Two Parkinson's patients receive deep brain stimulation (DBS) in their subthalamic nuclei. Despite accurate electrode placement, one patient is able to stand up and walk effortlessly around the room while the other breaks down into uncontrolled sobbing that only stops once the stimulator is turned off. This paradox exposes one of the major roadblocks in developing therapies for brain disorders: the elaborate and diffuse nature of neural circuits. Physically proximal neurons are often engaged in functionally different pathways; whereas modulation of one pathway might be therapeutic, modulation of those surrounding it may produce debilitating side effects. The problem with high-amplitude electrical stimulation, as applied during DBS, is that it affects not only the activity of neurons around the electrode, but also the activity of neurons whose long extensions happen to pass by the electrode.
One of the open issues in developing large-scale computational models of the brain is how the transfer of information between communicating cortical regions is controlled. Here, we present a model where the basal ganglia implement such a conditional information routing system. The basal ganglia are a set of subcortical nuclei that play a central role in cognition. Like a switchboard, the model basal ganglia direct the communication between cortical regions by alerting the destination regions to the presence of important signals coming from the source regions. This way, they can impose serial control on the massive parallel communication between cortical areas. The model also incorporates a possible mechanism by which subsequent transfers of information control the release of dopamine. This signal is used to produce novel stimulus-response associations by internalizing the representation being transferred in the striatum. We discuss how this neural circuit can be seen as a biological implementation of a production system. This comparison highlights the basal ganglia as bridge between computational models of small-size brain circuits and high-level characterizations of complex cognition, such as cognitive architectures.
A breakthrough that led to the creation of new neurons in mice could be used to transplant brain cells in Parkinson's patients and cure them of the disease. University of California San Diego School of Medicine researchers created neurons in mice using a new, much simpler method that involved rewriting genes. Parkinson's disease is characterised by a loss of dopaminergic neurons in a region of the brain responsible for reward and movement - replacing those cells could help to reduce or even reverse the symptoms of the degenerative disease. A small study involving mice with Parkinson's saw those given the'new neuron treatment' return to normal within three months and stay disease free for life. The researchers said it could one day be used to'cure' any disease caused by the loss of neurons but warned this was a long way off and hadn't been tested. Left: mouse cells (green) before reprogramming and then right shows neurons (red) induced from mouse cells after reprogramming.
Exercising consistently could help curb the symptoms of Parkinson's disease Regular exercise prevents the degradation of neurons vital for movement in rats with symptoms of Parkinson's disease, emphasising the importance of physical activity in the condition. The finding could also lead to new treatments for the disease. Parkinson's disease is a neurodegenerative disorder caused by a loss of dopamine-producing neurons in the substantia nigra, an area of the brain involved in movement. This can lead to tremors, loss of motor control, impaired balance or speech and other symptoms. Previous research has shown intense exercise can slow the progression of early-stage Parkinson's disease.
In this work we propose a new biophysical computational model of brain regions relevant to Parkinson's Disease based on local field potential data collected from the brain of marmoset monkeys. Parkinson's disease is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled single-neuron mean firing rates and spectral signatures of local field potentials from healthy and parkinsonian marmoset brain data. As far as we are concerned, this is the first computational model of Parkinson's Disease based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results show that the proposed model could facilitate the investigation of the mechanisms of PD and support the development of techniques that can indicate new therapies. It could also be applied to other computational neuroscience problems in which biological data could be used to fit multi-scale models of brain circuits.