?PROJECT 3: DYNAMIC NETWORKS. Convergent results suggest that distinct age-associated cascades affect separate brain networks. The present project seeks to better understand how pathophysiological compromises affect brain networks and, by doing so, provide insight into measureable network features that can distinguish dysfunction before clinical symptoms emerge. Unlike prior work where we have focused on group-level estimates of static properties of network organization, here we focus on network estimates in individuals including dynamic interactions between networks. We hypothesize the transient coupling between the medial temporal lobe (MTL) and partner cortical systems is critical to normal function and a potential biomarker of dysfunction. The overall goal of this project is to develop within-subject measures of dynamic network interactions and explore their relation to age-associated markers of neurodegeneration. The project exploits recent innovations of the NIH Human Connectome Project (HCP) including acquisition and hardware technology for advanced diffusion imaging.
Aim 1 : To analyze longitudinal data from the HABS cohort to identify, at the level of the subject group, the network topography indicative of preclinical Alzheimer's disease (AD), and distinct from age effects, in subjects with very low levels of A? burden (measured via PIB binding).
Aim 2 : To image a subset of 100 individuals representing a range of A? deposition, assessed by PiB- PET imaging, and Tau deposition, assessed by T807 binding, utilizing a protocol optimized to estimate dynamic interactions among core regions of the MTL network within individuals.
Aim 3 : To explore the relationship of functional network disruption to optimized measures of white matter tract integrity acquired using NIH HCP inspired technology. Of particular focus will be whether evidence of neurodegeneration can be detected in localized white matter tracts concurrent with functional disruption.
Aim 4 : Aggregating data from Aims 2 and 3 and the emerging behavioral data from Project 4, we will explore multivariate relationships between biomarkers of A? and Tau deposition, white-matter disruption, and volume loss, with within-subject estimates of functional network integrity and behavior. !

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National Institute on Aging (NIA)
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Massachusetts General Hospital
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Schultz, Aaron P; Chhatwal, Jasmeer P; Hedden, Trey et al. (2017) Phases of Hyperconnectivity and Hypoconnectivity in the Default Mode and Salience Networks Track with Amyloid and Tau in Clinically Normal Individuals. J Neurosci 37:4323-4331
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Wooten, Dustin W; Guehl, Nicolas J; Verwer, Eline E et al. (2017) Pharmacokinetic Evaluation of the Tau PET Radiotracer (18)F-T807 ((18)F-AV-1451) in Human Subjects. J Nucl Med 58:484-491
Hsu, David; Marshall, Gad A (2017) Primary and Secondary Prevention Trials in Alzheimer Disease: Looking Back, Moving Forward. Curr Alzheimer Res 14:426-440
Vannini, Patrizia; Hanseeuw, Bernard; Munro, Catherine E et al. (2017) Hippocampal hypometabolism in older adults with memory complaints and increased amyloid burden. Neurology 88:1759-1767
Hanseeuw, Bernard J; Betensky, Rebecca A; Schultz, Aaron P et al. (2017) Fluorodeoxyglucose metabolism associated with tau-amyloid interaction predicts memory decline. Ann Neurol 81:583-596

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