Open Access
Add to BibSonomyAbstract
We aimed to investigate experimentally how anesthetic levels affect cerebral metabolism measured by near-infrared spectroscopy (NIRS) and to identify a robust marker among NIRS parameters to discriminate various stages of anesthetic depth in rats under isoflurane anesthesia. In order to record the hemodynamic changes and local field potential (LFP) in the brain, fiber-optic cannulae and custom-made microelectrodes were implanted in the frontal cortex of the skull. The NIRS and LFP signals were continuously monitored before, during and after isoflurane anesthesia. The level of oxyhemoglobin and total hemoglobin concentration of the frontal cortex decreased gradually as isoflurane concentration is reduced while deoxyhemoglobin increased. The reflectance ratio between 730 nm and 850 nm and burst suppression ratio (BSR) correspond similarly with the change of oxyhemoglobin during the variation of isoflurane concentration. These results suggest that NIRS signals in addition to EEG may provide a possibility of developing a new anesthetic depth index.
© 2016 Optical Society of America
1. Introduction
In clinical practice, estimating the depth of anesthesia (DOA) is important for monitoring safety during surgical treatment. Light anesthesia may cause intraoperative awareness leading to negative psychological effects and consequent medico-legal problems [1]. Deep level of anesthesia can affect the hemodynamic status during surgery and recovery. Since the brain is the effect-site of anesthetic drugs, commercially available anesthetic depth indicators employ different algorithms based on the analysis of spectral changes in electroencephalogram (EEG) [2]. However, this may give rise to erroneous DOA estimates in patients with abnormal EEG [3]. For instance, the bispectral index (BIS), a widely used anesthetic depth monitor, has been reported to underestimate or overestimate anesthetic depth in many clinical scenarios [3].
Anesthetic drugs inhibit neuronal activity in the brain and affect cerebral metabolism [4–6]. Brain oxygenation levels can be monitored to evaluate cerebral metabolism using near-infrared spectroscopy (NIRS) [7, 8]. NIRS is a widely used noninvasive optical technique that allows the measurement of hemoglobin oxygenation to evaluate oxygen consumption within a living tissue based on the differences in near-infrared (NIR) absorption [9–11]. It can be used to measure the changes of oxyhemoglobin (OHb) and deoxyhemoglobin (RHb) concentration and total blood volume (sum of OHb and RHb) in intact tissues and has been applied to breast cancer detection [12], brain functional study [13] and critical care research [14]. In addition, NIRS is noninvasive, stable from electronic artifacts, cheaper, simple to make and compatible with other modalities such as EEG [15]. In this study, we aim to identify a robust marker to discriminate DOA with NIRS in rats under isoflurane anesthesia.
2. Experimental methods
2.1 Animal care and use
The experimental procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of the Gwangju Institute of Science and Technology. Eight adult male Long Evans rats weighing 370~610 g were used for this study. The rats were housed at constant room temperature (24 ± 1°C) maintained on a day-night cycle (lights on at 9:00 AM, and off at 6:00 PM) with freely access to water. All experiments were performed during the day time.
2.2 Surgical implantation of electrodes and fiber-optic cannulae
Custom-made epidural screw electrodes and fiber-optic cannulae were implanted in the frontal skull of each rat for recording blood oxygenation level changes and the local field potential (LFP) in the brain. Before the surgical implantation, the rat was anesthetized with a mixture of ketamine hydrochloride (100 mg kg−1) and xylazine (7 mg kg−1). The rat was then moved to a stereotaxic frame and anesthesia was maintained by 1.5% isoflurane administered through a nasal cone. We made an incision of ~2 cm of scalp along the midline and the pericranium was removed to expose the skull. A durotomy was followed by making small holes in the skull. Through the holes, we implanted electrodes and fiber-optic cannulae with the use of a stereotaxic frame. Electrodes were placed 5.5 mm in the anterior of the bregma and ± 3.0 mm lateral from the midline. Fiber-optic cannulae were placed 2.5 mm in the anterior of the bregma and ± 2.5 mm lateral from the midline. The implantation was completed by securing the electrodes and fiber-optic cannulae on top of the skull using dental cement. Rats were then transferred to a recovery box.
