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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 7 — Jul. 1, 2014
  • pp: 2184–2195
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Exploring diazepam’s effect on hemodynamic responses of mouse brain tissue by optical spectroscopic imaging

David Abookasis, Ariel Shochat, Elimelech Nesher, and Albert Pinhasov  »View Author Affiliations


Biomedical Optics Express, Vol. 5, Issue 7, pp. 2184-2195 (2014)
http://dx.doi.org/10.1364/BOE.5.002184


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Abstract

In this study, a simple duel-optical spectroscopic imaging apparatus capable of simultaneously determining relative changes in brain oxy-and deoxy-hemoglobin concentrations was used following administration of the anxiolytic compound diazepam in mice with strong dominant (Dom) and submissive (Sub) behavioral traits. Three month old mice (n = 30) were anesthetized and after 10 min of baseline imaging, diazepam (1.5 mg/kg) was administered and measurements were taken for 80 min. The mouse head was illuminated by white light based LED's and diffused reflected light passing through different channels, consisting of a bandpass filter and a CCD camera, respectively, was collected and analyzed to measure the hemodynamic response. This work’s major findings are threefold: first, Dom and Sub animals showed statistically significant differences in hemodynamic response to diazepam administration. Secondly, diazepam was found to more strongly affect the Sub group. Thirdly, different time-series profiles were observed post-injection, which can serve as a possible marker for the groups’ differentiation. To the best of our knowledge, this is the first report on the effects of an anxiolytic drug on brain hemodynamic responses in mice using diffused light optical imaging.

© 2014 Optical Society of America

1. Introduction

Depressive and anxiety disorders are clinically problematic with a majority of cases showing multiple recurrences and frequent progression to the chronic stage [1

1. R. C. Kessler, P. Berglund, O. Demler, R. Jin, K. R. Merikangas, and E. E. Walters, “Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication,” Arch. Gen. Psychiatry 62(6), 593–602 (2005). [CrossRef] [PubMed]

, 2

2. R. C. Kessler, A. M. Ruscio, K. Shear, and H. U. Wittchen, “Epidemiology of anxiety disorders,” Curr. Top. Behav. Neurosci. 2, 21–35 (2009). [CrossRef] [PubMed]

]. Unfortunately, our limited understanding of the neurobiology of these disorders hinders the development of new therapies and therapeutic strategies [3

3. V. Krishnan and E. J. Nestler, “The molecular neurobiology of depression,” Nature 455(7215), 894–902 (2008). [CrossRef] [PubMed]

]. Although treatment success rates for depressive and anxiety disorders have increased over the past two decades, they still remain low [4

4. M. T. Berlim, M. P. Fleck, and G. Turecki, “Current trends in the assessment and somatic treatment of resistant/refractory major depression: an overview,” Ann. Med. 40(2), 149–159 (2008). [CrossRef] [PubMed]

]. Therapeutic compounds’ efficacy is assessed mainly based on behavioral changes in both preclinical and clinical studies, while objective physiological parameters of treatment’s success have yet to be developed. Functional MRI (fMRI) studies of psychotropic drugs administration as well as electroencephalograms (EEGs) mapping show neural activity patterns that are congruent with the behavioral changes [5

5. C. J. Harmer, G. M. Goodwin, and P. J. Cowen, “Why do antidepressants take so long to work? A cognitive neuropsychological model of antidepressant drug action,” Br. J. Psychiatry 195(2), 102–108 (2009). [CrossRef] [PubMed]

7

7. R. G. Wise, B. J. Lujan, P. Schweinhardt, G. D. Peskett, R. Rogers, and I. Tracey, “The anxiolytic effects of midazolam during anticipation to pain revealed using fMRI,” Magn. Reson. Imaging 25(6), 801–810 (2007). [CrossRef] [PubMed]

]. The effect of diazepam has been investigated recently with the help of positron emission tomography (PET) and computed tomography (CT) [8

8. R. Rakheja, A. Ciarallo, Y. Z. Alabed, and M. Hickeson, “Intravenous administration of diazepam significantly reduces brown fat activity on 18F-FDG PET/CT,” Am. J. Nucl. Med. Mol. Imaging 1(1), 29–35 (2011). [PubMed]

]. Diazepam, a member of the benzodiazepine family, acts through modulation of GABAergic neurotransmission by producing anxiolytic, sedative or/and anticonvulsive effects [9

9. J. Divljaković, M. Milić, T. Timić, and M. M. Savić, “Tolerance liability of diazepam is dependent on the dose used for protracted treatment,” Pharmacol. Rep. 64(5), 1116–1125 (2012). [CrossRef] [PubMed]

]. It is used in treatment of disorders characterized by anxiety, agitation, tremors, delirium, seizures, as well as hallucinations resulting from alcohol withdrawal [10

10. M. P. Peppers, “Benzodiazepines for alcohol withdrawal in the elderly and in patients with liver disease,” Pharmacotherapy 16(1), 49–57 (1996). [PubMed]

, 11

11. K. Rickels, E. Schweizer, N. DeMartinis, L. Mandos, and C. Mercer, “Gepirone and diazepam in generalized anxiety disorder: a placebo-controlled trial,” J. Clin. Psychopharmacol. 17(4), 272–277 (1997). [CrossRef] [PubMed]

]. It is also used to relieve muscle spasms in some neurological diseases, and for sedation during surgery [9

9. J. Divljaković, M. Milić, T. Timić, and M. M. Savić, “Tolerance liability of diazepam is dependent on the dose used for protracted treatment,” Pharmacol. Rep. 64(5), 1116–1125 (2012). [CrossRef] [PubMed]

]. While the above mentioned techniques have greatly advanced our ability to understand brain function and the effects of drug administration on brain physiology, each of them suffer from certain constraints (expensive and not portable, require physical restraint, are complex in structure, etc.) or do not provide quantitative information regarding chromophore concentration levels, such as oxyhemoglobin (HbO2) and deoxyhemoglobin (Hbr). Hence, the influence of the psychotropic drugs on brain hemodynamics needs to be more clearly understood.

