This K25 proposal is for a five-year mentored training program to enable Dr. Ariana Anderson from UCLA to transition from a statistician to an independent investigator, with an active research plan in identifying functional biomarkers of cognitive decline and aging. Dr. Anderson's proposal includes a comprehensive training program involving formal training in calibrated fMRI, fMRI data collection, neuropsychological assessments, volunteering, directed readings, coursework, mentoring, conferences, and career development, which are designed to complement and complete the applicant's previous training in mathematics and statistics. The proposed research addresses an important issue that affects nearly all fMRI studies; the modeled blood-flow response to neuronal stimuli is assumed to be constant across ages, genotypes and diseases, even though we know that this assumption is categorically false. This lowers the statistical power of fMRI studies, increases necessary sample sizes, and introduces bias into fMRI studies of disease and aging. Moreover, a poor understanding of hemodynamic change with age in healthy patients makes identifying biomarkers of unhealthy aging difficult. We will use cerebral blood flow measurements (ASL), hypercapnic and hemodynamic changes, along with genetic risk factors, as biomarkers to predict future cognitive decline. The relationship between the hemodynamic response, cognitive decline, aging and disease will be unraveled through the following three aims.
Aim 1.) Create age-corrected hemodynamic response functions, after adjusting for cardiac and respiratory artifacts recorded during scan-time, so that future age studies can use age-corrected models of blood flow. We will estimate this in subjects with and without genetic risk for Alzheimer's disease. Age-corrected hemodynamic response functions will reduce bias and increase statistical power (increase the sensitivity, and/or reduce the required sample sizes).
Aim 2.) Evaluate whether age- abnormal hemodynamics predict abnormal cognitive ability. Modeling normal hemodynamics will allow us to identify abnormal hemodynamics, creating biomarkers for specific diseases such as vascular dementia.
Aim 3.) Create a new HRF model to account for age-related CBF changes, using calibrated fMRI. Abnormal hemodynamics may better predict which patients are more likely to experience cognitive decline, leading to earlier treatment.

Public Health Relevance

This research investigates how the brain's blood-flow response to stimuli changes with cognitive decline, aging and genetic risk for Alzheimer's disease, and creates theoretical models to increase accuracy of fMRI neuroimaging studies. It will reduce necessary sample sizes in fMRI studies of aging, and will improve analysis methods that incorrectly assume that all people react in the same manner to stimuli.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25AG051782-04
Application #
9691106
Study Section
Neuroscience of Aging Review Committee (NIA)
Program Officer
Wagster, Molly V
Project Start
2016-08-01
Project End
2021-04-30
Budget Start
2019-05-15
Budget End
2020-04-30
Support Year
4
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of California Los Angeles
Department
Type
Schools of Medicine
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Anderson, Ariana E; Jones, Jacob D; Thaler, Nicholas S et al. (2018) Intraindividual variability in neuropsychological performance predicts cognitive decline and death in HIV. Neuropsychology 32:966-972
Schreiner, Matthew; Forsyth, Jennifer K; Karlsgodt, Katherine H et al. (2017) Intrinsic Connectivity Network-Based Classification and Detection of Psychotic Symptoms in Youth With 22q11.2 Deletions. Cereb Cortex 27:3294-3306
Trammell, Janet P; MacRae, Priscilla G; Davis, Greta et al. (2017) The Relationship of Cognitive Performance and the Theta-Alpha Power Ratio Is Age-Dependent: An EEG Study of Short Term Memory and Reasoning during Task and Resting-State in Healthy Young and Old Adults. Front Aging Neurosci 9:364
Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian et al. (2017) Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms. J Neurosci Methods 282:81-94
Anderson, Ariana E; Mansolf, Maxwell; Reise, Steven P et al. (2017) Measuring pathology using the PANSS across diagnoses: Inconsistency of the positive symptom domain across schizophrenia, schizoaffective, and bipolar disorder. Psychiatry Res 258:207-216
Anderson, Ariana E; Kerr, Wesley T; Thames, April et al. (2016) Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study. J Biomed Inform 60:162-8