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Diagnostics 2018, 8(1), 14; https://doi.org/10.3390/diagnostics8010014

Multimodal Discrimination between Normal Aging, Mild Cognitive Impairment and Alzheimer’s Disease and Prediction of Cognitive Decline

1
Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
2
Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
3
Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
4
Department of Anatomy and Neurobiology, Center for Biomedical Imaging, Boston University School of Medicine, Boston, MA 02118, USA
*
Author to whom correspondence should be addressed.
Received: 24 October 2017 / Revised: 8 January 2018 / Accepted: 31 January 2018 / Published: 6 February 2018
(This article belongs to the Special Issue Alzheimer's Disease Imaging Biomarkers)
Full-Text   |   PDF [273 KB, uploaded 6 February 2018]

Abstract

Alzheimer’s Disease (AD) and mild cognitive impairment (MCI) are associated with widespread changes in brain structure and function, as indicated by magnetic resonance imaging (MRI) morphometry and 18-fluorodeoxyglucose position emission tomography (FDG PET) metabolism. Nevertheless, the ability to differentiate between AD, MCI and normal aging groups can be difficult. Thus, the goal of this study was to identify the combination of cerebrospinal fluid (CSF) biomarkers, MRI morphometry, FDG PET metabolism and neuropsychological test scores to that best differentiate between a sample of normal aging subjects and those with MCI and AD from the Alzheimer’s Disease Neuroimaging Initiative. The secondary goal was to determine the neuroimaging variables from MRI, FDG PET and CSF biomarkers that can predict future cognitive decline within each group. To achieve these aims, a series of multivariate stepwise logistic and linear regression models were generated. Combining all neuroimaging modalities and cognitive test scores significantly improved the index of discrimination, especially at the earliest stages of the disease, whereas MRI gray matter morphometry variables best predicted future cognitive decline compared to other neuroimaging variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores and CSF biomarkers may provide significantly better discrimination than any modality alone. View Full-Text
Keywords: Alzheimer’s disease; mild cognitive impairment; multimodal neuroimaging; cognitive decline Alzheimer’s disease; mild cognitive impairment; multimodal neuroimaging; cognitive decline
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Bauer, C.M.; Cabral, H.J.; Killiany, R.J. Multimodal Discrimination between Normal Aging, Mild Cognitive Impairment and Alzheimer’s Disease and Prediction of Cognitive Decline. Diagnostics 2018, 8, 14.

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