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Uncovering the Connectomic and Functional Components of Neurodegenerative Disease Spread Using Dynamic Network Modeling.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Uncovering the Connectomic and Functional Components of Neurodegenerative Disease Spread Using Dynamic Network Modeling./
Author:	Dadgar-Kiani, Ehsan.
Description:	1 online resource (56 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Brain. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30399003click for full text (PQDT)
ISBN:	9798379470616

Uncovering the Connectomic and Functional Components of Neurodegenerative Disease Spread Using Dynamic Network Modeling.
Dadgar-Kiani, Ehsan.

Uncovering the Connectomic and Functional Components of Neurodegenerative Disease Spread Using Dynamic Network Modeling. - 1 online resource (56 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

An emerging view regarding neurodegenerative diseases is that discreet seeding of misfolded proteins leads to widespread pathology. However, the mechanisms by which misfolded proteins seed distinct brain regions and cause differential whole-brain pathology remain elusive. Using whole-brain tissue clearing and high-resolution imaging, I longitudinally mapped pathology in an α-synuclein preformed fibril injection model of Parkinson's disease. Cleared brains at different time points of disease progression were quantitatively segmented and registered to a standardized atlas, revealing distinct phases of spreading and decline. I then developed a computational network model with parameters that represent α-synuclein pathological spreading, aggregation, decay, and gene expression pattern to this longitudinal dataset. This model generalized to predicting α-synuclein spreading patterns from several distinct brain regions and could also estimate their origins. Altogether, these results empower a more mechanistic understanding and accurate prediction of neurodegenerative disease progression.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379470616Subjects--Topical Terms:

525115
Brain.
Index Terms--Genre/Form:

542853
Electronic books.

Uncovering the Connectomic and Functional Components of Neurodegenerative Disease Spread Using Dynamic Network Modeling.
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245 1 0 $a Uncovering the Connectomic and Functional Components of Neurodegenerative Disease Spread Using Dynamic Network Modeling.
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500 $a Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
500 $a Advisor: Ding, Jun;Lin, Michael Z.;Lee, Jin Hyung.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a An emerging view regarding neurodegenerative diseases is that discreet seeding of misfolded proteins leads to widespread pathology. However, the mechanisms by which misfolded proteins seed distinct brain regions and cause differential whole-brain pathology remain elusive. Using whole-brain tissue clearing and high-resolution imaging, I longitudinally mapped pathology in an α-synuclein preformed fibril injection model of Parkinson's disease. Cleared brains at different time points of disease progression were quantitatively segmented and registered to a standardized atlas, revealing distinct phases of spreading and decline. I then developed a computational network model with parameters that represent α-synuclein pathological spreading, aggregation, decay, and gene expression pattern to this longitudinal dataset. This model generalized to predicting α-synuclein spreading patterns from several distinct brain regions and could also estimate their origins. Altogether, these results empower a more mechanistic understanding and accurate prediction of neurodegenerative disease progression.
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538 $a Mode of access: World Wide Web
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650 4 $a Pathology. $3 643180
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