The limitations and drawbacks of conventional neuro-imaging techniques have led to extensive interest in optical diagnosis techniques such as diffuse optical imaging (DOI). DOI has gained great interest due to its unique properties, such as the ability to assess real-time changes in the concentration of hemoglobin in finer resolution than the aforementioned methodologies [12

12. D. A. Boas, C. Pitris, and N. Ramanujam, Handbook of Biomedical Optics (CRC Press, Boca Raton, 2011).

]. DOI techniques have been successfully applied in medicine and biomedical research to monitor normal and abnormal tissue structure and function. Particularly, DOI has been used in clinical diagnostics and management in different fields of neuroscience such as neurology, neurosurgery, psychiatry, and rehabilitation [13

13. E. Watanabe, A. Maki, F. Kawaguchi, Y. Yamashita, H. Koizumi, and Y. Mayanagi, “Noninvasive cerebral blood volume measurement during seizures using multichannel near infrared spectroscopic topography,” J. Biomed. Opt. 5(3), 287–290 (2000). [CrossRef] [PubMed]

17

17. M. Costantini, A. Di Vacri, A. M. Chiarelli, F. Ferri, G. Luca Romani, and A. Merla, “Studying social cognition using near-infrared spectroscopy: the case of social Simon effect,” J. Biomed. Opt. 18(2), 025005 (2013). [CrossRef] [PubMed]

]. Within this context: (1) biological tissues are relatively transparent to light in the near infrared (NIR) range between 650 and 950nm [18

18. G. Strangman, D. A. Boas, and J. P. Sutton, “Non-invasive neuroimaging using near-infrared light,” Biol. Psychiatry 52(7), 679–693 (2002). [CrossRef] [PubMed]

]; (2) there is high correlation between brain activities to changes in brain tissue’s optical properties dependent upon hemodynamics [19

19. R. D. Frostig, In vivo Optical Imaging of Brain Function, 2nd ed. (CRC Press, Boca Raton, 2009).

]; (3) neural activity can be detected through the skull surface [20

20. G. Gratton, P. M. Corballis, E. Cho, M. Fabiani, and D. C. Hood, “Shades of gray matter: noninvasive optical images of human brain responses during visual stimulation,” Psychophysiology 32(5), 505–509 (1995). [CrossRef] [PubMed]

23

23. M. Izzetoglu, S. C. Bunce, K. Izzetoglu, B. Onaral, and K. Pourrezaei, “Functional brain imaging using near-infrared technology,” IEEE Eng. Med. Biol. Mag. 26(4), 38–46 (2007). [CrossRef] [PubMed]

]; and (4) optical techniques have some unique properties: being noninvasive and inexpensive, offer unsurpassed high spatiotemporal resolution, are portable and relatively low-cost, require minimal patient restraint, etc.. These traits offer advantages in comparison to conventional functional neuroimaging methodologies.

This manuscript describes an attempt to monitor variations in brain hemodynamics in response to drug administration. This is obtained by applying a white light source to the scalp followed by the acquisition of a series of images of the diffuse reflected light filtered at 470 and 650 nm, which is mainly sensitive to HbO2 and Hbr concentrations, respectively. We analyzed these images to examine the spatiotemporal characteristics of a set of hemodynamic parameters. We observed distinct hemodynamic reactions between groups of animals in response to diazepam over time. It should be stressed that following imaging, for example at 470nm, the achieved data will include absorption from both HbO2 and Hbr. However, since HbO2 is approximately ten-fold more absorptive than Hbr, we can consider the acquired image to be mainly influenced by HbO2. One can reduce the mutual influence between the two using frequency or time domain approaches [12

12. D. A. Boas, C. Pitris, and N. Ramanujam, Handbook of Biomedical Optics (CRC Press, Boca Raton, 2011).

].

Our hypothesis was tested on mice with genetic predisposition to strong dominant and submissive behavioral phenotype [29

29. Y. Feder, E. Nesher, A. Ogran, A. Kreinin, E. Malatynska, G. Yadid, and A. Pinhasov, “Selective breeding for dominant and submissive behavior in Sabra mice,” J. Affect. Disord. 126(1-2), 214–222 (2010). [CrossRef] [PubMed]

, 30

30. E. Nesher, M. Gross, S. Lisson, T. Tikhonov, G. Yadid, and A. Pinhasov, “Differential responses to distinct psychotropic agents of selectively bred dominant and submissive animals,” Behav. Brain Res. 236(1), 225–235 (2013). [PubMed]

]. These animals were developed based on dominant-submissive relationship model used for psychotropic drug screening [31

31. A. Pinhasov, J. Crooke, D. Rosenthal, D. Brenneman, and E. Malatynska, “Reduction of Submissive Behavior Model for antidepressant drug activity testing: study using a video-tracking system,” Behav. Pharmacol. 16(8), 657–664 (2005). [CrossRef] [PubMed]

34

34. E. Malatynska, A. Pinhasov, C. J. Creighton, J. J. Crooke, A. B. Reitz, D. E. Brenneman, and M. S. Lubomirski, “Assessing activity onset time and efficacy for clinically effective antidepressant and antimanic drugs in animal models based on dominant-submissive relationships,” Neurosci. Biobehav. Rev. 31(6), 904–919 (2007). [CrossRef] [PubMed]

]. Our recent studies demonstrated that these animals differentially react to acute diazepam administration: while submissive animals showed obvious anxiolytic and sedative response to diazepam administration dominant mice demonstrated paradoxical response that was manifested in anxiogenic and hyperactive behavior [30

30. E. Nesher, M. Gross, S. Lisson, T. Tikhonov, G. Yadid, and A. Pinhasov, “Differential responses to distinct psychotropic agents of selectively bred dominant and submissive animals,” Behav. Brain Res. 236(1), 225–235 (2013). [PubMed]

]. Thus, we hypothesized that differential behavioral response of these animals to diazepam may be also reflected in the changes in hemodynamic parameters.

2. Material and methods

2.1. Animal and experimental protocol

This study was carried out using a protocol approved by the Institute Animal Care and Use Committee of Ariel University. The populations of selectively bred dominant (Dom) and submissive (Sub) mice (n = 30, males, mean weight = 45 gr, 3 month old) used in this study are descendants of the outbred Sabra strain. Animal’s behavioral features of dominance and submissiveness are confirmed using dominant-submissive relationship (DSR) test. Details of the behavioral procedure and the breeding have been published previously [29

29. Y. Feder, E. Nesher, A. Ogran, A. Kreinin, E. Malatynska, G. Yadid, and A. Pinhasov, “Selective breeding for dominant and submissive behavior in Sabra mice,” J. Affect. Disord. 126(1-2), 214–222 (2010). [CrossRef] [PubMed]

]. In addition to the Dom and Sub groups, background wild-type (WT) Sabra mice [35

35. E. Nesher, V. Peskov, A. Rylova, O. Raz, and A. Pinhasov, “Comparative analysis of the behavioral and biomolecular parameters of four mouse strains,” J. Mol. Neurosci. 46(2), 276–284 (2012). [CrossRef] [PubMed]

] were used as a control group.

