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


METHODS
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.


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.


CONCLUSIONS
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.
Department of Physiology, Hallym University College of Medicine, Chuncheon 200-702, Korea.
Jae Hong
JH
Park
Chang-Eop
CE
Kim
Jaewoo
J
Shin
Changkyun
C
Im
Chin Su
CS
Koh
In Seok
IS
Seo
Sang Jeong
SJ
Kim
Hyung-Cheul
HC
Shin
D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D055724:Electrophysiological Phenomena / Q000502:physiology; D005260:Female; D016014:Linear Models; D010812:Physical Stimulation; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D012677:Sensation / Q000502:physiology; D013116:Spinal Cord / Q000166:cytology / Q000502:physiology*; D060388:Support Vector Machine; D001743:Urinary Bladder / Q000294:innervation / Q000502:physiology*; D014554:Urination