2020
DOI: 10.5213/inj.2040318.159
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Past, Present, and Future in the Study of Neural Control of the Lower Urinary Tract

Abstract: The neurological coordination of the lower urinary tract can be analyzed from the perspective of motor neurons or sensory neurons. First, sensory nerves with receptors in the bladder and urethra transmits stimuli to the cerebral cortex through the periaqueductal gray (PAG) of the midbrain. Upon the recognition of stimuli, the cerebrum carries out decision-making in response. Motor neurons are divided into upper motor neurons (UMNs) and lower motor neurons (LMNs) and UMNs coordinate storage and urination in the… Show more

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Cited by 28 publications
(19 citation statements)
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“…The pathophysiology of OAB is not completely understood and is believed to be multifactorial [ 24 , 25 , 29 , 33 ]. One theory is that the urgency and related symptoms stem from a cortical immaturity of the centers responsible for controlling urination [ 15 , 34 ].…”
Section: Theoretical and Clinical Backgroundmentioning
confidence: 99%
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“…The pathophysiology of OAB is not completely understood and is believed to be multifactorial [ 24 , 25 , 29 , 33 ]. One theory is that the urgency and related symptoms stem from a cortical immaturity of the centers responsible for controlling urination [ 15 , 34 ].…”
Section: Theoretical and Clinical Backgroundmentioning
confidence: 99%
“…Then, cortical inhibitory pathways and the pontine micturition center along with periaqueductal grey matter, anterior cingulate gyrus, and the autonomic, somatic, and sensorial autonomic nervous systems are developed, and urination becomes voluntary [ 36 ]. The prefrontal cortex starts to maintain top-down control over more primitive afferent pathways of the brain, such as the limbic and paralimbic systems [ 24 ].…”
Section: Theoretical and Clinical Backgroundmentioning
confidence: 99%
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“…Various methods and learning algorithms for motion recognition have been proposed. Most studies have used static algorithms such as artificial neural networks [7][8][9][10][11] and K-means clustering [12][13][14], or dynamic time warping [15] in combination with algorithms. However, a time series algorithm should be used to predict or classify dynamically changing time series data.…”
Section: Introductionmentioning
confidence: 99%