PURPOSE: Similar regional anatomical distributions were reported for fibrillary amyloid deposition [measured by (11)C-Pittsburgh compound B (PIB) positron emission tomography (PET)] and brain hypometabolism [measured by (18)F-fluorodeoxyglucose (FDG) PET] in numerous Alzheimer's disease (AD) studies. However, there is a lack of longitudinal studies evaluating the interrelationships of these two different pathological markers in the same AD population. Our most recent AD study suggested that the longitudinal pattern of hypometabolism anatomically follows the pattern of amyloid deposition with temporal delay, which indicates that neuronal dysfunction may spread within the anatomical pattern of amyloid pathology. Based on this finding we now hypothesize that in early AD patients quantitative longitudinal decline in hypometabolism may be related to the amount of baseline amyloid deposition during a follow-up period of 2 years. METHODS: Fifteen patients with mild probable AD underwent baseline (T1) and follow-up (T2) examination after 24 ± 2.1 months with [(18)F]FDG PET, [(11)C]PIB PET, structural T1-weighted MRI and neuropsychological testing [Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological battery]. Longitudinal cognitive measures and quantitative PET measures of amyloid deposition and metabolism [standardized uptake value ratios (SUVRs)] were obtained using volume of interest (VOI)-based approaches in the frontal-lateral-retrosplenial (FLR) network and in predefined bihemispheric brain regions after partial volume effect (PVE) correction of PET data. Statistical group comparisons (SUVRs and cognitive measures) between patients and 15 well-matched elderly controls who had undergone identical imaging procedures once as well as Pearson's correlation analyses within patients were performed. RESULTS: Group comparison revealed significant cognitive decline and increased mean PIB/decreased FDG SUVRs in the FLR network as well as in several AD-typical regions in patients relative to controls. Concurrent with cognitive decline patients showed longitudinal increase in mean PIB/decrease in mean FDG SUVRs over time in the FLR network and in several AD-typical brain regions. Correlation analyses of FLR network SUVRs in patients revealed significant positive correlations between PIB T1 and delta FDG (FDG T1-T2) SUVRs, between PIB T1 and PIB T2 SUVRs, between FDG T1 and PIB T2 SUVRs as well as between FDG T1 and FDG T2 SUVRs, while significant negative correlations were found between FDG T1 and delta PIB (PIB T1-T2) SUVRs as well as between FDG T2 and delta FDG (FDG T1-T2) SUVRs. These findings were confirmed in locoregional correlation analyses, revealing significant associations in the same directions for two left hemispheric regions and nine right hemispheric regions, showing the strongest association for bilateral precuneus. CONCLUSION: Baseline amyloid deposition in patients with mild probable AD was associated with longitudinal metabolic decline. Additionally, mildly decreased/relatively preserved baseline metabolism was associated with a longitudinal increase in amyloid deposition. The latter bidirectional associations were present in the whole AD-typical FLR network and in several highly interconnected hub regions (i.e. in the precuneus). Our longitudinal findings point to a bidirectional quantitative interrelationship of the two investigated AD pathologies, comprising an initial relative maintenance of neuronal activity in already amyloid-positive hub regions (neuronal compensation), followed by accelerated amyloid deposition, accompanied by functional neuronal decline (neuronal breakdown) along with cognitive decline.
PURPOSE: Similar regional anatomical distributions were reported for fibrillary amyloid deposition [measured by (11)C-Pittsburgh compound B (PIB) positron emission tomography (PET)] and brain hypometabolism [measured by (18)F-fluorodeoxyglucose (FDG) PET] in numerous Alzheimer's disease (AD) studies. However, there is a lack of longitudinal studies evaluating the interrelationships of these two different pathological markers in the same AD population. Our most recent AD study suggested that the longitudinal pattern of hypometabolism anatomically follows the pattern of amyloid deposition with temporal delay, which indicates that neuronal dysfunction may spread within the anatomical pattern of amyloid pathology. Based on this finding we now hypothesize that in early ADpatients quantitative longitudinal decline in hypometabolism may be related to the amount of baseline amyloid deposition during a follow-up period of 2 years. METHODS: Fifteen patients with mild probable AD underwent baseline (T1) and follow-up (T2) examination after 24 ± 2.1 months with [(18)F]FDG PET, [(11)C]PIB PET, structural T1-weighted MRI and neuropsychological testing [Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological battery]. Longitudinal cognitive measures and quantitative PET measures of amyloid deposition and metabolism [standardized uptake value ratios (SUVRs)] were obtained using volume of interest (VOI)-based approaches in the frontal-lateral-retrosplenial (FLR) network and in predefined bihemispheric brain regions after partial volume effect (PVE) correction of PET data. Statistical group comparisons (SUVRs and cognitive measures) between patients and 15 well-matched elderly controls who had undergone identical imaging procedures once as well as Pearson's correlation analyses within patients were performed. RESULTS: Group comparison revealed significant cognitive decline and increased mean PIB/decreased FDG SUVRs in the FLR network as well as in several AD-typical regions in patients relative to controls. Concurrent with cognitive declinepatients showed longitudinal increase in mean PIB/decrease in mean FDG SUVRs over time in the FLR network and in several AD-typical brain regions. Correlation analyses of FLR network SUVRs in patients revealed significant positive correlations between PIB T1 and delta FDG (FDG T1-T2) SUVRs, between PIB T1 and PIB T2 SUVRs, between FDG T1 and PIB T2 SUVRs as well as between FDG T1 and FDG T2 SUVRs, while significant negative correlations were found between FDG T1 and delta PIB (PIB T1-T2) SUVRs as well as between FDG T2 and delta FDG (FDG T1-T2) SUVRs. These findings were confirmed in locoregional correlation analyses, revealing significant associations in the same directions for two left hemispheric regions and nine right hemispheric regions, showing the strongest association for bilateral precuneus. CONCLUSION: Baseline amyloid deposition in patients with mild probable AD was associated with longitudinal metabolic decline. Additionally, mildly decreased/relatively preserved baseline metabolism was associated with a longitudinal increase in amyloid deposition. The latter bidirectional associations were present in the whole AD-typical FLR network and in several highly interconnected hub regions (i.e. in the precuneus). Our longitudinal findings point to a bidirectional quantitative interrelationship of the two investigated AD pathologies, comprising an initial relative maintenance of neuronal activity in already amyloid-positive hub regions (neuronal compensation), followed by accelerated amyloid deposition, accompanied by functional neuronal decline (neuronal breakdown) along with cognitive decline.
