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Functional brain networks in the evaluation of patients with neurodegenerative disorders

Nature Reviews Neurology volume 19, pages 73–90 (2023)Cite this article

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A Publisher Correction to this article was published on 19 May 2023

This article has been updated

Abstract

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient’s scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

Key points

  • Parkinson disease, Alzheimer disease and other neurodegenerative disorders are characterized by specific disease-related functional topographies (brain networks) that can be identified and validated using metabolic PET or resting-state functional MRI.

  • Brain network activity can be quantified on an individual patient basis, and the resulting network expression levels can be used in research and clinical settings.

  • Expression levels for multiple disease-related topographies can be entered into computational algorithms used to classify patients according to the diagnostic likelihood of these diseases.

  • Expression levels for abnormal disease networks correlate with clinical symptom severity and can be modulated by effective treatment.

  • Network expression levels increase over time and can be used to predict the likelihood of transition from preclinical to symptomatic disease in at-risk individuals.

  • The characterization of treatment-induced networks opens the door to their future use as objective outcome measures in clinical trials.

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Fig. 1: Functional brain network alterations associated with Parkinson disease.
Fig. 2: Novel functional brain network induced by subthalamic gene therapy for Parkinson disease.
Fig. 3: Pattern-based classification algorithms for the differential diagnosis of parkinsonism.
Fig. 4: Abnormal metabolic covariance patterns associated with Alzheimer disease and dementia with Lewy bodies.
Fig. 5: Abnormal metabolic covariance pattern associated with frontotemporal dementia.

Change history

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Acknowledgements

M.P. and T.R. were supported by the Slovenian Research Agency (ARRS) through grant P1-0389 and projects J7-2600 and J7-3150. T.R. is a recipient of the Fulbright Foreign Student Program sponsored by the US Department of State’s Bureau of Educational and Cultural Affairs. The authors thank Yoon Young Choi for her invaluable editorial assistance in preparing the manuscript.

Competing interests

D.E. declares that he receives funding from the NIH and The Michael J. Fox Foundation for Parkinson’s Research.

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  1. These authors contributed equally: Matej Perovnik, Tomaž Rus.

Authors and Affiliations

  1. Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia

    Matej Perovnik & Tomaž Rus

  2. Medical Faculty, University of Ljubljana, Ljubljana, Slovenia

    Matej Perovnik & Tomaž Rus

  3. Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA

    Katharina A. Schindlbeck & David Eidelberg

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Glossary

Assortativity

Correlation coefficient between the degree of all nodes on two opposite ends of a link; a measure of the diversity of connections in a graph that provides an index of overall network stability.

Characteristic path length

The average number of edges in the shortest paths connecting the nodes of the network; a measure of the integration of information processing and the global efficiency of the network.

Clustering coefficient

The likelihood that the nearest neighbours of a given network node are themselves connected; an index of the segregation of information processing within the network.

Covariance topographies

Patterns of co-varying regional activity identified by principal component analysis of the individual’s scan data.

Degree centrality

The number of connections divided by the number of nodes in the same graph; a measure of the overall connectivity of nodes.

Dimensionality reduction

Mathematical procedures to identify one or more smaller matrices that contain information the same as or similar to the original large data matrix; this approach is used to extract relevant properties of the data (such as specific disease-related topographies) and remove extraneous effects.

Expression levels

Also termed subject scores. The principal component scalar, which quantifies the extent to which a given topographic pattern is represented in a specific individual’s scan.

Functional connectivity

Connections between brain regions defined by the magnitude of correlations in spontaneous signal fluctuations (resting-state functional MRI), local metabolic activity ([18F]fluorodeoxyglucose PET), electrical signals (electro-encephalography) or magnetic fields produced by electrical activity (magneto-encephalography).

Functional neuroimaging

Technique to map regional changes in neuronal activity, based typically on blood oxygenation, cerebral metabolism or other physiological signals.

Graph theory

Mathematical approach to studying properties of network structure (nodal organization) and function (information flow).

Modularity

The relationship of the number of edges linking the nodes within a community (module or subgraph) to those linking the different communities for the network as a whole; a means of partitioning the network into organizationally discrete nodal clusters.

Network patterns

Topographical patterns of neural activity in which interconnected brain regions form discrete networks.

Predictive models

Mathematical constructs that predict the likelihood of future events or outcomes based on a set of input data.

Small-worldness

Ratio of clustering coefficient to characteristic path length, normalized to corresponding values from an equivalent random graph; a measure of the balance between segregation and integration of information processing in the network space.

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Perovnik, M., Rus, T., Schindlbeck, K.A. et al. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 19, 73–90 (2023). https://doi.org/10.1038/s41582-022-00753-3

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