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

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.

Resting-state EEG distinguishes depression in Parkinson’s disease

“…Our machine learning approach, LEAPD, compress power spectra into a series of autoregressive coefficients that holistically captures the shape of each power spectra with a few numbers [17,20]. Here, we used LEAPD to classify PD vs PDDEP and PDDEP vs DEP from single channels, as well as combinations of two channels (Figure 2).…”

“…LEAPD is an algorithm for binary classification of the spectral content of EEG signals. This approach was developed by Anjum et al [17,20] to distinguish between PD patients and control participants. We implemented LEAPD to compare PD patients with depression (PDDEP) vs PD patients without depression (PD) and PDDEP vs depressed patients without PD (DEP).…”

“…Second, PD patients with depression had strong global differences in gamma rhythms relative to non-PD patients with depression. Third, we used a linear predictive coding of EEG Algorithm for PD (LEAPD) formulated by Anjum et al [17,20], which provides binary classification based on resting-state EEG power spectra. LEAPD-based classification accurately identified PD patients with depression relative to PD patients and non-PD depressed patients.…”

“…We report three main results. First, PD patients with depression had globally attenuated alpha (8-13 Hz) rhythms, as well as attenuated central beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma rhythms relative to PD patients without depression. Second, PD patients with depression had strong global differences in gamma rhythms relative to non-PD patients with depression.…”

“…Third, we used a linear predictive coding of EEG Algorithm for PD (LEAPD) formulated by Anjum et al. [17,20], which provides binary classification based on resting-state EEG power spectra.…”