Human upper extremity motor cortex activity shows distinct oscillatory signatures for stereotyped arm and leg movements.

Introduction: Stepping and arm swing are stereotyped movements that require coordination across multiple muscle groups. It is not known whether the encoding of these stereotyped movements in the human primary motor cortex is confined to the limbs' respective somatotopy. Methods: We recorded subdural electrocorticography activities from the hand/arm area in the primary motor cortex of 6 subjects undergoing deep brain stimulation surgery for essential tremor and Parkinson's disease who performed stepping (all patients) and arm swing (n = 3 patients) tasks. Results: We show stepping-related low frequency oscillations over the arm area. Furthermore, we show that this oscillatory activity is separable, both in frequency and spatial domains, from gamma band activity changes that occur during arm swing. Discussion: Our study contributes to the growing body of evidence that lower extremity movement may be more broadly represented in the motor cortex, and suggest that it may represent a way to coordinate stereotyped movements across the upper and lower extremities.

Lesion location and outcome following cingulotomy for obsessive-compulsive disorder.

OBJECTIVE: Obsessive-compulsive disorder (OCD) is among the most debilitating and medically refractory psychiatric disorders. While cingulotomy is an anatomically targeted neurosurgical treatment that has shown significant promise in treating OCD-related symptoms, the precise underlying neuroanatomical basis for its beneficial effects has remained poorly understood. Therefore, the authors sought to determine whether lesion location is related to responder status following cingulotomy. METHODS: The authors reviewed the records of 18 patients who had undergone cingulotomy. Responders were defined as patients who had at least a 35% improvement in the Yale-Brown Obsessive Compulsive Scale (YBOCS) score. The authors traced the lesion sites on T1-weighted MRI scans and used an anatomical registration matrix generated by the imaging software FreeSurfer to superimpose these lesions onto a template brain. Lesion placement was compared between responders and nonresponders. The placement of lesions relative to various anatomical regions was also compared. RESULTS: A decrease in postoperative YBOCS score was significantly correlated with more superiorly placed lesions (decrease -0.52, p = 0.0012). While all lesions were centered within 6 mm of the cingulate sulcus, responder lesions were placed more superiorly and posteriorly along the cingulate sulcus (1-way ANOVA, p = 0.003). The proportions of the cingulum bundle, cingulate gyrus, and paracingulate cortex affected by the lesions were the same between responders and nonresponders. However, all responders had lesions covering a larger subregion of Brodmann area (BA) 32. In particular, responder lesions covered a significantly greater proportion of the posterior BA32 (1-way ANOVA, p = 0.0064). CONCLUSIONS: Lesions in patients responsive to cingulotomy tended to be located more superiorly and posteriorly and share greater coverage of a posterior subregion of BA32 than lesions in patients not responsive to this treatment.

Dopamine signals as temporal difference errors: recent advances.

In the brain, dopamine is thought to drive reward-based learning by signaling temporal difference reward prediction errors (TD errors), a 'teaching signal' used to train computers. Recent studies using optogenetic manipulations have provided multiple pieces of evidence supporting that phasic dopamine signals function as TD errors. Furthermore, novel experimental results have indicated that when the current state of the environment is uncertain, dopamine neurons compute TD errors using 'belief states' or a probability distribution over potential states. It remains unclear how belief states are computed but emerging evidence suggests involvement of the prefrontal cortex and the hippocampus. These results refine our understanding of the role of dopamine in learning and the algorithms by which dopamine functions in the brain.

Initial Approach to the Patient with Multiple Newly Diagnosed Brain Metastases.

Brain metastases are the most common intracranial tumor in adults, with increasing incidence owing to prolonged survival times. Roughly half of patients diagnosed with new brain metastases have greater than 1 brain metastasis at the time of diagnosis, raising the question of how to optimize patient care with multiple brain metastases. The authors review studies relevant to the care of patients with brain metastasis, with emphasis on those relevant to the care of patients with multiple brain metastases. They discuss evolving strategies involving multiple modalities and the benefit of surgical management in patients with a large symptomatic brain metastasis.

A distributional code for value in dopamine-based reinforcement learning.

Since its introduction, the reward prediction error theory of dopamine has explained a wealth of empirical phenomena, providing a unifying framework for understanding the representation of reward and value in the brain1-3. According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. Here we propose an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning4-6. We hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea implies a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.

The Medial Prefrontal Cortex Shapes Dopamine Reward Prediction Errors under State Uncertainty.

Animals make predictions based on currently available information. In natural settings, sensory cues may not reveal complete information, requiring the animal to infer the "hidden state" of the environment. The brain structures important in hidden state inference remain unknown. A previous study showed that midbrain dopamine neurons exhibit distinct response patterns depending on whether reward is delivered in 100% (task 1) or 90% of trials (task 2) in a classical conditioning task. Here we found that inactivation of the medial prefrontal cortex (mPFC) affected dopaminergic signaling in task 2, in which the hidden state must be inferred ("will reward come or not?"), but not in task 1, where the state was known with certainty. Computational modeling suggests that the effects of inactivation are best explained by a circuit in which the mPFC conveys inference over hidden states to the dopamine system. VIDEO ABSTRACT.

Dopamine reward prediction errors reflect hidden-state inference across time.

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a 'belief state'). Here we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling showed a notable difference between two tasks that differed only with respect to whether reward was delivered in a deterministic manner. Our results favor an associative learning rule that combines cached values with hidden-state inference.