2.3 Experimental protocol
The experiment was performed at least 4 days after the implantation of fiber-optic cannulae and electrodes to allow rats to recover fully from postoperative complications such as hemorrhage, shock, pain and infection from surgery before performing the experiments. The optical fibers in the implanted fiber-optic cannulae were connected to the light source (left cannula) and CCD spectrometer (right cannula) through optical patch cables. The implanted electrodes were connected to a custom-made LFP recording device. Prior to obtaining NIRS signals and LFP under isoflurane anesthesia, both signals were recorded from freely moving rats (awake state). We considered the awake state signals as the individual baseline reference. After acquiring a baseline reference data for 30 minutes, signals were obtained at various levels of anesthesia with a protocol shown as below.
Isoflurane anesthesia was introduced with 50% oxygen through a nasal cone. Initially, 3.0% isoflurane was administered to induce anesthesia for 3 minutes. Isoflurane concentration was then decreased to 2.5% and administered for 7 minutes. The initial 2 minutes was to allow the blood plasma and the brain to reach pseudo state equilibrium. The DOA was evaluated for each isoflurane concentration administered constantly from 2.5% and decreased by 0.5% after every 5 minutes. After administering 1.0% isoflurane for 5 minutes, the anesthesia was discontinued and we waited to detect a first movement caused by awakening from the anesthesia state. A gas analyzer (B40 model, GE healthcare) was used to monitor the inhalational oxygen percentage, isoflurane concentration, end tidal CO2 during the measurement. The NIRS signals and LFP were simultaneously and continuously recorded from the awake state until 30 minutes after the first movement (recovery state). At the end of each experiment, the rat was allowed to fully recover from anesthesia.
2.4 Continuous-wave near-infrared spectroscopy (CWNIRS)
For this study, a continuous-wave near-infrared spectroscopy (CWNIRS) system was used to measure the change of oxy-(OHb), deoxy-(RHb), and total hemoglobin (THb) concentration in the brain. The CWNIRS system consists of a tungsten-halogen light source (HL-2000-HP, Ocean Optics Inc.) and a 16-bit CCD spectrometer (USB4000, Ocean Optics Inc.) as detector [16]. Light was delivered to the left frontal lobe via a home-made fiber optic cannula having an optical fiber (Thorlabs FT400EMT, 0.39 NA, Ø400 µm Core) and the diffuse reflectance was measured from the right frontal lobe by another home-made fiber optic cannula. The separation between the light source and detector cannula was 5 mm.
2.5 NIRS signals process
The NIR spectra were acquired at a frequency of 3 Hz and intensity values of five wavelengths (730, 750, 800, 830 and 850 nm) were recorded on a computer to calculate the changes of OHb, RHb and THb concentration. If we assume that the absorbance of OHb and RHb dominates the attenuation of the NIR range and also scattering is constant, the change in [RHb] and [OHb] can be obtained based on the Beer-Lambert’s law [17, 18].
where is the change of optical density at wavelength λ, and are the extinction coefficients of RHb and OHb at wavelength λ respectively [19].
The path length is determined by , d is the source-detector separation distance and DPF is the differential path length factor [20]. The change in total hemoglobin concentration (∆[THb]) can be obtained by taking the sum of ∆[RHb] and ∆[OHb].
To reduce an effect of cerebral blood volume change (CBV) during anesthesia on NIRS signals, we calculated a ratio of reflectance intensity (RRI) between 730 nm and 850 nm by dividing the reflectance of both 730 nm (R730) and 850 nm (R850) by 800 nm (R800) in which RHb and OHb have identical extinction coefficient values.
