Biology subjects

Nicolelis, M. A.

Publications and source records attributed to Nicolelis, M. A..

3 recordsLinked to original sources

bioRxiv2022

Closed-loop spinal cord stimulation is superior in restoring locomotion in rodent models of Parkinson's Disease

Dorsal column stimulation (DCS) of the spinal cord is emerging as a promising new technology to treat Parkinsons disease (PD). However, optimal stimulation settings that maximize its therapeutic effect on PD symptoms are yet to be determined. Here we demonstrate a closed-loop DCS (CLDCS) paradigm - a substantial advancement from previously tested continuous high-frequency DCS - in a bilateral intrastriatal 6-hydroxydopamine (6-OHDA) rodent model of PD. Firstly, CLDCS, triggered by corticostriatal beta frequency oscillations facilitated a pro-locomotion brain state that restored locomotion and reduced akinesia. Secondly, CLDCS was better at disrupting ongoing beta oscillations and achieved it with lesser overall charge delivery than continuous open-loop stimulation. These results indicate that CLDCS is markedly better than traditional spinal cord stimulation methods and can potentially be highly effective in treating PD symptoms. We envision that the CLDCS approach can be beneficial in the treatment of other neurological disorders which showcase similar pathological neuronal oscillations.

neuroscience↗

bioRxiv2019

What, if anything, is the true neurophysiological significance of rotational dynamics?

Back in 2012, Churchland and his colleagues proposed that \"rotational dynamics\", uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchlands own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal shift in peak firing rates exhibited by individual neurons, and (2) the temporal sequence of peak rates remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchlands data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.s exaggerated assessment that such rotations are related to \"an unexpected yet surprisingly simple structure in the population response\", which \"explains many of the confusing features of individual neural responses.\" Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.

neuroscience↗

bioRxiv2017

Pitfalls in the Assessment of Brain-Machine Interfaces Using Information Transfer Rate

Information transfer rate (ITR), measured in bits/s, can be applied to evaluate motor performance, including the capacity of brain-machine interfaces (BMIs) to control external actuators. In a 2013 article entitled \"Transfer of information by BMI\" and published in Neuroscience, Tehovnik and his colleagues utilized ITR to assess the performance of several BMIs reported in the literature. We examined these analyses closely and found several fundamental flaws in their evaluation of ITR. Here we discuss the pitfalls in Tehovniks measurements of ITR, as well as several other issues raised in \"Transfer of information by BMI\", including the claim that BMIs cannot be a reasonable option for paralyzed patients.\n\nHighlightsO_LIInformation transfer rate is discussed for BMI experiments, where subjects reach to targets.\nC_LIO_LITask settings, not just the number of possible targets, are important to calculate information correctly.\nC_LIO_LIActive tactile exploration can be quantified as information transfer, but the number of targets is insufficient for such quantification.\nC_LIO_LIInformation transfer rate increases with the number of neural recording channels.\nC_LIO_LIFor practical applications, improvement in quality of life is essential, not information transfer rate per se.\nC_LI

neuroscience↗