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Latency of auditory evoked potential monitoring the effects of general anesthetics on nerve fibers and synapses.

Huang B, Liang F, Zhong L, Lin M, Yang J, Yan L, Xiao J, Xiao Z - Sci Rep (2015)

Bottom Line: Auditory evoked potential (AEP) is an effective index for the effects of general anesthetics.However, it's unknown if AEP can differentiate the effects of general anesthetics on nerve fibers and synapses.Therefore, we conclude that, AEP latency is superior to amplitude for the effects of general anesthetics, ∆L monitors the effect of hypothermia on nerve fibers, and ∆I monitors a combined effect of anesthesia and hypothermia on synapses.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, PR China [2] Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, PR China.

ABSTRACT
Auditory evoked potential (AEP) is an effective index for the effects of general anesthetics. However, it's unknown if AEP can differentiate the effects of general anesthetics on nerve fibers and synapses. Presently, we investigated AEP latency and amplitude changes to different acoustic intensities during pentobarbital anesthesia. Latency more regularly changed than amplitude during anesthesia. AEP Latency monotonically decreased with acoustic intensity increase (i.e., latency-intensity curve) and could be fitted to an exponential decay equation, which showed two components, the theoretical minimum latency and stimulus-dependent delay. From the latency-intensity curves, the changes of these two components (∆L and ∆I) were extracted during anesthesia. ∆L and ∆I monitored the effect of pentobarbital on nerve fibers and synapses. Pentobarbital can induce anesthesia, and two side effects, hypoxemia and hypothermia. The hypoxemia was not related with ∆L and ∆I. However, ∆L was changed by the hypothermia, whereas ∆I was changed by the hypothermia and anesthesia. Therefore, we conclude that, AEP latency is superior to amplitude for the effects of general anesthetics, ∆L monitors the effect of hypothermia on nerve fibers, and ∆I monitors a combined effect of anesthesia and hypothermia on synapses. When eliminating the temperature factor, ∆I monitors the anesthesia effect on synapses.

No MeSH data available.


Related in: MedlinePlus

Changes in ∆L and ∆I.(a) Latency-intensity curves (n = 301) for each recording session of twenty mice. (b) Data fitting to DCASF. Data were presented as those in Fig. 4c. (c,d) Summary of ∆L- and ∆I-time curves (n = 20). (e,f) The normalized ∆L- and ∆I-time curves of twenty mice and the fitting curves to a polynomial regression equation (polynomial order = 4) (red (R2 = 0.836, intercept = −0.225, B1 = 4.036, B2 = −6.073, B3 = 7.034, B4 = −4.622) and green curves (R2 = 0.670, intercept = −0.020, B1 = 3.822, B2 = −3.821, B3 = −0.269, B4 = 0.410)). Normalized and fitting methods were similar to those in Fig. 2e. (g) Comparison between the two fitting curves (peak time: 0.688 ± 0.103 vs. 0.547 ± 0.115, **P < 0.01, unpaired t test). (h) Comparison between two groups of absolute values of residuals obtained by fitting normalized ∆L- and ∆I-time curves (P25 = 0.038, P50 = 0.074, P75 = 0.122, mean = 0.096 and SD = 0.087 for ∆L; P25 = 0.058, P50 = 0.118, P75 = 0.202, mean = 0.141 and SD = 0.112 for ∆I; **P < 0.01, 2 Independent Samples Tests).
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f5: Changes in ∆L and ∆I.(a) Latency-intensity curves (n = 301) for each recording session of twenty mice. (b) Data fitting to DCASF. Data were presented as those in Fig. 4c. (c,d) Summary of ∆L- and ∆I-time curves (n = 20). (e,f) The normalized ∆L- and ∆I-time curves of twenty mice and the fitting curves to a polynomial regression equation (polynomial order = 4) (red (R2 = 0.836, intercept = −0.225, B1 = 4.036, B2 = −6.073, B3 = 7.034, B4 = −4.622) and green curves (R2 = 0.670, intercept = −0.020, B1 = 3.822, B2 = −3.821, B3 = −0.269, B4 = 0.410)). Normalized and fitting methods were similar to those in Fig. 2e. (g) Comparison between the two fitting curves (peak time: 0.688 ± 0.103 vs. 0.547 ± 0.115, **P < 0.01, unpaired t test). (h) Comparison between two groups of absolute values of residuals obtained by fitting normalized ∆L- and ∆I-time curves (P25 = 0.038, P50 = 0.074, P75 = 0.122, mean = 0.096 and SD = 0.087 for ∆L; P25 = 0.058, P50 = 0.118, P75 = 0.202, mean = 0.141 and SD = 0.112 for ∆I; **P < 0.01, 2 Independent Samples Tests).

