Cornell University
Library
Cornell UniversityLibrary

eCommons

Help
Log In(current)
  1. Home
  2. Weill Cornell Medicine
  3. Weill Cornell Theses and Dissertations
  4. Weill Cornell Theses and Dissertations
  5. Multimodal Connectome Mapping of Sex Differences and Cognitive Abilities: a Machine Learning Approach

Multimodal Connectome Mapping of Sex Differences and Cognitive Abilities: a Machine Learning Approach

File(s)
eld2024.pdf (58.43 MB)
Permanent Link(s)
https://hdl.handle.net/1813/118269
Collections
Weill Cornell Theses and Dissertations
Author
Dhamala, Elvisha
Abstract

Tens of billions of neurons interconnect in the human brain. White matter pathways between these neurons facilitate neuronal co-activation patterns in the brain. These structural and functional connections underlie human cognition. In this dissertation, we identify functional and structural sex differences in the brain, quantify multimodal correlates of cognition, and identify sex-specific functional correlates of cognition. First, we evaluate sex differences in regional temporal dependence of resting‐state brain activity. We find that males have more persistent temporal dependence in regions within temporal, parietal, and occipital cortices, and regions in the cerebellum, amygdala, and frontal and occipital cortices strongly contribute to sex classification based on temporal dependence. We also show that even after matching of total grey matter volume, significant volumetric sex differences persist; males have larger subcortical and cerebellar structures, while females have larger cingulates, and frontal and parietal cortices. Next, we quantify the extent to which functional and structural connectivity predict individual crystallised and fluid cognitive abilities. We demonstrate functional connectivity is generally more predictive of cognitive scores than structural connectivity and integrating the two modalities does not generally increase explained variance. We show the quality of cognitive prediction is influenced by choice of grey matter parcellation. We also identify that distinct functional and structural connections predict crystallised and fluid abilities. Finally, we investigate sex-independent and sex-specific relationships between functional connectivity and cognition. We establish that sex-independent models comparably predict crystallised abilities in both sexes, but more accurately predict fluid abilities in males. We demonstrate sex-specific models comparably predict crystallised abilities within and between sexes but fail to predict fluid abilities in either sex. We reveal that largely overlapping connections between visual, dorsal attention, ventral attention, and temporal parietal networks are associated with better performance on crystallised and fluid cognitive tests in males and females, while connections within visual, somatomotor, and temporal parietal networks are associated with poorer performance. This work provides an understanding of how functional and structural properties of the healthy brain differ between the sexes and underlie cognition. These insights provide an important foundation with which to delineate disease-related sex-specific changes in cognitive functioning.

Date Issued
2021-04-01
Keywords
WCM Library Coordinated Deposit
•
Brain connectivity
•
Cognitive
•
Function
•
Machine learning
•
Sex differences
•
Structure
Committee Chair
Kuceyeski, Amy
Committee Member
Sabuncu, Mert
Kosofsky, Barry
Liston, Conor
Jacobs, Emily
Degree Discipline
Neuroscience
Degree Name
Ph. D., Neuroscience
Degree Level
Doctor of Philosophy
Type
dissertation or thesis

Site Statistics | Help

About eCommons | Policies | Terms of use | Contact Us

copyright © 2002-2026 Cornell University Library | Privacy | Web Accessibility Assistance