Alzheimer's disease (AD) is the most common form of age-related dementia. It is overwhelming 5.3 million individuals71 and families in the US, 46.8 million people world-wide, and medical and health care systems. Advances in research better identify prodromal phases (e.g., Mild Cognitive Impairment, MCI) although it is unclear who will and who will not progress to AD. Prodromal phases are being classified using neuroimaging and biospecimen biomarkers (e.g., amyloid and tau burden, regional atrophy). Yet, cognitive changes are also occurring during prodromal phases, even 8-12 years prior to onset22,27. Our challenge is choosing a cognitive marker that is sensitive to early disease change, corroborates with underlying neuropathology, and can predict those who will convert. We propose one such measure, namely the Serial Position Effect (SPE). We hypothesize that SPE markers are highly sensitive to AD, and that they can predict conversion to AD from prodromal states. We will retrieve cognitive and imaging data from the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset to investigate healthy adults, persons with mild cognitive impairment (MCI) who convert and do not convert to AD, and persons with AD. SPE is a measure of word recall as a function of its position in a word list, and measures will be calculated to generate profiles at learning and recall. AD performance differs significantly from controls, corroborating their deteriorating semantic-memory systems.
Aim 1 : We will demonstrate unique associations between characteristic SPE markers at learning and delay recall and underlying neurodegenerative biomarkers including regional atrophy, cortical thinning, amyloid and tau burden.
Aim 2 : We will test the predictive strength of SPE markers to establish the relative risk of developing disease given SPE profile characteristics. The significance of the study is that if SPE markers corroborate with underlying neurological biomarkers of AD, and predict the elevated risk to conversion to AD, SPE can serve as a functional and cost-effective tool for disease prediction, detection, and potentially for drug efficacy. Also, as SPE measures are easily derived from any list, they can be culture-free and not dependent on linguistic differences. The project's first innovation is that despite hundreds of ADNI investigations, none have applied SPE analyses. Second, this approach shifts a prevalent practice of using non-specific cognitive tasks to adopting a cognitive instrument that is theoretically-driven and corroborated by underlying disease-specific neurological processes of AD.
Alzheimer's disease is devastating over 5 million American individuals and families, and with only 1 in 4 people getting diagnosed, this is a major public health problem. Unfortunately, the costs of current neuroimaging to document underlying brain changes will not be feasible for so many people, and community and primary care settings increasingly need an effective, easy-to-use method to predict who truly has or will develop the disease. The present study investigates a measure derived from a list- learning task, called the Serial Position Effect, and asks two key questions to test its accuracy: a) does this measure correlate with the underlying brain changes that we know occur in Alzheimer's disease, and b) can it predict who will or will not be susceptible Alzheimer's disease at a later point in time.
| Vila-Castelar, Clara; Ly, Jenny J; Kaplan, Lillian et al. (2018) Attention Measures of Accuracy, Variability, and Fatigue Detect Early Response to Donepezil in Alzheimer's Disease: A Randomized, Double-blind, Placebo-Controlled Pilot Trial. Arch Clin Neuropsychol : |