A solution of Xylazine (Xylazine 20 Inj. Kepro B.V., Netherlands) was used in ratio 1:6 to Ketamine (Ketanest. Fort Dodge, USA), further diluted 1:1 with Saline (NaCl, 0.9%) for anesthesia kept the mouse motionless throughout the experiment. Mice received intraperitoneal injection according their weight (6 µl of total solution per gram of mouse). The depth of anesthesia was ascertained by pinching of the toes or tail and by monitoring rate of breathing. Hair causes a multiple scattering which affect the captured photon density and contributes to a large fraction of the overall light. These cause image degradation and blurring which reduce the ability to observe the head surface by the camera. In order to obtain a clear image, hair was removed to increase efficacy of light transmission and reflection [36

36. J. T. Moon and S. R. Marschner, “Simulating multiple scattering in hair using a photon mapping approach,” ACM Trans. Graph. 25(3), 1067–1074 (2006). [CrossRef]

, 37

37. F. H. Mustafa and M. S. Jaafar, “Shaving area of unwanted hair before laser operation is useful in cosmetic procedure: A simulation study,” J. Saudi Society Dermat. Surg. (Article in Press).

]. A folded heating plate was placed under the mouse to keep the body temperature at a constant level of ~34°C and a thermocouple rectal probe (YSI) was inserted to measure core body temperature. Other physiological (systematic) parameters such as heart rate and arterial oxygen saturation (SpO2) were continuously monitored independently through a veterinary pulse oximeter (Nonin 8600, Plymouth, MN, USA) attached to the forelimb.

The experimental protocol consisted of a 10 min baseline reflectance images recording of the head, injection of diazepam (1.5 mg/kg) without interrupting the optical monitoring, and 80 min of reflectance measurements post diazepam injection. After the experiments, the animals were euthanized by carbon dioxide (CO2).

2.2. Experimental setup

Figure 1
Fig. 1 Sketch of the dual-imaging setup. BPF, band pass filter; BS, beam splitter; L, lens (f = 100 mm). Representative images with the selected ROI are shown for each channel. ROI size: 50 × 60 pixels correspond to ~5mm × 5mm. The same ROI is observed simultaneously on both cameras.
illustrates the imaging system. Collimated white light based LED’s technology shining the head (~30mm in diameter) at an incident angle of ~30 deg off the normal to the head surface. The diffusely reflected light (intrinsic optical signals) from the head, which embodies tissue physiological properties, is split equally into two directions (channels) using 50:50 plate beamsplitter (BS) (Thorlabs, BSW10R). Since in mouse the layers of the head are optically thin and highly transparent to light, we consider the diffuse reflected signals to have originated from the brain itself. Each channel consists of different 10nm wide optical bandpass filter (BPF, Thorlabs) of 470nm and 650nm, respectively and 14-bit CCD camera (GuppyPRO F-013B, Allied Vision Technology, 480 × 640 pixels resolution, Germany) equipped with a zoom lens system (Computar, 75-150 mm, f#/2.8, New York, USA). In that way, the diffusely reflected light is first filtered accordingly and then recorded simultaneously by the CCD cameras which view the same region through the BS. Imaging acquisition, synchronization, and data processing are achieved using software implemented in the Matlab platform (Version 2010a, The MathWorks, Inc., Natick, Massachusetts) controlled via personal computer (Intel Core, E6750). The CCD gain and image integration time were manually adjusted during imaging. The setup is similar to one presented previously [38

38. J. Qin, L. Shi, S. Dziennis, R. Reif, and R. K. Wang, “Fast synchronized dual-wavelength laser speckle imaging system for monitoring hemodynamic changes in a stroke mouse model,” Opt. Lett. 37(19), 4005–4007 (2012). [CrossRef] [PubMed]

, 39

39. P. B. Jones, H. K. Shin, D. A. Boas, B. T. Hyman, M. A. Moskowitz, C. Ayata, and A. K. Dunn, “Simultaneous multispectral reflectance imaging and laser speckle flowmetry of cerebral blood flow and oxygen metabolism in focal cerebral ischemia,” J. Biomed. Opt. 13(4), 044007 (2008). [CrossRef] [PubMed]

] but differs in its light source engine and configuration, channel filtering, data processing, and application.

2.3. Hemodynamics quantification

2.4. Data analysis

Raw images from the camera were processed by in-house software developed in Matlab. Prior to data analysis, the collected diffuse raw images were first normalized to overcome the nonlinearity of the camera quantum efficiency at the above wavelengths and second, filtered (fspecial function in MATLAB) and averaged to reduce physiological fluctuations such as pulsations of the brain due to respiration, heartbeat, and arterial oscillations, and to eliminate high frequency noise originated from the camera during recording. For measurement of small fluctuations in reflectance, a simple algorithm based on a ratio that provides diffuse reflectance signal change in terms of fractional change from baseline was implemented. Consecutive ten baseline raw images were captured and averaged together (Io) and image at the i'th time point (Ii) was subtracted and divided by Io based on the following calculation:ΔR=ΔI/IO where ΔI=IiIO, yielding a reflectance image in dimensionless units. ΔR represents the mean percent change in reflectance signal and it is inversely proportional to light absorption; reflectance increase absorption decrease and vise verse. Since optical intrinsic signal are usually very small or noisy we calculated the changes in light reflectance from concomitantly collected images in order to emphasize signal intensity and to improve signal-to-noise ratio. Because we were not concerned with real-time display of results, data analysis was performed off-line.

2.5. Statistical analysis

Statistical significance of difference between pre- and post-drug administration and between different mice groups was evaluated using one-way ANOVA with Bonferroni-corrected post-hoc t-test analysis (GraphPad Prism software). Statistical differences are shown as * at p < 0.05, ** at p < 0.01, and *** at p < 0.001. All data are given as mean value ± standard deviation.

3. Results and discussion

Figure 2
Fig. 2 Changes in brain hemoglobin concentrations over time following diazepam administration in: (a) wild-type (WT, n=10), (b) dominant (Dom, n=10) and (c) submissive (Sub, n=10) animals. The change in diffuse reflectance ∆R in 470nm is proportional to HbO2 while at 650nm to Hbr. Data is presented as mean plus standard deviation (error bar) normalized to baseline. The response in diffuse reflectance to diazepam differed markedly between the three groups.
shows a plot of the time courses of ΔR calculated over the ROI (dashed boxed region appears in Fig. 1) both before and after i.p. administration of diazepam at different wavelengths for the different animal groups. The mean value for each time point was taken from ten mice for the λ = 470nm (HbO2) and 650nm (Hbr). The error bars (standard deviation) represent the variation in the calculated mean reflectance over ROI. These variations can be explained by the individual differences (e.g., behavior, weight, level of dominancy), unexpected variances (breathing, grasping, uncontrolled general activity, etc.), partial volume effect, etc. As can be seen, in contrast to the WT (Fig. 2(a)), Dom (Fig. 2(b)) and Sub (Fig. 2(c)) groups produce opposing patterns of ΔHbO2 and ΔHbr. Moreover, while ΔHbO2 and ΔHbr levels of WT mice remain relatively constant overtime, these values for Dom and Sub mice deviated from the baseline. Among the two, ΔHbr in the Sub group strongly deviated from baseline (~3 times than ΔHbO2). As shown in Fig. 2, the levels of ΔHbr in Sub mice were lower prior to diazepam administration in comparison to Dom and WT groups. Even more, these levels of ΔHbr were reduced in Sub mice in comparison to WT and Dom groups following injection. This is an interesting observation suggesting that submissiveness may correlate with elevated hemoglobin oxygenation levels. The strong changes in ΔHbr seen among Sub mice in comparison to other groups may be supported by our recent study showing differential effects of diazepam on these mice [30

