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Integrating Network Science and Computational Topology with Applications in Neuroscience Data Analytics.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Integrating Network Science and Computational Topology with Applications in Neuroscience Data Analytics./
Author:	Vaiana, Michael.
Description:	1 online resource (146 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
Subject:	Mathematics. -
Online resource:	click for full text (PQDT)
ISBN:	9780438048881

Integrating Network Science and Computational Topology with Applications in Neuroscience Data Analytics.
Vaiana, Michael.

Integrating Network Science and Computational Topology with Applications in Neuroscience Data Analytics. - 1 online resource (146 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2018.

Includes bibliographical references

Real world systems are complex, dynamic and exist across multiple scales. Recent revolutions in data collection and storage have provided researchers with unprecedented access to information about these systems in greater detail and variety than ever before. It is therefore imperative that we develop models and methods that are able to describe and detect changes in these systems through time and across scales. In this dissertation, I present work integrating multilayer networks and computational topology to perform multi-scale analysis of brain activity in epileptic mice. First, I present work providing theoretical advances to multilayer network theory. I prove that multilayer networks suffer from a resolution limit that prevents a popular method of community detection from detecting changes in community structure across layers of the network. I then propose an improvement to the multilayer network model that is more intuitive and helps mitigate the constraints of the resolution limit. Next, I describe work drawing from the tools of topological data analysis using persistent homology to localize and segment cells in biomedical images. Finally, I employing tools from computational topology and multilayer networks to study the evolution of neuronal dynamics in the brains of epileptic mice in the period leading up to seizure. This intricate, multi-scale analysis of brain dynamics enables us to identify evolving functional groups of neurons that drive the progression to a seizure.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438048881Subjects--Topical Terms:

527692
Mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

Integrating Network Science and Computational Topology with Applications in Neuroscience Data Analytics.
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500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: Sarah Muldoon.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2018.
504 $a Includes bibliographical references
520 $a Real world systems are complex, dynamic and exist across multiple scales. Recent revolutions in data collection and storage have provided researchers with unprecedented access to information about these systems in greater detail and variety than ever before. It is therefore imperative that we develop models and methods that are able to describe and detect changes in these systems through time and across scales. In this dissertation, I present work integrating multilayer networks and computational topology to perform multi-scale analysis of brain activity in epileptic mice. First, I present work providing theoretical advances to multilayer network theory. I prove that multilayer networks suffer from a resolution limit that prevents a popular method of community detection from detecting changes in community structure across layers of the network. I then propose an improvement to the multilayer network model that is more intuitive and helps mitigate the constraints of the resolution limit. Next, I describe work drawing from the tools of topological data analysis using persistent homology to localize and segment cells in biomedical images. Finally, I employing tools from computational topology and multilayer networks to study the evolution of neuronal dynamics in the brains of epileptic mice in the period leading up to seizure. This intricate, multi-scale analysis of brain dynamics enables us to identify evolving functional groups of neurons that drive the progression to a seizure.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mathematics. $3 527692
650 4 $a Neurosciences. $3 593561
650 4 $a Computer science. $3 573171
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710 2 $a State University of New York at Buffalo. $b Computational and Data Enabled Sciences. $3 1193723
773 0 $t Dissertation Abstracts International $g 79-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10823066 $z click for full text (PQDT)