國立虎尾科技大學 |

Large-scale analyses of functional interactions in the human brain.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Large-scale analyses of functional interactions in the human brain./
作者:	Wang, Yida.
面頁冊數:	1 online resource (171 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781339815879

Large-scale analyses of functional interactions in the human brain.
Wang, Yida.

Large-scale analyses of functional interactions in the human brain. - 1 online resource (171 pages)

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

Thesis (Ph.D.)--Princeton University, 2016.

Includes bibliographical references

A grand challenge in neuroscience is to understand how a human brain functions. Much previous research has focused on studying the activities of brain regions. However, the functional interactions between brain regions are not well understood. This dissertation focuses on computational methods to analyze data from functional magnetic resonance imaging (fMRI) scanners to study the functional interactions in the human brain.

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

Mode of access: World Wide Web

ISBN: 9781339815879Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Large-scale analyses of functional interactions in the human brain.
LDR:03317ntm a2200385K 4500 001 913636
005 20180622095234.5
006 m o u
007 cr mn||||a|a||
008 190606s2016 xx obm 000 0 eng d
020 $a 9781339815879
035 $a (MiAaPQ)AAI10120405
035 $a (MiAaPQ)princeton:11756
035 $a AAI10120405
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Wang, Yida. $3 1186551
245 1 0 $a Large-scale analyses of functional interactions in the human brain.
264 0 $c 2016
300 $a 1 online resource (171 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: 77-11(E), Section: B.
500 $a Adviser: Kai Li.
502 $a Thesis (Ph.D.)--Princeton University, 2016.
504 $a Includes bibliographical references
520 $a A grand challenge in neuroscience is to understand how a human brain functions. Much previous research has focused on studying the activities of brain regions. However, the functional interactions between brain regions are not well understood. This dissertation focuses on computational methods to analyze data from functional magnetic resonance imaging (fMRI) scanners to study the functional interactions in the human brain.
520 $a This dissertation proposes, designs and implements efficient systems to conduct full correlation matrix analysis (FCMA) as an unbiased way to explore functional interactions in the human brain. Since a straightforward way to study FCMA would take years to complete one run with a typical neuroscience study dataset on a modern compute server, no previous attempt has been made in the past. This dissertation makes several contributions. First, we proposed and implemented parallel algorithms and optimizations on a multi-processor cluster, which improved FCMA computation by three orders of magnitude.
520 $a Second, we demonstrated that FCMA can effectively show functional interactions in the human brain by conducting a neuroscience study on an fMRI dataset. Our study successfully identified functional interactions between certain brain regions.
520 $a Third, we proposed and implemented optimization methods for FCMA for emerging many-core processors such as the IntelRTM Xeon(TM) Phi coprocessors and improved the performance of computing FCMA by another order of magnitude. On a 96-node Xeon Phi cluster, our system can finish an FCMA study with a typical dataset in minutes.
520 $a Finally, we proposed, designed and implemented a service for real-time, closed loop neuroscience studies. Our real-time FCMA can process and analyze brain volumes from multiple fMRI experiments on a 40-node compute cluster simultaneously and send the neurofeedback to each fMRI scanner over the Internet within 1.5 seconds. This system uses a novel method to improve the performance and utilization of compute nodes while meeting the real-time requirements in the presence of node failures.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Neurosciences. $3 593561
650 4 $a Psychobiology. $3 655331
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0317
690 $a 0349
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Princeton University. $b Computer Science. $3 1179801
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10120405 $z click for full text (PQDT)