30. E. Nesher, M. Gross, S. Lisson, T. Tikhonov, G. Yadid, and A. Pinhasov, “Differential responses to distinct psychotropic agents of selectively bred dominant and submissive animals,” Behav. Brain Res. 236(1), 225–235 (2013). [PubMed]

]. In general, the changes of ΔHbr concentration are of particular clinical interest in most of the optical modalities as it is closely linked to the BOLD contrast used in fMRI [56

56. J. Berwick, C. Martin, J. Martindale, M. Jones, D. Johnston, Y. Zheng, P. Redgrave, and J. Mayhew, “Hemodynamic response in the unanesthetized rat: intrinsic optical imaging and spectroscopy of the barrel cortex,” J. Cereb. Blood Flow Metab. 22(6), 670–679 (2002). [CrossRef] [PubMed]

]. It is worth noticing that following injection, the ΔHbO2 gradually increased over time in both Dom (Fig. 2(b)) and Sub (Fig. 2(c)) mice, while no change in ΔHbO2 for WT (Fig. 2(a)) were observed.

In addition, in the Dom and Sub groups, the maximum change in reflectance for both wavelengths is reached about ~50 minutes post-injection, while in the WT no such change over time is observed. Importantly, each group displays a unique time-response profile which may serve as a possible marker to distinguish between the groups. This hypothesis remains to be tested thoroughly in the future with larger animal populations. In all recordings presented in Fig. 2, the measurement noise level was also evaluated by calculation of the coefficient of variation (CV) defined as the ratio of standard deviation and the mean value. The CV was found to be lower than 1.5%.

Figure 3(a)
Fig. 3 (a) Bar graph summarizing the mean concentrations of ΔHbO2 and ΔHbr for wild type (WT), dominant (Dom) and submissive (Sub) mice group, normalized to baseline. A significant increase in the difference between ΔHbr to ΔHbO2 in the Sub group is seen. The error bars represent standard deviation. The statistical significance between animal groups for ΔHbO2 (b) and ΔHbr (c) was assessed using one-way ANOVA with post-hoc Bonferroni test, indicated by (***) at p <0.001 (n = 10/group).
presents the average of ∆HbO2 and ∆Hbr over time for different groups derived from Fig. 2. Significant differences between ∆HbO2 (Fig. 3(b)) and ∆Hbr (Fig. 3(c)) among the groups with p<0.001 were found. A differential diazepam-induced oxy/deoxy hemoglobin response (increase in ∆Hbr and a decrease in ∆HbO2) was seen among the distinct animal populations. We assume that these changes may result from differential modulation of the GABAergic system induced by diazepam upon genetically distinct animal groups. This in turn may cause changes in hemoglobin fractions. As mentioned above, from the clinical point of interest, the information presented here on ΔHbr is the basis of the BOLD signal in fMRI studies.

Time traces for the changes in additional physiologically important parameters (THC, StO2) for each group were calculated as mentioned earlier (subsection 2.3) and are plotted in Fig. 4
Fig. 4 Time course of changes in (a) THC and (b) StO2 parameters extracted from the raw data of HbO2 and Hbr for the wild type (WT), dominant (Dom) and submissive (Sub) mice group. Each data point represents the mean ± standard deviation (error bar). These graphs highlight the difference of the submissive group in comparison to others.
. The percent change in THC in the Sub group (Fig. 4(a)) was found to be more than 5% lower from the rest; the THC in both Dom and WT groups did not remarkably change between pre and post injection. At the same time, StO2 in the Sub group (Fig. 4(b)) increased gradually over time respective to its pre-injection, reaching the range of the other groups. In addition, StO2 and THC levels in Sub mice were significantly lower in comparison to others pre-injection. We may speculate that these changes in StO2 and THC over time may indicate that Sub animals’ glucose metabolism may differ from that of the other groups. This intriguing phenomena should be further elucidated in future study.

Figure 5
Fig. 5 a) Bar graph shows a comparison of the mean THC and mean StO2 pre- and post-diazepam injection for wild type (WT), dominant (Dom) and submissive (Sub) mice group. Sub animals distinctively differ from the other two groups. The statistical significances between animal groups for THC pre-injection (b) and post-injection (c), as well as for StO2 pre-injection (d) and post-injection (e) were assessed using one-way ANOVA with post-hoc Bonferroni test, indicated by (***) at p <0.001 and ns as non-significant (n=10/group).
shows averages THC and StO2 levels of each animal group, before and after diazepam treatment. While no significant differences were found in THC levels pre-injection (Fig. 5(b)) and in StO2 levels post-injection (Fig. 5(e)) between Dom and WT groups, all other compared groups show highly significant differences (Fig. 5(a)-5(e)). As mentioned above, diazepam induced distinct hemodynamic changes among mice groups. Taken together, the above observations demonstrate that the proposed optical platform used here may be valuable in neuroscience rodent research for the evaluation of drug administration’s effects as well as changes in brain hemodynamics in pathological and physiological conditions.

4. Conclusions

References and links

1.

R. C. Kessler, P. Berglund, O. Demler, R. Jin, K. R. Merikangas, and E. E. Walters, “Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication,” Arch. Gen. Psychiatry 62(6), 593–602 (2005). [CrossRef] [PubMed]

2.

R. C. Kessler, A. M. Ruscio, K. Shear, and H. U. Wittchen, “Epidemiology of anxiety disorders,” Curr. Top. Behav. Neurosci. 2, 21–35 (2009). [CrossRef] [PubMed]

3.

V. Krishnan and E. J. Nestler, “The molecular neurobiology of depression,” Nature 455(7215), 894–902 (2008). [CrossRef] [PubMed]

4.

M. T. Berlim, M. P. Fleck, and G. Turecki, “Current trends in the assessment and somatic treatment of resistant/refractory major depression: an overview,” Ann. Med. 40(2), 149–159 (2008). [CrossRef] [PubMed]

5.

C. J. Harmer, G. M. Goodwin, and P. J. Cowen, “Why do antidepressants take so long to work? A cognitive neuropsychological model of antidepressant drug action,” Br. J. Psychiatry 195(2), 102–108 (2009). [CrossRef] [PubMed]

6.

