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  • Perspective
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Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight

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 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.

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Fig. 1: Clinico-pathological spectrum of neurodegenerative dementia syndromes.
Fig. 2: Common methods for assessing the influence of network architecture on disease progression.
Fig. 3: Brain networks as conduits and drivers of disease progression.
Fig. 4: The next generation of connectome-based models can innovate along three dimensions.

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Acknowledgements

The authors thank P. Chaggar for comments and suggestions on the manuscript. J.W.V. acknowledges support from the SciLifeLab & Wallenberg Data Driven Life Science Program (grant no. KAW 2020.0239). D.S.B. acknowledges support from the Michael J. Fox Foundation ASAP Initiative.

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J.W.V., M.E., N.C.-L., N.F., J.A.B. and A.M. researched data for the article and along with J.B.P. made a substantial contribution to discussion of content, wrote, reviewed and edited the manuscript before submission. A substantial contribution to the discussion of content, reviewing and editing of the manuscript before submission was also made by H.B., W.W.S., D.S.B. and D.T.J.

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Correspondence to Jacob W. Vogel or Michael Ewers.

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M.E. and N.F. receive research funding from Eli Lilly. M.E. is a consultant for Prevail Therapeutics.

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Nature Reviews Neuroscience thanks A. Fornito and B. Misic for their contribution to the peer review of this work.

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Glossary

Amnestic

Presenting with primary impairment in memory and memory-related processes.

APOE

The strongest genetic risk factor for sporadic Alzheimer disease with three common alleles (ε2, ε3 and ε4): ε4 is a risk factor and ε2 is protective against Alzheimer disease relative to the ε3 allele.

Diffusion-weighted imaging

(DWI). A type of structural MRI sequence that measures the motion of water molecules in the brain. The water in the brain diffuses more easily along the direction of the white matter fibres (myelinated axons) than perpendicular to them and diffusion anisotropy can be used to reconstruct the anatomical connections.

Electrophysiology

A branch of physiology that studies the electrical properties of neurons and brain tissues by using electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex.

Functional MRI

(fMRI). A type of MRI sequence that measures changes in blood flow that occur with brain activity and can be used to identify the functional brain connections, which are obtained from the temporal correlations in the activity between pairs of grey matter regions.

Graph theory

A mathematical system used to study pairwise relationships between objects. In graphs, objects are represented as ‘nodes’, whereas their relationships are represented as ‘edges’.

Neurodegenerative diseases

A disorder characterized by the accumulation of pathological protein aggregates, which result in progressive damage to neurons and brain connections that are essential for cognition and/or sensorimotor functions.

Overfitting

An undesirable situation when a mathematical model gives accurate predictions of data that were used to train it, but not of new, unseen data.

Positron emission tomography

(PET). A type of molecular imaging technique that uses radiotracers on a fluorine radioisotope, which cross the blood–brain barrier and can bind to neurons that show abnormal pathological processes such as hypometabolism, tau accumulation or amyloid deposition.

Structural MRI

(sMRI). The most common non-invasive neuroimaging modality used to quantify grey and white matter atrophy in neurodegenerative disorders through the use of strong magnetic fields that produce contrast between different brain tissue classes.

Synucleinopathies

A class of neurodegenerative disorders characterized by the abnormal accumulation of α-synuclein aggregates in the brain, which can lead to the development of inclusions called Lewy bodies inside the neurons. Parkinson disease and dementia with Lewy bodies are the most common forms of synucleinopathies.

Tauopathy

A class of neurodegenerative diseases that involve the abnormal aggregation of tau into neurofibrillary tangles in the brain. Depending on the major tau isoforms appearing in the aggregates, tauopathies can be classified into groups on the basis of the number of repeats (R): 3R tauopathies (for example, Pick disease), 4R tauopathies (for example, progressive supranuclear palsy) and mixed 3R/4R tauopathies (for example, Alzheimer disease).

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Vogel, J.W., Corriveau-Lecavalier, N., Franzmeier, N. et al. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat. Rev. Neurosci. 24, 620–639 (2023). https://doi.org/10.1038/s41583-023-00731-8

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