The aperiodic exponent of subthalamic field potentials reflects excitation/inhibition balance in Parkinsonism
Abstract
Periodic features of neural time series data, such as local field potentials (LFP), are often quantified using power spectra. While the aperiodic exponent of spectra is typically disregarded, it is nevertheless modulated in a physiologically-relevant manner and was recently hypothesised to reflect excitation/inhibition (E/I) balance in neuronal populations. Here, we used a cross-species in vivo electrophysiological approach to test the E/I hypothesis in the context of experimental and idiopathic Parkinsonism. We demonstrate in dopamine-depleted rats that aperiodic exponents and power at 30-100 Hz in subthalamic nucleus (STN) LFPs reflect defined changes in basal ganglia network activity; higher aperiodic exponents tally with lower levels of STN neuron firing and a balance tipped towards inhibition. Using STN-LFPs recorded from awake Parkinson's patients, we show that higher exponents accompany dopaminergic medication and deep brain stimulation (DBS) of STN, consistent with untreated Parkinson's manifesting as reduced inhibition and hyperactivity of STN. These results suggest that the aperiodic exponent of STN-LFPs in Parkinsonism reflects E/I balance, and might be a candidate biomarker for adaptive DBS.
Data availability
The code was uploaded directly to eLife and in addition can be found here https://doi.org/10.5287/bodleian:rJ7jyjX97. The animal data (Mallet et al., 2022) used for this project is available at the Medical Research Council Brain Networks Dynamics Unit (MRC BNDU) Data Sharing Platform at the University of Oxford https://data.mrc.ox.ac.uk/stn-rat and DOI: https://doi.org/10.5287/bodleian:wx6D7oenk. The human data (Wiest et al., 2022) is also available at the MRC BNDU Data Sharing Platform https://data.mrc.ox.ac.uk/stn-lfp-on-off-and-dbs and DOI: https://doi.org/10.5287/bodleian:mzJ7YwXvo.
-
Wideband recordings from silicon probes in the subthalamic nucleus of 6-OHDA hemi-lesioned rats during anaesthesiaOxford University Research Archive, doi:10.5287/bodleian:wx6D7oenk.
-
STN local field potential recordings from awake patients with Parkinson's, ON and OFF meds, and during 130 Hz DBSOxford University Research Archive, doi:10.5287/bodleian:mzJ7YwXvo.
Article and author information
Author details
Funding
Medical Research Council (MC_UU_00003/2)
- Huiling Tan
Medical Research Council (MC_UU_00003/5)
- Peter J Magill
Medical Research Council (MC_UU_00003/6)
- Andrew Sharott
National Institute for Health Research Oxford Biomedical Research Centre
- Huiling Tan
Rosetrees Trust
- Huiling Tan
Parkinson's UK (G-0806)
- Peter J Magill
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: Experiments were performed on adult male Sprague Dawley rats (Charles River), and were conducted in accordance with the Animals (Scientific Procedures) Act, 1986 (UK) and the provisions of the Society for Neuroscience Policies on the Use of Animals in Neuroscience Research. Animal data that was analysed in this paper has been generated under the project licence numbers 30/2131 and 30/2629. All details on the 6-OHDA lesion and electrophysiological recordings were published before (Mallet et al., 2008a).
Human subjects: This protocol was approved by the Health Research Authority UK, the National Research Ethics Service local Research Ethics Committee (IRAS: 46576) and the local ethics committee at the University of Mainz (837.208.17(11042)). Patients were recruited at St. George's University Hospital NHS Foundation Trust, London, King's College Hospital NHS Foundation Trust, London, and the University Medical Center Mainz. Written informed consent, and consent to publish, was obtained before surgery in line with the Declaration of the Principles of Helsinki. We analysed data from 24 patients from these 3 centres.
Copyright
© 2023, Wiest et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,226
- views
-
- 340
- downloads
-
- 33
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
Combining electrophysiological, anatomical and functional brain maps reveals networks of beta neural activity that align with dopamine uptake.
-
- Neuroscience
During rest and sleep, memory traces replay in the brain. The dialogue between brain regions during replay is thought to stabilize labile memory traces for long-term storage. However, because replay is an internally-driven, spontaneous phenomenon, it does not have a ground truth - an external reference that can validate whether a memory has truly been replayed. Instead, replay detection is based on the similarity between the sequential neural activity comprising the replay event and the corresponding template of neural activity generated during active locomotion. If the statistical likelihood of observing such a match by chance is sufficiently low, the candidate replay event is inferred to be replaying that specific memory. However, without the ability to evaluate whether replay detection methods are successfully detecting true events and correctly rejecting non-events, the evaluation and comparison of different replay methods is challenging. To circumvent this problem, we present a new framework for evaluating replay, tested using hippocampal neural recordings from rats exploring two novel linear tracks. Using this two-track paradigm, our framework selects replay events based on their temporal fidelity (sequence-based detection), and evaluates the detection performance using each event's track discriminability, where sequenceless decoding across both tracks is used to quantify whether the track replaying is also the most likely track being reactivated.