G. R. McClelland and P. Raptopoulos, “EEG and blood level of the potential antidepressant paroxetine after a single oral dose to normal volunteers,” Psychopharmacology (Berl.) 83(4), 327–329 (1984). [CrossRef] [PubMed]

7.

R. G. Wise, B. J. Lujan, P. Schweinhardt, G. D. Peskett, R. Rogers, and I. Tracey, “The anxiolytic effects of midazolam during anticipation to pain revealed using fMRI,” Magn. Reson. Imaging 25(6), 801–810 (2007). [CrossRef] [PubMed]

8.

R. Rakheja, A. Ciarallo, Y. Z. Alabed, and M. Hickeson, “Intravenous administration of diazepam significantly reduces brown fat activity on 18F-FDG PET/CT,” Am. J. Nucl. Med. Mol. Imaging 1(1), 29–35 (2011). [PubMed]

9.

J. Divljaković, M. Milić, T. Timić, and M. M. Savić, “Tolerance liability of diazepam is dependent on the dose used for protracted treatment,” Pharmacol. Rep. 64(5), 1116–1125 (2012). [CrossRef] [PubMed]

10.

M. P. Peppers, “Benzodiazepines for alcohol withdrawal in the elderly and in patients with liver disease,” Pharmacotherapy 16(1), 49–57 (1996). [PubMed]

11.

K. Rickels, E. Schweizer, N. DeMartinis, L. Mandos, and C. Mercer, “Gepirone and diazepam in generalized anxiety disorder: a placebo-controlled trial,” J. Clin. Psychopharmacol. 17(4), 272–277 (1997). [CrossRef] [PubMed]

12.

D. A. Boas, C. Pitris, and N. Ramanujam, Handbook of Biomedical Optics (CRC Press, Boca Raton, 2011).

13.

E. Watanabe, A. Maki, F. Kawaguchi, Y. Yamashita, H. Koizumi, and Y. Mayanagi, “Noninvasive cerebral blood volume measurement during seizures using multichannel near infrared spectroscopic topography,” J. Biomed. Opt. 5(3), 287–290 (2000). [CrossRef] [PubMed]

14.

R. P. Kennan, D. Kim, A. Maki, H. Koizumi, and R. T. Constable, “Non-invasive assessment of language lateralization by transcranial near infrared optical topography and functional MRI,” Hum. Brain Mapp. 16(3), 183–189 (2002). [CrossRef] [PubMed]

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K. Matsuo, T. Kato, M. Fukuda, and N. Kato, “Alteration of hemoglobin oxygenation in the frontal region in elderly depressed patients as measured by near-infrared spectroscopy,” J. Neuropsychiatry Clin. Neurosci. 12(4), 465–471 (2000). [CrossRef] [PubMed]

16.

P. M. Arenth, J. H. Ricker, and M. T. Schultheis, “Applications of functional near-infrared spectroscopy (fNIRS) to Neurorehabilitation of cognitive disabilities,” Clin. Neuropsychol. 21(1), 38–57 (2007). [CrossRef] [PubMed]

17.

M. Costantini, A. Di Vacri, A. M. Chiarelli, F. Ferri, G. Luca Romani, and A. Merla, “Studying social cognition using near-infrared spectroscopy: the case of social Simon effect,” J. Biomed. Opt. 18(2), 025005 (2013). [CrossRef] [PubMed]

18.

G. Strangman, D. A. Boas, and J. P. Sutton, “Non-invasive neuroimaging using near-infrared light,” Biol. Psychiatry 52(7), 679–693 (2002). [CrossRef] [PubMed]

19.

R. D. Frostig, In vivo Optical Imaging of Brain Function, 2nd ed. (CRC Press, Boca Raton, 2009).

20.

G. Gratton, P. M. Corballis, E. Cho, M. Fabiani, and D. C. Hood, “Shades of gray matter: noninvasive optical images of human brain responses during visual stimulation,” Psychophysiology 32(5), 505–509 (1995). [CrossRef] [PubMed]

21.

D. A. Benaron, S. R. Hintz, A. Villringer, D. Boas, A. Kleinschmidt, J. Frahm, C. Hirth, H. Obrig, J. C. van Houten, E. L. Kermit, W. F. Cheong, and D. K. Stevenson, “Noninvasive functional imaging of human brain using light,” J. Cereb. Blood Flow Metab. 20(3), 469–477 (2000). [CrossRef] [PubMed]

22.

A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997). [CrossRef] [PubMed]

23.

M. Izzetoglu, S. C. Bunce, K. Izzetoglu, B. Onaral, and K. Pourrezaei, “Functional brain imaging using near-infrared technology,” IEEE Eng. Med. Biol. Mag. 26(4), 38–46 (2007). [CrossRef] [PubMed]

24.

L. V. Wang and H.-i. Wu, Biomedical optics: principles and imaging. (Wiley-Interscience, Hoboken, N.J., 2007).

25.

J. G. Fujimoto and D. L. Farkas, Biomedical Optical Imaging (Oxford University Press, Oxford; New York, 2009).

26.

F. Crespi, M. Donini, A. Bandera, F. Congestri, F. Formenti, V. Sonntag, C. Heidbreder, and L. Rovati, “Near-infrared oxymeter biosensor prototype for non-invasive in vivo analysis of rat brain oxygenation: effects of drugs of abuse,” J. Opt. A, Pure Appl. Opt. 8(7), 528 (2006). [CrossRef]

27.

K. Kohmura, K. Iwamoto, B. Aleksic, K. Sasada, N. Kawano, H. Katayama, Y. Noda, A. Noda, T. Iidaka, and N. Ozaki, “Effects of sedative antidepressants on prefrontal cortex activity during verbal fluency task in healthy subjects: a near-infrared spectroscopy study,” Psychopharmacology (Berl.) 226(1), 75–81 (2013). [CrossRef] [PubMed]

28.

E. A. Verhagen, E. M. Kooi, P. P. van den Berg, and A. F. Bos, “Maternal antihypertensive drugs may influence cerebral oxygen extraction in preterm infants during the first days after birth,” J. Matern. Fetal Neonatal Med. 26(9), 871–876 (2013). [CrossRef] [PubMed]

29.

Y. Feder, E. Nesher, A. Ogran, A. Kreinin, E. Malatynska, G. Yadid, and A. Pinhasov, “Selective breeding for dominant and submissive behavior in Sabra mice,” J. Affect. Disord. 126(1-2), 214–222 (2010). [CrossRef] [PubMed]

30.

E. Nesher, M. Gross, S. Lisson, T. Tikhonov, G. Yadid, and A. Pinhasov, “Differential responses to distinct psychotropic agents of selectively bred dominant and submissive animals,” Behav. Brain Res. 236(1), 225–235 (2013). [PubMed]

31.

