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Detecting bladder fullness through the ensemble activity patterns of the spinal cord unit population in a somatovisceral convergence environment

Authors
 Jae Hong Park  ;  Chang-Eop Kim  ;  Jaewoo Shin  ;  Changkyun Im  ;  Chin Su Koh  ;  In Seok Seo  ;  Sang Jeong Kim  ;  Hyung-Cheul Shin 
Citation
 JOURNAL OF NEURAL ENGINEERING, Vol.10(5) : 56009, 2013 
Journal Title
JOURNAL OF NEURAL ENGINEERING
ISSN
 1741-2560 
Issue Date
2013
MeSH
Algorithms ; Animals ; Data Interpretation, Statistical ; Electrophysiological Phenomena/physiology ; Female ; Linear Models ; Physical Stimulation ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Sensation/physiology ; Spinal Cord/cytology ; Spinal Cord/physiology* ; Support Vector Machine ; Urinary Bladder/innervation ; Urinary Bladder/physiology* ; Urination
Keywords
Algorithms ; Animals ; Data Interpretation, Statistical ; Electrophysiological Phenomena/physiology ; Female ; Linear Models ; Physical Stimulation ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Sensation/physiology ; Spinal Cord/cytology ; Spinal Cord/physiology* ; Support Vector Machine ; Urinary Bladder/innervation ; Urinary Bladder/physiology* ; Urination
Abstract
OBJECTIVE:
Chronic monitoring of the state of the bladder can be used to notify patients with urinary dysfunction when the bladder should be voided. Given that many spinal neurons respond both to somatic and visceral inputs, it is necessary to extract bladder information selectively from the spinal cord. Here, we hypothesize that sensory information with distinct modalities should be represented by the distinct ensemble activity patterns within the neuronal population and, therefore, analyzing the activity patterns of the neuronal population could distinguish bladder fullness from somatic stimuli.
APPROACH:
We simultaneously recorded 26-27 single unit activities in response to bladder distension or tactile stimuli in the dorsal spinal cord of each Sprague-Dawley rat. In order to discriminate between bladder fullness and tactile stimulus inputs, we analyzed the ensemble activity patterns of the entire neuronal population. A support vector machine (SVM) was employed as a classifier, and discrimination performance was measured by k-fold cross-validation tests.
MAIN RESULTS:
Most of the units responding to bladder fullness also responded to the tactile stimuli (88.9-100%). The SVM classifier precisely distinguished the bladder fullness from the somatic input (100%), indicating that the ensemble activity patterns of the unit population in the spinal cord are distinct enough to identify the current input modality. Moreover, our ensemble activity pattern-based classifier showed high robustness against random losses of signals.
SIGNIFICANCE:
This study is the first to demonstrate that the two main issues of electroneurographic monitoring of bladder fullness, low signals and selectiveness, can be solved by an ensemble activity pattern-based approach, improving the feasibility of chronic monitoring of bladder fullness by neural recording.
Full Text
http://iopscience.iop.org/1741-2552/10/5/056009/
DOI
10.1088/1741-2560/10/5/056009
Appears in Collections:
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Shin, Jaewoo(신재우) ORCID logo https://orcid.org/0000-0002-6335-1292
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/89227
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