國立虎尾科技大學 |

Spatial and Temporal Modeling of Large-Scale Brain Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Spatial and Temporal Modeling of Large-Scale Brain Networks./
作者:	Najafi, Mahshid.
面頁冊數:	1 online resource (167 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355291919

Spatial and Temporal Modeling of Large-Scale Brain Networks.
Najafi, Mahshid.

Spatial and Temporal Modeling of Large-Scale Brain Networks. - 1 online resource (167 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2017.

Includes bibliographical references

The human brain is the most fascinating and complex organ. It directs all our actions and thoughts. Despite the large body of brain studies, little is known about the neural basis of its large-scale structure. In this dissertation, I take advantage of several network-based and statistical techniques to investigate the spatial and temporal aspects of large-scale functional networks of the human brain during "rest" and "task" conditions using functional MRI data.

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

Mode of access: World Wide Web

ISBN: 9780355291919Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Spatial and Temporal Modeling of Large-Scale Brain Networks.
LDR:03610ntm a2200349K 4500 001 913927
005 20180628100931.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355291919
035 $a (MiAaPQ)AAI10282347
035 $a (MiAaPQ)umd:18126
035 $a AAI10282347
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Najafi, Mahshid. $3 1186959
245 1 0 $a Spatial and Temporal Modeling of Large-Scale Brain Networks.
264 0 $c 2017
300 $a 1 online resource (167 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Advisers: Luiz Pessoa; Jonathan Z. Simon.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2017.
504 $a Includes bibliographical references
520 $a The human brain is the most fascinating and complex organ. It directs all our actions and thoughts. Despite the large body of brain studies, little is known about the neural basis of its large-scale structure. In this dissertation, I take advantage of several network-based and statistical techniques to investigate the spatial and temporal aspects of large-scale functional networks of the human brain during "rest" and "task" conditions using functional MRI data.
520 $a Large-scale analysis of human brain function has revealed that brain regions can be grouped into networks or communities. Most studies adopt a framework in which brain regions belong to only one community. Yet studies in general fields of knowledge suggest that in most cases complex networks consist of interwoven sets of overlapping communities. A mixed-membership framework can better characterize the complex networks. In this dissertation, I employed a mixed-membership Bayesian model to characterize overlapping community structure of the brain at both "rest" and "task" conditions. The approach allowed us to quantify how task performance reconfigures brain communities at rest, and determine the relationship between functional diversity (how diverse is a region's functional activation repertoire) and membership diversity (how diverse is a region's affiliation to communities). Furthermore, I could study the distribution of key regions, named "bridges", in transferring information across the brain communities. Our findings revealed that the overlapping framework described the brain in ways that were not captured by disjoint clustering, and thus provided a richer landscape of large-scale brain networks. Overall, I suggest that overlapping networks are better suited to capture the flexible and task-dependent mapping between brain regions and their functions.
520 $a Finally, I developed a dynamic intersubject network analysis technique to study the temporal changes of the emotional brain at the level of large-scale brain networks by formulating a manipulation in which threat levels varied continuously during the experiment. Our results illustrate that cohesion within and between networks changed dynamically with threat level. Together, our findings reveal that characterizing emotional processing should be done at the level of distributed networks, and not simply at the level of evoked responses in specific brain regions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Neurosciences. $3 593561
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0317
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Maryland, College Park. $b Electrical Engineering. $3 845418
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10282347 $z click for full text (PQDT)