2.6 Statistical analysis of NIRS signals
NIRS signal measurements were recorded from each rat (N = 8) during the experiment. For the statistical analysis of NIRS parameters, we selected the last 1 minute data from 5 minutes of each four different isoflurane concentration, 5 minutes data in awake state (20-25 minutes from start point of awake state) and recovery state (7-12 minutes from first spontaneous movement of subject including eye blinking and whisker movement). We then calculated the mean value with the standard deviation from each data. Two-tailed paired t-test was performed to compare the [OHb], [RHb], [THb] and RRI among the awake state, the different anesthetic levels, and the recovery state after identifying the normality of NIRS data using Shapiro–Wilk test. If the null-hypothesis of this test is rejected, Wilcoxon-rank sum test was conducted to investigate the statistical differences of NIRS signals related to isoflurane concentration.
2.7 LFP signals process
To compare the performance of NIRS signals as an anesthetic depth indicator, we obtained LFP to identify burst suppression, a validated index of anesthetic depth [21]. After the surgery of microelectrodes implantation, we connected microelectrodes to the custom made LFP recording device. LFPs like EEG are easily contaminated by external noises generated by various external factors. To remove noises in LFPs at each targeted areas, we applied notch filtering to reject the 60Hz line noise. LFPs data were preprocessed and analyzed using custom made analysis software. To visually compare the change of burst suppression lightening on anesthetic levels, we selected representative artifact-free 30 sec LFP epoch from four different anesthetic levels, awake state and recovery state (Fig. 2).
In order to quantify burst suppression, we calculated burst suppression ratio (BSR) which is defined as the fraction of time spent in suppression per epoch. 1 minute data from each isoflurane concentration and 5 minutes data from awake and recovery state were selected as each epoch same as NIRS parameters analysis shown in a previous section. All the raw data were filtered between 0.5 and 32 Hz which covers the most of cerebral signals before the BSR calculation [22]. If there is no presence of suppression during an epoch, then BSR is “0” while BSR will be “1” when suppression exists throughout the all the time of an epoch.
In this study, a suppression period was defined as follows: a suppression period starts when LFP amplitudes remain below the threshold value for more than 0.5 s. The suppression period lasts as long as amplitudes remain below the threshold value. The time point when amplitude rises above the threshold was defined as the end of suppression period. The threshold value was manually set to be 33% of standard deviation value from awake state for each rat (Fig. 5).
3. Results and discussion
The [OHb] and [THb] increased sharply during the anesthetic induction at 3.0% of isoflurane, while [RHb] decreased. After the anesthetic induction, [OHb] and [THb] decreased gradually with the reduction of isoflurane concentration, while [RHb] increased until 1.5% of isoflurane. However, some rats exhibited an abrupt increase of [OHb] and [THb], and decrease of [RHb] during 1.0% isoflurane administration.
Depending on the presence or absence of this phenomenon, we divided the results into two groups with either ‘awaken during anesthesia (AA)’ (the presence of abrupt increase of [OHb] and [THb]) or ‘non- awaken during anesthesia (NAA)’ (the absence of abrupt increase of [OHb] and [THb]) (Fig. 1) Fig. 1(A) and 1(B) show the change of [OHb], [RHb] and [THb] from representative rats from each group.
Fig. 1 The representative changes of [OHb], [RHb] and [THb] before, during and after anesthesia from AA and NAA groups. Depending on the existence of rapid increase in the [OHb] level change during the administration of 1.0% of isoflurane, the results are divided into ‘AA’ group (n = 4) (A) and ‘NAA’ group (n = 4) (B).
In both groups, the LFP consists of low amplitude and high frequency during the awake and recovery state (Fig. 2). On the other hand, a burst suppression pattern was observed while 2.5%, 2.0% and 1.5% of the isoflurane was administered. The duration time of burst suppression was reduced as the anesthetic concentration went down. At 1.0% isoflurane concentration, different results were obtained from the ‘AA’ and ‘NAA’ group. In the ‘AA’ group, the burst suppression was not observed and the LFP pattern is similar to the awake state. In the ‘NAA’ group, the burst suppression was observed though the duration time of burst suppression was reduced compared to higher isoflurane concentrations.
