Brain connectivity during Alzheimer’s disease progression and its
cognitive impact in a transgenic rat model
Author:
Muñoz-Moreno Emma12ORCID, Tudela Raúl11, López-Gil Xavier1, Soria Guadalupe13
Affiliation:
1. Experimental 7T MRI Unit, Institut d’Investigacions Bimediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain 2. Magnetic Resonance Image Core Facility, Institut d’Investigacions Biomdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain 3. Consorcio Centro de Investigacin Biomdica en Red (CIBER) de Bioingeniera, Biomateriales y Nanomedicina (CIBER-BBN), Group of Biomedical Imaging, University of Barcelona, Barcelona, Spain
Abstract
Abstract
The research of Alzheimer’s disease (AD) in its early stages and its progression till symptomatic onset is essential to understand the pathology and investigate new treatments. Animal models provide a helpful approach to this research, since they allow for controlled follow-up during the disease evolution. In this work, transgenic TgF344-AD rats were longitudinally evaluated starting at 6 months of age. Every 3 months, cognitive abilities were assessed by a memory-related task and magnetic resonance imaging (MRI) was acquired. Structural and functional brain networks were estimated and characterized by graph metrics to identify differences between the groups in connectivity, its evolution with age, and its influence on cognition. Structural networks of transgenic animals were altered since the earliest stage. Likewise, aging significantly affected network metrics in TgF344-AD, but not in the control group. In addition, while the structural brain network influenced cognitive outcome in transgenic animals, functional network impacted how control subjects performed. TgF344-AD brain network alterations were present from very early stages, difficult to identify in clinical research. Likewise, the characterization of aging in these animals, involving structural network reorganization and its effects on cognition, opens a window to evaluate new treatments for the disease.
Funder
Instituto de Salud Carlos III Fundació la Marató de TV3 FP7 Health Secretaria d’Universitats i Recerca del Departament d’Empresa I Coneixement de la Generalitat de Catalunya European Community
Publisher
MIT Press - Journals
Subject
Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience
Reference57 articles.
1. Anckaerts,
C., Blockx,
I., Summer,
P.,
Michael, J.,
Hamaide,
J., Kreutzer,
C., … Van der
Linden, A.
(2019). Early functional connectivity deficits and
progressive microstructural alterations in the TgF344-AD rat model of Alzheimer’s
disease: A longitudinal MRI study. Neurobiology of
Disease, 124,
93–107. 2. Arenaza-Urquijo, E.
M., Landeau,
B., La
Joie, R.,
Mevel, K.,
Mézenge,
F., Perrotin,
A., …
Chételat,
G. (2013).
Relationships between years of education and gray matter volume,
metabolism and functional connectivity in healthy elders.
NeuroImage, 83,
450–457. 3. Avants, B.
B., Epstein,
C. L.,
Grossman,
M., &
Gee, J.
C. (2008).
Symmetric diffeomorphic image registration with cross-correlation:
Evaluating automated labeling of elderly and neurodegenerative brain.
Medical Image Analysis, 12,
26–41. 4. Badhwar, A.
P., Tam,
A.,
Dansereau,
C., Orban,
P.,
Hoffstaedter,
F., &
Bellec,
P. (2017).
Resting-state network dysfunction in Alzheimer’s disease: A
systematic review and meta-analysis. Alzheimer’s &
Dementia: Diagnosis, Assessment & Disease Monitoring, 8,
73–85. 5. Benjamin,
Y., &
Hochberg,
Y. (1995).
Controlling the false discovery rate: A practical and powerful approach
to multiple testing. Journal of the Royal Statistical Society.
Series B (Methodological), 57(1),
289–300.
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|