Reappraising contemporary theories of subcortical participation in language: Proposing an interhemispheric regulatory function for the subthalamic nucleus (STN) in the mediation of high-level linguistic processes

Apropos the basal ganglia, the dominant striatum and globus pallidus internus (GPi) have been hypothesised to represent integral components of subcortical language circuitry. Working subcortical language theories, however, have failed thus far to consider a role for the STN in the mediation of linguistic processes, a structure recently defined as the driving force of basal ganglia output. The aim of this research was to investigate the impact of surgically induced functional inhibition of the STN upon linguistic abilities, within the context of established models of basal ganglia participation in language. Two males with surgically induced 'lesions' of the dominant and non-dominant dorsolateral STN, aimed at relieving Parkinsonian motor symptoms, served as experimental subjects. General and high-level language profiles were compiled for each subject up to 1 month prior to and 3 months following neurosurgery, within the drug-on state (i.e., when optimally medicated). Comparable post-operative alterations in linguistic performance were observed subsequent to surgically induced functional inhibition of the left and right STN. More specifically, higher proportions of reliable decline as opposed to improvement in post-operative performance were demonstrated by both subjects on complex language tasks, hypothesised to entail the interplay of cognitive-linguistic processes. The outcomes of the current research challenge unilateralised models of functional basal ganglia organisation with the proposal of a potential interhemispheric regulatory function for the STN in the mediation of high-level linguistic processes.

A comparison of different choices for the regularization parameter in inverse electrocardiography models

Calculating the potentials on the heart’s epicardial surface from the body surface potentials constitutes one form of inverse problems in electrocardiography (ECG). Since these problems are ill-posed, one approach is to use zero-order Tikhonov regularization, where the squared norms of both the residual and the solution are minimized, with a relative weight determined by the regularization parameter. In this paper, we used three different methods to choose the regularization parameter in the inverse solutions of ECG. The three methods include the L-curve, the generalized cross validation (GCV) and the discrepancy principle (DP). Among them, the GCV method has received less attention in solutions to ECG inverse problems than the other methods. Since the DP approach needs knowledge of norm of noises, we used a model function to estimate the noise. The performance of various methods was compared using a concentric sphere model and a real geometry heart-torso model with a distribution of current dipoles placed inside the heart model as the source. Gaussian measurement noises were added to the body surface potentials. The results show that the three methods all produce good inverse solutions with little noise; but, as the noise increases, the DP approach produces better results than the L-curve and GCV methods, particularly in the real geometry model. Both the GCV and L-curve methods perform well in low to medium noise situations.