Abstract
Non-invasive recordings of magnetoencephalography (MEG) have been used for developing biomarkers for neural changes associated with Parkinson’s Disease (PD) but have yielded inconsistent findings. Here, we investigated whether analysing motor cortical activity within the context of large-scale brain network activity provides a more sensitive marker of changes in PD using MEG.
We extracted motor cortical beta power and beta bursts from resting-state MEG scans of individuals with PD (N=28) and well-matched healthy controls (N=36). To situate beta bursts in their brain network contexts, we used a time-delay embedded Hidden Markov Model (TDE-HMM) to extract brain network activity and investigated co-occurrence patterns between brain networks and beta bursts.
PD was associated with decreased beta power in motor cortex and decreased occurrences of the sensorimotor network, while motor cortical beta-burst dynamics were not changed. By comparing conventional burst and large-scale network occurrences, we observed that motor beta bursts occurred during both sensorimotor network and non-sensorimotor network activations. When using the large-scale network information provided by the TDE-HMM to focus on bursts that were active during sensorimotor network activations, significant decreases in burst dynamics could be observed in individuals with PD.
In conclusion, our findings suggest that decreased motor cortical beta power in PD is prominently associated with changes in sensorimotor network dynamics using MEG. Thus, investigating large-scale networks or considering the large-scale network context of motor cortical activations may be crucial for identifying alterations in the sensorimotor network that are prevalent in PD, and might help resolve contradicting findings in the literature.
Highlights
Sensorimotor network occurrences are decreased in Parkinson’s Disease.
Motor cortical beta bursts occur during both sensorimotor network and non-sensorimotor network activations.
Focusing on motor beta bursts occurring during sensorimotor network activations enables for better discrimination between controls and individuals with PD.
The spatiotemporal details provided by large-scale network analysis may help to overcome discrepancies found in the PD literature.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This research was funded by the Marie Skłodowska-Curie Innovative Training Network European School of Network Neuroscience (euSNN) (860563), a Wellcome Trust Senior Investigator Award to A.C.N. (104571/Z/14/Z), and a James S. McDonnell Foundation Understanding Human Cognition Collaborative Award (220020448). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z and 203139/A/16/Z). This research was supported by the NIHR Oxford Health Biomedical Research Centre (NIHR203316). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The OPDC Discovery cohort is funded by Parkinson's UK and the Oxford NIHR Biomedical Research Centre.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
Included studies were approved by the NHS Oxford Research Ethics Committee and the Research Ethics Committee of the University of Oxford and followed the Declaration of Helsinki.
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Data Availability
The conditions of our ethics approval do not permit public archiving of the data supporting this study. Sharing data requires a formal data-sharing agreement in accordance with ethics procedures governing the re-use of sensitive data. Readers seeking access to the data should contact the first author.
Abbreviations
- ECoG
- Electrocorticogram
- EEG
- Electroencephalogram
- HC
- Healthy Control
- ICA
- Independent Component Analysis
- MEG
- Magnetoencephalogram
- NABB
- Network-associated Beta Bursts
- PD
- Parkinson’s Disease
- PCA
- Principal Component Analysis
- SD
- Standard Deviation
- STN
- Subthalamic Nucleus
- TDE-HMM
- Time-delay Embedded Hidden Markov Model
- UPDRS
- Unified Parkinson’s disease Rating Scale