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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.


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One example of DCASF.(a,b) Latency-time curves to 30–90 dB acoustic stimuli and latency changes with acoustic intensity for all recording sessions lasting 190 min, hereafter, latency-intensity curves (c) Data fitting to DCASF. The average latency-intensity curve of all latency-intensity curves in Fig. 4b was fit to Equation (1) (the red line was the fitting curve, i.e., reference curve (corresponding to Curve 1 in the insert)). Then, each curve in Fig. 4b as the target curve (corresponding to Curve 2 in the insert) was fit to Equation (2) to obtain ∆L and ∆I (insert). This result can be simplified as the target curve (Curve 2) first shifts ∆I to overlap with Curve 3 and then shifts ∆L to overlap with the reference curve (Curve 1) along the axes (the insert) (details introduced in Results). The latency shift (∆L) and intensity shift (∆I) reflect the changes of latencies in nerve fibers and synapses, respectively. When each data point in Fig. 4b subtracted its corresponding shifts (∆L and ∆I) and those newly obtained data were re-plotted, the black dots were obtained. (d) ∆L and ∆I changes with anesthesia time were hereafter labeled as ∆L- and ∆I-time curves.
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f4: One example of DCASF.(a,b) Latency-time curves to 30–90 dB acoustic stimuli and latency changes with acoustic intensity for all recording sessions lasting 190 min, hereafter, latency-intensity curves (c) Data fitting to DCASF. The average latency-intensity curve of all latency-intensity curves in Fig. 4b was fit to Equation (1) (the red line was the fitting curve, i.e., reference curve (corresponding to Curve 1 in the insert)). Then, each curve in Fig. 4b as the target curve (corresponding to Curve 2 in the insert) was fit to Equation (2) to obtain ∆L and ∆I (insert). This result can be simplified as the target curve (Curve 2) first shifts ∆I to overlap with Curve 3 and then shifts ∆L to overlap with the reference curve (Curve 1) along the axes (the insert) (details introduced in Results). The latency shift (∆L) and intensity shift (∆I) reflect the changes of latencies in nerve fibers and synapses, respectively. When each data point in Fig. 4b subtracted its corresponding shifts (∆L and ∆I) and those newly obtained data were re-plotted, the black dots were obtained. (d) ∆L and ∆I changes with anesthesia time were hereafter labeled as ∆L- and ∆I-time curves.

Mentions: Recently, we have developed a method (DCASF) to first spike latency-intensity curves recorded in single cells, to extract changes in the theoretical minimum and stimulus-dependent components of latency29, which can represent the changes in spike latencies in nerve fibers and synapses, respectively. The latency changes of AEPs with anesthesia time (latency-time curves) from No. 5 mouse (M20110410) (Fig. 4a) were similar to those shown in Fig. 3a. When the data were re-plotted in latency-intensity curve for each recording session (Fig. 4b), the latency exponentially decayed as acoustic intensity increased, as previous results from single cells2627, although AEP is summary of the acoustic evoked electrical activities of auditory pathways30. Thus, we can analyze the effects of general anesthetics on nerve fibers and synapses by using DCASF29.


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)

One example of DCASF.(a,b) Latency-time curves to 30–90 dB acoustic stimuli and latency changes with acoustic intensity for all recording sessions lasting 190 min, hereafter, latency-intensity curves (c) Data fitting to DCASF. The average latency-intensity curve of all latency-intensity curves in Fig. 4b was fit to Equation (1) (the red line was the fitting curve, i.e., reference curve (corresponding to Curve 1 in the insert)). Then, each curve in Fig. 4b as the target curve (corresponding to Curve 2 in the insert) was fit to Equation (2) to obtain ∆L and ∆I (insert). This result can be simplified as the target curve (Curve 2) first shifts ∆I to overlap with Curve 3 and then shifts ∆L to overlap with the reference curve (Curve 1) along the axes (the insert) (details introduced in Results). The latency shift (∆L) and intensity shift (∆I) reflect the changes of latencies in nerve fibers and synapses, respectively. When each data point in Fig. 4b subtracted its corresponding shifts (∆L and ∆I) and those newly obtained data were re-plotted, the black dots were obtained. (d) ∆L and ∆I changes with anesthesia time were hereafter labeled as ∆L- and ∆I-time curves.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: One example of DCASF.(a,b) Latency-time curves to 30–90 dB acoustic stimuli and latency changes with acoustic intensity for all recording sessions lasting 190 min, hereafter, latency-intensity curves (c) Data fitting to DCASF. The average latency-intensity curve of all latency-intensity curves in Fig. 4b was fit to Equation (1) (the red line was the fitting curve, i.e., reference curve (corresponding to Curve 1 in the insert)). Then, each curve in Fig. 4b as the target curve (corresponding to Curve 2 in the insert) was fit to Equation (2) to obtain ∆L and ∆I (insert). This result can be simplified as the target curve (Curve 2) first shifts ∆I to overlap with Curve 3 and then shifts ∆L to overlap with the reference curve (Curve 1) along the axes (the insert) (details introduced in Results). The latency shift (∆L) and intensity shift (∆I) reflect the changes of latencies in nerve fibers and synapses, respectively. When each data point in Fig. 4b subtracted its corresponding shifts (∆L and ∆I) and those newly obtained data were re-plotted, the black dots were obtained. (d) ∆L and ∆I changes with anesthesia time were hereafter labeled as ∆L- and ∆I-time curves.
Mentions: Recently, we have developed a method (DCASF) to first spike latency-intensity curves recorded in single cells, to extract changes in the theoretical minimum and stimulus-dependent components of latency29, which can represent the changes in spike latencies in nerve fibers and synapses, respectively. The latency changes of AEPs with anesthesia time (latency-time curves) from No. 5 mouse (M20110410) (Fig. 4a) were similar to those shown in Fig. 3a. When the data were re-plotted in latency-intensity curve for each recording session (Fig. 4b), the latency exponentially decayed as acoustic intensity increased, as previous results from single cells2627, although AEP is summary of the acoustic evoked electrical activities of auditory pathways30. Thus, we can analyze the effects of general anesthetics on nerve fibers and synapses by using DCASF29.

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