ABSTRACT
Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large-scale modalities. Here we introduce GhostiPy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. GhostiPy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high-channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis.
Subject(s)
Signal Processing, Computer-Assisted , Software , AlgorithmsABSTRACT
The globus pallidus internus (GPi) is the main output nucleus of the basal ganglia, which is associated with a variety of functions including motor performance and cognition. The GPi is one of the primary targets of deep brain stimulation (DBS) in patients with movement disorders. However, the therapeutic mechanism of GPi-DBS is poorly understood and rodent models have not been characterized. Cognitive side effects, such as impulsivity and depression, of DBS treatment for Parkinson's disease are known, but their relationship to the efficacy of the treatment is not well explained. The goal of this study is to illuminate the effects of GPi-DBS on both motor and cognitive function in a hemi-Parkinsonian rat model. In this work, we study the motor performance of the rodents in multiple behaviors, as well as of impulsivity and depression, and consider the relationship between these behavioral variables and the stimulation frequency of the DBS signal. For the first time, the connection is directly established between stimulating the GPi, motor performance and cognition is directly established in the hemi-Parkinsonian rodent model.
Subject(s)
Cognition Disorders/etiology , Cognition Disorders/physiopathology , Deep Brain Stimulation/adverse effects , Globus Pallidus/physiopathology , Movement Disorders/etiology , Movement Disorders/physiopathology , Parkinsonian Disorders/physiopathology , Animals , Behavior, Animal , Male , Movement , Parkinsonian Disorders/complications , Parkinsonian Disorders/therapy , Rats , Rats, Long-Evans , Treatment OutcomeABSTRACT
The globus pallidus interna (GPi) is the main output nucleus of the basal ganglia, the neural circuit involved in motor and cognitive performance which is impacted by Parkinson's Disease (PD). Although deep brain stimulation (DBS) of the GPi is an effective treatment for the motor symptoms of PD in humans, the link between the stimulation signal space and the therapeutic benefits of DBS is not well understood. The rodent model of PD is useful for characterization of ameliorative DBS, though prior work focuses on the rodent model for DBS of the subthalamic nucleus (STN). This work investigates GPi-DBS in the rat model of PD under the framework of an amphetamine-induced rotational behavior. This work elucidates the relationship between stimulation current intensity and the motor effects of the dopaminergic lesion. Our results show that rotational behavior is modulated by the current intensity and validates GPi-DBS as a beneficial treatment of PD.
Subject(s)
Behavior, Animal , Deep Brain Stimulation/methods , Electricity , Globus Pallidus/physiopathology , Parkinson Disease/therapy , Rotation , Amphetamine/pharmacology , Animals , Basal Ganglia/drug effects , Basal Ganglia/pathology , Disease Models, Animal , Globus Pallidus/drug effects , Humans , Male , Parkinson Disease/pathology , Parkinson Disease/physiopathology , Rats, Long-Evans , Subthalamic Nucleus/drug effects , Subthalamic Nucleus/pathology , Subthalamic Nucleus/physiopathology , Tyrosine 3-Monooxygenase/metabolismABSTRACT
Neural prostheses have received considerable attention due to their potential to dramatically improve the quality of life of severely disabled patients. Cortically-controlled prostheses are able to translate neural activity from cerebral cortex into control signals for guiding computer cursors or prosthetic limbs. Non-invasive and invasive electrode techniques can be used to measure neural activity, with the latter promising considerably higher levels of performance and therefore functionality to patients. We review here some of our recent experimental and computational work aimed at establishing a principled design methodology to increase electrode-based cortical prosthesis performance to near theoretical limits. Studies discussed include translating unprecedentedly brief periods of "plan" activity into high information rate (6.5 bits/s)control signals, improving decode algorithms and optimizing visual target locations for further performance increases, and recording from chronically implanted arrays in freely behaving monkeys to characterize neuron stability. Taken together, these results should substantially increase the clinical viability of cortical prostheses.
