The role of cortical morphometry of functional networks in predicting age-related cognition in older adults

Over the next three decades, the 65-and-over population is projected to nearly double, increasing from 8.5% to 16.7% of the world’s total population (He, Goodkind, and Kowal, 2016). Alarmingly, despite longer life expectancies, health is not necessarily improving (He, Goodkind, and Kowal, 2016). While all organs are affected by aging, decline in the brain’s ability to function (cognitive aging) is one of the most impactful consequences of aging on day-to-day activities and one of the most common complaints of older adults (Blazer et al., 2015). In fact, in a recent survey, almost half of individuals aged 65 or older report changes in mental ability (AARP Brain Health Survey, Fall 2015). While almost all older adults acknowledge the importance of brain health, only half actually engage in activities found to be beneficial for brain health (AARP Brain Health Survey, Fall 2015). Thus, understanding individual variability in the older adult brain, both in terms of structure and function, and its relationship with cognition and age is essential (Hedden and Gabrieli 2004). Despite the well-established widespread relationships of age and cognition with cortical structure, the nature and organization of this relationship remains underspecified. In this thesis, I investigate the nature of the relationships between cortical morphometry, cognition, and age in older adults through a contemporary neuroscience lens of the brain as a system of functional networks. In chapter one, I employ a widely-used functional network architecture as the organizing principle of the cortex to investigate how the cortical morphometry of individual networks predicts cognition and mediates the age-cognition relationship in older adults (using both cortical thickness and surface area—phenotypes both implicated in relationships with cognition but not tested in the same sample of older adults). I use a machine learning and cross-validation prediction framework to compare the predictive ability of cortical morphometry of individual functional networks to age-related cognitive abilities (declarative memory and executive function). In a second set of analyses, I apply a novel inferential test to exploratory, whole brain analyses. Specifically, I examine the number of significant point-by-point regional associations within functional networks, providing a test of the spatial extent of each functional network’s relationship with age-related cognitive abilities (compared to chance). Ultimately, making impactful theoretical and practical contributions to the field requires assessing the reproducibility and generalizability of conclusions derived from data-driven techniques. Thus, in chapter 2, I test if regions robustly associated with cognitive ability (executive function) discovered in chapter 1 and regions associated with cognitive task performance discovered in a previous study (Sun et al., 2016) predict well-established cognitive reference abilities in an independent sample of older adults. General patterns of functional connectivity (i.e., group-average functional networks) across a population(s), such as the one used in Chapter 1, provide a picture of the common functional architecture and distinct functional networks across the cortex of healthy adults (i.e., Yeo et al., 2011). These group-based networks of the functional connectome were used to assess the importance of cortical structure of functional networks in Chapter 1. However, this ignores individual differences in the integrity of these functional networks and how these individual differences relate to individual differences in cortical structure. If functional connectivity causes (or is caused by) differences in mechanisms marked by cortical structure or vice versa (e.g., individual variability in older adults’ cortical thickness may be indexing the number of synapses or intracortical myelin important for connectivity between regions as is theorized in previous studies; see Fjell et al., 2015), one would expect the two to be related and share overlapping variance in their relationship with age and cognition. Thus, in chapter 3, I examine whether individual differences in functional connectivity mediates the relationship of cortical structure with age and cognitive ability (as the relationship of structure with cognition emerges as a result of the functional system measured by functional connectivity).

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Kranz, Michael Benjamin

Contributors dc:contributor

• Kramer, Arthur F.
• Voss, Michelle
• Barbey, Aron
• Beck, Diane
• Wang, Ranxiao

• Cognitive neuroscience, aging, cortical structure, functional networks, executive function, fluid intelligence

Statement dc:rights

• 2019 Michael Kranz

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/104810

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/104810