Mentions: The latency-intensity curves of all recording sessions from twenty sampled mice had similar shapes and showed an exponential decay relationship between latency and acoustic intensity (Fig. 5a, n = 301). The average curve of all latency-intensity curves in Fig. 5a was also fit well to Equation (1) (R2 = 0.998; L0, I0, K and τ were 19.607, 29.754, 10.984 and 21.443, respectively). Taking the fitting curve (Fig. 5b, the red curve) as the reference curve, the corresponding shifts (∆L and ∆I) for each latency-intensity curve were obtained by performing DCASF on all latency-intensity curves in Fig. 5a. After subtracting the corresponding ∆L and ∆I, all latency-intensity curves overlapped well with the reference curve (Fig. 5b, the black dots), which were well fit to Equation (2). The ∆L, ∆I and R2 values were 0.00016, –0.00021 and 0.998, respectively. That is, the fitting curve almost was the same as the reference curve. To show the shifts (∆L and ∆I) during anesthesia in each mouse, ∆L and ∆I for the first recording session were referred to zero, and all other shifts were normalized to those and plotted with recording session, i.e., time (Fig. 5c,d).


Latency of auditory evoked potential monitoring the effects of general anesthetics on nerve fibers and synapses.

Huang B, Liang F, Zhong L, Lin M, Yang J, Yan L, Xiao J, Xiao Z - Sci Rep (2015)

Changes in ∆L and ∆I.(a) Latency-intensity curves (n = 301) for each recording session of twenty mice. (b) Data fitting to DCASF. Data were presented as those in Fig. 4c. (c,d) Summary of ∆L- and ∆I-time curves (n = 20). (e,f) The normalized ∆L- and ∆I-time curves of twenty mice and the fitting curves to a polynomial regression equation (polynomial order = 4) (red (R2 = 0.836, intercept = −0.225, B1 = 4.036, B2 = −6.073, B3 = 7.034, B4 = −4.622) and green curves (R2 = 0.670, intercept = −0.020, B1 = 3.822, B2 = −3.821, B3 = −0.269, B4 = 0.410)). Normalized and fitting methods were similar to those in Fig. 2e. (g) Comparison between the two fitting curves (peak time: 0.688 ± 0.103 vs. 0.547 ± 0.115, **P < 0.01, unpaired t test). (h) Comparison between two groups of absolute values of residuals obtained by fitting normalized ∆L- and ∆I-time curves (P25 = 0.038, P50 = 0.074, P75 = 0.122, mean = 0.096 and SD = 0.087 for ∆L; P25 = 0.058, P50 = 0.118, P75 = 0.202, mean = 0.141 and SD = 0.112 for ∆I; **P < 0.01, 2 Independent Samples Tests).
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4526847&req=5

