title: Characterizing Alzheimer's disease using a hypometabolic convergence index.
abstract: This article introduces a hypometabolic convergence index (HCI) for the assessment of Alzheimer's disease (AD); compares it to other biological, cognitive and clinical measures; and demonstrates its promise to predict clinical decline in mild cognitive impairment (MCI) patients using data from the AD Neuroimaging Initiative (ADNI). The HCI is intended to reflect in a single measurement the extent to which the pattern and magnitude of cerebral hypometabolism in an individual's fluorodeoxyglucose positron emission tomography (FDG-PET) image correspond to that in probable AD patients, and is generated using a fully automated voxel-based image-analysis algorithm. HCIs, magnetic resonance imaging (MRI) hippocampal volume measurements, cerebrospinal fluid (CSF) assays, memory test scores, and clinical ratings were compared in 47 probable AD patients, 21 MCI patients who converted to probable AD within the next 18months, 76 MCI patients who did not, and 47 normal controls (NCs) in terms of their ability to characterize clinical disease severity and predict conversion rates from MCI to probable AD. HCIs were significantly different in the probable AD, MCI converter, MCI stable and NC groups (p=9e-17) and correlated with clinical disease severity. Using retrospectively characterized threshold criteria, MCI patients with either higher HCIs or smaller hippocampal volumes had the highest hazard ratios (HRs) for 18-month progression to probable AD (7.38 and 6.34, respectively), and those with both had an even higher HR (36.72). In conclusion, the HCI, alone or in combination with certain other biomarker measurements, has the potential to help characterize AD and predict subsequent rates of clinical decline. More generally, our conversion index strategy could be applied to a range of imaging modalities and voxel-based image-analysis algorithms.
authors.affiliation: Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA. Kewei.Chen@bannerhealth.com
authors.forename: Kewei
authors.initials: K
authors.lastname: Chen
authors.forename: Napatkamon
authors.initials: N
authors.lastname: Ayutyanont
authors.forename: Jessica B S
authors.initials: JB
authors.lastname: Langbaum
authors.forename: Adam S
authors.initials: AS
authors.lastname: Fleisher
authors.forename: Cole
authors.initials: C
authors.lastname: Reschke
authors.forename: Wendy
authors.initials: W
authors.lastname: Lee
authors.forename: Xiaofen
authors.initials: X
authors.lastname: Liu
authors.forename: Dan
authors.initials: D
authors.lastname: Bandy
authors.forename: Gene E
authors.initials: GE
authors.lastname: Alexander
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
authors.forename: Leslie
authors.initials: L
authors.lastname: Shaw
authors.forename: John Q
authors.initials: JQ
authors.lastname: Trojanowski
authors.forename: Clifford R
authors.initials: CR
authors.lastname: Jack
authors.forename: Susan M
authors.initials: SM
authors.lastname: Landau
authors.forename: Norman L
authors.initials: NL
authors.lastname: Foster
authors.forename: Danielle J
authors.initials: DJ
authors.lastname: Harvey
authors.forename: Michael W
authors.initials: MW
authors.lastname: Weiner
authors.forename: Robert A
authors.initials: RA
authors.lastname: Koeppe
authors.forename: William J
authors.initials: WJ
authors.lastname: Jagust
authors.forename: Eric M
authors.initials: EM
authors.lastname: Reiman
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000378:metabolism* / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D003072:Cognition Disorders / Q000378:metabolism* / Q000473:pathology*; D018450:Disease Progression; D005260:Female; D006624:Hippocampus / Q000378:metabolism / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D049268:Positron-Emission Tomography