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

FindBook

Google Book

Amazon

博客來

Multivariate Machine Learning Approach to Integrate Multimodal Neuroimaging Datasets.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multivariate Machine Learning Approach to Integrate Multimodal Neuroimaging Datasets./
作者:	Dashtestani, Hadis.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	99 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28031070
ISBN:	9798691213113

Multivariate Machine Learning Approach to Integrate Multimodal Neuroimaging Datasets.
Dashtestani, Hadis.

Multivariate Machine Learning Approach to Integrate Multimodal Neuroimaging Datasets. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 99 p.

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ph.D.)--University of Maryland, Baltimore County, 2020.

This item is not available from ProQuest Dissertations & Theses.

Non-invasive functional neuroimaging techniques have evolved remarkably over the last decades. Consequently, developing new procedures for more efficient description of neuroimaging data is emerging as a critical requirement. Such procedures, which enable automated extraction of clinically relevant information from neuroimaging data and can display it in an appropriate form, will enable more rapid and efficient evaluations. The main objective of this work is to apply multi-set Canonical Correlation Analysis (mCCA) for two distinct neuroimaging datasets: functional Near Infra-Red Spectroscopy (fNIRS) and ElectroEncephaloGraphy (EEG), to extract an enhanced analysis. However, fNIRS and EEG datasets are enormously different in nature and present challenges in leveraging the information content of both data sets. For example, EEG has excellent temporal resolution (ms range) and scattered spatial resolution, while fNIRS temporal resolution is slow in the range of seconds but its spatial resolution can be more local than EEG. Hence, there is a high possibility that that non-instantaneous relationships can be observed between brain neural and hemodynamic responses. To investigate the relationship between the mentioned datasets, we applied mCCA to find subject associations within each modality and also the correlation between groups of subjects across the two modalities. Utilizing a hybrid EEG/fNIRS correlation system should provide complementary information to better understand and explain the underlying neural/functional brain processes.In addition, we will present fNIRS data analysis case studies relating to a Moral Judgment (MJ) task. Using statistical learning techniques, such as mixed effect modeling, we investigate functional brain activity during personal (emotionally salient) vs impersonal (less emotional and more logical) MJ decision making. We show the proposed methodology can account for the covariance and causality structures present in the signal. This feature makes conventional methods when combined with fNIR more capable than conventional univariate methods alone. Furthermore, using mCCA, we were able to find a relationship between the behavioral datasets and neuroimaging data. Behavioral dataset consists of psychopathic traits measured by a self-report questionnaire called Psychopathic Personality Inventory-Revised (PPI-R). This case study, to our knowledge, is the first to determine the psychopathic core traits most correlated with brain functional activation.

ISBN: 9798691213113Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Data fusion

Multivariate Machine Learning Approach to Integrate Multimodal Neuroimaging Datasets.
LDR:04186nmm a2200517 4500 001 2346816
005 20220706051257.5
008 241004s2020 ||||||||||||||||| ||eng d
020 $a 9798691213113
035 $a (MiAaPQ)AAI28031070
035 $a AAI28031070
040 $a MiAaPQ $c MiAaPQ
100 1 $a Dashtestani, Hadis. $3 3686007
245 1 0 $a Multivariate Machine Learning Approach to Integrate Multimodal Neuroimaging Datasets.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 99 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
500 $a Advisor: Halem, Milton;Gandjbakhche, Amir H.
502 $a Thesis (Ph.D.)--University of Maryland, Baltimore County, 2020.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a Non-invasive functional neuroimaging techniques have evolved remarkably over the last decades. Consequently, developing new procedures for more efficient description of neuroimaging data is emerging as a critical requirement. Such procedures, which enable automated extraction of clinically relevant information from neuroimaging data and can display it in an appropriate form, will enable more rapid and efficient evaluations. The main objective of this work is to apply multi-set Canonical Correlation Analysis (mCCA) for two distinct neuroimaging datasets: functional Near Infra-Red Spectroscopy (fNIRS) and ElectroEncephaloGraphy (EEG), to extract an enhanced analysis. However, fNIRS and EEG datasets are enormously different in nature and present challenges in leveraging the information content of both data sets. For example, EEG has excellent temporal resolution (ms range) and scattered spatial resolution, while fNIRS temporal resolution is slow in the range of seconds but its spatial resolution can be more local than EEG. Hence, there is a high possibility that that non-instantaneous relationships can be observed between brain neural and hemodynamic responses. To investigate the relationship between the mentioned datasets, we applied mCCA to find subject associations within each modality and also the correlation between groups of subjects across the two modalities. Utilizing a hybrid EEG/fNIRS correlation system should provide complementary information to better understand and explain the underlying neural/functional brain processes.In addition, we will present fNIRS data analysis case studies relating to a Moral Judgment (MJ) task. Using statistical learning techniques, such as mixed effect modeling, we investigate functional brain activity during personal (emotionally salient) vs impersonal (less emotional and more logical) MJ decision making. We show the proposed methodology can account for the covariance and causality structures present in the signal. This feature makes conventional methods when combined with fNIR more capable than conventional univariate methods alone. Furthermore, using mCCA, we were able to find a relationship between the behavioral datasets and neuroimaging data. Behavioral dataset consists of psychopathic traits measured by a self-report questionnaire called Psychopathic Personality Inventory-Revised (PPI-R). This case study, to our knowledge, is the first to determine the psychopathic core traits most correlated with brain functional activation.
590 $a School code: 0434.
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
650 4 $a Neurosciences. $3 588700
650 4 $a Medical imaging. $3 3172799
650 4 $a Behavioral sciences. $3 529833
650 4 $a Information technology. $3 532993
650 4 $a Health care management. $3 2122906
653 $a Data fusion
653 $a EEG
653 $a Near Infra-Red Spectroscopy
653 $a Machine learning
653 $a Multimodal neuroimaging
653 $a Multivariate data analysis
653 $a Non-invasive imaging
653 $a Neuroimaging data
653 $a ElectroEncephaloGraphy
653 $a Brain function activation
653 $a Psychopathic core traits
653 $a Psychopathic Personality Inventory-Revised
690 $a 0984
690 $a 0574
690 $a 0800
690 $a 0489
690 $a 0602
690 $a 0317
690 $a 0769
710 2 $a University of Maryland, Baltimore County. $b Computer Science. $3 1018441
773 0 $t Dissertations Abstracts International $g 82-05B.
790 $a 0434
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28031070