f5: Changes in ∆L and ∆I.(a) Latency-intensity curves (n = 301) for each recording session of twenty mice. (b) Data fitting to DCASF. Data were presented as those in Fig. 4c. (c,d) Summary of ∆L- and ∆I-time curves (n = 20). (e,f) The normalized ∆L- and ∆I-time curves of twenty mice and the fitting curves to a polynomial regression equation (polynomial order = 4) (red (R2 = 0.836, intercept = −0.225, B1 = 4.036, B2 = −6.073, B3 = 7.034, B4 = −4.622) and green curves (R2 = 0.670, intercept = −0.020, B1 = 3.822, B2 = −3.821, B3 = −0.269, B4 = 0.410)). Normalized and fitting methods were similar to those in Fig. 2e. (g) Comparison between the two fitting curves (peak time: 0.688 ± 0.103 vs. 0.547 ± 0.115, **P < 0.01, unpaired t test). (h) Comparison between two groups of absolute values of residuals obtained by fitting normalized ∆L- and ∆I-time curves (P25 = 0.038, P50 = 0.074, P75 = 0.122, mean = 0.096 and SD = 0.087 for ∆L; P25 = 0.058, P50 = 0.118, P75 = 0.202, mean = 0.141 and SD = 0.112 for ∆I; **P < 0.01, 2 Independent Samples Tests).
Mentions: The latency-intensity curves of all recording sessions from twenty sampled mice had similar shapes and showed an exponential decay relationship between latency and acoustic intensity (Fig. 5a, n = 301). The average curve of all latency-intensity curves in Fig. 5a was also fit well to Equation (1) (R2 = 0.998; L0, I0, K and τ were 19.607, 29.754, 10.984 and 21.443, respectively). Taking the fitting curve (Fig. 5b, the red curve) as the reference curve, the corresponding shifts (∆L and ∆I) for each latency-intensity curve were obtained by performing DCASF on all latency-intensity curves in Fig. 5a. After subtracting the corresponding ∆L and ∆I, all latency-intensity curves overlapped well with the reference curve (Fig. 5b, the black dots), which were well fit to Equation (2). The ∆L, ∆I and R2 values were 0.00016, –0.00021 and 0.998, respectively. That is, the fitting curve almost was the same as the reference curve. To show the shifts (∆L and ∆I) during anesthesia in each mouse, ∆L and ∆I for the first recording session were referred to zero, and all other shifts were normalized to those and plotted with recording session, i.e., time (Fig. 5c,d).

Bottom Line: Auditory evoked potential (AEP) is an effective index for the effects of general anesthetics.However, it's unknown if AEP can differentiate the effects of general anesthetics on nerve fibers and synapses.Therefore, we conclude that, AEP latency is superior to amplitude for the effects of general anesthetics, ∆L monitors the effect of hypothermia on nerve fibers, and ∆I monitors a combined effect of anesthesia and hypothermia on synapses.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, PR China [2] Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, PR China.

ABSTRACT
Auditory evoked potential (AEP) is an effective index for the effects of general anesthetics. However, it's unknown if AEP can differentiate the effects of general anesthetics on nerve fibers and synapses. Presently, we investigated AEP latency and amplitude changes to different acoustic intensities during pentobarbital anesthesia. Latency more regularly changed than amplitude during anesthesia. AEP Latency monotonically decreased with acoustic intensity increase (i.e., latency-intensity curve) and could be fitted to an exponential decay equation, which showed two components, the theoretical minimum latency and stimulus-dependent delay. From the latency-intensity curves, the changes of these two components (∆L and ∆I) were extracted during anesthesia. ∆L and ∆I monitored the effect of pentobarbital on nerve fibers and synapses. Pentobarbital can induce anesthesia, and two side effects, hypoxemia and hypothermia. The hypoxemia was not related with ∆L and ∆I. However, ∆L was changed by the hypothermia, whereas ∆I was changed by the hypothermia and anesthesia. Therefore, we conclude that, AEP latency is superior to amplitude for the effects of general anesthetics, ∆L monitors the effect of hypothermia on nerve fibers, and ∆I monitors a combined effect of anesthesia and hypothermia on synapses. When eliminating the temperature factor, ∆I monitors the anesthesia effect on synapses.

No MeSH data available.


Related in: MedlinePlus