Motor Pathways, Basal Ganglia Physiology, and Pathophysiology

Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance

Brain-computer interfaces (BCIs) translate brain signals into commands to external effectors, and mainly target severely disabled users. The usability of BCIs may be improved by reducing their major constraints, such as the necessity for special training sessions to initially calibrate and later keep up to date the neural signal decoders. In this study, we show that it is possible to train and update BCI decoders during free use of motor BCIs. In addition to the neural signal decoder controlling effectors (control decoder), one more classifier is proposed to detect neural correlates of BCI motor task performances (MTP). MTP decoders reveal whether the actions performed by BCI effectors matched the user's intentions. The combined outputs of MTP and control decoders allow forming training datasets to update the control decoder online and in real time during free use of BCIs. The usability of the proposed auto-adaptive BCI (aaBCI) is demonstrated for two principle BCIs paradigms: with discrete outputs (4 classes BCI, virtual 4-limb exoskeleton), and with continuous outputs (cursor 2D control). The proof of concept was performed in an online simulation study using an ECoG dataset collected from a tetraplegic during a BCI clinical trial. The control decoder reached a multiclass area under the ROC curve of 0.7404 using aaBCI, compared to a chance level of 0.5173 and to 0.8187 for supervised training for the multiclass BCI, and a cosine similarity of 0.1211 using aaBCI, compared to a chance level of 0.0036 and to 0.2002 for supervised training for the continuous BCI.

A dynamical model for the basal ganglia-thalamo-cortical oscillatory activity and its implications in Parkinson's disease

We propose to investigate brain electrophysiological alterations associated with Parkinson’s disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson’s disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.

High-Frequency Stimulation at the Subthalamic Nucleus Suppresses Excessive Self-Grooming in Autism-Like Mouse Models

Approximately one quarter of individuals with an autism spectrum disorder (ASD) display self-injurious behavior (SIB) ranging from head banging to self-directed biting and punching. Sometimes, these behaviors are extreme and unresponsive to pharmacological and behavioral therapies. We have found electroconvulsive therapy (ECT) can produce life-changing results, with more than 90% suppression of SIB frequency. However, these patients typically require frequent maintenance ECT (mECT), as often as every 5 days, to sustain the improvement gained during the acute course. Long-term consequences of such frequent mECT started as early as childhood in some cases are unknown. Accordingly, there is a need for alternative forms of chronic stimulation for these patients. To explore the feasibility of deep brain stimulation (DBS) for intractable SIB seen in some patients with an ASD, we utilized two genetically distinct mouse models demonstrating excessive self-grooming, namely the Viaat-Mecp2(-/y) and Shank3B(-/-) lines, and administered high-frequency stimulation (HFS) via implanted electrodes at the subthalamic nucleus (STN-HFS). We found that STN-HFS significantly suppressed excessive self-grooming in both genetic lines. Suppression occurs both acutely when stimulation is switched on, and persists for several days after HFS is stopped. This effect was not explained by a change in locomotor activity, which was unaffected by STN-HFS. Likewise, social interaction deficits were not corrected by STN-HFS. Our data show STN-HFS suppresses excessive self-grooming in two autism-like mouse models, raising the possibility DBS might be used to treat intractable SIB associated with ASDs. Further studies are required to explore the circuitry engaged by STN-HFS, as well as other potential stimulation sites. Such studies might also yield clues about pathways, which could be modulated by non-invasive stimulatory techniques.