17 December 2016 Intraoperative intrinsic optical imaging of human somatosensory cortex during neurosurgical operations
Author Affiliations +
Intrinsic optical imaging as developed by Grinvald et al. is a powerful technique for monitoring neural function in the



Optical imaging of neural activity in the in vivo central nervous system (CNS) is based on the extrinsic optical signals obtained with voltage-sensitive dyes (VSDs) and the intrinsic optical signal without VSDs.12.3 In the imaging of membrane potential changes in the CNS, it is known that voltage-dependent extrinsic optical signals are often distorted and/or contaminated by slower intrinsic optical signals.4,5 In response to these preliminary observations, Grinvald et al.6 have developed a unique intrinsic optical imaging technique for monitoring cortical activity.

The intrinsic optical signal detected in vivo in the CNS is considered to be at least three different components: (1) activity dependent changes in the oxygen saturation level of hemoglobin; (2) changes in blood volume in an area containing electrically active neurons; and (3) light-scattering changes that accompany cortical activation, which are caused by ion/water movement, expansion and contraction of extracellular spaces, capillary expansion, or neurotransmitter release.56.7.8 Components 1 and 2 dominate intrinsic signals at wavelengths of incident light between 400 and 630 nm, while component 3 becomes a significant source of intrinsic signals above 630 nm and dominates the signals in the near-infrared region above 800 nm.7 As pioneered by Grinvald et al.,9 this method has been applied widely to various regions in the mammalian CNS, e.g., the cat/monkey visual system (for reviews see Refs. 10 and 11), the rodent somatosensory cortex (for a review see Ref. 12), the rat spinal cord,13,14 and the rat brainstem.15


Intraoperative Intrinsic Optical Imaging of Human Brain Function

Intrinsic optical imaging is applicable to the human cortex during neurosurgical operations because of its dye-free nature (for reviews see Refs. 16 and 17). Functional imaging of the human brain has significant uses in basic medical and clinical physiology. During surgical operations on brain tumors and intractable epilepsies, operators need to obtain functional local maps of the human brain for making decisions regarding the resection area.

Intraoperative intrinsic optical imaging of human brain function was first reported by Haglund et al.18 They succeeded in monitoring stimulation-evoked epileptiform after discharges and cognitively evoked functional activity in the human cerebral cortex. Subsequently, intraoperative intrinsic optical imaging has been carried out by some groups to monitor brain function related to sensation,1920. language task,30,31 tongue movement,32 and intractable epilepsy.33,34

In general, the imaging apparatus is composed of image forming optics, a detector, and an acquisition computer. In our laboratory, a charge coupled device camera fitted to an operating microscope detected reflected light from the cerebral cortex, which passed through an interference filter with a passband at 605±5  nm. This wavelength produced the largest optical signals in rat whisker barrel experiments.35,36 Signal acquisition and imaging were performed using differential video acquisition systems, IMAGER 2001 (for Fig. 1) or IMAGER 2001 VSD+ (for Fig. 2) (Optical Imaging Ltd., Germantown, New York). These recording systems were developed in Professor Grinvald’s laboratory and the newest version is available from Optical Imaging Ltd.

Fig. 1

(a) A vascular image of the recording region and color-coded images of intrinsic optical signals induced by digits I and V stimulation. The digits were individually stimulated transcutaneously with surface electrodes driven by an electrical stimulator at 5 Hz for 2 s. The detected optical signals are superimposed on the vascular image. Eight images were collected in 5 s (1  frame/0.7  s) from the right somatosensory cortex of a 57-year-old patient who suffered from anaplastic oligodendroglioma, and the first one was used as a reference image. Both digits I and V stimulation induced two separate response areas, areas I1 and I2, and areas V1 and V2, respectively. Asterisks indicate noise due to blood flow. In the vascular image, the yellow curves indicate the central and postcentral sulci, and the red and blue curves show the digits I and V response areas identified in the color-coded images. The optical response area for each finger stimulation was defined as the area with the fractional change (ΔR/R)>2.0×103. (b) Time courses of changes in the intrinsic optical signal size in area V1 (red line) and area V2 (blue line). The positive direction corresponds to a decrease in light reflectance. (c) The recording site of the intrinsic optical signals is illustrated with a red ellipse on a three-dimensional (3-D) magnetic resonance (MR) image. The green area indicates the brain tumor. The major veins are illustrated in blue. See Ref. 24 for more details.


