ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
|Principal Investigator's Name:||Ozioma Okonkwo|
|Institution:||Brown Medical School|
|Proposed Analysis:||CEREBRAL ATROPHY, CEREBROVASCULAR DISEASE, AND FUNCTIONAL CHANGE IN MCI Background: There is a growing clinical and research interest in identifying older adults who, relative to their peers, are at elevated risk of developing Alzheimer?s disease (AD) and other dementias (Petersen et al., 2001). Arguably, this would permit timely therapeutic interventions that could potentially delay the progression of the pathology while offering the prospect of considerable reduction in the individual and societal cost of the disease (Burns & Zaudig, 2002; Petersen & Morris, 2005). Amnestic mild cognitive impairment (MCI) has become widely accepted as the classification for the group of individuals in the transitional stage between normal aging and AD (Gauthier et al., 2006). Current diagnostic criteria for MCI require generally intact functional abilities (Petersen et al., 2001). This stipulation reflects the observation that deterioration in the ability to perform activities necessary for independent functioning in the community is a hallmark of AD and related diseases (APA, 2000). However, several studies have shown that persons with MCI experience limitations in functional abilities relative to older adults without cognitive impairment (Artero, Touchon, & Ritchie, 2001; Okonkwo et al., 2007; Tuokko, Morris, & Ebert, 2005), and that restriction in functional abilities predicts progression to AD (Albert, Tabert, Dienstag, Pelton, & Devanand, 2002; Devanand et al., 2008). Neuroimaging has gained wide currency as a viable tool for studying the brain changes that accompany MCI. Evidence from numerous studies suggests that cerebral atrophy, particularly of mesial temporal structures, is a defining feature of MCI even in its earliest stages (C. D. Smith, Chebrolu, Markesbery, & Liu, 2008; Spulber et al., 2008). Other studies have reported strong correlations between cerebral atrophy, severity of cognitive impairment, and rate of cognitive decline in MCI (Desikan et al., 2008; Sluimer et al., 2008; Walhovd et al., 2008). There is also a growing realization that cerebrovascular disease, typically quantified as amount of white matter hyperintensities (WMH) on T2-weighted cranial MRI sequences, is prevalent among persons with MCI and is associated with decreased brain volume, cognitive impairment, cognitive decline, and progression to AD (DeCarli et al., 2001; E. E. Smith et al., 2008; van Straaten et al., 2008; Yoshita et al., 2006). In contrast with the advances that have been made in understanding the relationship between brain dysfunction and cognitive change in MCI, relative little effort has been devoted to uncovering the neuroanatomical basis of functional decline in MCI. For instance, it is unclear whether neuropathological alterations in specific brain regions uniquely underlie functional decline in MCI or whether functional decline is a result of global cerebral pathology (Cahn-Weiner et al., 2007; Farias, Mungas, Reed, Haan, & Jagust, 2004). This is a significant knowledge gap given the centrality of functional change to the clinical diagnosis of AD, the burden that such change imposes on patients and their families, and the present push to identity factors related to progression from MCI to AD such that appropriate interventions could be instituted early on (APA, 2000; Marson, 2002; Petersen & Morris, 2005). The overarching aim of this proposal is to investigate the functional significance of brain atrophy and cerebrovascular disease in MCI. Specifically, we would examine the relative contributions of mesial temporal atrophy, frontal lobe atrophy, global atrophy, and vascular disease (WMH and other vascular risk factors such as hypertension, diabetes, tobacco use, and atrial fibrillation) to functional abilities at baseline and over time among persons with MCI. Unique facets of this proposal include (i) determining how changes in these measures predict changes in functional abilities over time and (ii) determining whether cerebral