Elsevier

NeuroImage

Volume 56, Issue 1, 1 May 2011, Pages 52-60
NeuroImage

Characterizing Alzheimer's disease using a hypometabolic convergence index

https://doi.org/10.1016/j.neuroimage.2011.01.049Get rights and content

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 18 months, 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.

Research Highlights

► Hypometabolic convergence index (HCI) is a FDG-PET based global single index. ► HCI reflects a person's cerebral hypometabolism pattern similarity to that of AD. ► HCI correlated with cognitive measures. ► HCI distinguished AD, MCI who converted to AD in 18 months, MCI who did not, and NC. ► MCIs with high HCI or small hippocampal volume had higher risk to convert to AD.

Introduction

Alzheimer's disease (AD) is the most common form of dementia in older adults, affecting approximately 10% of adults over age 65 and almost 50% of adults over age 85 (Corrada et al., 2008, Evans et al., 1989). Mild cognitive impairment (MCI) (Petersen et al., 1999) is associated with an increased rate of progression to AD dementia and affects about 15–20% of adults over age 65 (Lopez et al., 2003). There is growing interest in using brain imaging and other biomarkers for differential diagnosis, detection, and tracking of AD, especially in its earliest non-symptomatic and symptomatic stages. Brain imaging may also help identify genetic and non-genetic risk factors, and evaluate putative AD-slowing treatments in a cost-effective way (Reiman and Langbaum, 2009).

To date, the best established biomarkers of clinical AD progression are fluorodeoxyglucose positron emission tomography (FDG-PET) measurements of the regional cerebral metabolic rate for glucose (CMRgl) decline (Alexander et al., 2002, Reiman and Langbaum, 2009, Reiman et al., 2001) and volumetric magnetic resonance imaging (MRI) measurements of hippocampal or whole brain shrinkage, regional gray matter loss and cortical thinning (Fleisher et al., 2008, Fox et al., 2000, Huang et al., 2010, Jack et al., 1999, Jack et al., 2008b, Whitwell et al., 2007). The best established biomarkers of AD pathology are fibrillar amyloid-β (Aβ) PET measurements using Pittsburgh Compound B (PiB) (Ikonomovic et al., 2008, Klunk et al., 2004) or other recently developed radioligand (Wong et al., 2010) and cerebrospinal fluid (CSF) amyloid-beta1–42 (Aβ1–42) levels alone or in combination with total tau (t-tau) or phosphorylated tau levels (p-tau181) (Fagan et al., 2007, Hansson et al., 2006, Li et al., 2007, Shaw et al., 2009).

When analyzing FDG-PET or any other brain images, it may be helpful to capitalize on as much of the data in the image as possible (rather than one or more preselected regions of interest [ROIs]), while overcoming the problem of inflated Type I error due to multiple regional comparisons. We developed a voxel-based data analysis method that capitalizes on all data in person's image and captures the extent to which the pattern and magnitude of a person's brain alterations, relative to a normal control (NC) group, correspond to the pattern and magnitude of the brain alterations in AD patients.

Here we used FDG-PET data from the multi-center AD Neuroimaging Initiative (ADNI), and an automated brain-mapping algorithm (SPM) to compute an “AD-related hypometabolic convergence index (HCI)” for each person. As described below, the HCI provides a single measurement of the extent to which a person's pattern and magnitude of cerebral hypometabolism correspond to that in a specific group—in this case clinically diagnosed AD patients. We compared the HCI to the more widely used measurements, namely, MRI measures of hippocampal volume, CSF assays, memory test scores and clinical ratings in their ability to distinguish probable AD patients, MCI patients who converted to probable AD in the next 18 months, MCI patients who remained stable during that time, and NCs. Finally, we examined biomarker, memory test score, and clinical rating thresholds that had optimal specificity and sensitivity to predict progression from MCI to probable AD within 18 months. We compared their ability to predict rates of progression from MCI to probable AD over the same time-period.

Section snippets

Participants

To date, ADNI has enrolled 819 adults 55–90 years of age, including 192 patients with mild probable AD, 398 patients with amnestic MCI (Petersen et al., 2001), and 229 NCs from 58 clinical sites in the United States and Canada. Mild AD patients had Mini-Mental State Examination (MMSE) (Folstein et al., 1975) scores of 20–26, had a Clinical Dementia Rating (CDR) global scores (Morris, 1993) of 0.5 or 1.0, and met NINCDS/ADRDA criteria for probable AD (McKhann et al., 1984). MCI patients had MMSE

Results

The probable AD, MCI converter, MCI stable, and NC groups' demographic characteristics, clinical ratings, and memory test scores, and their proportion of APOE ε4 homozygotes, heterozygotes and non-carriers are shown in Table 1. The groups did not differ in their mean age, gender distribution or educational level. As expected, the groups differed in their clinical ratings, memory test scores, and proportion of number APOE ε4 alleles.

As shown in Table 1 and Fig. 1, HCIs were different in the four

Discussion

This study introduces the concept and use of the AD-related HCI. Using data from ADNI, we showed it could distinguish between probable AD patients, MCI patients who did or did not convert within 18 months after baseline, and NCs. HCIs were closely associated with categorical measures of disease severity and significantly correlated with other AD biomarkers. Finally, we demonstrate possible advantages of using HCIs, alone or in combination with hippocampal volumes, over other promising

Acknowledgments

Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; Principal Investigator: Michael Weiner; NIH grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and through generous contributions from the following: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Merck & Co. Inc., AstraZeneca AB, Novartis

References (50)

  • K.K. Leung et al.

    Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease

    Neuroimage

    (2010)
  • J.H. Morra et al.

    Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer's disease mild cognitive impairment, and elderly controls

    Neuroimage

    (2008)
  • I. Yakushev et al.

    Choice of reference area in studies of Alzheimer's disease using positron emission tomography with fluorodeoxyglucose-F18

    Psychiatry Res.

    (2008)
  • G.E. Alexander et al.

    Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer's disease treatment studies

    Am. J. Psychiatry

    (2002)
  • A.L. Bokde et al.

    The effect of brain atrophy on cerebral hypometabolism in the visual variant of Alzheimer disease

    Arch. Neurol.

    (2001)
  • G.E. Christensen et al.

    Volumetric transformation of brain anatomy

    IEEE Trans. Med. Imaging

    (1997)
  • M. Chupin et al.

    Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI

    Hippocampus

    (2009)
  • M.M. Corrada et al.

    Prevalence of dementia after age 90. Results from The 90+ Study

    Neurology

    (2008)
  • D.A. Evans et al.

    Prevalence of Alzheimer's disease in a community population of older persons. Higher than previously reported

    JAMA

    (1989)
  • A.M. Fagan et al.

    Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults

    Arch. Neurol.

    (2007)
  • A.S. Fleisher et al.

    Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment

    Neurology

    (2008)
  • N.C. Fox et al.

    Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects

    Arch. Neurol.

    (2000)
  • V. Ibanez et al.

    Regional glucose metabolic abnormalities are not the result of atrophy in Alzheimer's disease

    Neurology

    (1998)
  • M.D. Ikonomovic et al.

    Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer's disease

    Brain

    (2008)
  • C.R. Jack et al.

    The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods

    J. Magn. Reson. Imaging

    (2008)
  • Cited by (0)

    1

    These two authors contributed equally to this research.

    2

    Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analyses or writing of this report. The ADNI investigator list is at www.loni.ucla.edu/ADNI/Data/ADNI_Authorship_List.pdf.

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