top 10 fast and slow accumulating regions. Conclusion: Pseudotemporal analysis of baseline PET images is capable of predicting the regional pattern of longitudinal Aβ accumulation in AD at a group level. This approach may be useful in exploring spatial patterns of Aβ accumulation in other amyloid-associated disorders such as Lewy body disease and atypical forms of AD. In addition, the method allows identification of brain regions with a high accumulation rate of Aβ, which are of particular interest for antiamyloid clinical trials.
Knowledge about spatial and temporal patterns of β-amyloid (Aβ) accumulation is essential for understanding Alzheimer disease (AD) and for design of antiamyloid drug trials. Here, we tested whether the regional pattern of longitudinal Aβ accumulation can be predicted by baseline amyloid PET. Methods: Baseline and 2-y follow-up 18F-florbetapir PET data from 58 patients with incipient and manifest dementia due to AD were analyzed. With the determination of how fast amyloid deposits in a given region relative to the whole-brain gray matter, a pseudotemporal accumulation rate for each region was calculated. The actual accumulation rate of 18F-florbetapir was calculated from follow-up data. Results: Pseudotemporal measurements from baseline PET data explained 87% (P < 0.001) of the variance in longitudinal accumulation rate across 62 regions. The method accurately predicted the top 10 fast and slow accumulating regions. Conclusion: Pseudotemporal analysis of baseline PET images is capable of predicting the regional pattern of longitudinal Aβ accumulation in AD at a group level. This approach may be useful in exploring spatial patterns of Aβ accumulation in other amyloid-associated disorders such as Lewy body disease and atypical forms of AD. In addition, the method allows identification of brain regions with a high accumulation rate of Aβ, which are of particular interest for antiamyloid clinical trials.
Authors: Andrei G Vlassenko; Mark A Mintun; Chengjie Xiong; Yvette I Sheline; Alison M Goate; Tammie L S Benzinger; John C Morris Journal: Ann Neurol Date: 2011-11 Impact factor: 10.422
Authors: Nicolas Villain; Gaël Chételat; Blandine Grassiot; Pierrick Bourgeat; Gareth Jones; Kathryn A Ellis; David Ames; Ralph N Martins; Francis Eustache; Olivier Salvado; Colin L Masters; Christopher C Rowe; Victor L Villemagne Journal: Brain Date: 2012-05-23 Impact factor: 13.501
Authors: Victor L Villemagne; Kerryn E Pike; Gaël Chételat; Kathryn A Ellis; Rachel S Mulligan; Pierrick Bourgeat; Uwe Ackermann; Gareth Jones; Cassandra Szoeke; Olivier Salvado; Ralph Martins; Graeme O'Keefe; Chester A Mathis; William E Klunk; David Ames; Colin L Masters; Christopher C Rowe Journal: Ann Neurol Date: 2011-01 Impact factor: 10.422
Authors: Stephanie Jb Vos; Chengjie Xiong; Pieter Jelle Visser; Mateusz S Jasielec; Jason Hassenstab; Elizabeth A Grant; Nigel J Cairns; John C Morris; David M Holtzman; Anne M Fagan Journal: Lancet Neurol Date: 2013-09-04 Impact factor: 44.182
Authors: Wai-Ying Wendy Yau; Dana L Tudorascu; Eric M McDade; Snezana Ikonomovic; Jeffrey A James; Davneet Minhas; Wenzhu Mowrey; Lei K Sheu; Beth E Snitz; Lisa Weissfeld; Peter J Gianaros; Howard J Aizenstein; Julie C Price; Chester A Mathis; Oscar L Lopez; William E Klunk Journal: Lancet Neurol Date: 2015-06-29 Impact factor: 44.182
Authors: Juha O Rinne; David J Brooks; Martin N Rossor; Nick C Fox; Roger Bullock; William E Klunk; Chester A Mathis; Kaj Blennow; Jerome Barakos; Aren A Okello; Sofia Rodriguez Martinez de Liano; Enchi Liu; Martin Koller; Keith M Gregg; Dale Schenk; Ronald Black; Michael Grundman Journal: Lancet Neurol Date: 2010-02-26 Impact factor: 44.182
Authors: Henry Engler; Anton Forsberg; Ove Almkvist; Gunnar Blomquist; Emma Larsson; Irina Savitcheva; Anders Wall; Anna Ringheim; Bengt Långström; Agneta Nordberg Journal: Brain Date: 2006-07-19 Impact factor: 13.501
Authors: Rachel A Yotter; Jimit Doshi; Vanessa Clark; Jitka Sojkova; Yun Zhou; Dean F Wong; Luigi Ferrucci; Susan M Resnick; Christos Davatzikos Journal: Neurobiol Aging Date: 2013-07-13 Impact factor: 4.673
Authors: Victor L Villemagne; Samantha Burnham; Pierrick Bourgeat; Belinda Brown; Kathryn A Ellis; Olivier Salvado; Cassandra Szoeke; S Lance Macaulay; Ralph Martins; Paul Maruff; David Ames; Christopher C Rowe; Colin L Masters Journal: Lancet Neurol Date: 2013-03-08 Impact factor: 44.182
Authors: Eric R Siemers; Karen L Sundell; Christopher Carlson; Michael Case; Gopalan Sethuraman; Hong Liu-Seifert; Sherie A Dowsett; Michael J Pontecorvo; Robert A Dean; Ronald Demattos Journal: Alzheimers Dement Date: 2015-08-01 Impact factor: 21.566
Authors: Chin Hong Tan; Luke W Bonham; Chun Chieh Fan; Elizabeth C Mormino; Leo P Sugrue; Iris J Broce; Christopher P Hess; Jennifer S Yokoyama; Gil D Rabinovici; Bruce L Miller; Kristine Yaffe; Gerard D Schellenberg; Karolina Kauppi; Dominic Holland; Linda K McEvoy; Walter A Kukull; Duygu Tosun; Michael W Weiner; Reisa A Sperling; David A Bennett; Bradley T Hyman; Ole A Andreassen; Anders M Dale; Rahul S Desikan Journal: Brain Date: 2019-02-01 Impact factor: 13.501
Authors: Andrew T Templin; Daniel T Meier; Joshua R Willard; Tami Wolden-Hanson; Kelly Conway; Yin-Guo Lin; Patrick J Gillespie; Krister B Bokvist; Giorgio Attardo; Steven E Kahn; Donalyn Scheuner; Rebecca L Hull Journal: Diabetologia Date: 2018-07-25 Impact factor: 10.122