Biomedical subjects

Justin C Sanchez

Publications and source records attributed to Justin C Sanchez.

8 recordsLinked to original sources

PubMed2006

Evolving into epilepsy: Multiscale electrophysiological analysis and imaging in an animal model.

Epilepsy research for the design of seizure detection/prediction neuroprosthetics has been faced with the search for electrophysiologic control parameters that can be used to infer the epileptic state of the animal and be leveraged at a later time to deliver neurotherapeutic feedback. The analysis presented here uses multi-microelectrode array technology to provide an electrophysiologic quantification of a hippocampal neural ensemble during the latent period of epileptogenesis. Through the use of signal processing system identification methodologies, we were able to assess the spatial and temporal interrelations of ensembles of hippocampal neurons and relate them to the evolution of the epileptic condition. High-field magnetic resonance (MR) imaging was used to determine the location of electrode placement and to evaluate hippocampal pyramidal cell structural damage. Long-term single unit activity analysis suggests that hippocampal neurons in both CA1-2 and dentate regions increase the number of occurrences and duration of their bursting activity after injury to the contra-lateral hippocampus. The trends inferred from both single neuron and ensemble analysis suggests that the evolution into epilepsy is not abrupt but modulates gradually from the time of injury.

Action Potentials↗

PubMed2006

Deep brain stimulation of the internal segment of the globus pallidus in delayed runaway dyskinesia.

BACKGROUND: Dyskinesias that occur during a period without medication after embryonic cell transplantation have been commonly reported in double-blind trials; however, to date, they have not been reported in the few patients who participated in open-label pilot studies. DESIGN: Single case observation with preoperative and postoperative data, and intraoperative single-cell physiology. PATIENT: A patient who underwent embryonic cell transplantation in 1993 as part of the University of South Florida open-label study was referred for evaluation of intractable dyskinesia of the right arm. The dyskinesia was present during evaluation of the patient after a 12-hour period without medication and was clinically disabling. It was manifested as a severe groping movement of the hand. Intraoperative physiologic evaluation revealed decreased firing rates in the internal segment of the globus pallidus. RESULTS: Deep brain stimulation of the internal segment of the globus pallidus resulted in resolution of the dyskinesia. CONCLUSION: This case highlights the delayed development of runaway dyskinesia after a period without medication as an important potential long-term adverse effect of embryonic cell transplantation in patients with Parkinson disease.

Deep Brain Stimulation↗

PubMed2006

Structural modifications in chronic microwire electrodes for cortical neuroprosthetics: a case study.

Long-term viability of chronic invasive neural probes is a necessary condition for extracting robust control signals directly from neural tissue. Although immune/tissue response is a leading factor in the degradation of single neuron recording, we investigate a second component of signal degradation connected to the structural changes associated with microwire electrodes chronically exposed to extracelluar environments in vivo. Scanning electron microscopy is used to assess the surface modifications to the electrodes after an implantation duration of four weeks in rats. The electrode developed a smooth fracture surface, a reduction of the metal diameter, and pitting in the insulation of the electrode structure. Over the duration of implantation, recording properties of the electrode were marked by a reduction in the peak-to-peak amplitude in neuronal firing.

Action Potentials↗

PubMed2005

The effect of a bellows leak in an Ohmeda 7810 ventilator on room contamination, inspired oxygen, airway pressure, and tidal volume.

We investigated the effect of a small bellows leak (bellows full at end-expiration) on inspired oxygen fraction (Fio(2)), exhaled tidal volume (Vt), airway pressure, and room contamination in an oxygen-driven anesthesia ventilator (Ohmeda 7810, Madison, WI). CO(2) concentration at the ventilator exhalation valve, Fio(2), Vt, and airway pressure were measured (n = 3) while ventilating a CO(2)-producing test lung at 8 breaths/min and an inspiratory/expiratory ratio of 1:2, with and without a bellows leak (4-mm-long tear). Set Vt was 400, 600, 800, and 1000 mL. Fresh gas flow (FGF) was 0.3 L/min O(2) and (a) 5.0 L/min air, (b) 2.0 L/min air, and (c) 0.2 L/min nitrogen. There was no clinical difference in Fio(2), Vt, PIP (peak inspiratory pressure) and PEEP (positive end-expiratory pressure), with and without a 4-mm bellows tear, at all FGFs and Vt settings. CO(2) at the ventilator exhalation valve was always nonzero with a bellows leak, indicating that CO(2)-laden circuit gas was contaminating the drive gas via the bellows leak. A 4-mm bellows tear in an Ohmeda 7810 ventilator allows anesthetic gases to contaminate ambient air but does not cause clinically significant changes in Fio(2), exhaled Vt, PIP, or PEEP.

Air Pressure↗

PubMed2005

Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface.

We propose the use of optimized brain-machine interface (BMI) models for interpreting the spatial and temporal neural activity generated in motor tasks. In this study, a nonlinear dynamical neural network is trained to predict the hand position of primates from neural recordings in a reaching task paradigm. We first develop a method to reveal the role attributed by the model to the sampled motor, premotor, and parietal cortices in generating hand movements. Next, using the trained model weights, we derive a temporal sensitivity measure to asses how the model utilized the sampled cortices and neurons in real-time during BMI testing.

Algorithms↗

PubMed2005

Staged deep brain stimulation for refractory craniofacial dystonia with blepharospasm: case report and physiology.

OBJECTIVE AND IMPORTANCE: We report the intraoperative results, subsequent course, and 1-year follow-up evaluation of a patient with medication-refractory craniofacial dystonia for whom we planned bilateral globus pallidus internus (GPi) deep brain stimulation (DBS) implantation but delayed the left GPi DBS implantation because of robust intraoperative effects of right GPi DBS. CLINICAL PRESENTATION: A 47-year-old patient had a 5-year history of progressively severe, bilateral craniofacial dystonia with blepharospasm (Meige's syndrome) that was refractory to medications and to botulinum toxin (A and B) injections. Blepharospasm interfered with his ability to perform his duties as a Special Forces soldier and ended his military career. INTERVENTION: Under stereotactic guidance (magnetic resonance imaging and computed tomographic image fusion, Cosman-Roberts-Wells frame, and University of Florida surgical navigation software) and with detailed microelectrode mapping (four microelectrode passes), a DBS electrode was implanted in the right posteroventral GPi. Microelectrode recordings were taken to document electrophysiological activity of neurons in the region, and intraoperative macrostimulation was performed. The patient was followed up for 6 months with right unilateral GPi DBS, and later a left GPi DBS electrode was placed. CONCLUSION: Although DBS for primary generalized dystonia is commonly performed by simultaneously implanting bilateral GPi electrodes, it may be reasonable in cases of refractory blepharospasm and/or craniofacial dystonia to use a staged procedure for implantation in selected patients. Additionally, the physiology, especially that encountered in the striatum, may help to elucidate the pathophysiological basis for refractory blepharospasm and Meige's syndrome. More cases will be needed to determine the significance of the results reported in this article.

Deep Brain Stimulation↗

PubMed2004

Ascertaining the importance of neurons to develop better brain-machine interfaces.

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.

Action Potentials↗

PubMedDate not supplied

Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models.

This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network.

Artificial Intelligence↗