Predicting Alzheimer disease with β‐amyloid imaging: Results from the Australian imaging, biomarkers, and lifestyle study of ageing

Rowe, Christopher C.; Bourgeat, Pierrick; Ellis, Kathryn A.; Brown, Belinda M.; Lim, Yen Ying; Mulligan, Rachel; Jones, Gareth; Maruff, Paul; Woodward, Mark; Price, Roger I.; Robins, Peter; Tochon-Danguy, Henri; O’Keefe, Graeme; Pike, Kerryn E.; Yates, Paul; Szoeke, Cassandra; Salvado, Olivier; Macaulay, S. Lance; O’Meara, Timothy; Head, Richard; Cobiac, Lynne; Savage, Greg; Martins, Ralph N.; Masters, Colin L.; Ames, David; Villemagne, Victor L.

doi:10.1002/ana.24040

Cited by 205 publications

(170 citation statements)

References 39 publications

(61 reference statements)

Supporting

Mentioning

151

Contrasting

Unclassified

Order By: Relevance

“…Neuropathological and CSF biomarker studies show that in Alzheimer's disease, cognitive impairment and synaptic loss are associated more strongly with the presence and number of neurofibrillary tangles than amyloid-b plaques (Giannakopoulos et al, 2003;Bennett et al, 2004;Ingelsson et al, 2004). However, neuroimaging studies in preclinical Alzheimer's disease report that higher cortical amyloid-b load is associated with greater rates of cognitive decline and progression to MCI (Rowe et al, 2013;Lim et al, 2014a), with these effects mediated by the effect of amyloid-b on neurodegeneration (Jack and Holtzman, 2013;Lim et al, 2015a). In this context, dissociation of the effects of BDNF on amyloid-b and tau associated cognitive impairment observed here are important because they provide evidence that BDNF Met 66 influences disease progression through effects on neuronal dysfunction and cognitive impairment associated with tau.…”

Section: Discussionmentioning

confidence: 99%

BDNFVal66Met moderates memory impairment, hippocampal function and tau in preclinical autosomal dominant Alzheimer’s disease

Lim

Hassenstab

Cruchaga

et al. 2016

Brain

Self Cite

View full text Add to dashboard Cite

) for a scientific commentary on this article. The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism is implicated in synaptic excitation and neuronal integrity, and has previously been shown to moderate amyloid-b-related memory decline and hippocampal atrophy in preclinical sporadic Alzheimer's disease. However, the effect of BDNF in autosomal dominant Alzheimer's disease is unknown. We aimed to determine the effect of BDNF Val66Met on cognitive function, hippocampal function, tau and amyloid-b in preclinical autosomal dominant Alzheimer's disease. We explored effects of apolipoprotein E (APOE) "4 on these relationships. The Dominantly Inherited Alzheimer Network conducted clinical, neuropsychological, genetic, biomarker and neuroimaging measures at baseline in 131 mutation noncarriers and 143 preclinical autosomal dominant Alzheimer's disease mutation carriers on average 12 years before clinical symptom onset. BDNF genotype data were obtained for mutation carriers (95 Val 66 homozygotes, 48 Met 66 carriers). Among preclinical mutation carriers, Met 66 carriers had worse memory performance, lower hippocampal glucose metabolism and increased levels of cerebrospinal fluid tau and phosphorylated tau (p-tau) than Val 66 homozygotes. Cortical amyloid-b and cerebrospinal fluid amyloidb 42 levels were significantly different from non-carriers but did not differ between preclinical mutation carrier Val 66 homozygotes and Met 66 carriers. There was an effect of APOE on amyloid-b levels, but not cognitive function, glucose metabolism or tau. As in sporadic Alzheimer's disease, the deleterious effects of amyloid-b on memory, hippocampal function, and tau in preclinical autosomal dominant Alzheimer's disease mutation carriers are greater in Met 66 carriers. To date, this is the only genetic factor found to moderate downstream effects of amyloid-b in autosomal dominant Alzheimer's disease.

show abstract

Section: Discussionmentioning

confidence: 99%

BDNFVal66Met moderates memory impairment, hippocampal function and tau in preclinical autosomal dominant Alzheimer’s disease

