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OBJECTIVE: In patients with Parkinson Disease (PD), self-imitated or internally cued (IC) actions are thought to be compromised by the disease process, as exemplified by impairments in action initiation. In contrast, externally-cued (EC) actions which are made in response to sensory prompts can restore a remarkable degree of movement capability in PD, particularly alleviating freezing-of-gait. This study investigates the electrophysiological underpinnings of movement facilitation in PD through visuospatial cuing, with particular attention to the dynamics within the posterior parietal cortex (PPC) and lateral premotor cortex (LPMC) axis of the dorsal visual stream. METHODS: Invasive cortical recordings over the PPC and LPMC were obtained during deep brain stimulation lead implantation surgery. Thirteen PD subjects performed an action selection task, which was constituted by left or right joystick movement with directional visual cuing in the EC condition and internally generated direction selection in the IC condition. Time-resolved neural activities within and between the PPC and LPMC were compared between EC and IC conditions. RESULTS: Reaction times (RT) were significantly faster in the EC condition relative to the IC condition (paired t-test, p=0.0015). PPC-LPMC inter-site phase synchrony within the β-band (13–35 Hz) was significantly greater in the EC relative to the IC condition. Greater PPC-LPMC β debiased phase lag index (dwPLI) prior to movement onset was correlated with faster reaction times only in the EC condition. Multivariate granger causality (GC) was greater in the EC condition relative to the IC condition, prior to and during movement. CONCLUSION: Relative to IC actions, we report relative increase in inter-site phase synchrony and directional PPC to LPMC connectivity in the β-band during preparation and execution of EC actions. Furthermore, increased strength of connectivity is predictive of faster RT, which are pathologically slow in PD patients. Stronger engagement of the PPC-LPMC cortical network by an EC specifically through the channel of β-modulation is implicated in correcting the pathological slowing of action initiation seen in Parkinson’s patients. SIGNIFICANCE: These findings shed light on the electrophysiological mechanisms that underlie motor facilitation in PD patients through visuospatial cuing.

Pavlovian conditioning is thought to involve the formation of learned associations between stimuli and values, and between stimuli and specific features of outcomes. Here we leveraged human single neuron recordings in ventromedial prefrontal, dorsomedial frontal, hippocampus and amygdala neurons while patients performed a sequential Pavlovian conditioning task containing both stimulus-value and stimulus-stimulus associations. Neurons in the ventromedial prefrontal cortex encoded predictive value along with the amygdala, but also encoded predictions about the identity of stimuli that would subsequently be presented, suggesting a role for neurons in this region in encoding predictive information beyond value. Unsigned error signals were found in dorsomedial prefrontal areas and hippocampus, potentially supporting learning of non-value related outcome features. Our findings implicate distinct human prefrontal and medial temporal neuronal populations in mediating predictive associations which could partially support model-based mechanisms during Pavlovian conditioning.Significance statementPavlovian conditioning is a fundamental form of learning, allowing organisms to associate stimuli and outcomes. Recent Pavlovian work suggests that phenomena such as devaluation sensitivity and sensory preconditioning can be explained by a model-based learning framework. How human neurons perform model-based learning during Pavlovian conditioning is still an open question. We recorded single neurons from epilepsy patients during a two-step Pavlovian conditioning task and found that ventromedial prefrontal neurons encoded expected rewards along with amygdala neurons, but also predicted the identity of upcoming stimuli as required for model-based cognition. Additionally, medial frontal neurons were found to encode error signals that could be used for stimulus-outcome learning. This is the first study mapping model-based computations during Pavlovian conditioning in human neurons.

Integrated recording of neural electrical potentials from the brain poses great challenges due to stringent dynamic range requirements to resolve small-signal amplitudes buried in noise amidst large artifact and stimulation transients, as well as stringent power and volume constraints to enable minimally invasive untethered operation. Here, we present a 16-channel neural recording system-on-chip with greater than 90-dB input dynamic range and less than 1- $\mu \text{V}_{\mathrm {rms}}$ input-referred noise from dc to 500 Hz, at 0.8- $\mu \text{W}$ power consumption, and 0.024-mm2 area per channel in a 65-nm CMOS process. Each recording channel features a hybrid analog–digital second-order oversampling analog-to-digital converter (ADC), with the biopotential signal coupling directly to the second integrator for high conversion gain and dynamic offset subtraction in the digital domain. This bypasses the need for high-pass filtering pre-amplification in neural recording systems, which often leads to signal distortion. The integrated ADC-direct neural recording offers record figure-of-merit with a noise efficiency factor (NEF) of the combined front end and ADC of 1.81, and a corresponding power efficiency factor (PEF) of 2.6. Predictive digital autoranging of the binary quantizer further supports rapid transient recovery while maintaining fully dc-coupled operation. Hence, the neural ADC is capable of recording ${\le}$ 0.01-Hz slow potentials as well as recovering from $\ge$ 200-mVpp transients within ${\le}$ 1 ms that are important prerequisites to effective electrocortical recording for brain activity mapping. In vivo recordings from marmoset primate frontal cortex demonstrate its unique capabilities in resolving ultra-slow local field potentials indicative of subject arousal state.