Gene E Alexander

Gene E Alexander

Professor, Psychology
Professor, Psychiatry
Professor, Evelyn F Mcknight Brain Institute
Professor, Neuroscience - GIDP
Professor, Physiological Sciences - GIDP
Professor, BIO5 Institute
Primary Department
Department Affiliations
Contact
(520) 626-1704

Work Summary

My research focuses on advancing our understanding of how and why aging impacts the brain and associated cognitive abilities. I use neuroimaging scans of brain function and structure together with measures of cognition and health status to identify those factors that influence brain aging and the risk for Alzheimer's disease. My work also includes identifying how health and lifestyle interventions can help to delay or prevent the effects of brain aging and Alzheimer's disease.

Research Interest

Dr. Alexander is Professor in the Departments of Psychology and Psychiatry, the Evelyn F. McKnight Brain Institute, and the Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs of the University of Arizona. He is Director of the Brain Imaging, Behavior and Aging Lab, a member of the Internal Scientific Advisory Committee for the Arizona Alzheimer’s Consortium, and a member of the Scientific Advisory Board for the Arizona Evelyn F. McKnight Brain Institute. He received his post-doctoral training in neuroimaging and neuropsychology at Columbia University Medical Center and the New York State Psychiatric Institute. Prior to coming to Arizona, Dr. Alexander was Chief of the Neuropsychology Unit in the Laboratory of Neurosciences in the Intramural Research Program at the National Institute on Aging. Dr. Alexander has over 20 years experience as a neuroimaging and neuropsychology researcher in the study of aging and age-related neurodegenerative disease. He is a Fellow of the Association for Psychological Science and the American Psychological Association (Division 40) Society for Clinical Neuropsychology. His research has been supported by grants from the National Institutes of Health, the Evelyn F. McKnight Brain Research Foundation, the State of Arizona, and the Alzheimer’s Association. He uses structural and functional magnetic resonance imaging (MRI) and positron emission tomography (PET) combined with measures of cognition and behavior to investigate the effects of multiple health and lifestyle factors on the brain changes associated with aging and the risk for Alzheimer’s disease. Keywords: "Aging/Age-Related Disease", "Brain Imaging", "Cognitive Neurosicence", "Alzheimer's Disease"

Publications

Reiman, E. M., Quiroz, Y. T., Fleisher, A. S., Chen, K., Velez-Pardo, C., Jimenez-Del-Rio, M., Fagan, A. M., Shah, A. R., Alvarez, S., Arbelaez A, A., Giraldo, M., Acosta-Baena, N., Sperling, R. A., Dickerson, B., Stern, C. E., Tirado, V., Munoz, C., Reiman, R. A., Huentelman, M. J., , Alexander, G. E., et al. (2012). Brain abnormalities in young adults at genetic risk for autosomal dominant Alzheimer’s disease. The Lancet Neurology, 11, 1048-56.
Yoshimaru, E., Totenhagen, J., Alexander, G. E., & Trouard, T. P. (2014). Design, manufacture, and analysis of customized phantoms for enhanced quality control in small animal MRI systems. Magn Reson Med, 71, 880-84.
Roberson, E. D., DeFazio, R. A., Barnes, C. A., Alexander, G. E., Bizon, J. L., Bowers, D., Foster, T. C., Glisky, E. L., Levin, B. E., Ryan, L., Wright, C. B., & Geldmacher, D. S. (2012). Challenges and opportunities in characterizing cognitive aging across species. Frontiers in Aging Neuroscience, 4, 6.
Chen, K., Reiman, E. M., Alexander, G. E., Caselli, R. J., Gerkin, R., Bandy, D., Domb, A., Osborne, D., Fox, N., Crum, W. R., Saunders, A. M., & Hardy, J. (2007). Correlations between apolipoprotein E ε4 gene dose and whole brain atrophy rates. American Journal of Psychiatry, 164(6), 916-921.

PMID: 17541051;Abstract:

Objective: The purpose of this study was to characterize the relationship between whole brain atrophy rates and three levels of genetic risk for Alzheimer's disease in cognitively normal persons. The authors previously found accelerated whole brain atrophy rates in patients with probable Alzheimer's disease by computing changes in brain volume from sequential magnetic resonance images (MRIs). Methods: The authors assessed 36 late-middle-aged persons from three genetic groups: those with two, one, and no copies of the apolipoprotein E (APOE) ε4 allele, a common Alzheimer's disease susceptibility gene. The participants had clinical ratings, neuropsychological tests, and volumetric T1-weighted MRIs during a baseline visit and again approximately 2 years later. Two different image-analysis techniques, brain boundary shift integration and iterative principal component analysis, were used to compute whole brain atrophy rates. Results: While there were no baseline, follow-up, or between-visit differences in the clinical ratings or neuropsychological test scores among the three subject groups, whole brain atrophy rates were significantly greater in the ε4 homozygote group than in noncarriers and were significantly correlated with ε4 gene dose (i.e., the number of ε4 alleles in a person's APOE genotype). Conclusion: Since APOE ε4 gene dose is associated with an increased risk of Alzheimer's disease and a younger median age at dementia onset, this study suggests an association between the risk of Alzheimer's disease and accelerated brain atrophy rates before the onset of cognitive impairment.

Lin, L., Chen, K., Alexander, G. E., Jiping, H. e., & Reiman, E. M. (2002). Adaptive smoothing strategies to eliminate the scalp/ventricle artifact in statistical parametric mapping. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2, 1039-1040.

Abstract:

In positron emission tomographic (PET) or magnetic resonance imaging (MRI) neuroimaging studies, spatial smoothing technique is commonly applied to increase the signal to noise ratio and to condition neoroimaging data for subsequent statistical analysis, and to reduce errors associated with registration or spatial normalizations. Usually, the smoothing step is applied to the images without any masking. Thus, some artifacts adjacent but outside of the brain will enter the brain volume. Masking the brain volume before smoothing has been suggested as one way to eliminate the introduced artifact, but it will introduce zero-in (intensities within-mask voxels are reduced) and nonzero-out (intensities outside the mask become non-zero) artifacts. Here we proposed an adaptive smoothing method to reduce the influence of such artifacts. Unlike the conventional smoothing method, the adaptive strategy did not introduce artificial addition (due to nonzero-out artifact) and deletion (due to zero-in), suggesting that such adaptive smoothing methods may be helpful in reducing the influence of non-brain tissue in the analysis of neuroimaging data.