Functional Connectivity in Parkinson's Disease Systems

Parkinson’s disease is increasingly understood as a disorder of distributed neural networks rather than solely a dopaminergic deficit in the substantia nigra. Functional connectivity studies, primarily using resting-state functional magnetic resonance imaging, have revealed widespread alterations in communication between cortical regions, subcortical nuclei and the cerebellum. Disruption of cortico-basal ganglia-thalamic loops underlies core motor symptoms, while changes in frontoparietal, default mode and sensorimotor networks correlate with cognitive and non-motor manifestations. Graph-theory approaches have characterised Parkinson’s as a disconnection syndrome, identifying both reduced integration among hub regions and compensatory hyperconnectivity, for example increased coupling within sensorimotor circuits. Dopaminergic therapies and deep brain stimulation exert modulatory effects on network organisation, normalising some connectivity deficits while revealing frequency-specific alterations in low-frequency BOLD oscillations. Emerging evidence points to early functional signatures that may serve as biomarkers for disease progression and therapeutic response, highlighting the clinical and translational potential of connectivity mapping in Parkinson’s disease systems.

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Functional Connectivity in Parkinson's Disease Systems publication trend

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Technical terms

Functional connectivity: Statistical interdependence of neuronal activity between spatially distinct brain regions over time.

Resting-state fMRI: Functional MRI acquired while the subject is not performing an explicit task, used to infer intrinsic network activity.

Seed-based analysis: Correlation method that quantifies connectivity between a predefined region of interest and the rest of the brain.

Network-based analysis: Examination of connectivity patterns across multiple regions to characterise interactions within and between functional networks.

Graph-theory analysis: Modelling the brain as a network of nodes (regions) and edges (connections) to assess topological properties such as efficiency and centrality.

Eigenvector centrality: Graph-theory metric that assigns importance to nodes based on their connections to other highly connected nodes.

Default mode network: Set of brain regions that exhibit higher activity during rest than during goal-directed tasks, associated with self-referential processing.

References

1. Understanding the role of cerebellum in early Parkinson’s disease: a structural and functional MRI study. npj Parkinson's Disease (2024).
2. Resting-State Functional MRI Approaches to Parkinsonisms and Related Dementia. Current Neurology and Neuroscience Reports (2024).
3. Altered Resting State Cortico-Striatal Connectivity in Mild to Moderate Stage Parkinson's Disease. Frontiers in Systems Neuroscience (2010).
4. Enhanced Functional Connectivity between Putamen and Supplementary Motor Area in Parkinson’s Disease Patients. PLOS ONE (2013).
5. Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits. Frontiers in Systems Neuroscience (2014).
6. Dysfunction of the Default Mode Network in Drug-Naïve Parkinson’s Disease with Mild Cognitive Impairments: A Resting-State fMRI Study. Frontiers in Aging Neuroscience (2016).
7. L-DOPA changes spontaneous low-frequency BOLD signal oscillations in Parkinson's disease: a resting state fMRI study. Frontiers in Systems Neuroscience (2012).