A. Pinhasov, J. Crooke, D. Rosenthal, D. Brenneman, and E. Malatynska, “Reduction of Submissive Behavior Model for antidepressant drug activity testing: study using a video-tracking system,” Behav. Pharmacol. 16(8), 657–664 (2005). [CrossRef] [PubMed]

32.

E. Malatynska, A. Pinhasov, J. J. Crooke, V. L. Smith-Swintosky, and D. E. Brenneman, “Reduction of dominant or submissive behaviors as models for antimanic or antidepressant drug testing: technical considerations,” J. Neurosci. Methods 165(2), 175–182 (2007). [CrossRef] [PubMed]

33.

A. Moussaieff, M. Gross, E. Nesher, T. Tikhonov, G. Yadid, and A. Pinhasov, “Incensole acetate reduces depressive-like behavior and modulates hippocampal BDNF and CRF expression of submissive animals,” J. Psychopharmacol. (Oxford) 26(12), 1584–1593 (2012). [CrossRef] [PubMed]

34.

E. Malatynska, A. Pinhasov, C. J. Creighton, J. J. Crooke, A. B. Reitz, D. E. Brenneman, and M. S. Lubomirski, “Assessing activity onset time and efficacy for clinically effective antidepressant and antimanic drugs in animal models based on dominant-submissive relationships,” Neurosci. Biobehav. Rev. 31(6), 904–919 (2007). [CrossRef] [PubMed]

35.

E. Nesher, V. Peskov, A. Rylova, O. Raz, and A. Pinhasov, “Comparative analysis of the behavioral and biomolecular parameters of four mouse strains,” J. Mol. Neurosci. 46(2), 276–284 (2012). [CrossRef] [PubMed]

36.

J. T. Moon and S. R. Marschner, “Simulating multiple scattering in hair using a photon mapping approach,” ACM Trans. Graph. 25(3), 1067–1074 (2006). [CrossRef]

37.

F. H. Mustafa and M. S. Jaafar, “Shaving area of unwanted hair before laser operation is useful in cosmetic procedure: A simulation study,” J. Saudi Society Dermat. Surg. (Article in Press).

38.

J. Qin, L. Shi, S. Dziennis, R. Reif, and R. K. Wang, “Fast synchronized dual-wavelength laser speckle imaging system for monitoring hemodynamic changes in a stroke mouse model,” Opt. Lett. 37(19), 4005–4007 (2012). [CrossRef] [PubMed]

39.

P. B. Jones, H. K. Shin, D. A. Boas, B. T. Hyman, M. A. Moskowitz, C. Ayata, and A. K. Dunn, “Simultaneous multispectral reflectance imaging and laser speckle flowmetry of cerebral blood flow and oxygen metabolism in focal cerebral ischemia,” J. Biomed. Opt. 13(4), 044007 (2008). [CrossRef] [PubMed]

40.

A. Devor, A. K. Dunn, M. L. Andermann, I. Ulbert, D. A. Boas, and A. M. Dale, “Coupling of total hemoglobin concentration, oxygenation, and neural activity in rat somatosensory cortex,” Neuron 39(2), 353–359 (2003). [CrossRef] [PubMed]

41.

D. T. Delpy and M. Cope, “Quantification in tissue near-infrared spectroscopy,” Philos. Trans. R. Soc. Lond. B Biol. Sci. 352(1354), 649–659 (1997). [CrossRef]

42.

E. M. C. Hillman, “Optical brain imaging in vivo: techniques and applications from animal to man,” J. Biomed. Opt. 12(5), 051402 (2007). [CrossRef] [PubMed]

43.

E. M. Sevick, B. Chance, J. Leigh, S. Nioka, and M. Maris, “Quantitation of time- and frequency-resolved optical spectra for the determination of tissue oxygenation,” Anal. Biochem. 195(2), 330–351 (1991). [CrossRef] [PubMed]

44.

C. H. Chen-Bee, T. Agoncillo, Y. Xiong, and R. D. Frostig, “The triphasic intrinsic signal: implications for functional imaging,” J. Neurosci. 27(17), 4572–4586 (2007). [CrossRef] [PubMed]

45.

S. Sheth, M. Nemoto, M. Guiou, M. Walker, N. Pouratian, and A. W. Toga, “Evaluation of coupling between optical intrinsic signals and neuronal activity in rat somatosensory cortex,” Neuroimage 19(3), 884–894 (2003). [CrossRef] [PubMed]

46.

S. B. Chen, Z. Feng, P. C. Li, S. L. Jacques, S. Q. Zeng, and Q. M. Luo, “In vivo optical reflectance imaging of spreading depression waves in rat brain with and without focal cerebral ischemia,” J. Biomed. Opt. 11(3), 034002 (2006). [CrossRef] [PubMed]

47.

M. R. Zhao, M. A. Suh, H. T. Ma, C. Perry, A. Geneslaw, and T. H. Schwartz, “Focal increases in perfusion and decreases in hemoglobin oxygenation precede seizure onset in spontaneous human epilepsy,” Epilepsia 48(11), 2059–2067 (2007). [CrossRef] [PubMed]

48.

Z. C. Luo, Z. J. Yuan, Y. T. Pan, and C. W. Du, “Simultaneous imaging of cortical hemodynamics and blood oxygenation change during cerebral ischemia using dual-wavelength laser speckle contrast imaging,” Opt. Lett. 34(9), 1480–1482 (2009). [CrossRef] [PubMed]

49.

R. L. Grubb Jr, M. E. Raichle, J. O. Eichling, and M. M. Ter-Pogossian, “The effects of changes in PaCO2 on cerebral blood volume, blood flow, and vascular mean transit time,” Stroke 5(5), 630–639 (1974). [CrossRef] [PubMed]

50.

H. Liu, B. Chance, A. H. Hielscher, S. L. Jacques, and F. K. Tittel, “Influence of blood vessels on the measurement of hemoglobin oxygenation as determined by time-resolved reflectance spectroscopy,” Med. Phys. 22(8), 1209–1217 (1995). [CrossRef] [PubMed]

51.

W. G. Zijlstra, A. Buursma, and O. Willem van Assendelft, eds., Visible and Near Infrared Absorption Spectra of Human and Animal Haemoglobin: Determination and Application. 2000, VSP. 368.

52.

A. Roggan, M. Friebel, K. Do Rschel, A. Hahn, and G. Mu Ller, “Optical Properties of Circulating Human Blood in the Wavelength Range 400-2500 nm,” J. Biomed. Opt. 4(1), 36–46 (1999). [CrossRef] [PubMed]

53.