Fig. 2 The representative changes of LFP before, during and after anesthesia from ‘AA’ and ‘NAA’ groups. Since spectral changes in the two bipolar montages were almost identical, only the results of left frontal electrode are reported here. Numbers represent the percentage of isoflurane.
[OHb], [RHb], and [THb] values were compared among awake, during each isoflurane concentration and recovery state which are shown in Fig. 3. [OHb] and [THb] were significantly increased (P<0.05), while [RHb] were significantly decreased (P<0.05) during isoflurane anesthesia compared to the awake state. The changes of [OHb], [RHb] and [THb] corresponded linearly to the change in isoflurane concentration in NAA group while AA group showed a reversal of each parameter at 1.0% isoflurane. We also conducted a simple linear regression analysis of the data on anesthetic state for the NAA group to see how they correspond linearly to the change of isoflurane concentration (Fig. 3).
Fig. 3 The changes of [OHb], [THb], and [RHb] with a variation of isoflurane concentration from ‘AA’ and ‘NAA’ groups. The asterisk indicates p value less than 0.05 compared to awake state and the double asterisk indicates p value less than 0.05 compared to each other. The results of simple linear regression analysis is shown on the left column for the NAA group.
The RRI values which are not affected by blood volume change during anesthesia were also compared among awake, during each isoflurane concentration and recovery state (Fig. 4). Overall, RRI followed a similar trend of [OHb] (Fig. 3(C) and 3(D)) from both AA and NAA group.
Fig. 4 The changes of RRI value depending on the isoflurane concentration from ‘AA’ and ‘NAA’ groups. The asterisk indicates p value less than 0.05 compared to awake state and the double asterisk indicates p value less than 0.05 compared to each other.
Figure 5 illustrates the change of BSR compared with RRI of all eight subjects. In NAA group, the change of BSR value followed a similar tendency of RRI. On the other hand, AA group showed a little bit of different trends between BSR and RRI from 1.5% to recovery state. Unlike NAA group, BSR value at 1% isoflurane from all the subjects are close to the values of awake state which indicates the awake from anesthesia. However, RRI showed opposite trend and increased from the values at 1.5% isoflurane.
The technique of NIRS enables the noninvasive monitoring of the hemodynamic changes related to cortical activity [23]. For example, increasing task load gives rise to an increase in blood oxygenation as evaluated by NIRS in the frontal area involved in attention and cognitive perception [24]. In this sense, the technique facilitates the understanding of brain activation by providing markers associated with cerebral metabolism. Most of general anesthetics inhibits cerebral metabolism depending on the dose [25]. Therefore, cerebral oxygen utilization is expected to decrease proportionally with anesthetic depth which will induce a rise in [OHb] and a decline in [RHb] as anesthetic depth increases.
Consistent with this hypothesis, we have shown that [OHb] and [THb] are decreased gradually with the reduction of isoflurane concentration, while [RHb] increased in this study. Most of general anesthetics inhibits cerebral metabolism depending on the dose. Therefore, cerebral oxygen utilization is expected to decrease proportionally with anesthetic depth which will induce a rise in [OHb] and a decline in [RHb] as anesthetic depth increases. Consistent with this hypothesis, we have shown that [OHb] and [THb] are decreased gradually with the reduction of isoflurane concentration, while RHb increased in this study. This observation corresponds similarly to changes in LFP burst suppression patterns. To the best of our knowledge, this is the first study to identify a potential NIRS-based parameter to reflect anesthetic depth. Considering that NIRS signals proportionally changed in accordance to the change in different anesthetic levels, our results suggest that NIRS signals in addition to EEG have a potential as a new anesthetic depth index.
It is well known that alveolar concentration of anesthetic agent is a surrogate parameter of the concentration of anesthetic gas on target organ (brain) [26,27] and alveolar concentration is equal to the expiratory anesthetic gas concentration which can be monitored by using gas analyzer. The accuracy of the methods was proven and became standard of anesthetic care and monitoring [28].
