{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T08:48:20Z","timestamp":1790844500839,"version":"4.1.0"},"reference-count":91,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T00:00:00Z","timestamp":1517875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Alzheimer\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/diagnostics8010014","type":"journal-article","created":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T14:31:17Z","timestamp":1517927477000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Multimodal Discrimination between Normal Aging, Mild Cognitive Impairment and Alzheimer\u2019s Disease and Prediction of Cognitive Decline"],"prefix":"10.3390","volume":"8","author":[{"given":"Corinna","family":"Bauer","sequence":"first","affiliation":[{"name":"Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Howard","family":"Cabral","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald","family":"Killiany","sequence":"additional","affiliation":[{"name":"Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA"},{"name":"Department of Anatomy and Neurobiology, Center for Biomedical Imaging, Boston University School of Medicine, Boston, MA 02118, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2017). 2017 Alzheimer\u2019s disease facts and figures. 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