Neural activity in the β frequency range (13–30 Hz) is excessively synchronized in Parkinson’s disease (PD). Previous work using invasive intracranial recordings and non-invasive scalp electroencephalography (EEG) has shown that correlations between β phase and broad-band γ (>50 Hz) amplitude [i.e., phase amplitude coupling (PAC)] are elevated in PD, perhaps a reflection of this synchrony. Recently, it has also been shown, in invasive human recordings, that non-sinusoidal features of β oscillation shape also characterize PD. Here, we show that these features of β waveform shape also distinguish PD patients on and off medication using non-invasive recordings in a dataset of 15 PD patients with resting scalp EEG. Specifically, β oscillations over sensorimotor electrodes in PD patients off medication had greater sharpness asymmetry and steepness asymmetry than on medication (sign rank, p < 0.02, corrected). We also showed that β oscillations over sensorimotor cortex most often had a canonical shape, and that using this prototypical shape as an inclusion criteria increased the effect size of our findings. Together, our findings suggest that novel ways of measuring β synchrony that incorporate waveform shape could improve detection of PD pathophysiology in non-invasive recordings. Moreover, they motivate the consideration of waveform shape in future EEG studies.

In value-based decision making, options are selected according to subjective values assigned by the individual to available goods and actions. Despite the importance of this faculty of the mind, the neural mechanisms of value assignments, and how choices are directed by them, remain obscure. To investigate this problem, we used a classic measure of utility maximization, the Generalized Axiom of Revealed Preference, to quantify internal consistency of food preferences in Caenorhabditis elegans, a nematode worm with a nervous system of only 302 neurons. Using a novel combination of microfluidics and electrophysiology, we found that C. elegans food choices fulfill the necessary and sufficient conditions for utility maximization, indicating that nematodes behave exactly as if they maintain, and attempt to maximize, an underlying representation of subjective value. Food choices are well-fit by a utility function widely used to model human consumers. Moreover, as in many other animals, subjective values in C. elegans are learned, a process we now find requires intact dopamine signaling. Differential responses of identified chemosensory neurons to foods with distinct growth potential are amplified by prior consumption of these foods, suggesting that these neurons may be part of a value-assignment system. The demonstration of utility maximization in an organism with a nervous system of only 302 neurons sets a new lower bound on the computational requirements for its execution, and offers the prospect of an essentially complete explanation of value-based decision making at single neuron resolution.

The combination of electromyography (EMG) and transcranial magnetic stimulation (TMS) offers a powerful non-invasive approach for investigating corticospinal excitability in both humans and animals. Acquiring and analyzing the data produced with this combination of tools requires overcoming multiple technical hurdles. Due in part to these technical hurdles, the field lacks standard routines for EMG data collection and analysis. This poses a problem for study replication and direct comparisons. Although software toolboxes already exist that perform either online EMG data visualization or offline analysis, there currently are no openly available toolboxes that flexibly perform both and also interface directly with peripheral EMG and TMS equipment. Here, we introduce Visualize EMG TMS Analyze (VETA), a MATLAB-based toolbox that supports simultaneous EMG data collection and visualization as well as automated offline processing and is specially tailored for use with motor TMS. The VETA toolbox enables the simultaneous recording of EMG, timed administration of TMS, and presentation of behavioral stimuli from a single computer. These tools also provide a streamlined analysis pipeline with interactive data visualization. Finally, VETA offers a standard EMG data format to facilitate data sharing and open science.

The combination of electromyography (EMG) and transcranial magnetic stimulation (TMS) offers a powerful non-invasive approach for investigating corticospinal excitability in both humans and animals. Acquiring and analyzing the data produced with this combination of tools requires overcoming multiple technical hurdles. Due in part to these technical hurdles, the field lacks standard routines for EMG data collection and analysis. This poses a problem for study replication and direct comparisons. Although software toolboxes already exist that perform either online EMG data visualization or offline analysis, there currently are no openly available toolboxes that flexibly perform both and also interface directly with peripheral EMG and TMS equipment. Here, we introduce Visualize EMG TMS Analyze (VETA), a MATLAB-based toolbox that supports simultaneous EMG data collection and visualization as well as automated offline processing and is specially tailored for use with motor TMS. The VETA toolbox enables the simultaneous recording of EMG, timed administration of TMS, and presentation of behavioral stimuli from a single computer. These tools also provide a streamlined analysis pipeline with interactive data visualization. Finally, VETA offers a standard EMG data format to facilitate data sharing and open science.

Neural activity in the beta frequency range (13-30 Hz) is excessively synchronized in Parkinson's Disease (PD). Previous work using invasive intracranial recordings and non-invasive scalp electroencephalography (EEG) has shown that correlations between beta phase and broadband gamma amplitude (i.e., phase-amplitude coupling) are elevated in PD, perhaps a reflection of this synchrony. Recently, it has also been shown, in invasive human recordings, that nonsinusoidal features of beta oscillation shape also characterize PD. Here we show that these features of beta waveform shape also distinguish PD patients on and off medication using noninvasive recordings in a dataset of 15 PD patients with resting scalp EEG. Specifically, beta oscillations over sensorimotor electrodes in PD patients off medication had greater sharpness asymmetry and steepness asymmetry than on medication (sign rank, p=0.006, p=0.003 respectively). We also showed that beta oscillations over sensorimotor cortex most often had a canonical shape and that using this prototypical shape as an inclusion criterion increased the effect size of our findings. Together our findings suggest that novel ways of measuring beta synchrony that incorporate waveform shape could improve detection of PD pathophysiology in non-invasive recordings.