In our experiment, we used GE healthcare’s B40 monitor and also applied customized rat mask to minimize the dead space of respiratory circuit. Empirically, we found that the expiratory isoflurane gas concentration became equal to the setting value of isoflurane vaporizer after 20~30 seconds of adjustment. In other words, it only takes 20~30 seconds for the target organ (brain) to reach the isoflurane concentration which we set using the vaporizer. Empirically, we found that hemodynamic changes started to become stabilized in 5 minutes when isoflurane concentration was reduced by 0.5%. Since the isoflurane has higher partition coefficients on rat’s blood and fat [29], elongated anesthesia duration might induce isoflurane accumulation in the rat’s tissue which can affect the target organ (brain)’s concentration. Therefore, we decided that 5 minutes of interval would be long enough for the rat’s alveolar concentration reaches the target concentration and short enough to prevent too much isoflurane accumulation in brain.
In this study, we obtained NIRS signals in the frontal brain areas since the frontal brain area is closely related to cognitive perception and recognized as one of the potential sites of anesthetic mechanisms [30, 31]. In clinical practice, the frontal area is easily accessible to obtain bio-signals in the brain, making this region the popular site for sensor attachment. The BIS, the most widely used anesthetic depth monitor, utilizes EEG signals to calculate anesthetic index at frontal area obtained from sensors attached to the forehead. Therefore, we selected the frontal brain area to monitor NIRS signals.
In order to obtain signals from brain itself, we used invasive measurement of NIRS and LFP from implanted electrodes and fiber-optic cannulae. This invasive measurement can minimize the motion artifacts and also signal contamination by skull and scalp since the signals are obtained directly from the brain. In this regard, it is expected that the results of this study can be used as a reference when using a non-invasive way of future research.
Cerebral blood volume (CBV) can be affected by hypercapnia due to respiratory depression during anesthesia [32]. In clinical practice, a ventilator is generally used to prevent respiratory depression and maintain the normal breathing of the patient during anesthesia. However, in this study, subjects were having a spontaneous breathing without a ventilator. Therefore, it will be important to consider the effect of CBV change on NIRS signals. To minimize the potential effect of CBV change on NIRS signals, we presented reflectance ratio value (RRI) as a new NIRS parameter. The RRI is not affected by CBV change since the reflectance of 730nm and 850nm was normalized by the reflectance of 800nm which is the isosbestic point of OHb and RHb. The trend of RRI value during anesthesia was similar to that of [OHb] from both AA and NAA group, suggesting that this parameter reflects the change of cerebral oxygen metabolism associated with anesthesia irrespective of the modes of ventilation.
To evaluate certain inhalation anesthetic’s effect on brain, minimum alveolar concentration (MAC) is a useful parameter. The MAC of an inhaled anesthetic is the alveolar concentration that prevents movement in 50% of patients in response to a noxious stimulus such as skin incision. MAC allows comparisons of potency between agents, and provides a standard for experimental evaluations. In clinics, concentration of inhalation anesthetic agent should be maintained 1 MAC during surgery [33].
1 MAC value of isoflurane for rat is about 1.5%. So, we could consider that 2.5% (1.67 MAC, nearly comparable to MAC-BAR (1.7-2.0 MAC) which is defined as the concentration required to block autonomic reflexes to noxious stimuli) of isoflurane is a high enough concentration attenuating autonomic responses completely from noxious stimuli for rat and 1.0% (0.67 MAC) is low enough to see how cerebral hemodynamics change during insufficient anesthetic condition [34]. Therefore, 1~2.5% of isoflurane can be considered as clinically relevant concentrations.
Since 1% of isoflurane concentration is corresponding to 0.67 MAC, there are high chance of rat to awake from anesthesia (AA group). The abrupt increase of [OHb] and [THb] at 1% isoflurane from AA group can be explained as follows. To recover from anesthesia, disorganized neuronal connections should be restored, thereby, increasing the possibility of neurovascular coupling [35]. This inevitably leads to an increase in cerebral blood flow which will increase [OHb], [THb] and also RRI value. In AA group, BSR values at 1% isoflurane also became close to the values of awake state which supports our explanation of sudden changes of NIRS parameters at 1% isoflurane.
