Computational investigations of neuronal network responses to traumatic brain injury / Samantha N. Schumm.

Format:

Book

Thesis/Dissertation

Author/Creator:

Schumm, Samantha N., author.

Contributor:

Meaney, David F., degree supervisor.

University of Pennsylvania. Department of Bioengineering, degree granting institution.

Language:

English

Subjects (All):

Bioengineering.

Biomedical engineering.

Neurosciences.

Bioengineering--Penn dissertations.

Penn dissertations--Bioengineering.

Local Subjects:

Bioengineering.

Biomedical engineering.

Neurosciences.

Bioengineering--Penn dissertations.

Penn dissertations--Bioengineering.

Genre:

Academic theses.

Physical Description:

1 online resource (285 pages)

Contained In:

Dissertations Abstracts International 82-07B.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2020.

Language Note:

English

System Details:

Mode of access: World Wide Web.

text file

Summary:

Traumatic brain injury (TBI) and other neural pathologies are increasingly considered diseases of brain network organization. Symptoms of post-concussive syndrome, such as headaches and concentration problems, are thought to emerge from brain network changes occurring after TBI. Decades of TBI research have also elucidated the cellular mechanisms of injury. Yet, precisely how cellular pathology disrupts macroscale networks and leads to subsequent cognitive dysfunction remains unclear. Therefore, microcircuits encompassing thousands of neurons may be an important substrate for understanding manifestations of TBI. To investigate microcircuit responses to injury, we use computational network models comprised of thousands of nodes representing individual neurons. In a model of two interconnected neuronal clusters, we study neurodegeneration, one classic consequence of TBI, and how it influences synchronization. Highly coupled networks resist loss of synchrony at the expense of functional flexibility. Baseline coupling between subregions is predictive of the effect of neuronal loss. To extend our approach to a specific circuit in the brain, we develop a network-based model that simultaneously incorporates three of the primary regions of the hippocampus (the dentate gyrus, CA3, and CA1). We validate the function of the model via firing rate, signal frequency analysis, and stimulus-response curves. Furthermore, we implement plasticity impairment, an understudied mechanism of injury. Impairment reduces broadband power in CA3 and CA1 as well as phase coherence between theta oscillations of CA3 and CA1. With intrinsically high activity, CA3 is especially vulnerable to plasticity impairment. Finally, we train the hippocampal network and test execution of pattern separation, finding a magnitude decrement in learned outputs but no deficit in pattern separation. Collectively, the studies in this thesis demonstrate how features of microcircuits either expose the network to or protect it from specific types of injury. Given the diverse circuitry of the brain, distinguishing regional vulnerabilities yields insight into the heterogeneity of outcomes after TBI.

Notes:

Source: Dissertations Abstracts International, Volume: 82-07, Section: B.

Advisors: Meaney, David F.; Committee members: Danielle Bassett; Akiva Cohen; Brian Litt; Barclay Morrison.

Department: Bioengineering.

Ph.D. University of Pennsylvania 2020.

Local Notes:

School code: 0175

ISBN:

9798557064736

Access Restriction:

Restricted for use by site license.

This item must not be sold to any third party vendors.