Electronic Theses and Dissertations for Graduate School
Author Last Name
Neural network dynamics in amnestic mild cognitive impairment
Restricted (Penn State Only)
Brenner, Einat Karin
Master of Science
Date of Defense:
June 13, 2017
Frank G. Hillary, Thesis Advisor
Peter Andrew Arnett, Committee Member
Nancy Anne Coulter Dennis, Committee Member
amnestic mild cognitive impairment
Objective: Mild cognitive impairment (MCI) is widely regarded as an intermediate stage between typical aging and dementia, with nearly 50% of patients with amnestic MCI (aMCI) converting to Alzheimer’s dementia (AD) within 30 months of follow-up (Fischer et al., 2007). The growing literature using resting-state functional magnetic resonance imaging (rs-fMRI) reveals both increased and decreased connectivity in individuals with MCI and connectivity loss between the anterior and posterior components of the default mode network (DMN) with disease progression (Hillary et al., 2015; Sheline & Raichle, 2013; Tjims et al., 2013). Participants and Methods: In this paper, we use dynamic connectivity modeling and graph theory to identify unique brain “states,” or patterns of connectivity across distributed networks, that distinguish individuals with aMCI and healthy older adults (HOAs). We enrolled 44 individuals diagnosed with aMCI and 33 HOAs of comparable age and education. Results: Our results showed that HOAs spent significantly more time in a highly connected state than participants with aMCI. Furthermore, individuals with aMCI were less likely to move between states, spending significantly more time in one state in particular (state 4), whereas neural network analysis in the HOA sample revealed equivalent representation across four distinct states. In individuals with aMCI, when examining the ratios of time spent in the dominant state (state 4) to a state where participants exhibited high cost (state 3), a higher proportion of time in state 3 compared to state 4 predicted better language performance. Conclusion: This is the first report to examine state-dependent neural networks in individuals with MCI.
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