Connectome-based modelling of neurodegenerative diseases : towards precision medicine and mechanistic insight
(2023) In Nature Reviews Neuroscience 24(10). p.620-639- Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which ‘network-based neurodegeneration’ applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and... (More)
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which ‘network-based neurodegeneration’ applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
(Less)
- author
- organization
- publishing date
- 2023-10
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Nature Reviews Neuroscience
- volume
- 24
- issue
- 10
- pages
- 20 pages
- publisher
- Nature Publishing Group
- external identifiers
-
- pmid:37620599
- scopus:85168710103
- ISSN
- 1471-003X
- DOI
- 10.1038/s41583-023-00731-8
- language
- English
- LU publication?
- yes
- id
- 8697f9f5-c130-4b40-8831-25b312d6c27b
- date added to LUP
- 2023-12-22 15:46:21
- date last changed
- 2024-04-21 02:03:57
@article{8697f9f5-c130-4b40-8831-25b312d6c27b,
abstract = {{<p>Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which ‘network-based neurodegeneration’ applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.</p>}},
author = {{Vogel, Jacob W. and Corriveau-Lecavalier, Nick and Franzmeier, Nicolai and Pereira, Joana B. and Brown, Jesse A. and Maass, Anne and Botha, Hugo and Seeley, William W. and Bassett, Dani S. and Jones, David T. and Ewers, Michael}},
issn = {{1471-003X}},
language = {{eng}},
number = {{10}},
pages = {{620--639}},
publisher = {{Nature Publishing Group}},
series = {{Nature Reviews Neuroscience}},
title = {{Connectome-based modelling of neurodegenerative diseases : towards precision medicine and mechanistic insight}},
url = {{http://dx.doi.org/10.1038/s41583-023-00731-8}},
doi = {{10.1038/s41583-023-00731-8}},
volume = {{24}},
year = {{2023}},
}