Deep brain stimulation (DBS), a neurosurgical treatment that stimulates the brain with electrical signals, is used to treat Parkinson’s disease, dystonia, Tourette syndrome, depression, epilepsy and an ever-increasing list of neurological disorders. Despite growing numbers of applications, the mechanisms of DBS are still largely unknown. Furthermore, it remains unclear exactly how electrophysiological dynamics change in affected neural circuits in both normal and diseased conditions. Microelectrode exploration has enabled collection of extraordinary amounts of electrophysiological data from various neural circuits, providing a great opportunity to understand the dynamics in these systems. However, typically recordings are not performed under all possible conditions (normal, diseased, with and without different patterns of DBS applied) due mainly to lack of resources. Furthermore, typical data analyses entail computing 1st and 2nd-order statistics, which provide a coarse view of the inter- and intra-nuclei dynamics in neural activity. For example, short-term history dependence within a neuronal spike train is analyzed with inter-spike interval histograms (ISIHs), while long-term history dependence related to neural oscillations is often studied in the frequency domain using power spectra. Correlations between neurons and nuclei are studied by computing coherence and cross-correlelograms.
The failure of these techniques is evident by the failure to demonstrate clear and specific abnormalities in neuronal spike trains between different neurological conditions. For example, neuronal mean discharge frequencies and coefficients of variation for subthalamic nucleus (STN) are no different between patients with epilepsy and patients with Parkinson’s disease. However, the very different phenotypic manifestations argue that there must be some parallel differences in the neuronal activities. Thus, there is a critical need to take a new and novel approach to understanding information, and perhaps misinformation, in the neuronal spike train.
In this Research Topic, we will explore various movement disorders on the DBS horizon and introduce new experimental results and data analysis methods that shed new light on neuromodulation for neurophysiological disorders and the mechanisms of deep brain stimulation.
Deep brain stimulation (DBS), a neurosurgical treatment that stimulates the brain with electrical signals, is used to treat Parkinson’s disease, dystonia, Tourette syndrome, depression, epilepsy and an ever-increasing list of neurological disorders. Despite growing numbers of applications, the mechanisms of DBS are still largely unknown. Furthermore, it remains unclear exactly how electrophysiological dynamics change in affected neural circuits in both normal and diseased conditions. Microelectrode exploration has enabled collection of extraordinary amounts of electrophysiological data from various neural circuits, providing a great opportunity to understand the dynamics in these systems. However, typically recordings are not performed under all possible conditions (normal, diseased, with and without different patterns of DBS applied) due mainly to lack of resources. Furthermore, typical data analyses entail computing 1st and 2nd-order statistics, which provide a coarse view of the inter- and intra-nuclei dynamics in neural activity. For example, short-term history dependence within a neuronal spike train is analyzed with inter-spike interval histograms (ISIHs), while long-term history dependence related to neural oscillations is often studied in the frequency domain using power spectra. Correlations between neurons and nuclei are studied by computing coherence and cross-correlelograms.
The failure of these techniques is evident by the failure to demonstrate clear and specific abnormalities in neuronal spike trains between different neurological conditions. For example, neuronal mean discharge frequencies and coefficients of variation for subthalamic nucleus (STN) are no different between patients with epilepsy and patients with Parkinson’s disease. However, the very different phenotypic manifestations argue that there must be some parallel differences in the neuronal activities. Thus, there is a critical need to take a new and novel approach to understanding information, and perhaps misinformation, in the neuronal spike train.
In this Research Topic, we will explore various movement disorders on the DBS horizon and introduce new experimental results and data analysis methods that shed new light on neuromodulation for neurophysiological disorders and the mechanisms of deep brain stimulation.
