This project will study neuroimaging, blood, and CSF to measure neurodegeneration associated with AD. Few studies have focused on multiple measures of neurodegeneration in the same subjects by combining informative neuroimaging and peripheral biomarkers to provide a "biosignature," in order to improve early diagnosis and treatment monitoring. Neuroimaging studies will include PET scans using probes of amyloid plaques (PIB) and amyloid plaques and tau in tangles (FDDNP), and MRI measures of myelin and white matter tract integrity. Plasma measures of signaling proteins and cerebrospinal fluid (CSF) levels of proteins associated with plaques and tangles (e.g., elevated phosphorylated tau and low Abl-42) and demyelination (e.g., sulfatide) will also be obtained. The UCLA Clinical Core will recruit 80 subjects (40 AD patients and 40 older cognitively-intact controls), and the Center's Imaging and Biomarker Core will assist with data storage and analysis. All subjects will receive neuropsychological testing, scans, and blood tests (apolipoprotein E genotyping and plasma signaling proteins), and an estimated 50 will agree to lumbar punctures for CSF measures. We will test the following hypotheses: (1) Plasma signaling protein biomarkers will differentiate AD patients from controls. (2) CSF Ab and phosphorylated tau will differentiate AD patients from controls. Within the AD and control subject groups, CSF Ab biomarkers will correlate with PIB signals, while both Ab and phosphorylated tau CSF biomarkers will correlate with FDDNP signals. (3) MRI measures of myelin integrity will differentiate AD and control groups. We will also explore possible associations of these MRI measures with CSF sulfatide values. (4) While both FDDNP and PIB signals will differentiate AD from controls, binding patterns will differ. FDDNP will label regions predicted to show tangle as well as plaque deposition;PIB will label predicted plaque-rich regions. Within the AD and control groups, we will also explore correlations between cognitive measures and PET binding signals. We will explore research questions on how well a potential combined-measure or biosignature predicts clinical decline and whether stratifying subjects according to apolipoprotein E genotype influences findings. This project would lay the groundwork for better quantification and understanding of these critical neurodegenerative biomarkers.
Neuroimaging and biomarker information could lead to an informative biosignature that would identify at risk individuals for testing of prevention strategies, identify patients at earlier stages of disease for early intervention strategies, and improve overall diagnostic accuracy, thus facilitating innovative interventions and drug discovery throughout the full longitudinal course of neurodegeneration.
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