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

Resting State fMRI and Machine Learn...

Vergun, Svyatoslav.

FindBook

Google Book

Amazon

博客來

Resting State fMRI and Machine Learning Applications in Healthy and Patient Populations.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Resting State fMRI and Machine Learning Applications in Healthy and Patient Populations./
作者:	Vergun, Svyatoslav.
面頁冊數:	198 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
Contained By:	Dissertation Abstracts International77-11B(E).
標題:	Medical imaging. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10140155
ISBN:	9781339955742

Resting State fMRI and Machine Learning Applications in Healthy and Patient Populations.
Vergun, Svyatoslav.

Resting State fMRI and Machine Learning Applications in Healthy and Patient Populations. - 198 p.

Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2016.

Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without task difficulty associated with task fMRI. It allows noninvasive examination of neural activity and offers clinically valuable functional information about normal and patient populations and individuals. Advanced neuroimaging measures from fMRI and diffusion tensor imaging (DTI) along with clinical variables acquired during standard imaging protocols offer predictive value for patients when used in machine learning applications. Machine learning methods are well matched in a clinical fMRI setting and serve purposes of prediction and prognostication, diagnosis, insight into normal and patient populations and aid in automation of clinical tasks. In this dissertation, image analysis and machine learning are applied for classification and prediction tasks of: discriminating normal aging subjects and stroke patients, extracting and labeling resting state networks in epilepsy patients and predicting brain tumor patient outcomes. Consequent model interpretation provides insight into the respective underlying processes and reveals influential measures. High accuracy performance of 80-90% accuracy is achieved in binary classification for discriminating age, stroke disease, and prediction of brain tumor outcomes. 80--90% accuracy is also seen for multi-class classification of resting state networks in epilepsy patients. fMRI allows the investigation of neural activity and is well matched in machine learning applications. The studies in this work show that fMRI and DTI provide a rich source of structural and functional information for patient representation in machine learning methods of classification and prediction in normal aging subjects, as well as stroke, epilepsy and brain tumor patients.

ISBN: 9781339955742Subjects--Topical Terms:

3172799
Medical imaging.

Resting State fMRI and Machine Learning Applications in Healthy and Patient Populations.
LDR:02781nmm a2200301 4500 001 2076202
005 20161028121038.5
008 170521s2016 ||||||||||||||||| ||eng d
020 $a 9781339955742
035 $a (MiAaPQ)AAI10140155
035 $a AAI10140155
040 $a MiAaPQ $c MiAaPQ
100 1 $a Vergun, Svyatoslav. $3 3191638
245 1 0 $a Resting State fMRI and Machine Learning Applications in Healthy and Patient Populations.
300 $a 198 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
500 $a Adviser: Vivek Prabhakaran.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2016.
520 $a Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without task difficulty associated with task fMRI. It allows noninvasive examination of neural activity and offers clinically valuable functional information about normal and patient populations and individuals. Advanced neuroimaging measures from fMRI and diffusion tensor imaging (DTI) along with clinical variables acquired during standard imaging protocols offer predictive value for patients when used in machine learning applications. Machine learning methods are well matched in a clinical fMRI setting and serve purposes of prediction and prognostication, diagnosis, insight into normal and patient populations and aid in automation of clinical tasks. In this dissertation, image analysis and machine learning are applied for classification and prediction tasks of: discriminating normal aging subjects and stroke patients, extracting and labeling resting state networks in epilepsy patients and predicting brain tumor patient outcomes. Consequent model interpretation provides insight into the respective underlying processes and reveals influential measures. High accuracy performance of 80-90% accuracy is achieved in binary classification for discriminating age, stroke disease, and prediction of brain tumor outcomes. 80--90% accuracy is also seen for multi-class classification of resting state networks in epilepsy patients. fMRI allows the investigation of neural activity and is well matched in machine learning applications. The studies in this work show that fMRI and DTI provide a rich source of structural and functional information for patient representation in machine learning methods of classification and prediction in normal aging subjects, as well as stroke, epilepsy and brain tumor patients.
590 $a School code: 0262.
650 4 $a Medical imaging. $3 3172799
650 4 $a Neurosciences. $3 588700
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
690 $a 0574
690 $a 0317
690 $a 0984
690 $a 0800
710 2 $a The University of Wisconsin - Madison. $b Medical Physics. $3 2097834
773 0 $t Dissertation Abstracts International $g 77-11B(E).
790 $a 0262
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10140155