of work: The neuroanatomic and neurophysiologic underpinnings of age- associated cognitive and memory change remain unclear, as there is little information on longitudinal brain changes. We are performing annual magnetic resonance imaging (MRI), positron emission tomography (PET), and neuropsychological assessments in participants from the Baltimore Longitudinal Study of Aging (BLSA) to investigate the neurobiological basis of memory change. These evaluations will allow us to examine changes in brain structure and function which may be early predictors of cognitive change and impairment, including Alzheimer's Disease (AD). An understanding of these associations and early detection of brain changes will be critical in identifying individuals likely to benefit from new interventions. In addition, we are using neuroimaging tools to investigate modulators of cognitive and brain changes, including genetic risk factors and the effects of sex steroid and other hormones. We are performing studies of the effects of estrogen and androgenic hormones on cognition and the brain in older women and men, respectively. Over the last year, we continued the development and validation of new tools for processing images for longitudinal studies. We validated our approach for analysis of structural MRI data and developed an approach for landmark-based quantification of hippocampal volumes for large data sets. We applied these tools and published a report demonstrating cross-sectional and longitudinal age effects on white matter signal intensities on MR images from 91 participants in our longitudinal study. These age-related changes were independent of changes in brain volumes and indicated qualitative changes in tissue compososition. We have identified specific brain regions that show longitudinal volume changes even in very healthy elderly adults. These data have guided our development of specific probes for examination of cognitive and brain changes in the elderly. We performed and published studies of age differences in spatial navigation using computer-based virtual environments and are currently examining the neural basis of these age differences using functional magnetic resonance imaging. We continue to examine the effects of hormones and other possible modulators of brain-behavior associations in aging.
| Spira, Adam P; Gamaldo, Alyssa A; An, Yang et al. (2013) Self-reported sleep and ?-amyloid deposition in community-dwelling older adults. JAMA Neurol 70:1537-43 |
| Filipovych, Roman; Resnick, Susan M; Davatzikos, Christos (2012) JointMMCC: joint maximum-margin classification and clustering of imaging data. IEEE Trans Med Imaging 31:1124-40 |
| Hohman, Timothy J; Beason-Held, Lori L; Lamar, Melissa et al. (2011) Subjective cognitive complaints and longitudinal changes in memory and brain function. Neuropsychology 25:125-30 |
| Driscoll, Ira; Espeland, Mark A; Wassertheil-Smoller, Sylvia et al. (2011) Weight change and cognitive function: findings from the Women's Health Initiative Study of Cognitive Aging. Obesity (Silver Spring) 19:1595-600 |
| Espeland, Mark A; Shumaker, Sally A; Limacher, Marian et al. (2010) Relative effects of tamoxifen, raloxifene, and conjugated equine estrogens on cognition. J Womens Health (Larchmt) 19:371-9 |
| Driscoll, I; Davatzikos, C; An, Y et al. (2009) Longitudinal pattern of regional brain volume change differentiates normal aging from MCI. Neurology 72:1906-13 |
| O'Brien, Richard J; Resnick, Susan M; Zonderman, Alan B et al. (2009) Neuropathologic studies of the Baltimore Longitudinal Study of Aging (BLSA). J Alzheimers Dis 18:665-75 |
| Dotson, Vonetta M; Zonderman, Alan B; Davatzikos, Christos et al. (2009) Frontal Atrophy and Attention Deficits in Older Adults with a History of Elevated Depressive Symptoms. Brain Imaging Behav 3:358 |
| Davatzikos, C; Resnick, S M; Wu, X et al. (2008) Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. Neuroimage 41:1220-7 |
| Davatzikos, Christos; Fan, Yong; Wu, Xiaoying et al. (2008) Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 29:514-23 |
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