Abstract: A method includes engaging Parkinson's Disease (PD) subjects in a continuous motor performance task that elicits natural motor variability, quantifying natural motor variability of each PD subject with an array of motor metrics at short timescales, applying a machine-learning classification or regression algorithm to determine weights for each of these metrics to maximally differentiate each patient's motor performance from that of controls performing the same task, and combining the weights to determine a scalar metric of motor performance for each short epoch of motor behavior.

Abstract: Closed-loop deep brain stimulation using neural and behavioral biomarkers of specific motor features is provided via training a machine learning model to differentiate first neural signals generated by a biological subject that are associated with tremor in the biological subject from second neural signals generated by the biological subject that are associated with bradykinesia in the biological subject; placing a first plurality and a second plurality of deep brain stimulation electrodes in a subthalamic nucleus (STN) of the biological subject; placing a plurality of microelectrodes and a plurality of macroelectrodes in the STN; in response to identifying, by the machine learning model, a given neural signal as one of the first or second neural signals, activating a corresponding one of the first or second plurality of electrodes with a therapeutically effective voltage and current to affect deep brain stimulation in the biological subject to treat a neuromotor disorder.

Abstract: A method includes engaging Parkinson's Disease (PD) subjects in a continuous motor performance task that elicits natural motor variability, quantifying natural motor variability of each PD subject with an array of motor metrics at short timescales, applying a machine-learning classification or regression algorithm to determine weights for each of these metrics to maximally differentiate each patient's motor performance from that of controls performing the same task, and combining the weights to determine a scalar metric of motor performance for each short epoch of motor behavior.