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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 ,
Ranjan Duara ,
Ching-Wen Lee ,
Leonid Teverovsky ,
Beth E. Snitz ,
Chung-Chou H. Chang  and
Mary Ganguli
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
*
Correspondence should be addressed to: James T. Becker, PhD, Neuropsychology Research Program, Department of Psychiatry, University of Pittsburgh, Suite 830, 3501 Forbes Avenue, Pittsburgh, PA 15213, USA. Phone: +1 412-246-6970; Fax: +1 412-246-6873. Email: [email protected].
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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.

Keywords

agingAlzheimer's diseasecognitive disordersdementiaimagingneuroepidemiology
Type
Research Article
Information
International Psychogeriatrics , Volume 24 , Issue 7 , July 2012 , pp. 1065 - 1075
Copyright
Copyright © International Psychogeriatric Association 2012

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References

Breteler, M. M. (2011). Mapping out biomarkers for Alzheimer disease. JAMA, 305, 304–305.CrossRefGoogle Scholar
Carmichael, O. T. et al. (2005a). Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment. Neuroimage, 27, 979–990.Google Scholar
Carmichael, O. T. et al. (2005b). Dementia-associated ventricular volume changes in a community cohort. Neuroimage, 26, S47.Google Scholar
Carmichael, O. T. et al. (2007). Cerebral ventricular changes associated with transitions between normal cognitive function, mild cognitive impairment, and dementia. Alzheimer Disease and Related Disorders, 21, 14–24.Google Scholar
Chui, H. C., Victoroff, J. I., Margolin, D., Jagust, W., Shankle, R. and Katzman, R. (1992). Criteria for the diagnosis of ischemic vascular dementia proposed by the State of California Alzheimer's Disease Diagnostic and Treatment Centers. Neurology, 42, 473–480.Google Scholar
de Leon, M. J. et al. (1984). Positron emission tomography and computed tomography assessments of the aging human brain. Journal of Computer Assisted Tomography, 8, 88–94.Google Scholar
de Leon, M. J., George, A. E. and Reisberg, B. (1989). Alzheimer's disease: longitudinal CT studies of ventricular change. American Journal of Roentgenology, 152, 1257–1262.CrossRefGoogle Scholar
de Leon, M. J. et al. (1993). The radiologic prediction of Alzheimer disease: the atrophic hippocampal formation. American Journal of Neuroradiology, 14, 897–906.Google Scholar
Duara, R. et al. (2008). Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer disease. Neurology, 71, 1986–1992.Google Scholar
Dubois, B., Picard, G. and Sarazin, M. (2009). Early detection of Alzheimer's disease: new diagnostic criteria. Dialogues in Clinical Neurosciences, 11, 135–139.Google Scholar
Erickson, K. I. et al. (2010). Physical activity predicts gray matter volume in late adulthood: the cardiovascular health study. Neurology, 75, 1415–1422.CrossRefGoogle Scholar
Folstein, M. F., Folstein, S. E. and McHugh, P. R. (1975). “Mini-mental state”: a practical method grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.Google Scholar
Ganguli, M., Snitz, B., Vander Bilt, J. and Chang, C. C. (2009). How much do depressive symptoms affect cognition at the population level? The Monongahela-Youghiogheny Healthy Aging Team (MYHAT) study. International Journal of Geriatric Psychiatry, 24, 1277–1284.CrossRefGoogle Scholar
Ganguli, M., Chang, C. C., Snitz, B. E., Saxton, J. A., Vanderbilt, J. and Lee, C. W. (2010a). Prevalence of mild cognitive impairment by multiple classifications: the Monongahela-Youghiogheny Healthy Aging Team (MYHAT) project. American Journal of Geriatric Psychiatry, 18, 674–683.Google Scholar
Ganguli, M., Snitz, B. E., Lee, C. W., Vanderbilt, J., Saxton, J. A. and Chang, C. C. (2010b). Age and education effects and norms on a cognitive test battery from a population-based cohort: the Monongahela-Youghiogheny Healthy Aging Team. Aging and Mental Health, 14, 100–107.Google Scholar
Gorelick, P. B. et al. (2011). Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 42, 2672–2713.Google Scholar
Hughes, C. P., Berg, L. and Danzinger, W. L. (1982). A new clinical scale for the staging of dementia. British Journal of Psychiatry, 140, 566–572.CrossRefGoogle Scholar
McKhann, G. M. et al. (2011). The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging and the Alzheimer's Association workgroup. Alzheimer's and Dementia, 7, 263–269.Google Scholar
Morris, J. C. et al. (1993). The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part IV. Rates of cognitive change in the longitudinal assessment of probable Alzheimer's disease. Neurology, 43, 2457–2465.Google Scholar
Mueller, S. G. et al. (2005). The Alzheimer's disease neuroimaging initiative. Neuroimaging Clinics of North America, 15, 869–877.Google Scholar
Mungas, D., Reed, B. R., Tomaszewski-Farias, S. and DeCarli, C. (2005). Criterion-referenced validity of a neuropsychological test battery: equivalent performance in elderly Hispanics and non-Hispanic Whites. Journal of the International Neuropsychological Society, 11, 620–630.Google Scholar
Patenaude, B., Smith, S. M., Kennedy, D. N. and Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage, 56, 907–922.Google Scholar
Raji, C. A., Lopez, O. L., Kuller, L. H., Carmichael, O. T. and Becker, J. T. (2009). Age, Alzheimer disease, and brain structure. Neurology, 73, 1899–1905.Google Scholar
Raji, C. A. et al. (2010). Brain structure and obesity. Human Brain Mapping, 31, 353–364.Google Scholar
Raji, C. A. et al. (2012). White matter lesions and brain gray matter volume in cognitively normal elders. Neurobiology of Aging, 33, 834.e7–834.e16. Epub 2011 Sep 23.Google Scholar
Román, G. C. et al. (1993). Vascular dementia: diagnostic criteria for research studies: report of the NINDS-AIREN International Workshop. Neurology, 43, 250–260.CrossRefGoogle Scholar
Satz, P. (1991). Brain reserve capacity on symptom onset after brain injury: a formulation and review of evidence of threshold theory. Neuropsychology, 7, 273–295.Google Scholar
Satz, P. et al. (1993). Low education as a possible risk factor for early cognitive abnormalities in HIV1: new findings from the Multicenter AIDS Cohort Study (MACS). Journal of Acquired Immune Deficiency Syndromes, 6, 503–511.CrossRefGoogle Scholar
Stern, Y., Alexander, G. E., Prohovnik, I. and Mayeux, R. (1992). Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer's disease. Annals of Neurology, 32, 371–375.Google Scholar
Teverovsky, L. et al. (2011). Brain extraction by deformable registration, Presented at the 17th Annual Meeting of the Ogranizagtion for Human Brain Mapping, Quebec City, June 26–30.Google Scholar
Urs, R. et al. (2009). Visual rating system for assessing magnetic resonance images: a tool in the diagnosis of mild cognitive impairment and Alzheimer disease. Journal of Computer Assisted Tomography, 33, 73–78.Google Scholar
Weir, D. R. et al. (2011). Reducing case ascertainment costs in US population studies of Alzheimer's disease, dementia, and cognitive impairment–part 1. Alzheimer's and Dementia, 7, 94–109.Google Scholar