atrophy and vascular disease exert an additive or multiplicative effect on prospective change in everyday functioning. For comparative purposes, we would also perform the same analyses within a group of demographically-matched healthy older adults. We hypothesize that measures of frontal atrophy and cerebrovascular disease, but not mesial temporal atrophy, would predict functional abilities in MCI. Specific Aims: 1. Examine the impact of cerebral atrophy and vascular disease on functional abilities in MCI Hypothesis: At baseline, measures of frontal atrophy, global atrophy, and vascular disease, but not mesial temporal atrophy, will be significantly correlated with functional abilities in MCI. 2. Examine the relative contributions of cerebral atrophy and vascular disease to rate of change in functional abilities in MCI Hypothesis 1: Measures of vascular disease will account for significant amounts of variance in change in functional abilities in MCI over and above variance accounted for by measures of cerebral atrophy. Hypothesis 2: Rate of change in vascular burden would combine multiplicatively with rate of change in cerebral atrophy to predict rate of change in functional abilities in MCI. Methods: Participants for the proposed study are all the MCI and control subjects enrolled in ADNI. Upon approval of the proposal by the ADNI committee, data will be downloaded from the website. The measures of interest include demographics, everyday functioning (Functional Activities Questionnaire, CDR sum of boxes), MRI volumetrics (frontal, mesial temporal, whole brain, and white matter), medical history, cognitive, neurologic (Hachinski), and genetics (APOE-4 status). Aim 1?s hypothesis will be tested using a block-entry linear regression model. Outcome measures will be CDR sum of boxes scores and total score on the Functional Activities Questionnaire (these outcomes will be analyzed in two separate models). It is expected that for both regression models, only the beta coefficients for frontal atrophy, global atrophy, and vascular disease will be significant. Aim 2 hypothesis 1 will be tested using hierarchical linear regression. Outcome measures will be change scores (baseline score minus score on last evaluation) on CDR sum of boxes and the Functional Activities Questionnaire. Measures of cerebral atrophy will be entered into the first block of the regression whereas measures of vascular disease will be entered in the second block. It is expected that measures of vascular disease will account for a significant portion of variance in the outcome measures over and above the variance accounted for by measures of cerebral atrophy. Aim 2 hypothesis 2 will be tested using random effects regression models. All variables will be treated as time varying to enable us test how changes in vascular burden and cerebral atrophy impact rate of change in everyday functioning (CDR sum of boxes scores and total score on the Functional Activities Questionnaire). The models tested under this hypothesis will include terms for main effects of vascular disease and cerebral atrophy and their interaction. It is expected that the interaction terms will be significant, providing support for the synergistic/multiplicative effect of vascular disease and cerebral atrophy on everyday functioning in MCI. Investigators: Ozioma Okonkwo, MA: predoctoral neuropsychology intern at the Dementia Research program of Brown Medical School, Providence, Rhode Island. Geoffrey Tremont, PhD: assistant professor of psychiatry, Brown Medical School, Providence, Rhode Island. Brian Ott, MD: professor of clinical neuroscience (neurology), Brown Medical School, Providence, Rhode Island. Lawrence Sweet, PhD: assistant professor of psychiatry, Brown Medical School, Providence, Rhode Island. Beth Jerskey, PhD: assistant professor of psychiatry, Brown Medical School, Providence, Rhode Island. References Albert, S. M., Tabert, M. H., Dienstag, A., Pelton, G., & Devanand, D. (2002). The impact of mild cognitive impairment on functional abilities in the elderly. Current Psychiatry Reports, 4, 64-68. APA. (2000). Diagnostic and statistical manual