Schmidt, Benjamin
(2017)
Large Scale Functional Connectivity Networks of Resting State Magnetoencephalography.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Understanding relationships between cortical neural activity is an important area of research. Investigations of the neural dynamics associated with the healthy and disordered brains could lead to new insights about disease models. Functional connectivity is a promising method for investigating these neural dynamics by observing intrinsic neural activity arising during spontaneous cortical activations recorded via magnetoencephalography (MEG). MEG is a non-invasive measure of the magnetic fields produced during neural activity and provides information regarding neural synchrony within the brain.
Phase locking is a time frequency analysis method that provides frequency band specific results of neural communication. Leveraging multiple computers operating in a cluster extends the scale of these investigations to whole brain functional connectivity. Quantification of these large-scale networks would allow for the quantitative characterization of healthy connectivity in a mathematically rigorous manner.
However, the volume of data required to characterize these networks creates a multiple comparison problem (MCP) in which upward of 33 million simultaneous hypothesis are tested. Conservative approaches such as Bonferroni can eliminate most of the results while more liberal methods may under-correct therefore leading to an increase in the true type I error rate. Here we used a combination of functionally defined cortical surface clustering methods followed by non-parametric permutation testing paradigm to control the family wise error rate and provide robust statistical networks.
These methods were validated with simulation studies to characterize limitations in inferences from the resultant whole brain networks. We then examined healthy subject’s MEG during resting state recordings to characterize intrinsic network activity across four physiological frequency bands: theta – 4-8 Hz, alpha – 8-13 Hz, beta-low – 13-20 Hz, beta-high – 20-30 Hz.
Quantifying large-scale functional connectivity networks allowed for the investigation of healthy electrophysiological networks within specific frequency bands. Understanding the intrinsic network connections would allow for better understanding of the electrophysiological processes underlying brain function. Quantification of these networks would also allow future studies to explore the ability of network aberrations to predict disordered brain states.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
| Creators | Email | Pitt Username | ORCID  |
|---|
| Schmidt, Benjamin | bts9@pitt.edu | BTS9 | |
|
| ETD Committee: |
| Title | Member | Email Address | Pitt Username | ORCID |
|---|
| Thesis Advisor | Huppert, Theodore | huppertt@upmc.edu | HUPPERT1 | | | Ghuman, Avniel | | | | | Ibrahim, Tamer | | | | | Stetten, George | | | |
|
| Date: |
26 September 2017 |
| Date Type: |
Publication |
| Defense Date: |
3 April 2017 |
| Approval Date: |
26 September 2017 |
| Submission Date: |
8 July 2013 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
277 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Bioengineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Functional Connectivity, Magnetoencephalography, Distributed Computing |
| Date Deposited: |
26 Sep 2017 17:22 |
| Last Modified: |
26 Sep 2017 17:22 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/19272 |
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