Songbird subthalamic neurons project to dopaminergic midbrain and exhibit singing-related activity

Skill learning requires motor output to be evaluated against internal performance benchmarks. In songbirds, ventral tegmental area (VTA) dopamine neurons (DA) signal performance errors important for learning, but it remains unclear which brain regions project to VTA and how these inputs may contribute to DA error signaling. Here we find that the songbird subthalamic nucleus (STN) projects to VTA and that STN micro-stimulation can excite VTA neurons. We also discover that STN receives inputs from motor cortical, auditory cortical and ventral pallidal brain regions previously implicated in song evaluation. In the first neural recordings from songbird STN, we discover that the activity of most STN neurons is associated with body movements and not singing, but a small fraction of neurons exhibits precise song timing and performance error signals. Our results place the STN in a pathway important for song learning, but not song production, and expand the territories of songbird brain potentially associated with song learning.

Machine learning algorithm for decoding multiple subthalamic spike trains for speech brain-machine interfaces

Objective. The goal of this study is to decode the electrical activity of single neurons in the human subthalamic nucleus (STN) to infer the speech features that a person articulated, heard or imagined. We also aim to evaluate the amount of subthalamic neurons required for high accuracy decoding suitable for real-life speech brain-machine interfaces. Approach. We intraoperatively recorded single-neuron activity in the STN of 21 neurosurgical patients with Parkinson's disease undergoing implantation of deep brain stimulator (DBS) while patients produced, perceived or imagined the five monophthongal vowel sounds. Our decoder is based on machine learning algorithms that dynamically learn specific features of the speech-related firing patterns. Main results. In an extensive comparison of algorithms, our sparse decoder ("SpaDe"), based on sparse decomposition of the high dimensional neuronal feature space, outperformed the other algorithms in all three conditions: production, perception and imagery. For speech production, our algorithm, Spade, predicted all vowels correctly (accuracy: 100%; chance level: 20%). For perception accuracy was 96%, and for imagery: 88%. The accuracy of Spade showed a linear behavior in the amount of neurons for the perception data, and even faster for production or imagery. Significance. Our study demonstrates that the information encoded by single neurons in the STN about the production, perception and imagery of speech is suitable for high-accuracy decoding. It is therefore an important step towards brain-machine interfaces for restoration of speech faculties that bears an enormous potential to alleviate the suffering of completely paralyzed ("locked-in") patients and allow them to communicate again with their environment. Moreover, our research indicates how many subthalamic neurons may be necessary to achieve each level of decoding accuracy, which is of supreme importance for a neurosurgeon planning the implantation of a speech brain-machine interface.

“Distinct roles of dorsal and ventral subthalamic neurons in action selection and cancellation.”

The subthalamic nucleus (STN) supports action selection by inhibiting all motor programs except the desired one. Recent evidence suggests that STN can also cancel an already selected action when goals change, a key aspect of cognitive control. However, there is little neurophysiological evidence for a dissociation between selecting and cancelling actions in the human STN. We recorded single neurons in the STN of humans performing a stop-signal task. Movement-related neurons suppressed their activity during successful stopping whereas stop-signal neurons activated at low-latencies regardless of behavioral outcome. In contrast, STN and motor-cortical beta-bursting occurred only later in the stopping process. Task-related neuronal properties varied by recording location from dorsolateral movement to ventromedial stop-signal tuning. Therefore, action selection and cancellation coexist in STN but are anatomically segregated. These results show that human ventromedial STN neurons carry fast stop-related signals suitable for implementing cognitive control.

A disynaptic circuit in the globus pallidus controls locomotion inhibition

Basal ganglia (BG) inhibit movement through two independent pathways, the indirect- and the hyperdirect-pathways. The globus pallidus (GP) has always been viewed as a simple relay within these two pathways, but its importance has changed drastically with the discovery of two functionally-distinct cell types, namely the prototypic and the arkypallidal neurons. Classic BG models suggest that all GP neurons receive GABAergic inputs from striato-pallidal indirect spiny projection neurons and glutamatergic inputs from subthalamic neurons. However, whether this synaptic connectivity scheme applies to both GP cell-types is currently unknown. Here, we optogenetically dissect the input organization of prototypic and arkypallidal neurons and further define the circuit mechanism underlying action inhibition in BG. Our results highlight that an increased activity of arkypallidal neurons is required to inhibit locomotion. Finally, this work supports the view that arkypallidal neurons are part of a novel disynaptic feedback loop that broadcast inhibitory control on movement execution.

Multistable properties of human subthalamic nucleus neurons in Parkinson’s disease

To understand the function and dysfunction of neural circuits, it is necessary to understand the properties of the neurons participating in the behavior, the connectivity between these neurons, and the neuromodulatory status of the circuits at the time they are producing the behavior. Such knowledge of human neural circuits is difficult, at best, to obtain. Here, we study firing properties of human subthalamic neurons, using microelectrode recordings and microstimulation during awake surgery for Parkinson’s disease. We demonstrate that low-amplitude, brief trains of microstimulation can lead to persistent changes in neuronal firing behavior including switching between firing rates, entering silent periods, or firing several bursts then entering a silent period. We suggest that these multistable states reflect properties of finite state machines and could have implications for the function of circuits involving the subthalamic nucleus. Furthermore, understanding these states could lead to therapeutic strategies aimed at regulating the transitions between states.

Subthalamic Nucleus: Neuroanatomical Review

Discovered in 1865 by Jules Bernard Luys, the subthalamic nucleus is a set of small nuclei located in the diencephalon, inferior to the thalamus and superior to the substantia nigra, that can be visualized in a posterior coronal section. Histologically, it consists of neurons compactly distributed and filled with a large number of blood vessels and sparse myelinated fibers. This review presents an analysis of this anatomical region, considering what is most recent in the literature. Subthalamic neurons are excitatory and use glutamate as the neurotransmitter. In healthy individuals, these neurons are inhibited by nerve cells located in the side globus pallidus. However, if the fibers that make up the afferent circuit are damaged, the neurons become highly excitable, thus causing motor disturbances that can be classified as hyperkinetic, for example ballism and chorea, or hypokinetic, for example Parkinson disease (PD). The advent of deep brain stimulation has given the subthalamic nucleus great visibility. Studies reveal that the stimulation of this nucleus improves the motor symptoms of PD.