Fig. 2

(a) Color-coded images of intrinsic optical signals induced by digits I–V stimulation. Right digits I–V were individually stimulated with surface electrodes driven by an electrical stimulator at 5 Hz for 2 s, and intrinsic optical signals were recorded from the left somatosensory cortex of a 47-year-old patient who suffered from an oligodendroglioma. The detected optical signals are superimposed on the vascular image (left panels). The recording site is shown on a 3-D reconstructed MR image with a blue rectangle and on a photograph of the cortical surface with a red ellipse. The yellow line indicates the central sulcus. The central sulcus was determined by recording somatosensory evoked potentials in response to median nerve stimulation. The vascular image obtained at a wavelength of 540 nm is also shown in the lower right panel. (b) Functional maps of finger representations in the somatosensory cortex obtained from four different patients (cases 1 to 4). The optical response area for each finger stimulation was defined as the area with the fractional change (ΔR/R)>1.5×103. The response areas of different fingers are shown with different translucent colors (digit I: red; digit II: yellow; digit III: green; digit IV: blue; and digit V: red purple), and are superimposed on the vascular image. Each response area is outlined with a black solid curve. Maps were obtained from the left (cases 1 and 2) and right (cases 3 and 4) cortices. See Ref. 26 for more details.


Under informed consent, we have measured intrinsic optical signals from the cerebral cortex in 30 anesthetized patients undergoing surgery for brain tumors (27 patients) and intractable epilepsies (3 patients). In the 30 patients, we succeeded in mapping brain function in 26 cases. In Secs. 3 and 4, we introduce two functional mappings of great interest obtained in the human somatosensory cortex.


Functional Mapping from Subdivisions of the Primary Somatosensory Cortex

In an electrical stimulation study on the human cerebral cortex during neurosurgical operations, Penfield and Boldray37 demonstrated that the human somatosensory cortex is a highly sophisticated system for information processing. The primary somatosensory cortex is subdivided into four cytoarchitectonic areas, which are termed Brodmann’s areas 3a, 3b, 1, and 2.38,39 Physiological and anatomical studies of nonhuman primates have shown that there is a complete topographic representation of the body in each of the four Brodmann’s areas, and that these areas exhibit a hierarchy in sensory information processing (for a review see Ref. 40). Using intraoperative intrinsic optical imaging, we directly showed a similar hierarchy in the human primary somatosensory cortex.

Figure 1 shows an example of intrinsic optical images induced by individual stimulation of digits I and V in a 57-year-old patient. In this case, we identified two separate response areas for each digit stimulation, areas I1 and I2 and areas V1 and V2 [Fig. 1(a)]. Areas I1 and V1 were located near the central sulcus, whereas areas I2 and V2 were located near the postcentral sulcus. Interestingly, response areas near the central sulcus were completely separate from each other, whereas those near the postcentral sulcus were partially overlapping. A difference in the time course of the optical signal was seen between the two areas, although the time resolution of the present study was not so high (about 0.7 s) [Fig. 1(b)]. Similar maps were also obtained in four other cases (three in the finger region and one in the face region: see Figs. 6 and 7 of Ref. 24).

Where are the origins of these optical response areas? The borders of Brodmann areas 3a, 3b, 1, and 2 are somewhat different between studies. If we consider that the response areas near the central and postcentral sulci correspond to Brodmann area 1 and 2, respectively,38,39,41 our maps might indicate that neurons in Brodmann area 2 receive sensory information from larger peripheral fields than those in Brodmann area 1. Similar observations have been reported in the monkey somatosensory cortex.4243.44.45 On the other hand, if we consider that Brodmann area 1 occupies the crown of the postcentral gyrus and reaches down into the postcentral sulcus,46,47 it is possible that further functional subdivisions exist in Brodmann area 1.


Overlapping Representations in the Primary Somatosensory Cortex

In the primate, Kaas et al.48 showed complete somatotopic maps in each Brodmann area (3a, 3b, 1, and 2) with microelectrode recording. In the monkey somatosensory cortex, significant overlapping representations of fingers were found in the somatotopic maps. Do similar overlaps exist in the human brain?