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: Stefan Förster; Verena C Buschert; Hans-Georg Buchholz; Stefan J Teipel; Uwe Friese; Christian Zach; Christian la Fougere; Axel Rominger; Alexander Drzezga; Harald Hampel; Peter Bartenstein; Katharina Buerger Journal: J Alzheimers Dis Date: 2011 Impact factor: 4.472
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: Randy L Buckner; Abraham Z Snyder; Benjamin J Shannon; Gina LaRossa; Rimmon Sachs; Anthony F Fotenos; Yvette I Sheline; William E Klunk; Chester A Mathis; John C Morris; Mark A Mintun Journal: J Neurosci Date: 2005-08-24 Impact factor: 6.167
Authors: A Drzezga; T Grimmer; G Henriksen; M Mühlau; R Perneczky; I Miederer; C Praus; C Sorg; A Wohlschläger; M Riemenschneider; H J Wester; H Foerstl; M Schwaiger; A Kurz Journal: Neurology Date: 2009-04-01 Impact factor: 9.910
Authors: N M Scheinin; S Aalto; J Koikkalainen; J Lötjönen; M Karrasch; N Kemppainen; M Viitanen; K Någren; S Helin; M Scheinin; J O Rinne Journal: Neurology Date: 2009-09-02 Impact factor: 9.910
Authors: Sofie M Adriaanse; Koene R A van Dijk; Rik Ossenkoppele; Martin Reuter; Nelleke Tolboom; Marissa D Zwan; Maqsood Yaqub; Ronald Boellaard; Albert D Windhorst; Wiesje M van der Flier; Philip Scheltens; Adriaan A Lammertsma; Frederik Barkhof; Bart N M van Berckel Journal: Eur J Nucl Med Mol Imaging Date: 2014-03-11 Impact factor: 9.236
Authors: Val J Lowe; Stephen D Weigand; Matthew L Senjem; Prashanthi Vemuri; Lennon Jordan; Kejal Kantarci; Bradley Boeve; Clifford R Jack; David Knopman; Ronald C Petersen Journal: Neurology Date: 2014-05-02 Impact factor: 9.910
Authors: Diego Iacono; Peter Zandi; Myron Gross; William R Markesbery; Olga Pletnikova; Gay Rudow; Juan C Troncoso Journal: Oncotarget Date: 2015-06-10
Authors: Behrooz H Yousefi; Boris von Reutern; Daniela Scherübl; André Manook; Markus Schwaiger; Timo Grimmer; Gjermund Henriksen; Stefan Förster; Alexander Drzezga; Hans-Jürgen Wester Journal: EJNMMI Res Date: 2015-03-28 Impact factor: 3.138
Authors: Brian A Gordon; Tyler M Blazey; Yi Su; Amrita Hari-Raj; Aylin Dincer; Shaney Flores; Jon Christensen; Eric McDade; Guoqiao Wang; Chengjie Xiong; Nigel J Cairns; Jason Hassenstab; Daniel S Marcus; Anne M Fagan; Clifford R Jack; Russ C Hornbeck; Katrina L Paumier; Beau M Ances; Sarah B Berman; Adam M Brickman; David M Cash; Jasmeer P Chhatwal; Stephen Correia; Stefan Förster; Nick C Fox; Neill R Graff-Radford; Christian la Fougère; Johannes Levin; Colin L Masters; Martin N Rossor; Stephen Salloway; Andrew J Saykin; Peter R Schofield; Paul M Thompson; Michael M Weiner; David M Holtzman; Marcus E Raichle; John C Morris; Randall J Bateman; Tammie L S Benzinger Journal: Lancet Neurol Date: 2018-02-01 Impact factor: 44.182