Poor blood flow circulation can occur in the subcortical regions of the brain in many brain diseases. However, the limitations of light penetration imaging techniques hinder the detection of blood flow in deep brain tissues in vivo. Hence, in this Letter, we present a gradient index lens-based laser speckle contrast imaging system for time-lapse blood flow detection in subcortical regions of the brain. We monitored the hemodynamic changes in the thalamus of mouse models of acute hypoxia and transient middle cerebral artery occlusion as a proof of concept for practical applications.
© 2018 Optical Society of America
Imaging tools for monitoring microvascular and blood perfusion are crucial to the study of normal and pathological conditions of brain metabolism in basic and clinical researches . Conventional wide-field optical imaging techniques such as laser speckle contrast imaging (LSCI) and laser Doppler imaging are limited to the superficial surface of tissue , while multiphoton microscopy with special instrumentation design can provide a penetration depth up to . Photoacoustic microscopy is capable of high-speed imaging of the mouse brain at the capillary level and provides an imaging depth of . Even though both near-infrared II fluorescence imaging  and optical coherence tomography  offer acceptable spatial resolution () and acquisition time (), these techniques still lack sufficient tissue penetration () for some subcortical regions such as thalamus and striatum. Because numerous brain diseases such as ischemic stroke, Parkinson’s disease, and Alzheimer’s disease are associated with the nucleus deep in the brain, it is of utmost importance to investigate the hemodynamics and metabolisms of subcortical regions in the brain.
Microendoscopes and microprisms offer the opportunity to probe deep brain structures because of their miniaturized objective lenses that can be inserted into the brain [7,8]. Similar to electrophysiology and fiber photometry , although in vivo microendoscopy may cause minute invasive trauma of the brain tissue, the combination of microendoscopy and optical microscopy enables one to observe the cellular structure or neural activity in deep subcortical regions of the brain [8,10–12]. The vascular structure and blood cell flow can also be detected by means of a fluorescent indicator, followed by cross-correlation analysis [7,13]. However, this technique is disadvantageous because of the long computational time needed to reconstruct the blood flow maps and, at present, there is a lack of high-resolution wide-field real-time techniques for the detection of cerebral blood flow (CBF) in deep subcortical regions without the need of a dye agent. Hence, in this Letter, we present a technique based on optical microendoscopy and LSCI for in vivo time-lapse blood flow imaging of deep subcortical regions.
LSCI offers a cost-effective way to measure blood flow with high-spatiotemporal resolution in a wide field of view . LSCI has been used with endoscopes to image blood flow in the retinas and cochlea, as well as tissue perfusion [15,16]. The relative velocity of the blood cells is calculated by integrating the intensity fluctuations of the speckle pattern produced by the random interference of scattered coherent light over a finite period. Similar to laser Doppler flowmetry, which measures the Doppler shift to determine the relative velocity, the LSCI technique establishes a linear relationship between the relative velocity of the scattering particles and the electric field autocorrelation time. Although it is difficult to determine the absolute velocity of the scattering particles using LSCI, LSCI is still an effective tool to measure variations in the relative velocity of the scattering particles . The relative velocity map of the blood flow can be obtained by calculating the value of the speckle temporal contrast at each pixel, which is given by 17]. Because noise is an important factor that contributes to the decrease in sensitivity and linear response range of the relative velocity measurements for LSCI, in this Letter, we applied a correction method to improve the sensitivity of the relative velocity measurements, as described in detail previously in Ref. . In brief, for special types of cameras such as the Baumer camera, which subtracts the mean of dark counts (counts registered by the photonic detectors in the absence of light) and sets the negative counts to zero, the dark noise can be estimated as 20], and it is primarily dependent on the camera pixel size, speckle size, coherence, and polarization of the laser [19,21]. It shall be noted that Eq. (3) is only applicable to special types of cameras such as the Baumer camera. For standard cameras that do not account for dark noise, a different correction method should be used, which is described in detail in Ref. .
Figure 1(a) shows the schematic of our imaging system. The laser (He–Ne laser; CVI Melles Griot; 632.8 nm; 15 mW), was shaped by a beam expander. A polarization beam splitter (PBS251; Thorlabs, Inc.) was used to suppress the direct reflected light by cross-polarization, in order to increase the ratio of dynamic shifted photons. The laser beam was focused into the microendoscope through an objective lens (UPlanFLN, Olympus Corporation; , NA 0.3). A 0.5 mm diameter microendoscope (NEM-050-06-08-520-S-1.5p; Grintech GmbH; single; ; NA 0.5) was inserted into the brain of the mouse to collect the optical signals, which then passed through the PBS, and was recorded by the CCD camera (TXG14f; Baumer; ; pixel size: 6.45 μm; 12 bits) at a frame rate up to 30 Hz. The acquired laser speckle images with an image resolution of , exposure time of 45 ms, and sampling rate of 20 Hz were then saved to a hard disk. All of the components were computer-controlled by using LabVIEW systems engineering software.
