Abstract
Objective. Identifying neural activity biomarkers of brain disease is essential to provide objective estimates of disease burden, obtain reliable feedback regarding therapeutic efficacy, and potentially to serve as a source of control for closed-loop neuromodulation. In Parkinson’s disease (PD), microelectrode recordings (MER) are routinely performed in the basal ganglia to guide electrode implantation for deep brain stimulation (DBS). While pathologically-excessive oscillatory activity has been observed and linked to PD motor dysfunction broadly, the extent to which these signals provide quantitative information about disease expression and fluctuations, particularly at short timescales, is unknown. Furthermore, the degree to which informative signal features are similar or different across patients has not been rigorously investigated. We sought to determine the extent to which motor error in PD across patients can be decoded on a rapid timescale using spectral features of neural activity. Approach. Here, we recorded neural activity from the subthalamic nucleus (STN) of subjects with PD undergoing awake DBS surgery while they performed an objective, continuous behavioral assessment that synthesized heterogenous PD motor manifestations to generate a scalar measure of motor dysfunction at short timescales. We then leveraged natural motor performance variations as a ‘ground truth’ to identify corresponding neurophysiological biomarkers. Main results. Support vector machines using multi-spectral decoding of neural signals from the STN succeeded in tracking the degree of motor impairment at short timescales (as short as one second). Spectral power across a wide range of frequencies, beyond the classic ‘β’ oscillations, contributed to this decoding, and multi-spectral models consistently outperformed those generated using more isolated frequency bands. While generalized decoding models derived across subjects were able to estimate motor impairment, patient-specific models typically performed better. Significance. These results demonstrate that quantitative information about short-timescale PD motor dysfunction is available in STN neural activity, distributed across various patient-specific spectral components, such that an individualized approach will be critical to fully harness this information for optimal disease tracking and closed-loop neuromodulation.
Highlights
Parkinson’s Disease (PD), one of the most prevalent neurodegenerative conditions (Pringsheim et al, 2014), is typified by motor and cognitive dysfunction that occur in the setting of pathologically-increased oscillatory neural activity in the basal ganglia (Brown et al, 2001; Priori et al, 2004; Kühn et al, 2005; Weinberger et al, 2006)
Because the heterogeneity of motor impairment in PD was unlikely to be associated with some narrow aspect of performance on this task, we defined a library of 8 motor metrics (Supplementary Figure 1A) that tiled the space of potential movement error
These observations that the PD vs. control Motor Error Score” (MES) distributions differed in timescale, range, and value support the proposition that the tracking task provided an appropriate platform for detecting PD motor impairment
Summary
Parkinson’s Disease (PD), one of the most prevalent neurodegenerative conditions (Pringsheim et al, 2014), is typified by motor and cognitive dysfunction that occur in the setting of pathologically-increased oscillatory neural activity in the basal ganglia (Brown et al, 2001; Priori et al, 2004; Kühn et al, 2005; Weinberger et al, 2006). Those in the β (~12–30 Hz) range, have emerged as potential biomarkers for PD motor dysfunction based primarily upon relatively longer timescale observations of abundant β oscillations in the unmedicated PD state and decreased β power in response to therapy (dopaminergic medications or DBS) (Wingeier et al, 2006; Pogosyan et al, 2010; Neumann et al, 2016; Shreve et al, 2016; Neumann and Kühn, 2017; Tinkhauser et al, 2017b).
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