Characterizing Alzheimer's disease using a hypometabolic convergence index
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
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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.