In order to validate our method, a phantom experiment was performed to verify the linear relationship between the speckle flow index and the absolute flow rate. A 1% intralipid solution was pushed into a glass tube (inner diameter, 0.5 mm) by a syringe-based infusion pump (TJ-4A; Longer Precision Pump Co., Ltd.) with a velocity range of 0–20 mm/s. The relative velocity maps were reconstructed by performing laser speckle temporal contrast analysis at each velocity, and the relationship between the actual fluid velocity and the measured speckle contrast index is shown in Fig. 1(b). It is evident that the performance of our method with correction (, fitted by the least squares method) is comparable to the performance of a conventional wide-field LSCI system without microendoscopy ().
Hemodynamic changes in the subcortical structure of mice were monitored to demonstrate the performance of our method for CBF imaging. An ischemic stroke is a major cause of death and disability, and it is generally caused by hypoxia or thromboembolic occlusion. The complex microvascular and blood perfusion processes underlying the development of ischemic brain lesions in the subcortical structure are still not well understood. The thalamus is critical in processing and relaying sensory information to the cortex, and little is known regarding how the stroke affects its structure and function in a damaged brain . Therefore, in this Letter, we observed the hemodynamic changes in mouse models with acute hypoxia and transient middle cerebral artery occlusion (tMCAo) in the thalamus. We found that CBF in the thalamus was significantly affected during an ischemic stroke.
Adult C57BL/6 mice (male; 25–29 g; 8–16 weeks old) were used in this Letter. We implanted the microendoscope into the brain of each mouse in order to obtain repeated observations of the targeted structure, as shown in Fig. 1(c). The mice were anesthetized (2% chloral hydrate, 10% urethane, 8 μl/g; xylazine, 18 mg/kg) and placed on a stereotaxic apparatus during surgery. The animal body temperature was maintained at using a warming blanket throughout anesthesia. After the skull of the mouse was exposed and cleaned, a small hole (diameter: ) was drilled above the targeted structure (ventral posteromedial nucleus [VPM]; AP, , L: 1.8 mm, D: 3.4 mm). To prevent mechanical compression and damage, a cylindrical column of neocortical tissue directly above the targeted structure was removed by aspiration. Next, the microendoscope was held by a motorized micromanipulator (MX7600L; Siskiyou Corporation) and slowly inserted into the incision to minimize bleeding and mechanical damage. We filled the gaps between the microendoscope and skull with erythromycin ointment and then fixed the microendoscope in place within the skull using dental cement (Super-bond C&B; Sun Medical Co., Ltd.). After the dental cement had dried, a piece of flexible tube was affixed dressing over the microendoscope to protect it. The mice were allowed to fully recover (4–6 weeks) before the final preparation for the imaging experiments. Tolfedine (0.2 mg/kg; intraperitoneal) and penicillin (20000 units; intraperitoneal) were administered to minimize tissue swelling and inflammation for 3–7 days, as necessary.
The mouse models of acute hypoxia were treated by giving only nitrogen instead of air in order to reduce inhaled oxygen tension. The mice were anesthetized by injecting 10% urethane and 2% chloral hydrate (8 μl/g; intraperitoneal). In general, we tested the images for 2–5 min before each experimental period to ensure that the imaging components were securely fixed and accurately focused on the targeted structure deep in the brain. The mice were given only nitrogen for 10 s and then allowed to recover in air. The optical intrinsic signals were monitored to confirm the hypoxia condition (not shown in the Letter). Figure 2 shows the typical temporal evolutions of hemodynamic changes in the thalamus during acute hypoxia. After breathing in nitrogen, the CBF of the mice decreases abruptly to () of the baseline (defined as the average CBF for 10 s before the mice are ventilated with nitrogen) in (, defined as the time from the start of the ventilation of the mice with nitrogen to the time when the CBF is minimum in the thalamus). The mice were then rescued and, therefore, the CBF begins to recover due to reperfusion. The maximum CBF increases to () in (, defined as the time from which the CBF is minimum to the time at which the CBF is maximum) and then returns to its basal level. This three-phase change of the CBF with respect to time is similar to that observed in the cortex in previous studies [23,24], where the inhalation of nitrogen resulted in a decrease of the CBF, followed by massive hyperemia. However, there is a significant difference in the magnitude of change between the cerebral cortex and thalamus, where the minimum residual CBF in the cortex is lower than that in the thalamus (decreased to , ). In contrast, there is no significant difference in the degree of hyperemia in the cortex (increased to in cortex, ). In addition, there is a significant difference between the duration of hyperemia in the cortex and thalamus ( in cortex, in thalamus, ), which may indicate that the protective mechanism of the brain is probably more effective in the cerebral cortex compared to that in the subcortical structures.
