Predicting the Functional Effects of Brain Injuries Through Modeling and Experiment Adam Rayfield

Format:

Book

Thesis/Dissertation

Author/Creator:

Rayfield, Adam, author.

Contributor:

University of Pennsylvania. Bioengineering., degree granting institution.

Language:

English

Subjects (All):

0202.

0317.

0541.

Local Subjects:

0202.

0317.

0541.

Physical Description:

1 electronic resource (197 pages)

Contained In:

Dissertations Abstracts International 87-07B

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2025

Language Note:

English

Summary:

Traumatic brain injury (TBI) causes a substantial public health burden both in the United States and globally. Advances in medical imaging research, supported by computational modeling research, find that TBI, including concussion, alters the white matter structure connectivity (SC) and correlated activity, or functional connectivity (FC), of the brain, but associations between these traces of damage and patient symptoms remain unclear. We first aimed to develop a computational model of brain SC and FC for an animal model, the mouse, in order to predict the effects of brain injuries on FC and experimentally confirm if these relate to cognitive symptoms. First, we develop a simplified computational model of mouse brain dynamics on a directed structural network. We explore an optimization algorithm to better model empirical mouse brain FC networks, then simulate the effects of TBI on these optimized models, predicting that a mixture of hyperconnectivity and hypoconnectivity, supported by prior rodent studies of TBI, can be produced after injury. Next, we model lesions on a group of individual nodes of the directed mouse model and conduct excitotoxic lesions in vivo to determine if simulated FC changes are related to changes in mouse behavior profiles after injury. We are able to predict injured from sham mice using behavior with effectiveness varying by lesion site, but could not confirm that our experimental data was predicted by simulated FC changes. Finally, we apply the dynamical modeling methods and optimization algorithm to human brain networks from multiple subjects, then simulate impacts. We find that the simulated FC changes on our models vary by subject, which may suggest intersubject differences in injury vulnerability. These changes can be used to develop classifiers distinguishing nonconcussive and concussive impacts, and the intersubject differences are reduced by artificially exchanging interhemispheric connectivity between subjects, suggesting that SC variation affects individual vulnerability. Our research in this dissertation further develops means to predict the effects of brain injury on SC and FC, but identifies substantial challenges with reliably connecting these changes to specific cognitive disruptions

Notes:

Advisors: Meaney, David F. Committee members: Cohen, Yale E.; Cullen, D. Kacy; Satterthwaite, Theodore D.; Verma, Ragini

Source: Dissertations Abstracts International, Volume: 87-07, Section: B.

Ph.D. University of Pennsylvania 2025

Vendor supplied data

Local Notes:

School code: 0175

ISBN:

9798276006413

Access Restriction:

Restricted for use by site license