Characterizing Alzheimer's disease using a hypometabolic convergence index

Kewei Chen, Napatkamon Ayutyanont, Jessica B S Langbaum, Adam S. Fleisher, Cole Reschke, Wendy Lee, Xiaofen Liu, Dan Bandy, Gene E. Alexander, Paul M. Thompson, Leslie Shaw, John Q. Trojanowski, Clifford R. Jack, Susan M. Landau, Norman L. Foster, Danielle J Harvey, Michael W. Weiner, Robert A. Koeppe, William J. Jagust, Eric M. Reiman

Research output: Contribution to journalArticle

107 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)52-60
Number of pages9
JournalNeuroImage
Volume56
Issue number1
DOIs
StatePublished - May 1 2011

Fingerprint

Alzheimer Disease
Aptitude
Cognitive Dysfunction
Neuroimaging
Positron-Emission Tomography
Cerebrospinal Fluid
Biomarkers
Magnetic Resonance Imaging
Control Groups

Keywords

  • Alzheimer's disease
  • FDG
  • Hippocampal volume
  • Hypometabolic convergence index
  • MCI
  • PET

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Chen, K., Ayutyanont, N., Langbaum, J. B. S., Fleisher, A. S., Reschke, C., Lee, W., ... Reiman, E. M. (2011). Characterizing Alzheimer's disease using a hypometabolic convergence index. NeuroImage, 56(1), 52-60. https://doi.org/10.1016/j.neuroimage.2011.01.049

Characterizing Alzheimer's disease using a hypometabolic convergence index. / Chen, Kewei; Ayutyanont, Napatkamon; Langbaum, Jessica B S; Fleisher, Adam S.; Reschke, Cole; Lee, Wendy; Liu, Xiaofen; Bandy, Dan; Alexander, Gene E.; Thompson, Paul M.; Shaw, Leslie; Trojanowski, John Q.; Jack, Clifford R.; Landau, Susan M.; Foster, Norman L.; Harvey, Danielle J; Weiner, Michael W.; Koeppe, Robert A.; Jagust, William J.; Reiman, Eric M.

In: NeuroImage, Vol. 56, No. 1, 01.05.2011, p. 52-60.

Research output: Contribution to journalArticle

Chen, K, Ayutyanont, N, Langbaum, JBS, Fleisher, AS, Reschke, C, Lee, W, Liu, X, Bandy, D, Alexander, GE, Thompson, PM, Shaw, L, Trojanowski, JQ, Jack, CR, Landau, SM, Foster, NL, Harvey, DJ, Weiner, MW, Koeppe, RA, Jagust, WJ & Reiman, EM 2011, 'Characterizing Alzheimer's disease using a hypometabolic convergence index', NeuroImage, vol. 56, no. 1, pp. 52-60. https://doi.org/10.1016/j.neuroimage.2011.01.049
Chen K, Ayutyanont N, Langbaum JBS, Fleisher AS, Reschke C, Lee W et al. Characterizing Alzheimer's disease using a hypometabolic convergence index. NeuroImage. 2011 May 1;56(1):52-60. https://doi.org/10.1016/j.neuroimage.2011.01.049
Chen, Kewei ; Ayutyanont, Napatkamon ; Langbaum, Jessica B S ; Fleisher, Adam S. ; Reschke, Cole ; Lee, Wendy ; Liu, Xiaofen ; Bandy, Dan ; Alexander, Gene E. ; Thompson, Paul M. ; Shaw, Leslie ; Trojanowski, John Q. ; Jack, Clifford R. ; Landau, Susan M. ; Foster, Norman L. ; Harvey, Danielle J ; Weiner, Michael W. ; Koeppe, Robert A. ; Jagust, William J. ; Reiman, Eric M. / Characterizing Alzheimer's disease using a hypometabolic convergence index. In: NeuroImage. 2011 ; Vol. 56, No. 1. pp. 52-60.
@article{b060bb3eaf674cb2936551fbadf2b2e7,
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 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.",
keywords = "Alzheimer's disease, FDG, Hippocampal volume, Hypometabolic convergence index, MCI, PET",
author = "Kewei Chen and Napatkamon Ayutyanont and Langbaum, {Jessica B S} and Fleisher, {Adam S.} and Cole Reschke and Wendy Lee and Xiaofen Liu and Dan Bandy and Alexander, {Gene E.} and Thompson, {Paul M.} and Leslie Shaw and Trojanowski, {John Q.} and Jack, {Clifford R.} and Landau, {Susan M.} and Foster, {Norman L.} and Harvey, {Danielle J} and Weiner, {Michael W.} and Koeppe, {Robert A.} and Jagust, {William J.} and Reiman, {Eric M.}",
year = "2011",
month = "5",
day = "1",
doi = "10.1016/j.neuroimage.2011.01.049",
language = "English (US)",
volume = "56",
pages = "52--60",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "1",

}

TY - JOUR

T1 - Characterizing Alzheimer's disease using a hypometabolic convergence index

AU - Chen, Kewei

AU - Ayutyanont, Napatkamon

AU - Langbaum, Jessica B S

AU - Fleisher, Adam S.

AU - Reschke, Cole

AU - Lee, Wendy

AU - Liu, Xiaofen

AU - Bandy, Dan

AU - Alexander, Gene E.

AU - Thompson, Paul M.

AU - Shaw, Leslie

AU - Trojanowski, John Q.

AU - Jack, Clifford R.

AU - Landau, Susan M.

AU - Foster, Norman L.

AU - Harvey, Danielle J

AU - Weiner, Michael W.

AU - Koeppe, Robert A.

AU - Jagust, William J.

AU - Reiman, Eric M.

PY - 2011/5/1

Y1 - 2011/5/1

N2 - 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.

AB - 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.

KW - Alzheimer's disease

KW - FDG

KW - Hippocampal volume

KW - Hypometabolic convergence index

KW - MCI

KW - PET

UR - http://www.scopus.com/inward/record.url?scp=79953041925&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79953041925&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2011.01.049

DO - 10.1016/j.neuroimage.2011.01.049

M3 - Article

C2 - 21276856

AN - SCOPUS:79953041925

VL - 56

SP - 52

EP - 60

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 1

ER -