Characterizing Alzheimer's disease using a hypometabolic convergence index
Kewei Chen1, Napatkamon Ayutyanont, Jessica B S Langbaum
1Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA. Kewei.Chen@bannerhealth.com
Neuroimage
|February 1, 2011
Summary
A new hypometabolic convergence index (HCI) shows promise for assessing Alzheimer's disease (AD) and predicting cognitive decline in mild cognitive impairment (MCI) patients. This novel biomarker, derived from FDG-PET scans, offers a single-measurement approach to disease characterization.
Area of Science:
- Neuroimaging
- Biomarker Discovery
- Neurology
Background:
- Alzheimer's disease (AD) diagnosis and prognosis rely on multifaceted assessments.
- Current methods for predicting clinical decline in mild cognitive impairment (MCI) require improvement.
- Standardized, quantitative imaging biomarkers are needed for early AD detection.
Purpose of the Study:
- To introduce and validate a novel hypometabolic convergence index (HCI) for Alzheimer's disease (AD) assessment.
- To compare the efficacy of HCI against existing biomarkers (MRI, CSF, cognitive tests) in characterizing AD and predicting MCI conversion.
- To evaluate the predictive power of HCI for clinical decline in MCI patients using AD Neuroimaging Initiative (ADNI) data.
Main Methods:
- Developed a fully automated voxel-based algorithm to generate the HCI from fluorodeoxyglucose positron emission tomography (FDG-PET) scans.
- Compared HCI with MRI hippocampal volume, CSF assays, memory scores, and clinical ratings in probable AD, MCI converters, stable MCI, and normal controls (NCs).
- Utilized survival analysis to determine hazard ratios (HRs) for MCI to probable AD progression based on HCI and hippocampal volume.
Main Results:
- HCI demonstrated significant differences across diagnostic groups (probable AD, MCI converters, stable MCI, NCs) with p=9e-17.
- HCI correlated significantly with clinical disease severity in Alzheimer's disease.
- MCI patients with higher HCIs or smaller hippocampal volumes exhibited substantially increased hazard ratios for 18-month progression to probable AD (HRs 7.38 and 6.34, respectively); combined, the HR was 36.72.
Conclusions:
- The hypometabolic convergence index (HCI) shows significant potential as a biomarker for characterizing Alzheimer's disease (AD).
- HCI, particularly when combined with other biomarkers like hippocampal volume, can effectively predict clinical decline in patients with mild cognitive impairment (MCI).
- The conversion index strategy employed for HCI development is adaptable to various imaging modalities and analysis algorithms for broader neurodegenerative disease research.
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