Figure 2(a) shows an example of intrinsic optical images induced by individual stimulation of digits I–V in a 47-year-old patient. The recording site corresponded to the postcentral gyrus, and optical responses induced by digits I–V stimulation were clearly identified in different regions of the primary somatosensory cortex. Figure 2(b) shows functional maps of finger representations in the somatosensory cortex obtained from four different patients. In these maps, we could not clearly identify subdivisions in the primary somatosensory cortex shown in Fig. 1, possibly because of differences in measurement conditions. Nonetheless, these maps show that (1) optical response areas induced by digits I–V stimulation were sequentially aligned along the central sulcus in the crown of the postcentral gyrus; (2) the digit I area was located in the most latero-inferior region, whereas the digit V area was located in the most medio-superior region; (3) in most patients, the digit I area was the largest and the digits III–V areas were smaller; and (4) the neighboring response areas partially overlapped each other, and had interindividual variations. Similar overlapping representations were also observed in the face region of two patients (Fig. 5 of Ref. 26).

Considering these maps together with previously reported observations in nonhuman primates23,45,49,50 and humans,21,25 the overlap of the activated areas is considered to be a common characteristic in the somatosensory cortex not only in nonhuman animals, but also in humans. This overlap might be functionally important in sensory information processing.


Conclusion and Further Considerations

Intrinsic optical imaging has the best combination of spatial and temporal resolutions for mapping human brain function. Advances in intrinsic optical imaging of neural function have benefited from the development of imaging techniques by Grinvald et al. The high success rate for functional brain mapping implies that the intraoperative intrinsic optical imaging is a powerful and reliable method for evaluating human brain function during neurosurgical operations. Evaluation with functional brain mapping enables neurosurgeons to perform much more accurate operations. On the other hand, there are some points to be improved and considered to obtain clearer functional maps during the limited recording time. First, the human brain exhibits large mechanical movements due to respiration and cardiac beats, which require the stabilization of the cortex by a glass plate and a lot of signal averaging. To reduce the recording time, it should be necessary to develop new methods for stabilization of the cortex in addition to the hardware improvement. Second, the arachnoid membrane is often thickened in brain tumor patients, especially in elderly persons. In such cases, it is difficult to rule out movement artifacts from true signals as shown in Fig. 3 of Ref. 26. It seems very important to carefully consider the condition of the brain before optical imaging.


The authors have no competing interests to disclose.


We are grateful to Dr. Carl Petersen and Dr. Ron D. Frostig for inviting us to contribute to this special section. We particularly appreciate the late Dr. Kimiyoshi Hirakawa and Dr. Kikuo Ohno, emeritus professors of neurosurgery in Tokyo Medical and Dental University, for their support during our close collaboration. We also wish to acknowledge all of our collaborators at Tokyo Medical and Dental University. In 1996, Amiram visited our laboratory in Tokyo Medical and Dental University and helped us to improve our optical recording system for monitoring intrinsic signals. He energetically demonstrated how to record intrinsic signals in the rat barrel cortex using his own experiments, without even pausing for lunch or dinner. We owe our success in intrinsic optical imaging of neural function in the rat barrel cortex, spinal cord, brainstem, and human brain to his expert guidance. We would like to express our gratitude to him.


1. L. B. Cohen and B. M. Salzberg, “Optical measurement of membrane potential,” Rev. Physiol. Biochem. Pharmacol. 83, 35–88 (1978).RPBEA50303-4240 Google Scholar

2. B. M. Salzberg, “Optical recording of electrical activity in neurons using molecular probes,” in Current Methods in Cellular Neurobiology, Vol. 3, Electrophysiological Techniques, , J. L. Barker and J. F. McKelvy, Eds., pp. 139–187, Wiley, New York (1983). Google Scholar

3. A. Grinvald et al., “Optical imaging of neuronal activity,” Physiol. Rev. 68, 1285–1366 (1988).PHREA70031-9333 Google Scholar

4. H. S. Orbach, L. B. Cohen and A. Grinvald, “Optical mapping of electrical activity in rat somatosensory and visual cortex,” J. Neurosci. 5, 1886–1895 (1985).JNRSDS0270-6474 Google Scholar