This is clinically meaningful since this sudden increases of [OHb], [THb], and RRI can be an indicator that awake from anesthesia is imminent. Indeed, rats in AA group exhibited spontaneous movement within a few minutes after the observation of abrupt changes in NIRS signals. This suggests that NIRS signals can be used as anesthetic depth indicators as well as predictors of awake during general anesthesia. Integrating between various brain regions after discontinuation of anesthetics is necessary for recovery from anesthesia, requiring significant changes in neurovascular coupling and cerebral metabolism. In this sense, the current technique may give insight to elucidate the mechanism of recovery of consciousness from anesthesia and its long-term effect.
However, we note some limitations in this study. The effect on cerebral metabolism may be different depending on the type of inhalational or intravenous anesthetics [36]. Therefore, a further study is required to identify the usefulness of NIRS signals as an indicator of DOA depending on the types of anesthetics. Also, in this study, the quantitative relationship between anesthetic depth and changes of NIRS indicator could not be obtained from this result though we have found that NIRS signals proportionally changed with anesthetic levels. Direct pharmacodynamics relationship between inhaled anesthetics and NIRS signal including RRI can be investigated in a quantitative way, thereby monitoring DOA directly with NIRS. NIRS does not show the DOA information directly, only shows the relative change of hemodynamics associated with cerebral metabolism during anesthesia. However, it can be used as a complimentary device to monitor DOA especially when EEG provides erroneous DOA estimates with abnormal EEG. From the results, it looks feasible to prevent awakening during anesthesia by monitoring RRI changes. Future studies are needed to compare the performance of NIRS in evaluating DOA with index of NIRS combined with conventional methods.
4. Conclusion
NIRS was employed together with LFP during isoflurane inhalation to monitor the DOA in rats, and the results showed that [OHb], [RHb], and [THb] were linearly responded to the level of isoflurane concentration. The reflectance ratio between 730nm and 850nm, RRI, was calculated to minimize the effect of CBV during anesthesia and it was also well corresponded with the change of isoflurane concentration. Therefore, combining NIRS with EEG may provide a new anesthetic depth index in clinical settings in that NIRS signals change differentially depending on anesthetic depth.
Funding
SGER grant (NRF-2015R1D1A1A02062382) and Brain research program (NRF-2016M3C7A1905475) through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning; GIST Research Institute (GRI-GK0549A) in 2016; Biomedical Integrated Technology Research Project through a grant provided by GIST in 2016.
Acknowledgements
We are very grateful to Ms. Eloise Anguluan for her help in correcting grammar.
