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.

Figure 1

Hypometabolic Convergence Index (Mean±SD) in normal controls, MCI stable subjects, MCI converters, and probable AD patients

Figure 2

An example of the brain regions where A) an individual AD patient and B) a normal control subject shows the presence/absence of consistent hypometabolic pattern with AD. For illustration purposes, the overlap display was created with a cut-off of the Z_Pi • Z_Ai≥ 8.37. Note that this method uses all cerebral voxels without having to specify a preselected region of interest, so the search area is over the whole brain.

Figure 3

Receiver Operating Curves showing the sensitivity and 1-specificity of the HCI and hippocampal volume

Figure 4

Kaplan-Meier curves showing the probability of an MCI patient not converting to probable AD within 18 months from baseline in a) the 26 MCI patients with a higher HCI versus the 71 MCI patients with a lower HCI, b) the 21 MCI patients with a smaller hippocampal volume versus 76 MCI patients with a larger hippocampal volume, and c) the 20 MCI patients with both a higher HCI and smaller hippocampal volume versus the 77 other MCI patients, d) the 20 MCI patients with both a higher HCI and smaller hippocampal volume versus the 38 MCI patients with both a lower HCI and larger hippocampal volume.