5. B. M. Salzberg, A. L. Obaid and H. Gainer, “Large and rapid changes in light scattering accompany secretion by nerve terminals in the mammalian neurohypophysis,” J. Gen. Physiol. 86, 395–411 (1985).JGPLAD0022-1295 http://dx.doi.org/10.1085/jgp.86.3.395 Google Scholar

6. R. D. Frostig et al., “Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals,” Proc. Natl. Acad. Sci. U. S. A. 87, 6082–6086 (1990). http://dx.doi.org/10.1073/pnas.87.16.6082 Google Scholar

7. T. Bonhoeffer, A. Grinvald, “Optical imaging based on intrinsic signals: the methodology,” in Brain Mapping; the Methods, , A. W. Toga and J. C. Mazziotta, Eds., pp. 55–97, Academic Press, California (1996). Google Scholar

8. G. H. Kim et al., “A mechanical spike accompanies the action potential in mammalian nerve terminals,” Biophys. J. 92, 3122–3129 (2007).BIOJAU0006-3495 http://dx.doi.org/10.1529/biophysj.106.103754 Google Scholar

9. A. Grinvald et al., “Functional architecture of cortex revealed by optical imaging of intrinsic signals,” Nature 324, 361–364 (1986). http://dx.doi.org/10.1038/324361a0 Google Scholar

10. T. Bonhoeffer, M. Hübener, “Intrinsic optical imaging of functional map development in mammalian visual cortex,” Chapter 68 in Imaging in Neuroscience, , F. Helmchen, A. Konnerth and R. Yuste, Eds., pp. 633–638, Cold Spring Habor Laboratory Press, New York (2011). Google Scholar

11. A. Grinvald et al., “Imaging the neocortex functional architecture using multiple intrinsic signals: implications for hemodynamic–based functional imaging,” Chapter 68 in Imaging in Neuroscience, , F. Helmchen, A. Konnerth and R. Yuste, Eds., pp. 907–926, Cold Spring Habor Laboratory Press, New York (2011). Google Scholar

12. R. D. Frostig, C. H. Chen-Bee, “The use of intrinsic signal optical imaging for mapping cortical function,” in Handbook of Neuronal Activity Measurements, , R. Brette and A. Destexhe, Eds., pp. 1–43, Cambridge University Press, United Kingdom (2012). Google Scholar

13. S. Sasaki et al., “Optical imaging of intrinsic signals induced by peripheral nerve stimulation in the in vivo rat spinal cord,” NeuroImage 17, 1240–1255 (2002).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.2002.1286 Google Scholar

14. S. Sasaki et al., “Postnatal changes in intrinsic optical responses to peripheral nerve stimulation in the in vivo rat spinal cord,” NeuroImage 20, 2126–2134 (2003).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2003.08.005 Google Scholar

15. I. Yazawa et al., “Intrinsic optical imaging of neural responses in in vivo rat brainstem evoked by vagus nerve stimulation,” Neurosci. Res. 38, S91 (2000). Google Scholar

16. N. Pouratian et al., “Shedding light on brain mapping: advances in human optical imaging,” Trends Neurosci. 26, 277–282 (2003).TNSCDR0166-2236 http://dx.doi.org/10.1016/S0166-2236(03)00070-5 Google Scholar

17. K. C. Brennan, A. W. Toga, “Intraoperative optical imaging,” Chapter 13 in In Vivo Optical Imaging of Brain Function, 2nd ed., and R. D. Frostig, Ed., CRC Press/Taylor & Francis, Boca Raton, Florida (2009). Google Scholar

18. M. M. Haglund, G. A. Ojemann and D. W. Hochman, “Optical imaging of epileptiform and functional activity in human cerebral cortex,” Nature 358, 668–671 (1992). http://dx.doi.org/10.1038/358668a0 Google Scholar

19. A. W. Toga, A. F. Cannestra and K. L. Black, “The temporal/spatial evolution of optical signals in human cortex,” Cereb. Cortex 5, 561–565 (1995). http://dx.doi.org/10.1093/cercor/5.6.561 Google Scholar

20. A. F. Cannestra et al., “The evolution of optical signals in human and rodent cortex,” NeuroImage 3, 202–208 (1996).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.1996.0022 Google Scholar

