Subthalamic Oscillatory Activity of Reward and Loss Processing Using the Monetary Incentive Delay Task in Parkinson Disease

Theta-Gamma Decoupling - A neurophysiological marker of impaired reward processing in Parkinson's disease

Individuals with Parkinson's disease (PD) exhibit altered reward processing, reflected by a decreased amplitude of an event-related potential (ERP) marker called reward positivity (RewP). Most studies have used RewP to investigate reward behavior due to the high temporal resolution of EEG and its high sensitivity. However, traditional single-electrode ERP analyses often overlook the intricate dynamics of non-phase-locked oscillatory activity and the complex interactions within these neural oscillatory patterns. Studying oscillatory activity is crucial as it provides mechanistic insights into the functional, spatial, and temporal aspects of neuronal processing. To address this gap, we employed a data-driven approach to identify EEG-based markers associated with PD reward processing deficits. Using an openly available 64-channel EEG dataset of 28 age- and sex-matched PD and control participants during a reinforcement learning task, we conducted a comprehensive secondary analysis. First, we employed a cluster-based permutation method to extract ERP markers, finding a consistent decrease in reward positivity in PD, regardless of medication status. Additionally, through region of interest (ROI) analysis on time-frequency data, we identified specific oscillatory patterns during reward processing. PD patients exhibited attenuated theta power and increased gamma power compared to healthy controls (HC). Notably, within the PD group, those off medication showed anterior localization of high gamma power, while those on medication displayed higher posterior gamma power. Building upon these findings, we explored phase-amplitude coupling between theta phase and gamma amplitude measured by the modulation index. We observed a trend of decreased theta-gamma coupling in PD patients, with statistically significant differences between on and off medication conditions. These results highlight the potential role of theta-gamma coupling as a neuromodulatory target for improving goal-oriented behavior in PD. Our correlation analyses suggest that high gamma power is linked to longer disease duration, while reduced reward positivity and low theta-gamma coupling may serve as markers of the dopaminergic impact on reward processing. Thus, our study unveils the intricate time-frequency dynamics underlying reward processing deficits in PD, emphasizing the utility of a data-driven approach to elucidate neural mechanisms and to identify potential therapeutic targets.

Modeling impulsivity and risk aversion in the subthalamic nucleus with deep brain stimulation

Risk evaluation is ubiquitous in decisions. Deep brain stimulation of the subthalamic nucleus is effective for Parkinson's disease and obsessive-compulsive disorder, and can be associated with impulsivity and hypomania. Subthalamic stimulation has seemingly contrasting effects on impulsivity enhancing conflict-induced impulsivity but decreasing risk taking. Here, using a card gambling task paired with intracranial recordings (n = 25) and within-subject case control acute stimulation (n = 15) of the right subthalamic nucleus, we dissociated objective risk and uncertainty and subjective physiological markers of risk. Acute stimulation decreased risk taking (P = 0.010, Cohen's d = 0.72) and increased subthalamic theta activity (P < 0.001, Cohen's d = 0.72). Critically, stimulation negatively shifted the relationship between subthalamic physiology and a measure of evidence accumulation similar to observations with stimulation-induced conflict processing. This highlights the phenotypic and physiological heterogeneity of impulsivity, yet linking mechanisms underlying stimulation-induced conflict and risk. Finally, stimulation-induced risk seeking implicates the ventral subthalamic nucleus and dissociating anatomical and functional connectivity with the mesial prefrontal cortex. Our findings have implications for conceptualizations of impulsivity, and clinical relevance for neuropsychiatric disorders.

STN localization using local field potentials based on wavelet packet features and stacking ensemble learning

BACKGROUND

DBS entails the insertion of an electrode into the patient brain, enabling Subthalamic nucleus (STN) stimulation. Accurate delineation of STN borders is a critical but time-consuming task, traditionally reliant on the neurosurgeon experience in deciphering the intricacies of microelectrode recording (MER). While clinical outcomes of MER have been satisfactory, they involve certain risks to patient safety. Recently, there has been a growing interest in exploring the potential of local field potentials (LFP) due to their correlation with the STN motor territory.

METHOD

A novel STN detection system, integrating LFP and wavelet packet transform (WPT) with stacking ensemble learning, is developed. Initial steps involve the inclusion of soft thresholding to increase robustness to LFP variability. Subsequently, non-linear WPT features are extracted. Finally, a unique ensemble model, comprising a dual-layer structure, is developed for STN localization. We harnessed the capabilities of support vector machine, Decision tree and k-Nearest Neighbor in conjunction with long short-term memory (LSTM) network. LSTM is pivotal for assigning adequate weights to every base model.

RESULTS

Results reveal that the proposed model achieved a remarkable accuracy and F1-score of 89.49% and 91.63%.

COMPARISON WITH EXISTING METHODS

Ensemble model demonstrated superior performance when compared to standalone base models and existing meta techniques.

CONCLUSION

This framework is envisioned to enhance the efficiency of DBS surgery and reduce the reliance on clinician experience for precise STN detection. This achievement is strategically significant to serve as an invaluable tool for refining the electrode trajectory, potentially replacing the current methodology based on MER.

More widespread and rigid neuronal representation of reward expectation underlies impulsive choices

Impulsive choices prioritize smaller, more immediate rewards over larger, delayed, or potentially uncertain rewards. Impulsive choices are a critical aspect of substance use disorders and maladaptive decision-making across the lifespan. Here, we sought to understand the neuronal underpinnings of expected reward and risk estimation on a trial-by-trial basis during impulsive choices. To do so, we acquired electrical recordings from the human brain while participants carried out a risky decision-making task designed to measure choice impulsivity. Behaviorally, we found a reward-accuracy tradeoff, whereby more impulsive choosers were more accurate at the task, opting for a more immediate reward while compromising overall task performance. We then examined how neuronal populations across frontal, temporal, and limbic brain regions parametrically encoded reinforcement learning model variables, namely reward and risk expectation and surprise, across trials. We found more widespread representations of reward value expectation and prediction error in more impulsive choosers, whereas less impulsive choosers preferentially represented risk expectation. A regional analysis of reward and risk encoding highlighted the anterior cingulate cortex for value expectation, the anterior insula for risk expectation and surprise, and distinct regional encoding between impulsivity groups. Beyond describing trial-by-trial population neuronal representations of reward and risk variables, these results suggest impaired inhibitory control and model-free learning underpinnings of impulsive choice. These findings shed light on neural processes underlying reinforced learning and decision-making in uncertain environments and how these processes may function in psychiatric disorders.

Proceedings of the 10th annual deep brain stimulation think tank: Advances in cutting edge technologies, artificial intelligence, neuromodulation, neuroethics, interventional psychiatry, and women in neuromodulation

The deep brain stimulation (DBS) Think Tank X was held on August 17-19, 2022 in Orlando FL. The session organizers and moderators were all women with the theme women in neuromodulation. Dr. Helen Mayberg from Mt. Sinai, NY was the keynote speaker. She discussed milestones and her experiences in developing depression DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging DBS technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank X speakers was that DBS has continued to expand in scope however several indications have reached the "trough of disillusionment." DBS for depression was considered as "re-emerging" and approaching a slope of enlightenment. DBS for depression will soon re-enter clinical trials. The group estimated that globally more than 244,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia, and Australia; cutting-edge technologies, closed loop DBS, DBS tele-health, neuroethics, lesion therapy, interventional psychiatry, and adaptive DBS.