Time-varying Spectral Index of Electrodermal Activity to Predict Central Nervous System Oxygen Toxicity Symptoms in Divers: Preliminary results

Author(s):  
Hugo F. Posada-Quintero ◽  
Bruce J. Derrick ◽  
Christopher Winstead-Derlega ◽  
Sara I. Gonzalez ◽  
M. Claire Ellis ◽  
...  
1994 ◽  
Vol 77 (4) ◽  
pp. 1903-1906 ◽  
Author(s):  
R. Arieli ◽  
G. Hershko

Cumulative O2 toxicity (K) can be calculated using the expression K = t2 x PO2c, where t is exposure time and the power c is to be determined; the phenomenon is liable to occur when K reaches Kc, the threshold value of K at which a symptom is manifested. Six rats were each exposed six times to 6 ATA O2 at 2-day intervals until the first electrical discharge (FED) was noted in an electroencephalogram. There was no difference in latency to FED in the series of six exposures. Thirteen rats were exposed to O2 until FED was noted in an electroencephalogram. They were exposed to four constant PO2's of 5, 6, 7, and 8 ATA and to two combined profiles of 1) 5 min at 7 ATA followed by 5 ATA and 2) 15 min at 5 ATA followed by 7 ATA. The solution of the equation for each rat was used to predict its latency to FED on the combined profile. The correlation of predicted to measured latency was significant (P < 0.0001), and the slope was not different from 1. Solving for these parameters using the combination of all the data, we obtained Kc = 5.71 x 10(6) and c = 5.39, which correctly predicted the mean latency but failed to predict individual latency. It is preferable to use each rat as its own control. The significance of the correlation supports the validity of the power equation for calculating K.


2019 ◽  
Vol 36 (1) ◽  
pp. 193-203 ◽  
Author(s):  
Cheng-wei Xie ◽  
Zhong-zhuang Wang ◽  
Ya-nan Zhang ◽  
Yu-liang Chen ◽  
Run-ping Li ◽  
...  

2014 ◽  
Vol 2 (4) ◽  
pp. e00282 ◽  
Author(s):  
Heather E. Held ◽  
Raffaele Pilla ◽  
Geoffrey E. Ciarlone ◽  
Carol S. Landon ◽  
Jay B. Dean

1991 ◽  
Vol 202 (2) ◽  
pp. 171-175 ◽  
Author(s):  
Tzahala Tzuk-Shina ◽  
Noemi Bitterman ◽  
Dan Harel

2018 ◽  
Vol 20 (suppl_2) ◽  
pp. i47-i47
Author(s):  
Andrea M Cappellano ◽  
Jonathan Finlay ◽  
Bruna Mançano ◽  
Daniela Barbosa ◽  
Sergio Cavalheiro ◽  
...  

2019 ◽  
Author(s):  
Ken Takiyama ◽  
Hikaru Yokoyama ◽  
Naotsugu Kaneko ◽  
Kimitaka Nakazawa

AbstractHow the central nervous system (CNS) controls many joints and muscles is a fundamental question in motor neuroscience and related research areas. An attractive hypothesis is the module hypothesis: the CNS controls groups of joints or muscles (i.e., spatial modules) while providing time-varying motor commands (i.e., temporal modules) to the spatial modules rather than controlling each joint or muscle separately. Another fundamental question is how the CNS generates numerous repertories of movement patterns. One hypothesis is that the CNS modulates the spatial and/or temporal modules depending on the required tasks. It is thus essential to quantify the spatial module, the temporal module, and the task-dependent modulation of those modules. Although previous methods attempted to quantify these aspects, they considered the modulation in only the spatial or temporal module. These limitations were possibly due to the constraints inherent to conventional methods for quantifying the spatial and temporal modules. Here, we demonstrate the effectiveness of tensor decomposition in quantifying the spatial module, the temporal module, and the task-dependent modulation of these modules without such limitations. We further demonstrate that the tensor decomposition provides a new perspective on the task-dependent modulation of spatiotemporal modules: in switching from walking to running, the CNS modulates the peak timing in the temporal module while recruiting proximal muscles in the corresponding spatial module.Author summaryThere are at least two fundamental questions in motor neuroscience and related research areas: 1) how does the central nervous system (CNS) control many joints and muscles and 2) how does the CNS generate numerous repertories of movement patterns. One possible answer to question 1) is that the CNS controls groups of joints or muscles (i.e., spatial modules) while providing time-varying motor commands (i.e., temporal modules) to the spatial modules rather than controlling each joint or muscle separately. One possible answer to question 2) is that the CNS modulates the spatial and/or temporal module depending on the required tasks. It is thus essential to quantify the spatial module, the temporal module, and the task-dependent modulation of those modules. Here, we demonstrate the effectiveness of tensor decomposition in quantifying the modules and those task-dependent modulations while overcoming the shortcomings inherent to previous methods. We further show that the tensor decomposition provides a new perspective on how the CNS switches between walking and running. The CNS modulated the peak timing in the temporal module while recruiting proximal muscles in the corresponding spatial module.


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