Wu, C;
Matias, C;
Foltynie, T;
Limousin, P;
Zrinzo, L;
Akram, H;
(2021)
Dynamic Network Connectivity Reveals Markers of Response to Deep Brain Stimulation in Parkinson's Disease.
Frontiers in Human Neuroscience
, 15
, Article 729677. 10.3389/fnhum.2021.729677.
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Abstract
Background: Neuronal loss in Parkinson's Disease (PD) leads to widespread neural network dysfunction. While graph theory allows for analysis of whole brain networks, patterns of functional connectivity (FC) associated with motor response to deep brain stimulation of the subthalamic nucleus (STN-DBS) have yet to be explored. Objective/Hypothesis: To investigate the distributed network properties associated with STN-DBS in patients with advanced PD. Methods: Eighteen patients underwent 3-Tesla resting state functional MRI (rs-fMRI) prior to STN-DBS. Improvement in UPDRS-III scores following STN-DBS were assessed 1 year after implantation. Independent component analysis (ICA) was applied to extract spatially independent components (ICs) from the rs-fMRI. FC between ICs was calculated across the entire time series and for dynamic brain states. Graph theory analysis was performed to investigate whole brain network topography in static and dynamic states. Results: Dynamic analysis identified two unique brain states: a relative hypoconnected state and a relative hyperconnected state. Time spent in a state, dwell time, and number of transitions were not correlated with DBS response. There were no significant FC findings, but graph theory analysis demonstrated significant relationships with STN-DBS response only during the hypoconnected state - STN-DBS was negatively correlated with network assortativity. Conclusion: Given the widespread effects of dopamine depletion in PD, analysis of whole brain networks is critical to our understanding of the pathophysiology of this disease. Only by leveraging graph theoretical analysis of dynamic FC were we able to isolate a hypoconnected brain state that contained distinct network properties associated with the clinical effects of STN-DBS.
| Type: | Article |
|---|---|
| Title: | Dynamic Network Connectivity Reveals Markers of Response to Deep Brain Stimulation in Parkinson's Disease |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fnhum.2021.729677 |
| Publisher version: | http://dx.doi.org/10.3389/fnhum.2021.729677 |
| Language: | English |
| Additional information: | Copyright © 2021 Wu, Matias, Foltynie, Limousin, Zrinzo and Akram. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Parkinson’s disease, deep brain stimulation, dynamic functional connectivity, functional magnetic resonance imaging, graph theory |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10137644 |
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