Uncovering the connectomic and functional components of neurodegenerative disease spread using dynamic network modeling

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Abstract/Contents

Abstract
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.

Description

Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2023; ©2023
Publication date 2023; 2023
Issuance monographic
Language English

Creators/Contributors

Author Dadgar-Kiani, Ehsan
Degree supervisor Lee, Jin Hyung
Thesis advisor Lee, Jin Hyung
Thesis advisor Ding, Jun (Jun B.)
Thesis advisor Lin, Michael Z
Degree committee member Ding, Jun (Jun B.)
Degree committee member Lin, Michael Z
Associated with Stanford University, Department of Bioengineering

Subjects

Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Ehsan Dadgar-Kiani.
Note Submitted to the Department of Bioengineering.
Thesis Thesis Ph.D. Stanford University 2023.
Location https://purl.stanford.edu/nm719hy6952

Access conditions

Copyright
© 2023 by Ehsan Dadgar-Kiani
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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