Amyloid involvement in subcortical regions predicts cognitive decline
We estimated whether amyloid involvement in subcortical regions may predict cognitive impairment, and established an amyloid staging scheme based on degree of subcortical amyloid involvement.
Data from 240 cognitively normal older individuals, 393 participants with mild cognitive impairment, and 126 participants with Alzheimer disease were acquired at Alzheimer’s Disease Neuroimaging Initiative sites. To assess subcortical involvement, we analyzed amyloid deposition in amygdala, putamen, and caudate nucleus. We staged participants into a 3-stage model based on cortical and subcortical amyloid involvement: 382 with no cortical or subcortical involvement as stage 0, 165 with cortical but no subcortical involvement as stage 1, and 203 with both cortical and subcortical involvement as stage 2.
Amyloid accumulation was first observed in cortical regions and spread down to the putamen, caudate nucleus, and amygdala. In longitudinal analysis, changes in MMSE, ADAS-cog 13, FDG PET SUVR, and hippocampal volumes were steepest in stage 2 followed by stage 1 then stage 0 (p value <0.001). Stage 2 showed steeper changes in MMSE score (β [SE] = −0.02 [0.004], p < 0.001), ADAS-cog 13 (0.05 [0.01], p < 0.001), FDG PET SUVR (−0.0008 [0.0003], p = 0.004), and hippocampal volumes (−4.46 [0.65], p < 0.001) compared to stage 1.
We demonstrated a downward spreading pattern of amyloid, suggesting that amyloid accumulates first in neocortex followed by subcortical structures. Furthermore, our new finding suggested that an amyloid staging scheme based on subcortical involvement might reveal how differential regional accumulation of amyloid affects cognitive decline through functional and structural changes of the brain.
KeywordsAmyloid staging Amyloid PET Alzheimer’s dementia Thal staging
S.H.C., J.H.S., J.K.S. and S.W.S. contributed to the conceptualization of the study, analysis and interpretation of data, and drafting. J.H.S. and S.P contributed to analyses of imaging data, prepared the figures, and provided technical support. H.J.K., H.M.J., S.E.K., S.J.K., Y.K. and J.S.L. contributed to interpretation of data. S.L., G.D.R. and D.L.N. contributed to analysis and interpretation of data.
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. NRF-2017R1A2B2005081 and No. 2016R1A2B4014398) and National Institutes of Health (NIH) grant P30AG049638.
Compliance with ethical standards
Conflict of interest
All authors have no conflicts of interest to disclose.
Role of the funder
The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 6.Thal DR, Beach TG, Zanette M, Heurling K, Chakrabarty A, Ismail A, et al. [(18)F]flutemetamol amyloid positron emission tomography in preclinical and symptomatic Alzheimer's disease: specific detection of advanced phases of amyloid-beta pathology. Alzheimers Dement. 2015;11:975–85.CrossRefPubMedGoogle Scholar
- 22.Hanseeuw BJ, Betensky RA, Mormino EC, Schultz AP, Sepulcre J, Becker JA, et al. PET staging of amyloidosis using striatum. Alzheimers Dement. 2018.Google Scholar
- 25.Dickerson BC, Wolk DA. Biomarker-based prediction of progression in MCI: comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau. Front Aging Neurosci. 2013;5.Google Scholar