R. Nachabé, B. H. W. Hendriks, M. van der Voort, A. E. Desjardins, and H. J. C. M. Sterenborg, “Estimation of biological chromophores using diffuse optical spectroscopy: benefit of extending the UV-VIS wavelength range to include 1000 to 1600 nm,” Biomed. Opt. Express 1(5), 1432–1442 (2010). [CrossRef] [PubMed]

54.

S. H. Tseng, C. K. Hsu, J. Yu-Yun Lee, S. Y. Tzeng, W. R. Chen, and Y. K. Liaw, “Noninvasive evaluation of collagen and hemoglobin contents and scattering property of in vivo keloid scars and normal skin using diffuse reflectance spectroscopy: pilot study,” J. Biomed. Opt. 17(7), 077005 (2012). [CrossRef] [PubMed]

55.

G. M. Hale and M. R. Querry, “Optical constants of water in the 200-nm to 200-microm wavelength region,” Appl. Opt. 12(3), 555–563 (1973). [CrossRef] [PubMed]

56.

J. Berwick, C. Martin, J. Martindale, M. Jones, D. Johnston, Y. Zheng, P. Redgrave, and J. Mayhew, “Hemodynamic response in the unanesthetized rat: intrinsic optical imaging and spectroscopy of the barrel cortex,” J. Cereb. Blood Flow Metab. 22(6), 670–679 (2002). [CrossRef] [PubMed]

57.

S. Kawauchi, I. Nishidate, Y. Uozumi, H. Nawashiro, H. Ashida, and S. Sato, “Diffuse light reflectance signals as potential indicators of loss of viability in brain tissue due to hypoxia: charge-coupled-device-based imaging and fiber-based measurement,” J. Biomed. Opt. 18(1), 015003 (2013). [CrossRef] [PubMed]

58.

P. Delaveau, M. Jabourian, C. Lemogne, S. Guionnet, L. Bergouignan, and P. Fossati, “Brain effects of antidepressants in major depression: a meta-analysis of emotional processing studies,” J. Affect. Disord. 130(1-2), 66–74 (2011). [CrossRef] [PubMed]

59.

M. Lazebnik, D. L. Marks, K. Potgieter, R. Gillette, and S. A. Boppart, “Functional optical coherence tomography for detecting neural activity through scattering changes,” Opt. Lett. 28(14), 1218–1220 (2003). [CrossRef] [PubMed]

60.

D. M. Rector, K. M. Carter, P. L. Volegov, and J. S. George, “Spatio-temporal mapping of rat whisker barrels with fast scattered light signals,” Neuroimage 26(2), 619–627 (2005). [CrossRef] [PubMed]

61.

D. M. Hueber, M. A. Franceschini, H. Y. Ma, Q. Zhang, J. R. Ballesteros, S. Fantini, D. Wallace, V. Ntziachristos, and B. Chance, “Non-invasive and quantitative near-infrared haemoglobin spectrometry in the piglet brain during hypoxic stress, using a frequency-domain multidistance instrument,” Phys. Med. Biol. 46(1), 41–62 (2001). [CrossRef] [PubMed]

OCIS Codes
(170.0110) Medical optics and biotechnology : Imaging systems
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.4580) Medical optics and biotechnology : Optical diagnostics for medicine
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics

ToC Category:
Neuroscience and Brain Imaging

History
Original Manuscript: March 10, 2014
Revised Manuscript: April 27, 2014
Manuscript Accepted: May 2, 2014
Published: June 11, 2014

Citation
David Abookasis, Ariel Shochat, Elimelech Nesher, and Albert Pinhasov, "Exploring diazepam’s effect on hemodynamic responses of mouse brain tissue by optical spectroscopic imaging," Biomed. Opt. Express 5, 2184-2195 (2014)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-7-2184


