東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Control theoretic analysis of human ...

Gu, Shi.

FindBook

Google Book

Amazon

博客來

Control theoretic analysis of human brain networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Control theoretic analysis of human brain networks./
作者:	Gu, Shi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	181 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
Contained By:	Dissertation Abstracts International77-12B(E).
標題:	Neurosciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10134967
ISBN:	9781339929002

Control theoretic analysis of human brain networks.
Gu, Shi.

Control theoretic analysis of human brain networks. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 181 p.

Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2016.

The brain is a complex system with complicated structures and entangled dynamics. Among the various approaches to investigating the brain's mechanics, the graphical method provides a successful framework for understanding the topology of both the structural and functional networks, and discovering efficient diagnostic biomarkers for cognitive behaviors, brain disorders and diseases. Yet it cannot explain how the structure affects the functionality and how the brain tunes its transition among multiple states to manipulate the cognitive control. In my dissertation, I propose a novel framework of modeling the mechanics of the cognitive control, which involves in applying control theory to analyzing the brain networks and conceptually connecting the cognitive control with the engineering control. First, I examine the energy distribution among different states via combining the energetic and structural constraints of the brain's state transition in a free energy model, where the interaction between regions is explicitly informed by structural connectivity. This work enables the possibility of achieving a whole view of the brain's energy landscape and preliminarily indicates the feasibility of control theory to model the dynamics of cognitive control. In the following work, I exploit the network control theory to address two questions about how the large-scale circuitry of the human brain constrains its dynamics. First, is the human brain theoretically controllable? Second, which areas of the brain are most influential in constraining or facilitating changes in brain state trajectories? Further, I seek to examine the structural effect on the control actions through solving the optimal control problem under different boundary conditions. I quantify the efficiency of regions in terms of the energy cost for the brain state transition from the default mode to task modes. This analysis is extended to the perturbation analysis of trajectories and is applied to the comparison between the group with mild traumatic brain injury(mTBI) and the healthy group. My research is the first to demonstrate how control theory can be used to analyze human brain networks.

ISBN: 9781339929002Subjects--Topical Terms:

588700
Neurosciences.

Control theoretic analysis of human brain networks.
LDR:03140nmm a2200313 4500 001 2155257
005 20180426091041.5
008 190424s2016 ||||||||||||||||| ||eng d
020 $a 9781339929002
035 $a (MiAaPQ)AAI10134967
035 $a (MiAaPQ)upenngdas:12228
035 $a AAI10134967
040 $a MiAaPQ $c MiAaPQ
100 1 $a Gu, Shi. $3 3342998
245 1 0 $a Control theoretic analysis of human brain networks.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 181 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
500 $a Adviser: Danielle S. Bassett.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2016.
520 $a The brain is a complex system with complicated structures and entangled dynamics. Among the various approaches to investigating the brain's mechanics, the graphical method provides a successful framework for understanding the topology of both the structural and functional networks, and discovering efficient diagnostic biomarkers for cognitive behaviors, brain disorders and diseases. Yet it cannot explain how the structure affects the functionality and how the brain tunes its transition among multiple states to manipulate the cognitive control. In my dissertation, I propose a novel framework of modeling the mechanics of the cognitive control, which involves in applying control theory to analyzing the brain networks and conceptually connecting the cognitive control with the engineering control. First, I examine the energy distribution among different states via combining the energetic and structural constraints of the brain's state transition in a free energy model, where the interaction between regions is explicitly informed by structural connectivity. This work enables the possibility of achieving a whole view of the brain's energy landscape and preliminarily indicates the feasibility of control theory to model the dynamics of cognitive control. In the following work, I exploit the network control theory to address two questions about how the large-scale circuitry of the human brain constrains its dynamics. First, is the human brain theoretically controllable? Second, which areas of the brain are most influential in constraining or facilitating changes in brain state trajectories? Further, I seek to examine the structural effect on the control actions through solving the optimal control problem under different boundary conditions. I quantify the efficiency of regions in terms of the energy cost for the brain state transition from the default mode to task modes. This analysis is extended to the perturbation analysis of trajectories and is applied to the comparison between the group with mild traumatic brain injury(mTBI) and the healthy group. My research is the first to demonstrate how control theory can be used to analyze human brain networks.
590 $a School code: 0175.
650 4 $a Neurosciences. $3 588700
650 4 $a Applied mathematics. $3 2122814
650 4 $a Biomedical engineering. $3 535387
690 $a 0317
690 $a 0364
690 $a 0541
710 2 $a University of Pennsylvania. $b Applied Mathematics and Computational Science. $3 2094759
773 0 $t Dissertation Abstracts International $g 77-12B(E).
790 $a 0175
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10134967