Research Project 1: Explicit memory deficits are characteristic of amnestic mild cognitive impairment (aMCl), a condition that often precedes Alzheimer's disease. Although cognitive rehabilitation has Improved memory functioning following other types of neurologic Injury, there is a striking lack of research investigating such methods In aMCl. The proposed project builds directly on the promising results of our ADRC sponsored pilot study that has demonstrated significant behavioral Improvement and increased brain activity as measured by functional magnetic resonance Imaging (fMRI) following training in the use of mnemonic strategies (i.e. explicit memory training). A total of 75 patients with aMCl will be randomly assigned to either 1) explicit memory training 2) rehearsal-based training that relies on preserved implicit memory abilities (spaced retrieval) or 3) an educational control group. Patients will undergo pre- and post-training fMRI while they attempt to learn and remember object-location associations. Between these fMRI sessions, the treatment groups will receive three training sessions that Include both didactics and practice periods (i.e. when patients apply the strategies to associations), or just didactics forthe control group. Long-term retention will be assessed at 1 month. This design will allow us to directly compare the behavioral changes resulting from each Intervention (Specific Aim 1) as well as the neural substrates of these approaches (Specific Aim 2). We will also Identify individual characteristics that affect treatment effectiveness, which could be crucial for the development and selection of appropriate Interventions for future patients (Specific Aim 3).

Public Health Relevance

The Innovative study design may provide substantial insight Into the types of cognitive rehabilitation techniques that are most effective in aMCl. Identification of preserved and/or compensatory neural networks could foster the development of additional techniques that specifically utilize such areas. This line of Investigation could ultimately have significant public health Impact by delaying functional progression to Alzheimer's disease thereby reducing the costs associated with this rapidly growing population.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Specialized Center (P50)
Project #
5P50AG025688-10
Application #
8662667
Study Section
Special Emphasis Panel (ZAG1-ZIJ-4)
Project Start
Project End
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
10
Fiscal Year
2014
Total Cost
$181,998
Indirect Cost
$64,580
Name
Emory University
Department
Type
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322
Bishof, Isaac; Dammer, Eric B; Duong, Duc M et al. (2018) RNA-binding proteins with basic-acidic dipeptide (BAD) domains self-assemble and aggregate in Alzheimer's disease. J Biol Chem 293:11047-11066
Peng, Katherine Y; Pérez-González, Rocío; Alldred, Melissa J et al. (2018) Apolipoprotein E4 genotype compromises brain exosome production. Brain :
Gangishetti, Umesh; Christina Howell, J; Perrin, Richard J et al. (2018) Non-beta-amyloid/tau cerebrospinal fluid markers inform staging and progression in Alzheimer's disease. Alzheimers Res Ther 10:98
Zhang, Qi; Ma, Cheng; Gearing, Marla et al. (2018) Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer's disease. Acta Neuropathol Commun 6:19
Umoh, Mfon E; Dammer, Eric B; Dai, Jingting et al. (2018) A proteomic network approach across the ALS-FTD disease spectrum resolves clinical phenotypes and genetic vulnerability in human brain. EMBO Mol Med 10:48-62
Wang, Qi; Guo, Lei; Thompson, Paul M et al. (2018) The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. J Alzheimers Dis 64:149-169
Johnson, Erik C B; Dammer, Eric B; Duong, Duc M et al. (2018) Deep proteomic network analysis of Alzheimer's disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease. Mol Neurodegener 13:52
Wang, Tingyan; Qiu, Robin G; Yu, Ming (2018) Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks. Sci Rep 8:9161
Crum, Jana; Wilson, Jeffrey; Sabbagh, Marwan (2018) Does taking statins affect the pathological burden in autopsy-confirmed Alzheimer's dementia? Alzheimers Res Ther 10:104
Agogo, George O; Ramsey, Christine M; Gnjidic, Danijela et al. (2018) Longitudinal associations between different dementia diagnoses and medication use jointly accounting for dropout. Int Psychogeriatr 30:1477-1487

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