This renewal application of the MRI, Genetics and Cognitive Precursors of AD and Dementia compares which of 3 approaches for diagnosing Mild Cognitive Impairment (MCI) is best at accurately predicting incident clinical dementia. Of the two primary diagnostic methods currently used, psychometrically defined and clinical determined, application of criteria for psychometrically defined has been most variable. Further, a novel algorithmic method is emerging as an alternative unbiased approach. Using commonly applied clinical tools of cognitive performance and brain structure obtained from baseline and repeat administration of neuropsychological (NP) tests and magnetic imaging resonance (MRI) scans, this proposed project will first determine what profile of cross-sectional or longitudinal neuropsychological tests and threshold performance levels, combined with imaging markers is the most efficacious in predicting progression to dementia, particularly Alzheimer's disease. Another set of analyses will test the algorithmic method. The diagnostic models that emerge from these two sets of analyses will then be compared to diagnoses of MCI using standard clinical criteria to determine which of the three methods best predicts incident dementia. The clinical diagnoses of MCI and dementia are available through a separately funded initiative. Also central to this grant proposal is the inclusion of external seasoned investigators, who will provid their expertise in definition and study of MCI and the factors influencing progression to disease. This effort aims to provide enhanced sensitivity in application of NP and MRI tools to detect subtle impairment at an earlier stage on the preclinical continuum and improved specificity in determining the MCI subtypes most highly associated with AD versus other types of dementia. Increased MCI diagnostic accuracy will enhance opportunity for more effective interventions, which have been largely disappointing to date, and could eventually leading to a delay in the onset or prevention of the clinical expression of AD.

Public Health Relevance

The MRI, Genetics and Cognitive Precursors of AD and Dementia seeks to identify the most effective method and criteria for diagnosis of mild cognitive impairment (MCI) that is best predictive of incident Alzheimer's disease. Current efforts to treat AD have been ineffective because intervention occurs too late in the insidious process. The potential public health relevance of this proposed project is increased accuracy of diagnosis at the preclinical stage, where treatment opportunities may prove to be more successful and prevention is most feasible.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG016495-12
Application #
8545660
Study Section
Special Emphasis Panel (NOIT)
Program Officer
Anderson, Dallas
Project Start
1998-12-01
Project End
2017-05-31
Budget Start
2013-09-01
Budget End
2014-05-31
Support Year
12
Fiscal Year
2013
Total Cost
$609,801
Indirect Cost
$94,712
Name
Boston University
Department
Neurology
Type
Schools of Medicine
DUNS #
604483045
City
Boston
State
MA
Country
United States
Zip Code
02118
Tynkkynen, Juho; Chouraki, Vincent; van der Lee, Sven J et al. (2018) Association of branched-chain amino acids and other circulating metabolites with risk of incident dementia and Alzheimer's disease: A prospective study in eight cohorts. Alzheimers Dement 14:723-733
Li, Jinlei; Ogrodnik, Matthew; Devine, Sherral et al. (2018) Practical risk score for 5-, 10-, and 20-year prediction of dementia in elderly persons: Framingham Heart Study. Alzheimers Dement 14:35-42
Bianciardi, Marta; Strong, Christian; Toschi, Nicola et al. (2018) A probabilistic template of human mesopontine tegmental nuclei from in vivo 7T MRI. Neuroimage 170:222-230
Aganj, Iman; Harisinghani, Mukesh G; Weissleder, Ralph et al. (2018) Unsupervised Medical Image Segmentation Based on the Local Center of Mass. Sci Rep 8:13012
Wu, Jianxiao; Ngo, Gia H; Greve, Douglas et al. (2018) Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems. Hum Brain Mapp :
Magnain, Caroline; Augustinack, Jean C; Tirrell, Lee et al. (2018) Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann's area 32 and area 21. Brain Struct Funct :
Li, Yi; Barkovich, Matthew J; Karch, Celeste M et al. (2018) Regionally specific TSC1 and TSC2 gene expression in tuberous sclerosis complex. Sci Rep 8:13373
Siless, Viviana; Chang, Ken; Fischl, Bruce et al. (2018) AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity. Neuroimage 166:32-45
Polimeni, Jonathan R; Renvall, Ville; Zaretskaya, Natalia et al. (2018) Analysis strategies for high-resolution UHF-fMRI data. Neuroimage 168:296-320
Zaretskaya, Natalia; Fischl, Bruce; Reuter, Martin et al. (2018) Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE. Neuroimage 165:11-26

Showing the most recent 10 out of 199 publications