Lim

Hassenstab

Cruchaga

et al. 2016

Brain

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the pathophysiological processes of AD begin at least a decade before clinical diagnosis (18), suggesting that the emergence of this heterogeneity may occur before the onset of clinical dementia. In this study, we, therefore, examined how distinct atrophy factors identified in AD dementia patients were associated with longitudinal cognitive decline early in nondemented participants who were at risk for AD dementia based on elevated beta-amyloid (Aβ) (19)(20)(21).…”

mentioning

confidence: 99%

Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

Zhang¹,

Mormino

Sun³

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

159

166

View full text Add to dashboard Cite

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer's disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

show abstract

“…However, quantification may be important for several reasons. Recent work indicates that the amount of amyloid has a prognostic value (9) and that any assessment of longitudinal change requires quantification. Quantification also facilitates comparison of results across centers.…”

mentioning

confidence: 99%

Automated Quantification of ¹⁸F-Flutemetamol PET Activity for Categorizing Scans as Negative or Positive for Brain Amyloid: Concordance with Visual Image Reads

et al. 2014

View full text Add to dashboard Cite

Clinical trials of the PET amyloid imaging agent 18 F-flutemetamol have used visual assessment to classify PET scans as negative or positive for brain amyloid. However, quantification provides additional information about regional and global tracer uptake and may have utility for image assessment over time and across different centers. Using postmortem brain neuritic plaque density data as a truth standard to derive a standardized uptake value ratio (SUVR) threshold, we assessed a fully automated quantification method comparing visual and quantitative scan categorizations. We also compared the histopathology-derived SUVR threshold with one derived from healthy controls. Methods: Data from 345 consenting subjects enrolled in 8 prior clinical trials of 18 F-flutemetamol injection were used. We grouped subjects into 3 cohorts: an autopsy cohort (n 5 68) comprising terminally ill patients with postmortem confirmation of brain amyloid status; a test cohort (n 5 172) comprising 33 patients with clinically probable Alzheimer disease, 80 patients with mild cognitive impairment, and 59 healthy volunteers; and a healthy cohort of 105 volunteers, used to define a reference range for SUVR. Visual image categorizations for comparison were from a previous study. A fully automated PET-only quantification method was used to compute regional neocortical SUVRs that were combined into a single composite SUVR. An SUVR threshold for classifying scans as positive or negative was derived by ranking the PET scans from the autopsy cohort based on their composite SUVR and comparing data with the standard of truth based on postmortem brain amyloid status for subjects in the autopsy cohort. The derived threshold was used to categorize the 172 scans in the test cohort as negative or positive, and results were compared with categorization using visual assessment. Different reference and composite region definitions were assessed. Threshold levels were also compared with corresponding thresholds derived from the healthy group. Results: Automated quantification (using pons as the reference region) demonstrated 91% sensitivity and 88% specificity and gave 3 false-positive and 4 false-negative scans. All 3 false-positive cases were either borderline-normal by standard of truth or had moderate to heavy cortical diffuse plaque burden. In the test cohort, the concordance between quantitative and visual read categorization ranged from 97.1% to 99.4% depending on the selection of reference and composite regions. The threshold derived from the healthy group was close to the histopathology-derived threshold. Conclusion: Categorization of 18 F-flutemetamol amyloid imaging data using an automated PET-only quantification method showed good agreement with histopathologic classification of neuritic plaque density and a strong concordance with visual read results. Amyl oid PET radiotracers have been developed for in vivo assessment of amyloid b deposition in the brain and may prove useful both clinically and in Alzheimer disease research. The first widely us...

show abstract

Predicting Alzheimer disease with β‐amyloid imaging: Results from the Australian imaging, biomarkers, and lifestyle study of ageing

Cited by 205 publications

References 39 publications

BDNFVal66Met moderates memory impairment, hippocampal function and tau in preclinical autosomal dominant Alzheimer’s disease

BDNFVal66Met moderates memory impairment, hippocampal function and tau in preclinical autosomal dominant Alzheimer’s disease

Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

Automated Quantification of ¹⁸F-Flutemetamol PET Activity for Categorizing Scans as Negative or Positive for Brain Amyloid: Concordance with Visual Image Reads

Contact Info

Product

Resources

About

Predicting Alzheimer disease with β‐amyloid imaging: Results from the Australian imaging, biomarkers, and lifestyle study of ageing

Cited by 205 publications

References 39 publications

BDNFVal66Met moderates memory impairment, hippocampal function and tau in preclinical autosomal dominant Alzheimer’s disease

BDNFVal66Met moderates memory impairment, hippocampal function and tau in preclinical autosomal dominant Alzheimer’s disease

Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

Automated Quantification of 18F-Flutemetamol PET Activity for Categorizing Scans as Negative or Positive for Brain Amyloid: Concordance with Visual Image Reads

Contact Info

Product

Resources

About

Automated Quantification of ¹⁸F-Flutemetamol PET Activity for Categorizing Scans as Negative or Positive for Brain Amyloid: Concordance with Visual Image Reads