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The human connectome in Alzheimer disease — relationship to biomarkers and genetics

An Author Correction to this article was published on 11 August 2021

This article has been updated

Abstract

The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.

Key points

  • Amyloid-β (Aβ) pathology is associated with decreased hub connectivity in the default-mode network (DMN) during the preclinical stage of Alzheimer disease (AD) and the association extends to other brain networks as the disease progresses.

  • Selective hub vulnerability might explain the preferential accumulation of Aβ in the medial hubs of the DMN, and of tau in medial temporal lobe hubs, in preclinical AD.

  • Tau pathology spreads from the medial temporal lobe hubs — along structural connections — to other brain regions, supporting the pathogenic spread hypothesis.

  • Aβ pathology has a common role in driving DMN hypo-connectivity in late-onset AD, autosomal-dominant AD and early-onset AD; however, the association between Aβ pathology and DMN hypoconnectivity is regulated by different genetic variants across AD subtypes.

  • Spatial gene expression profiles might contribute to the relationships between the patterns of Aβ and tau accumulation and patterns of structural and functional connectome changes in AD.

  • Computational modelling studies will be important for understanding the role of the connectome in relation to progression of Aβ, tau and other pathogenic features of AD.

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Fig. 1: Network models.
Fig. 2: Patterns of tau accumulation in AD.
Fig. 3: Early Aβ accumulation in resting-state functional brain networks.
Fig. 4: Association between functional connectivity and covariance in tau-PET change.
Fig. 5: Hypothesized, empirical and predicted tau spreading patterns.
Fig. 6: Gene expression, AD pathology and the human connectomes.

Change history

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Acknowledgements

The preparation of this manuscript was supported in part by U.S. National Institutes of Health grants: R01 AG197711, P30 AG10133, 1U01AG024904, R01 CA129769, R01 AG057739, R01 LM013463, R01 AG068193 and U01 AG068057. The authors would like to thank Dr. Martijn van den Heuvel for assisting with the drawing of Fig. 1b. The authors thank Dr. Kwangsik Nho and Dr. Shannon Risacher for valuable discussions.

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M. Y. researched data for the article, made a substantial contribution to discussion of content, wrote the article, and reviewed and edited the manuscript before submission. O. S. and A. J. S. researched data for the article, made a substantial contribution to discussion of content, and reviewed and edited the manuscript before submission.

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Correspondence to Andrew J. Saykin.

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Nature Reviews Neurology thanks B. Bendlin, who co-reviewed with A. Kohli, A. Raj and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Allen Human Brain Atlas: http://human.brain-map.org/

Alzheimer’s Disease Neuroimaging Initiative: http://adni.loni.usc.edu/

BioFINDER: https://biofinder.se/

Human Connectome Project: https://www.humanconnectome.org/

Glossary

Cytoarchitectonics

The study of the spatial distribution pattern of neurons within the central nervous system; the size, shape, packing density and staining intensity of neuronal cell bodies in six layers are used to characterize a specific cytoarchitectural area.

Clustering coefficient

The fraction of a node’s directly connected neighbours that are also neighbours of each other.

Path length

The number of links connecting any two nodes in a network.

Centrality

Measures that quantify the importance of a node or a link in a network.

Modularity

A measure that quantifies the degree to which a network can be partitioned into subnetworks or modules.

Small-world

A network property of high average clustering coefficient and short average shortest path length.

Rich-club

A network has this property when nodes with high degree centrality are more densely interconnected between each other than expected.

Global efficiency

The average inverse shortest path length in the network.

Local efficiency

The inverse of the average shortest path length of all neighbours of the node and an alternative local connectivity metric to the clustering coefficient.

Degree centrality

The number of links a node has to other nodes in a network.

Epidemic spreading models

(ESM). Computational models simulating the spreading patterns of amyloid-β and tau from a preselected epicentre to different brain regions via structural connections.

Euclidean distance matrix

A symmetric matrix, in which each element is computed by estimating the Euclidean distance between the centre coordinates of two brain regions.

Network diffusion models

(NDMs). Computational models simulating Alzheimer disease progression on brain networks using a network heat equation.

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Yu, M., Sporns, O. & Saykin, A.J. The human connectome in Alzheimer disease — relationship to biomarkers and genetics. Nat Rev Neurol 17, 545–563 (2021). https://doi.org/10.1038/s41582-021-00529-1

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