References and links
1. B. A. Orser, C. D. Mazer, and A. J. Baker, “Awareness during anesthesia,” CMAJ 178(2), 185–188 (2008). [CrossRef] [PubMed]
2. S. M. Wilson, “Electroencephalography in Relation to Anaesthesia,” Proc. R. Soc. Med. 50(2), 105–109 (1957). [PubMed]
3. L. T. D. Duarte and R. Â. Saraiva, “When the bispectral index (bis) can give false results,” Rev. Bras. Anestesiol. 59(1), 99–109 (2009). [CrossRef] [PubMed]
4. W. Heinke and S. Koelsch, “The effects of anesthetics on brain activity and cognitive function,” Curr. Opin. Anaesthesiol. 18(6), 625–631 (2005). [CrossRef] [PubMed]
5. K. K. Kaisti, J. W. Långsjö, S. Aalto, V. Oikonen, H. Sipilä, M. Teräs, S. Hinkka, L. Metsähonkala, and H. Scheinin, “Effects of sevoflurane, propofol, and adjunct nitrous oxide on regional cerebral blood flow, oxygen consumption, and blood volume in humans,” Anesthesiology 99(3), 603–613 (2003). [CrossRef] [PubMed]
6. M. T. Alkire, R. J. Haier, S. J. Barker, N. K. Shah, J. C. Wu, and Y. J. Kao, “Cerebral metabolism during propofol anesthesia in humans studied with positron emission tomography,” Anesthesiology 82(2), 393–403 (1995). [CrossRef] [PubMed]
7. K. Iwasaki, Y. Nomoto, M. Ishiwata, T. Yokota, and R. Ogawa, “Vital capacity induction with 8% sevoflurane and N2o causes cerebral hyperemia,” J. Anesth. 17(1), 3–7 (2003). [CrossRef] [PubMed]
8. A. T. Lovell, H. Owen-Reece, C. E. Elwell, M. Smith, and J. C. Goldstone, “Continuous measurement of cerebral oxygenation by near infrared spectroscopy during induction of anesthesia,” Anesth. Analg. 88(3), 554–558 (1999). [CrossRef] [PubMed]
9. B. Chance, E. Anday, S. Nioka, S. Zhou, L. Hong, K. Worden, C. Li, T. Murray, Y. Ovetsky, D. Pidikiti, and R. Thomas, “A novel method for fast imaging of brain function, non-invasively, with light,” Opt. Express 2(10), 411–423 (1998). [CrossRef] [PubMed]
10. K. Izzetoglu, S. Bunce, B. Onaral, K. Pourrezaei, and B. Chance, “Functional optical brain imaging using near-infrared during cognitive tasks,” Int. J. Hum. Comput. Interact. 17(2), 211–227 (2004). [CrossRef]
11. D. A. Boas, T. Gaudette, G. Strangman, X. Cheng, J. J. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” Neuroimage 13(1), 76–90 (2001). [CrossRef] [PubMed]
12. S. Nioka and B. Chance, “NIR spectroscopic detection of breast cancer,” Technol. Cancer Res. Treat. 4(5), 497–512 (2005). [CrossRef] [PubMed]
13. A. Villringer, J. Planck, C. Hock, L. Schleinkofer, and U. Dirnagl, “Near infrared spectroscopy (NIRS): a new tool to study hemodynamic changes during activation of brain function in human adults,” Neurosci. Lett. 154(1-2), 101–104 (1993). [CrossRef] [PubMed]
14. S. G. Simonson and C. A. Piantadosi, “Near-infrared spectroscopy. Clinical applications,” Crit. Care Clin. 12(4), 1019–1029 (1996). [CrossRef] [PubMed]
15. S. Ahn, T. Nguyen, H. Jang, J. G. Kim, and S. C. Jun, “Exploring neuro-physiological correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data,” Front. Hum. Neurosci. 10, 219 (2016). [CrossRef] [PubMed]
16. J. G. Kim, J. Lee, J. Roe, B. J. Tromberg, M. Brenner, and T. J. Walters, “Hemodynamic changes in rat leg muscles during tourniquet-induced ischemia-reperfusion injury observed by near-infrared spectroscopy,” Physiol. Meas. 30(7), 529–540 (2009). [CrossRef] [PubMed]
17. J. G. Kim and H. Liu, “Variation of haemoglobin extinction coefficients can cause errors in the determination of haemoglobin concentration measured by near-infrared spectroscopy,” Phys. Med. Biol. 52(20), 6295–6322 (2007). [CrossRef] [PubMed]