The tMCAo mouse models were also used to induce focal cerebral ischemia in order to demonstrate the validity of our method. The mice were anesthetized by the injection of 10% urethane and 2% chloral hydrate (8 μl/g; intraperitoneal). Prior to the tMCAo surgery, the baseline CBF was obtained by LSCI over a period of 20 min. The experimental procedure was performed according to the procedure described in detail in Ref. . In brief, a standardized suture coated with silicone rubber (602156PK5Re; Doccol Corporation) was inserted into the ipsilateral common carotid artery (CCA) and advanced over the internal carotid artery to occlude the origin of the middle cerebral artery (MCA). The wound was then sutured and each mouse was fixed on a mount for imaging. The CBF maps were obtained by LSCI for 2 min in every 10 min. After 90 min of occlusion, the suture was withdrawn to allow tissue reperfusion. An additional 4 h of data were recorded to demonstrate the impact of reperfusion. We also occluded the contralateral MCA responding to the hemisphere where we implanted the microendoscope using the same surgical procedure as control. Figure 3 shows the typical temporal evolutions of the hemodynamic changes. It can be seen that after application of the occlusion suture, the CBF in the thalamus vessel decreases by almost 100%, and there is no significant difference between the CBF in the vessel and parenchyma tissue (). After removal of the occlusion suture, the CBF in the thalamus recovers to of the baseline in 4 h, which indicates that there is sufficient occlusion and reperfusion in the vessel. In the control group, the CBF in the contralateral thalamus only decreases by during the tMCAo, and the CBF then recovers to of the baseline after removal of the suture. The impact of tMCAo on the CBF is minor in the contralateral thalamus compared with that in the ipsilateral thalamus. It is worth noting that the total cerebral blood circulation is cut by almost 50%, because one of the two CCAs is ligated for suture insertion. Both hemispheres are compromised by this shortage of blood supply. Therefore, the decrease of CBF in the contralateral thalamus is primarily caused by CCA ligation, rather than the tMCAo itself. There is also a reduction of CBF after ischemia in the contralateral hemisphere and hyperperfusion in the ipsilateral side. A similar phenomenon was observed in a previous study . However, the reduction of CBF was small and not accompanied by a decrease in contralateral somatosensory evoked potential amplitudes. In fact, the reduction of CBF can be largely attributed to the CCA occlusion . Furthermore, we noticed that there is a more severe drop of blood flow in the deep regions of the brain  and a longer period of hyperperfusion after reperfusion . Long-term hyperperfusion was observed in a previous study, which is likely because the thalamus is located in the penumbra during the MCAo preparation. Frykholm et al.  observed post-ischemic hyperperfusion for more than 8 h in the penumbra regions of a primate model. This long period of hyperperfusion may be attributed to the degree of neurological deficit, as well as the volume of histological infarction, which depends on both the duration of the occlusion and level of residual flow. Moreover, there may be different mechanisms between ischemic injury and reconstruction of blood circulation in the cortex and subcortical structures.
In summary, we developed an optical method in which LSCI is combined with microendoscopy to achieve time-lapse blood flow detection in deep regions of the brain, which will greatly facilitate in investigating the physiological and pathological mechanisms of blood flow regulation in regions below the cortex.
National Key Research and Development Program of China (2017YFB1002503); National Natural Science Foundation of China (NSFC) (31471083, 61721092, 61775071); Fundamental Research Funds for the Central Universities, HUST (2018KFYXKJC035); Director Fund of Wuhan National Laboratory for Optoelectronics.
1. A. K. Dunn, T. Bolay, M. A. Moskowitz, and D. A. Boas, J. Cereb. Blood Flow Metab. 21, 195 (2001). [CrossRef]
2. F. Helmchen and W. Denk, Nat. Methods 2, 932 (2005). [CrossRef]
3. P. Theer, M. T. Hasan, and W. Denk, Opt. Lett. 28, 1022 (2003). [CrossRef]
4. J. Yao, L. Wang, J. M. Yang, K. I. Maslov, T. T. Wong, L. Li, C. H. Huang, J. Zou, and L. V. Wang, Nat. Methods 12, 407 (2015). [CrossRef]
5. G. Hong, J. C. Lee, J. T. Robinson, U. Raaz, L. Xie, N. F. Huang, J. P. Cooke, and H. Dai, Nat. Med. 18, 1841 (2012). [CrossRef]
6. S. P. Chong, C. W. Merkle, D. F. Cooke, T. Zhang, H. Radhakrishnan, L. Krubitzer, and V. J. Srinivasan, Opt. Lett. 40, 4911 (2015). [CrossRef]
7. R. P. Barretto, T. H. Ko, J. C. Jung, T. J. Wang, G. Capps, A. C. Waters, Y. Ziv, A. Attardo, L. Recht, and M. J. Schnitzer, