Understanding Individual Differences within Large-scale Brain Networks across Cognitive Contexts

Authors

Katherine L. Bottenhorn, Florida International UniversityFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Psychology

First Advisor's Name

Angela Laird

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Matthew Sutherland

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Erica Musser

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Shanna Burke

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

Timothy Hayes

Fifth Advisor's Committee Title

Committee member

Keywords

fMRI, individual differences, dense sampling, methods development, education, physiology, hormones, sleep, stress, menstruation

Date of Defense

6-24-2021

Abstract

Historically, human neuroimaging has studied brain regions “activated” during behavior and how they differ between groups of people. This approach has improved our understanding of healthy and disordered brain function, but has two key shortcomings. First, focusing on brain activation restricts how we understand the brain, ignoring vital, behind-the-scenes processing. In the past decade, the focus has shifted to communication between brain regions, or connectivity, revealing networks that exhibit subtle, consistent differences across behaviors and diagnoses. Without activation-focused research’s constraints, connectivity-focused neuroimaging research more comprehensively assesses brain function. Second, focusing on group differences ignores substantial within-group heterogeneity and often imposes false dichotomies. Recent findings show that brain network variability within an individual is nearly as great as across a group. Altogether, this illustrates a need for understanding individual variability in brain networks and how it relates to behavior. Therefore, I have developed a pipeline for investigating individual differences in brain connectivity, adapting robust statistical methods to address unique challenges of neuroimaging data analysis. Here, I describe this pipeline and apply it to two datasets. First, I explore between-individual variability in brain connectivity underlying intelligence and academic performance to better understand factors contributing to student success. Second, I assess the relative contributions of stress, sleep, and hormones to within-individual variability in brain connectivity across the menstrual cycle to illuminate little-studied phenomena affecting the everyday lives of half the population. Finally, I introduce a novel signal processing workflow for cleaning electrophysiological measures of bodily stress and arousal in neuroimaging research.

Identifier

FIDC010186

ORCID

0000-0002-7796-8795

Previously Published In

Bottenhorn, K. L., Bartley, J. E., Riedel, M. C., Salo, T., Bravo, E. I., Odean, R., Nazareth, A., Laird, R. W., Musser, E. D., Pruden, S. M., Brewe, E., Sutherland, M. T., & Laird, A. R. (2021). Intelligence and academic performance: Is it all in your head? BioRxiv, 2021.01.23.427928. https://doi.org/10.1101/2021.01.23.427928 Bottenhorn, K. L., Salo, T., Riedel, M. C., Sutherland, M. T., Robinson, J. L., Musser, E. D., & Laird, A. R. (2021). Denoising physiological data collected during multi-band, multi-echo EPI sequences. BioRxiv, 2021.04.01.437293. https://doi.org/10.1101/2021.04.01.437293

Creative Commons License

This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.

Recommended Citation

Bottenhorn, Katherine L., "Understanding Individual Differences within Large-scale Brain Networks across Cognitive Contexts" (2021). FIU Electronic Theses and Dissertations. 4776.
https://digitalcommons.fiu.edu/etd/4776