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003 OCoLC;

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007 cr unu||||||||;

008 240731s2024 ncua obm 000 0 eng d;

035 a| (OCoLC)1450451777;

090 a| QA166;

049 a| EREE;

100 1 a| Adams, Nicolas Gavin,e| author.?| UNAUTHORIZED;

245 10 a| Graph theoretical analysis of brain functional connectivity differences between musicians and nonmusicians /c| by Nicolas Gavin Adams.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2024.;

300 a| 1 online resource (55 pages) :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| application/pdfc| 1.73 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2024.;

500 a| Presented to the Faculty of the Department of Engineering;

500 a| Advisor: Sunghan Kim;

500 a| Title from PDF t.p. (viewed August 21, 2025).;

520 3 a| This thesis study evaluated the effect of musical training on brain functional connectivity using graph theoretical concepts from a holistic perspective. While most studies based on this topic include the effects of the types of music or noise on a population of musicians or nonmusicians, this study was designed to try and differentiate the two populations directly by comparing their functional connectivity while listening to classical musical versus listening to white noise to compare the functional connectivity differences between the two populations using graph theory measures. It was found that musical training largely impacts the theta band during passive visual attention tasks such as facial recognition. Musicians and nonmusicians showed statistically significant difference in the average nodal degree, global efficiency, clustering coefficient, network densities, and modularity when considering the difference between the music and noise conditions in the theta band. Any differences between the populations in the alpha, mu, and beta bands were found to be statistically insignificant with a significance level of 0.05.;

504 a| Includes bibliographical references.;

650 0 a| Graph theory.=| ^A31040;

650 0 a| Music therapy.=| ^A9663;

650 0 a| Electroencephalography.=| ^A1408;

650 0 a| Neurophysiology.=| ^A1171;

650 0 a| Holistic medicine.=| ^A9899;

653 a| Functional Connectivity;

653 a| Musical Training;

653 a| EEG;

700 1 a| Kim, Sunghan,d| 1975-e| degree supervisor.1| https://id.oclc.org/worldcat/entity/E39PCjwGh6Dqv9BjgW6VV9mWpd=| ^A1434862;

710 2 a| East Carolina University.b| Department of Engineering.=| ^A1231592;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/13423;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 6462788;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6462788-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 7/31/2024x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6462788-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 7/31/2024x| ETDz| HERESOURCE;

Graph theoretical analysis of brain functional connectivity differences between musicians and nonmusicians / by Nicolas Gavin Adams.

Author/creator	Adams, Nicolas Gavin author.
Other author	Kim, Sunghan, 1975- degree supervisor.
Other author	East Carolina University. Department of Engineering.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2024.
Description	1 online resource (55 pages) : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Graph theory. Music therapy. Electroencephalography. Neurophysiology. Holistic medicine.

Summary	This thesis study evaluated the effect of musical training on brain functional connectivity using graph theoretical concepts from a holistic perspective. While most studies based on this topic include the effects of the types of music or noise on a population of musicians or nonmusicians, this study was designed to try and differentiate the two populations directly by comparing their functional connectivity while listening to classical musical versus listening to white noise to compare the functional connectivity differences between the two populations using graph theory measures. It was found that musical training largely impacts the theta band during passive visual attention tasks such as facial recognition. Musicians and nonmusicians showed statistically significant difference in the average nodal degree, global efficiency, clustering coefficient, network densities, and modularity when considering the difference between the music and noise conditions in the theta band. Any differences between the populations in the alpha, mu, and beta bands were found to be statistically insignificant with a significance level of 0.05.
General note	Presented to the Faculty of the Department of Engineering
General note	Advisor: Sunghan Kim
General note	Title from PDF t.p. (viewed August 21, 2025).
Dissertation note	M.S. East Carolina University 2024.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

Graph theoretical analysis of brain functional connectivity differences between musicians and nonmusicians / by Nicolas Gavin Adams.

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