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Cross-validation of brain structural biomarkers and cognitive aging in a community-based study

Published online by Cambridge University Press:  16 March 2012

James T. Becker
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Ranjan Duara
Affiliation:
Wien Center for Alzheimer's Disease & Memory Disorders, Mt. Sinai Medical Center, Miami Beach, Florida, USA Miller School of Medicine, University of Miami, Coral Gables, Florida, USA Wertheim College of Medicine, Florida International University, Miami, Florida, USA
Ching-Wen Lee
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Leonid Teverovsky
Affiliation:
Department of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
Beth E. Snitz
Affiliation:
Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Chung-Chou H. Chang
Affiliation:
Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Mary Ganguli
Affiliation:
Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Corresponding
E-mail address:

Abstract

Background: Population-based studies face challenges in measuring brain structure relative to cognitive aging. We examined the feasibility of acquiring state-of-the-art brain MRI images at a community hospital, and attempted to cross-validate two independent approaches to image analysis.

Methods: Participants were 49 older adults (29 cognitively normal and 20 with mild cognitive impairment (MCI)) drawn from an ongoing cohort study, with annual clinical assessments within one month of scan, without overt cerebrovascular disease, and without dementia (Clinical Dementia Rating (CDR) < 1). Brain MRI images, acquired at the local hospital using the Alzheimer's Disease Neuroimaging Initiative protocol, were analyzed using (1) a visual atrophy rating scale and (2) a semi-automated voxel-level morphometric method. Atrophy and volume measures were examined in relation to cognitive classification (any MCI and amnestic MCI vs. normal cognition), CDR (0.5 vs. 0), and presumed etiology.

Results: Measures indicating greater atrophy or lesser volume of the hippocampal formation, the medial temporal lobe, and the dilation of the ventricular space were significantly associated with cognitive classification, CDR = 0.5, and presumed neurodegenerative etiology, independent of the image analytic method. Statistically significant correlations were also found between the visual ratings of medial temporal lobe atrophy and the semi-automated ratings of brain structural integrity.

Conclusions: High quality MRI data can be acquired and analyzed from older adults in population studies, enhancing their capacity to examine imaging biomarkers in relation to cognitive aging and dementia.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2012

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