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

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

doi:10.3389/fnins.2020.00394

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

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

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

Frontiers in neuroscience, Vol.14, pp.394-394

2020

DOI: 10.3389/fnins.2020.00394

PMCID: PMC7193738

PMID: 32390797

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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.

local field potential

Parkinson's disease

linear predictive coding

levodopa

mice

Details

Title: Subtitle: Linear Predictive Approaches Separate Field Potentials in Animal Model of Parkinson's Disease
Creators: Md Fahim Anjum - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, United States
Joshua Haug - DISTek Integration Inc., Cedar Falls, IA, United States
Stephanie L Alberico - Department of Neurology, Medical School, University of Minnesota, Minneapolis, MN, United States
Soura Dasgupta - Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center, Jinan, China
Raghuraman Mudumbai - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, United States
Morgan A Kennedy - Department of Neurology, Papajohn Biomedical Institute, The University of Iowa, Iowa City, IA, United States
Nandakumar S Narayanan - Department of Neurology, Papajohn Biomedical Institute, The University of Iowa, Iowa City, IA, United States
Resource Type: Journal article
Publication Details: Frontiers in neuroscience, Vol.14, pp.394-394
DOI: 10.3389/fnins.2020.00394
PMID: 32390797
PMCID: PMC7193738
NLM abbreviation: Front Neurosci
ISSN: 1662-4548
eISSN: 1662-453X
Publisher: Switzerland
Grant note: R01 MH116043 / NIMH NIH HHS
Language: English
Date published: 2020
Academic Unit: Neurology; Electrical and Computer Engineering; Iowa Neuroscience Institute
Record Identifier: 9984070011302771

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