Neurofeedback Control in Parkinsonian Patients Using Electrocorticography Signals Accessed Wirelessly With a Chronic, Fully Implanted Device

Abstract

Parkinson's disease (PD) is characterized by motor symptoms such as rigidity and bradykinesia that prevent normal movement. Beta band oscillations (13-30 Hz) in neural local field potentials (LFPs) have been associated with these motor symptoms. Here, three PD patients implanted with a therapeutic deep brain neural stimulator that can also record and wirelessly stream neural data played a neurofeedback game where they modulated their beta band power from sensorimotor cortical areas. Patients' beta band power was streamed in real-time to update the position of a cursor that they tried to drive into a cued target. After playing the game for 1-2 hours each, all three patients exhibited above chance-level performance regardless of subcortical stimulation levels. This study, for the first time, demonstrates using an invasive neural recording system for at-home neurofeedback training. Future work will investigate chronic neurofeedback training as a potentially therapeutic tool for patients with neurological disorders.

Fig. 1

System design of neurofeedback task. (A) Patients implanted with the Activa PC + S and cortical leads have the implantable pulse generator located over the pectoralis muscle. A telemetry module has an antenna that sits on the skin surface in close proximity to the IPG and wirelessly acquires neural data and transmits the data to a Windows 7 machine via serial port. The Medtronic Nexus-D application program interface provides functions called from Matlab 2014b to acquire data from the serial port. Neural data is then translated into cursor position. (B) Task timeline begins with a target appearing. The patient then must make the cursor enter the target and hold (in all sessions reported, hold < 400 ms making the hold time effectively 0 ms) after which the target turns yellow and the score count is incremented. An inter-trial interval of 1.6 seconds follows before the next trial begins.

Fig. 2

Patient power spectral densities during online neurofeedback control. Patient PSDs for cases with stimulation off and stimulation on at 130 Hz or 160 Hz. Despite stimulation, beta peaks are still resolvable. Colored horizontal lines denoted by black arrow show the beta frequency range used for online control for each patient (Patient 1: 10–20 Hz, Patient 2: 12.5–17.5 Hz, Patient 3: 20 – 30 Hz). Note that the beta band used for neurofeedback control in Patient 2 was specially configured for streaming power estimates (instead of time domain data), and does not match the actual beta peak (see Methods)

Fig. 3

Assist parameter used over the course of training for Patients 1–3. Training blocks are concatenated together for visualization, even if time elapsed between blocks. All patients either reduce their reliance on the assist, or maintain a constant assist level throughout the course of training

Fig.4

Patients perform the neurofeedback task above chance levels. (A–C) Patient chance level is illustrated by the blue cumulative distribution (see Experimental Procedures for calculation method). The x axis is total rewards from simulated performance and the y axis is a cumulative normalized count of how many simulations yields that number of rewards. Actual patient performance (total rewards) is shown with the vertical orange line. P-values are printed and are the percent of chance level simulations greater than actual performance (multiplied by two for a two-tailed test). Only data from late learning (constant assist) and from targets with mean time to target greater than four seconds are included in the chance level performance calculation (targets included are denoted in title, see Experimental Procedures). (D–F) For each patient, the time to target is plotted versus session time for the target with most improvement (restricted to late session data) is plotted. Note that for patient 3, the negative slope indicative of reduced time to reach target is sognifocantly different than zero (Student’s t-test, p < 0.05).

Fig. 5

Neural changes emerge with training. (A–F) Trial averaged beta power estimates used to drive the cursor are plotted for each patient two seconds before target acquisition to time of targets acquisition (0 sec on x axis). Different traces are for the different targets as indicated by the target color key in (A) and (D). (A–C) show Patient 1–3 neural activity for early in the session (high assist levels). (D–F) show Patient 1–3 neural activity for late in the session (lower constant assist level, same data as Fig 3.). Asterisks indicate significant group differences (Kruskal–Wallis test, * p < 0.05, ** p < 0.01, *** p < 0.001). (G–I) Modulation of full spectrum during neurofeedback task. Traces show trial-averaged z-scored power spectral densities (z-scored by subtracting mean and dividing by standard deviation of aggregated data from late training session) calculated in the 800 ms before target acquisition. Red traces are for the high beta target, teal traces are for the lowest beta target (G, H), and blue trace is for the mid-low beta target (I). Shaded gray indicates significant difference between the top and bottom target plotted for each subject (Kruskal-Wallis test, p < 0.05).