21. A. F. Cannestra et al., “Topological and temporal specificity of human intraoperative optical intrinsic signals,” NeuroReport 9, 2557–2563 (1998).NERPEZ0959-4965 http://dx.doi.org/10.1097/00001756-199808030-00024 Google Scholar

22. A. F. Cannestra et al., “Temporal spatial differences observed by functional MRI and human intraoperative optical imaging,” Cereb Cortex 11, 773–782 (2001). http://dx.doi.org/10.1093/cercor/11.8.773 Google Scholar

23. D. Shoham and A. Grinvald, “The cortical representations of the hand in macaque and human area S-I: high resolution optical imaging,” J. Neurosci. 21, 6820–6835 (2001).JNRSDS0270-6474 Google Scholar

24. K. Sato et al., “Intraoperative intrinsic optical imaging of neuronal activity from subdivisions of the human primary somatosensory cortex,” Cereb Cortex 12, 269–280 (2002). http://dx.doi.org/10.1093/cercor/12.3.269 Google Scholar

25. T. H. Schwartz et al., “Intraoperative optical imaging of human face cortical topography: a case study,” NeuroReport 15, 1527–1531 (2004).NERPEZ0959-4965 http://dx.doi.org/10.1097/01.wnr.0000131006.59315.2f Google Scholar

26. K. Sato et al., “Functional representation of the finger and face in the human somatosensory cortex: intraoperative intrinsic optical imaging,” NeuroImage 25, 1292–1301 (2005).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2004.12.049 Google Scholar

27. T. Nariai et al., “Visualization of somatotopic representation of sensory cortex with intrinsic optical signals as guides for brain tumor surgery,” J. Neurosurg. 103, 414–423 (2005).JONSAC0022-3085 http://dx.doi.org/10.3171/jns.2005.103.3.0414 Google Scholar

28. T. Meyer et al., “Intraoperative optical imaging of functional brain areas for improved image-guided surgery,” Biomed. Tech. 58, 225–236 (2013). http://dx.doi.org/10.1515/bmt-2012-0072 Google Scholar

29. S. B. Sobottka et al., “Intraoperative optical imaging of intrinsic signals: a reliable method for visualizing stimulated functional brain areas during surgery,” J. Neurosurg. 119, 853–863 (2013).JONSAC0022-3085 http://dx.doi.org/10.3171/2013.5.JNS122155 Google Scholar

30. A. F. Cannestra et al., “Temporal and topological characterization of language cortices using intraoperative optical intrinsic signals,” NeuroImage 12, 41–54 (2000).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.2000.0597 Google Scholar

31. N. Pouratian et al., “Optical imaging of bilingual cortical representations,” J. Neurosurg. 93, 676–681 (2000).JONSAC0022-3085 http://dx.doi.org/10.3171/jns.2000.93.4.0676 Google Scholar

32. N. Pouratian et al., “Spatial/temporal correlation of BOLD and optical intrinsic signals in humans,” Magn. Reson. Med. 47, 766–776 (2002).MRMEEN0740-3194 http://dx.doi.org/10.1002/(ISSN)1522-2594 Google Scholar

33. H. Ma et al., “The importance of latency in the focality of perfusion and oxygenation changes associated with triggered after discharges in human cortex,” J. Cereb. Blood Flow Metab. 29, 1003–1014 (2009). http://dx.doi.org/10.1038/jcbfm.2009.26 Google Scholar

34. Y. Song et al., “Intraoperative optical mapping of epileptogenic cortices during non-ictal periods in pediatric patients,” NeuroImage 11, 423–434 (2016). http://dx.doi.org/10.1016/j.nicl.2016.02.015 Google Scholar

35. T. Tanaka et al., “Consistency behind trial-to-trial variations in intrinsic optical responses to single-whisker movement in the rat D1-barrel cortex,” Neurosci. Res. 36, 193–207 (2000). http://dx.doi.org/10.1016/S0168-0102(99)00117-0 Google Scholar

36. I. Yazawa et al., “Developmental changes in trial-to-trial variations in whisker barrel responses studied using intrinsic optical imaging: comparison between normal and de-whiskered rats,” J. Neurophysiol. 86, 392–401 (2001).JONEA40022-3077 Google Scholar