18. H. Liu, Y. Song, K. L. Worden, X. Jiang, A. Constantinescu, and R. P. Mason, “Noninvasive investigation of blood oxygenation dynamics of tumors by near-infrared spectroscopy,” Appl. Opt. 39(28), 5231–5243 (2000). [CrossRef] [PubMed]
19. J. G. Kim, J. Lee, S. B. Mahon, D. Mukai, S. E. Patterson, G. R. Boss, B. J. Tromberg, and M. Brenner, “Noninvasive monitoring of treatment response in a rabbit cyanide toxicity model reveals differences in brain and muscle metabolism,” J. Biomed. Opt. 17(10), 1050051 (2012). [CrossRef] [PubMed]
20. D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of optical pathlength through tissue from direct time of flight measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988). [CrossRef] [PubMed]
21. P. L. van den Broek, C. M. van Rijn, J. van Egmond, A. M. Coenen, and L. H. Booij, “An effective correlation dimension and burst suppression ratio of the EEG in rat. Correlation with sevoflurane induced anaesthetic depth,” Eur. J. Anaesthesiol. 23(5), 391–402 (2006). [CrossRef] [PubMed]
22. W. O. Tatum, “Ellen R. Grass Lecture: Extraordinary EEG,” Neurodiagn. J. 54(1), 3–21 (2014). [PubMed]
23. A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997). [CrossRef] [PubMed]
24. K. Izzetoglu, S. Bunce, M. Izzetoglu, B. Onaral, and K. Pourrezaei, “fNIR spectroscopy as a measure of cognitive task load,” in Engineering in Medicine and Biology Society,2003.Proceedings of the 25th Annual International Conference of the IEEE, (IEEE, 2003), 3431–3434. [CrossRef]
25. L. Uhrig, S. Dehaene, and B. Jarraya, “Cerebral mechanisms of general anesthesia,” in Annales francaises d'anesthesie et de reanimation, (Elsevier, 2014), 72–82.
26. E. I. Eger 2nd, “A brief history of the origin of minimum alveolar concentration (MAC),” Anesthesiology 96(1), 238–239 (2002). [CrossRef] [PubMed]
27. E. I. Eger 2nd, L. J. Saidman, and B. Brandstater, “Minimum Alveolar Anesthetic Concentration: A Standard of Anesthetic Potency,” Anesthesiology 26(6), 756–763 (1965). [CrossRef] [PubMed]
28. B. Walder, R. Lauber, and A. M. Zbinden, “Accuracy and cross-sensitivity of 10 different anesthetic gas monitors,” J. Clin. Monit. 9(5), 364–373 (1993). [CrossRef] [PubMed]
29. C. J. Meulenberg and H. P. Vijverberg, “Empirical relations predicting human and rat tissue:air partition coefficients of volatile organic compounds,” Toxicol. Appl. Pharmacol. 165(3), 206–216 (2000). [CrossRef] [PubMed]
30. A. Muzur, E. F. Pace-Schott, and J. A. Hobson, “The prefrontal cortex in sleep,” Trends Cogn. Sci. (Regul. Ed.) 6(11), 475–481 (2002). [CrossRef] [PubMed]
31. C. Libedinsky and M. Livingstone, “Role of prefrontal cortex in conscious visual perception,” J. Neurosci. 31(1), 64–69 (2011). [CrossRef] [PubMed]
32. H. Ito, I. Kanno, M. Ibaraki, J. Hatazawa, and S. Miura, “Changes in human cerebral blood flow and cerebral blood volume during hypercapnia and hypocapnia measured by positron emission tomography,” J. Cereb. Blood Flow Metab. 23(6), 665–670 (2003). [CrossRef] [PubMed]
33. A. L. Quasha, E. I. Eger 2nd, and J. H. Tinker, “Determination and applications of MAC,” Anesthesiology 53(4), 315–334 (1980). [CrossRef] [PubMed]
34. E. I. Eger 2nd, Y. Xing, M. Laster, J. Sonner, J. F. Antognini, and E. Carstens, “Halothane and isoflurane have additive minimum alveolar concentration (MAC) effects in rats,” Anesth. Analg. 96(5), 1350–1353 (2003). [CrossRef] [PubMed]
35. A. G. Hudetz, “General anesthesia and human brain connectivity,” Brain Connect. 2(6), 291–302 (2012). [CrossRef] [PubMed]
36. H. Van Aken and J. Van Hemelrijck, “Influence of anesthesia on cerebral blood flow and cerebral metabolism: an overview,” Agressologie 32(6-7), 303–306 (1991). [PubMed]
