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Yates, B. J.

Publications and source records attributed to Yates, B. J..

2 recordsLinked to original sources

Responses of Neurons in the Rostral Ventrolateral Medulla (RVLM) of Conscious Felines to Anticipated and Passive Movements

Considerable evidence demonstrates that the vestibular system contributes to regulating sympathetic nerve activity and blood pressure. Initial studies in decerebrate animals showed that presumed pre-sympathetic neurons in the rostral ventrolateral medulla (RVLM) respond to small-amplitude (<10{degrees}) rotations of the body, as in other brain areas that process vestibular signals, despite the fact that such movements do not appreciably affect blood distribution in the body. However, a subsequent experiment in conscious animals showed that few RVLM neurons respond to small-amplitude movements. This study tested the hypothesis that vestibular inputs to RVLM neurons are modulated in conscious animals, such that vestibulosympathetic responses are only elicited when changes in body position are large enough to require changes in sympathetic nerve activity. The activity of approximately a third of RVLM neurons whose firing rate was related to the cardiac cycle, and thus likely received baroreceptor inputs, responded to vestibular inputs elicited by 40{degrees} head-up tilts in conscious cats, but not during 10{degrees} sinusoidal rotations in the pitch plane that affected the activity of neurons in brain regions providing inputs to the RVLM. These data suggest the existence of brain circuitry that suppresses vestibular influences on the activity of RVLM neurons and the sympathetic nervous system unless these inputs are physiologically warranted. We also determined that RVLM activity is not altered prior to tilts when a light cue is provided signaling the movement. The simplest interpretation of this findings is that feedforward cardiovascular responses are associated with active movement such as occurs during exercise, but not passive movements that require cardiovascular adjustments.

physiology

Machine learning prediction of emesis and gastrointestinal state in ferrets

Although electrogastrography (EGG) could be a critical tool in the diagnosis and treatment of patients with gastrointestinal (GI) disease, it remains under-utilized. The lack of spatial and temporal resolution using current EGG methods presents a significant roadblock to more widespread usage. Human and preclinical studies have shown that GI myoelectric electrodes can record signals containing significantly more information than can be derived abdominal surface electrodes. The current study sought to assess the efficacy of multi-electrode arrays, surgically implanted on the serosal surface of the GI tract, from gastric fundus to duodenum, in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features. Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous recordings from up to six GI recording sites in both anesthetized and chronically implanted free-moving ferrets. Testing conditions to produce different gastric states included gastric distension, intragastric infusion of emetine (a prototypical emetic agent), and feeding. Despite the observed variability in GI signals, machine learning algorithms, including k nearest neighbors and support vector machines, were able to detect the state of the stomach with high overall accuracy (>80%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching and could provide insight into methodologies to treat GI diseases and control symptoms such as nausea and vomiting.

neuroscience