Linear Predictive Approaches Separate Field Potentials in Animal Model of Parkinson's Disease.

Md Fahim Anjum,Morgan A Kennedy,Stephanie L Alberico,Joshua Haug,Soura Dasgupta,Raghuraman Mudumbai,Nandakumar S Narayanan

doi:10.3389/fnins.2020.00394

Abstract

Parkinson's disease (PD) causes impaired movement and cognition. PD can involve profound changes in cortical and subcortical brain activity as measured by electroencephalography or intracranial recordings of local field potentials (LFP). Such signals can adaptively guide deep-brain stimulation (DBS) as part of PD therapy. However, adaptive DBS requires the identification of triggers of neuronal activity dependent on real time monitoring and analysis. Current methods do not always identify PD-related signals and can entail delays. We test an alternative approach based on linear predictive coding (LPC), which fits autoregressive (AR) models to time-series data. Parameters of these AR models can be calculated by fast algorithms in real time. We compare LFPs from the striatum in an animal model of PD with dopamine depletion in the absence and presence of the dopamine precursor levodopa, which is used to treat motor symptoms of PD. We show that in dopamine-depleted mice a first order AR model characterized by a single LPC parameter obtained by LFP sampling at 1 kHz for just 1 min can distinguish between levodopa-treated and saline-treated mice and outperform current methods. This suggests that LPC may be useful in online analysis of neuronal signals to guide DBS in real time and could contribute to DBS-based treatment of PD.

Highlights

Parkinson’s disease (PD) and other neurodegenerative conditions involve impaired movement and cognition
Neuronal activity as measured by electroencephalography (EEG) or local field potentials (LFPs) can be abnormal in PD patients (McCarthy et al, 2011), and aspects of these abnormalities can be recapitulated in rodent models of PD that are based on dopamine depletion (Parker et al, 2015; Alberico et al, 2017)
We provide a proof-of-principle demonstration that changes in this parameter can distinguish between levodopa-treated and saline-treated dopamine-depleted mice and discuss the implications for future Adaptive DBS (aDBS) approaches

Summary

Introduction

Parkinson’s disease (PD) and other neurodegenerative conditions involve impaired movement and cognition. PD is a complex disease, a feature common to all cases is loss of midbrain dopamine neurons that project throughout the brain, including the cerebral cortex and basal ganglia (Narayanan et al, 2013). One effective treatment for PD is deep brain stimulation (DBS), in which high-frequency electrical current (generated by an implanted, battery-powered impulse generator) is applied directly to the subthalamic nucleus (STN) or globus pallidus to modulate PD-related neuronal activity and alleviate motor symptoms (Deuschl et al, 2006). Linear Predictive Approaches Separate LFP performing DBS are generally adjusted only rarely—weeks or months apart. This conventional DBS (cDBS) is associated with an average battery life of 4 years with replacement requiring general anesthesia and substantial hardware costs (BinMahfoodh et al, 2003)

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