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References

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  21. D. A. Benaron, S. R. Hintz, A. Villringer, D. Boas, A. Kleinschmidt, J. Frahm, C. Hirth, H. Obrig, J. C. van Houten, E. L. Kermit, W. F. Cheong, and D. K. Stevenson, “Noninvasive functional imaging of human brain using light,” J. Cereb. Blood Flow Metab.20(3), 469–477 (2000). [CrossRef] [PubMed]
  22. A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci.20(10), 435–442 (1997). [CrossRef] [PubMed]
  23. M. Izzetoglu, S. C. Bunce, K. Izzetoglu, B. Onaral, and K. Pourrezaei, “Functional brain imaging using near-infrared technology,” IEEE Eng. Med. Biol. Mag.26(4), 38–46 (2007). [CrossRef] [PubMed]
  24. L. V. Wang and H.-i. Wu, Biomedical optics: principles and imaging. (Wiley-Interscience, Hoboken, N.J., 2007).
  25. J. G. Fujimoto and D. L. Farkas, Biomedical Optical Imaging (Oxford University Press, Oxford; New York, 2009).
  26. F. Crespi, M. Donini, A. Bandera, F. Congestri, F. Formenti, V. Sonntag, C. Heidbreder, and L. Rovati, “Near-infrared oxymeter biosensor prototype for non-invasive in vivo analysis of rat brain oxygenation: effects of drugs of abuse,” J. Opt. A, Pure Appl. Opt.8(7), 528 (2006). [CrossRef]
  27. K. Kohmura, K. Iwamoto, B. Aleksic, K. Sasada, N. Kawano, H. Katayama, Y. Noda, A. Noda, T. Iidaka, and N. Ozaki, “Effects of sedative antidepressants on prefrontal cortex activity during verbal fluency task in healthy subjects: a near-infrared spectroscopy study,” Psychopharmacology (Berl.)226(1), 75–81 (2013). [CrossRef] [PubMed]
  28. E. A. Verhagen, E. M. Kooi, P. P. van den Berg, and A. F. Bos, “Maternal antihypertensive drugs may influence cerebral oxygen extraction in preterm infants during the first days after birth,” J. Matern. Fetal Neonatal Med.26(9), 871–876 (2013). [CrossRef] [PubMed]
  29. Y. Feder, E. Nesher, A. Ogran, A. Kreinin, E. Malatynska, G. Yadid, and A. Pinhasov, “Selective breeding for dominant and submissive behavior in Sabra mice,” J. Affect. Disord.126(1-2), 214–222 (2010). [CrossRef] [PubMed]
  30. E. Nesher, M. Gross, S. Lisson, T. Tikhonov, G. Yadid, and A. Pinhasov, “Differential responses to distinct psychotropic agents of selectively bred dominant and submissive animals,” Behav. Brain Res.236(1), 225–235 (2013). [PubMed]
  31. A. Pinhasov, J. Crooke, D. Rosenthal, D. Brenneman, and E. Malatynska, “Reduction of Submissive Behavior Model for antidepressant drug activity testing: study using a video-tracking system,” Behav. Pharmacol.16(8), 657–664 (2005). [CrossRef] [PubMed]
  32. E. Malatynska, A. Pinhasov, J. J. Crooke, V. L. Smith-Swintosky, and D. E. Brenneman, “Reduction of dominant or submissive behaviors as models for antimanic or antidepressant drug testing: technical considerations,” J. Neurosci. Methods165(2), 175–182 (2007). [CrossRef] [PubMed]
  33. A. Moussaieff, M. Gross, E. Nesher, T. Tikhonov, G. Yadid, and A. Pinhasov, “Incensole acetate reduces depressive-like behavior and modulates hippocampal BDNF and CRF expression of submissive animals,” J. Psychopharmacol. (Oxford)26(12), 1584–1593 (2012). [CrossRef] [PubMed]
  34. E. Malatynska, A. Pinhasov, C. J. Creighton, J. J. Crooke, A. B. Reitz, D. E. Brenneman, and M. S. Lubomirski, “Assessing activity onset time and efficacy for clinically effective antidepressant and antimanic drugs in animal models based on dominant-submissive relationships,” Neurosci. Biobehav. Rev.31(6), 904–919 (2007). [CrossRef] [PubMed]
  35. E. Nesher, V. Peskov, A. Rylova, O. Raz, and A. Pinhasov, “Comparative analysis of the behavioral and biomolecular parameters of four mouse strains,” J. Mol. Neurosci.46(2), 276–284 (2012). [CrossRef] [PubMed]
  36. J. T. Moon and S. R. Marschner, “Simulating multiple scattering in hair using a photon mapping approach,” ACM Trans. Graph.25(3), 1067–1074 (2006). [CrossRef]
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  38. J. Qin, L. Shi, S. Dziennis, R. Reif, and R. K. Wang, “Fast synchronized dual-wavelength laser speckle imaging system for monitoring hemodynamic changes in a stroke mouse model,” Opt. Lett.37(19), 4005–4007 (2012). [CrossRef] [PubMed]
  39. P. B. Jones, H. K. Shin, D. A. Boas, B. T. Hyman, M. A. Moskowitz, C. Ayata, and A. K. Dunn, “Simultaneous multispectral reflectance imaging and laser speckle flowmetry of cerebral blood flow and oxygen metabolism in focal cerebral ischemia,” J. Biomed. Opt.13(4), 044007 (2008). [CrossRef] [PubMed]
  40. A. Devor, A. K. Dunn, M. L. Andermann, I. Ulbert, D. A. Boas, and A. M. Dale, “Coupling of total hemoglobin concentration, oxygenation, and neural activity in rat somatosensory cortex,” Neuron39(2), 353–359 (2003). [CrossRef] [PubMed]
  41. D. T. Delpy and M. Cope, “Quantification in tissue near-infrared spectroscopy,” Philos. Trans. R. Soc. Lond. B Biol. Sci.352(1354), 649–659 (1997). [CrossRef]
  42. E. M. C. Hillman, “Optical brain imaging in vivo: techniques and applications from animal to man,” J. Biomed. Opt.12(5), 051402 (2007). [CrossRef] [PubMed]
  43. E. M. Sevick, B. Chance, J. Leigh, S. Nioka, and M. Maris, “Quantitation of time- and frequency-resolved optical spectra for the determination of tissue oxygenation,” Anal. Biochem.195(2), 330–351 (1991). [CrossRef] [PubMed]
  44. C. H. Chen-Bee, T. Agoncillo, Y. Xiong, and R. D. Frostig, “The triphasic intrinsic signal: implications for functional imaging,” J. Neurosci.27(17), 4572–4586 (2007). [CrossRef] [PubMed]
  45. S. Sheth, M. Nemoto, M. Guiou, M. Walker, N. Pouratian, and A. W. Toga, “Evaluation of coupling between optical intrinsic signals and neuronal activity in rat somatosensory cortex,” Neuroimage19(3), 884–894 (2003). [CrossRef] [PubMed]
  46. S. B. Chen, Z. Feng, P. C. Li, S. L. Jacques, S. Q. Zeng, and Q. M. Luo, “In vivo optical reflectance imaging of spreading depression waves in rat brain with and without focal cerebral ischemia,” J. Biomed. Opt.11(3), 034002 (2006). [CrossRef] [PubMed]
  47. M. R. Zhao, M. A. Suh, H. T. Ma, C. Perry, A. Geneslaw, and T. H. Schwartz, “Focal increases in perfusion and decreases in hemoglobin oxygenation precede seizure onset in spontaneous human epilepsy,” Epilepsia48(11), 2059–2067 (2007). [CrossRef] [PubMed]
  48. Z. C. Luo, Z. J. Yuan, Y. T. Pan, and C. W. Du, “Simultaneous imaging of cortical hemodynamics and blood oxygenation change during cerebral ischemia using dual-wavelength laser speckle contrast imaging,” Opt. Lett.34(9), 1480–1482 (2009). [CrossRef] [PubMed]
  49. R. L. Grubb, M. E. Raichle, J. O. Eichling, and M. M. Ter-Pogossian, “The effects of changes in PaCO2 on cerebral blood volume, blood flow, and vascular mean transit time,” Stroke5(5), 630–639 (1974). [CrossRef] [PubMed]
  50. H. Liu, B. Chance, A. H. Hielscher, S. L. Jacques, and F. K. Tittel, “Influence of blood vessels on the measurement of hemoglobin oxygenation as determined by time-resolved reflectance spectroscopy,” Med. Phys.22(8), 1209–1217 (1995). [CrossRef] [PubMed]
  51. W. G. Zijlstra, A. Buursma, and O. Willem van Assendelft, eds., Visible and Near Infrared Absorption Spectra of Human and Animal Haemoglobin: Determination and Application. 2000, VSP. 368.
  52. A. Roggan, M. Friebel, K. Do Rschel, A. Hahn, and G. Mu Ller, “Optical Properties of Circulating Human Blood in the Wavelength Range 400-2500 nm,” J. Biomed. Opt.4(1), 36–46 (1999). [CrossRef] [PubMed]
  53. R. Nachabé, B. H. W. Hendriks, M. van der Voort, A. E. Desjardins, and H. J. C. M. Sterenborg, “Estimation of biological chromophores using diffuse optical spectroscopy: benefit of extending the UV-VIS wavelength range to include 1000 to 1600 nm,” Biomed. Opt. Express1(5), 1432–1442 (2010). [CrossRef] [PubMed]
  54. S. H. Tseng, C. K. Hsu, J. Yu-Yun Lee, S. Y. Tzeng, W. R. Chen, and Y. K. Liaw, “Noninvasive evaluation of collagen and hemoglobin contents and scattering property of in vivo keloid scars and normal skin using diffuse reflectance spectroscopy: pilot study,” J. Biomed. Opt.17(7), 077005 (2012). [CrossRef] [PubMed]
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