of mental disorders (4th, text revision ed.). Washington, DC: APA. Artero, S., Touchon, J., & Ritchie, K. (2001). Disability and mild cognitive impairment: A longitudinal population-based study. International Journal of Geriatric Psychiatry, 16, 1092-1097. Burns, A., & Zaudig, M. (2002). Mild cognitive impairment in older people. Lancet, 360, 1963-1965. Cahn-Weiner, D. A., Farias, S. T., Julian, L., Harvey, D. J., Kramer, J. H., Reed, B. R., et al. (2007). Cognitive and neuroimaging predictors of instrumental activities of daily living. Journal of the International Neuropsychological Society, 13, 747-757. DeCarli, C., Miller, B. L., Swan, G. E., Reed, T., Wolf, P. A., & Carmelli, D. (2001). Cerebrovascular and brain morphologic correlates of mild cognitive impairment in the National Heart, Lung, and Blood Institute Twin Study. Archives of Neurology, 58, 643-647. Desikan, R. S., Fischl, B., Cabral, H. J., Kemper, T. L., Guttmann, C. R., Blacker, D., et al. (2008). MRI measures of temporoparietal regions show differential rates of atrophy during prodromal AD. Neurology, 71, 819-825. Devanand, D. P., Liu, X., Tabert, M. H., Pradhaban, G., Cuasay, K., Bell, K., et al. (2008). Combining early markers strongly predicts conversion from mild cognitive impairment to Alzheimer's disease. Biological Psychiatry. Farias, S. T., Mungas, D., Reed, B., Haan, M. N., & Jagust, W. J. (2004). Everyday functioning in relation to cognitive functioning and neuroimaging in community-dwelling Hispanic and non-Hispanic older adults. Journal of the International Neuropsychological Society, 10, 342-354. Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C., Ritchie, K., Broich, K., et al. (2006). Mild cognitive impairment. Lancet, 367, 1262-1270. Marson, D. C. (2002). Competency assessment and research in an aging society. Generations, 26, 99-102. Okonkwo, O. C., Griffith, H. R., Belue, K., Lanza, S., Zamrini, E., Harrell, L. E., et al. (2007). Medical decision-making capacity in patients with mild cognitive impairment. Neurology, 69(15), 1528-1535. Petersen, R. C., & Morris, J. C. (2005). Mild cognitive impairment as a clinical entity and treatment target. Archives of Neurology, 62, 1160-1163. Petersen, R. C., Stevens, J. C., Ganguli, M., Tangalos, E., Cummings, J. L., & DeKosky, S. T. (2001). Practice parameter: Early detection of dementia: Mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology, 56, 1133-1142. Sluimer, J. D., van der Flier, W. M., Karas, G. B., Fox, N. C., Scheltens, P., Barkhof, F., et al. (2008). Whole-brain atrophy rate and cognitive decline: longitudinal MR study of memory clinic patients. Radiology, 248, 590-598. Smith, C. D., Chebrolu, H., Markesbery, W. R., & Liu, J. (2008). Improved predictive model for presymptomatic mild cognitive impairment and Alzheimer's disease. Neurological Research. Smith, E. E., Egorova, S., Blacker, D., Killiany, R. J., Muzikansky, A., Dickerson, B. C., et al. (2008). Magnetic resonance imaging white matter hyperintensities and brain volume in the prediction of mild cognitive impairment and dementia. Archives of Neurology, 65, 94-100. Spulber, G., Niskanen, E., Macdonald, S., Smilovici, O., Chen, K., Reiman, E. M., et al. (2008). Whole brain atrophy rate predicts progression from MCI to Alzheimer's disease. Neurobiology of Aging. Tuokko, H., Morris, C., & Ebert, P. (2005). Mild cognitive impairment and everyday functioning in older adults. Neurocase, 11, 40-47. van Straaten, E. C., Harvey, D., Scheltens, P., Barkhof, F., Petersen, R. C., Thal, L. J., et al. (2008). Periventricular white matter hyperintensities increase the likelihood of progression from amnestic mild cognitive impairment to dementia. Journal of Neurology. Walhovd, K. B., Fjell, A. M., Dale, A. M., McEvoy, L. K., Brewer, J., Karow, D. S., et al. (2008). Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiology of Aging. Yoshita, M., Fletcher, E., Harvey, D., Ortega, M., Martinez, O., Mungas, D. M., et al. (2006). Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurology, 67(1), 2192-2198.|
|Investigator's Name:||Geoffrey Tremont|
|Proposed Analysis:||Same as for Ozioma OKonkwo|