37. W. Penfield and E. Boldray, “Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation,” Brain 60, 389–443 (1937).BRAIAK0006-8950 http://dx.doi.org/10.1093/brain/60.4.389 Google Scholar

38. K. Brodmann, Vergleichende Lokalisationslehre der Großhirnrinde, Barth JA, Leipzig (1909). Google Scholar

39. C. Vogt and O. Vogt, “Allgemeinere Ergebnisse unserer Hirnforschung,” J. Psychol. Neurol. 25, 292–398 (1919). Google Scholar

40. J. H. Kaas, “What, if anything, is SI? Organization of first somatosensory area of cortex,” Physiol. Rev. 63, 206–231 (1983).PHREA70031-9333 Google Scholar

41. L. E. White et al., “Structure of the human sensorimotor system I: morphology and cytoarchitecture of the central sulcus,” Cereb. Cortex 7, 18–30 (1997).53OPAV1047-3211 http://dx.doi.org/10.1093/cercor/7.1.18 Google Scholar

42. Y. Iwamura et al., “Diversity in receptive field properties of vertical neuronal arrays in the crown of the postcentral gyrus of the conscious monkey,” Exp. Brain Res. 58, 400–411 (1985).EXBRAP0014-4819 http://dx.doi.org/10.1007/BF00235321 Google Scholar

43. Y. Iwamura et al., “Vertical neuronal arrays in the postcentral gyrus signaling active touch: a receptive field study in the conscious monkey,” Exp. Brain Res. 58, 412–420 (1985).EXBRAP0014-4819 http://dx.doi.org/10.1007/BF00235322 Google Scholar

44. T. P. Pons et al., “The somatotopic organization of area 2 in macaque monkeys,” J. Comp. Neurol. 241, 445–466 (1985).JCNEAM0021-9967 http://dx.doi.org/10.1002/(ISSN)1096-9861 Google Scholar

45. E. P. Gardner, “Somatosensory cortical mechanisms of feature detection in tactile and kinesthetic discrimination,” Can. J. Physiol. Pharmacol. 66, 439–454 (1988).CJPPA30008-4212 http://dx.doi.org/10.1139/y88-074 Google Scholar

46. S. Geyer, A. Schleicher and K. Zilles, “Areas 3a, 3b, and 1 of human primary somatosensory cortex 1. Microstructual organization and interindividual variability,” NeuroImage 10, 63–83 (1999).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.1999.0440 Google Scholar

47. S. Geyer et al., “Areas 3a, 3b, and 1 of human primary somatosensory cortex 2. Spatial normalization to standard anatomical space,” NeuroImage 11, 684–696 (2000).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.2000.0548 Google Scholar

48. J. H. Kaas et al., “Multiple representations of the body within the primary somatosensory cortex of primates,” Science 204, 521–523 (1979).SCIEAS0036-8075 http://dx.doi.org/10.1126/science.107591 Google Scholar

49. Y. Iwamura et al., “Functional subdivision representing different finger regions in area 3 of the first somatosensory cortex of the conscious monkey,” Exp. Brain Res. 51, 315–326 (1983).EXBRAP0014-4819 http://dx.doi.org/10.1007/BF00237868 Google Scholar

50. Y. Iwamura et al., “Converging patterns of finger representation and complex response properties of neurons in area 1 of the first somatosensory cortex of the conscious monkey,” Exp. Brain Res. 51, 327–337 (1983).EXBRAP0014-4819 http://dx.doi.org/10.1007/BF00237869 Google Scholar

Biographies for the authors are not available.

© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Katsushige Sato, Katsushige Sato, Tadashi Nariai, Tadashi Nariai, Yoko Momose-Sato, Yoko Momose-Sato, Kohtaro Kamino, Kohtaro Kamino, } "Intraoperative intrinsic optical imaging of human somatosensory cortex during neurosurgical operations," Neurophotonics 4(3), 031205 (17 December 2016). https://doi.org/10.1117/1.NPh.4.3.031205 . Submission: Received: 29 August 2016; Accepted: 28 November 2016
Received: 29 August 2016; Accepted: 28 November 2016; Published: 17 December 2016

Back to Top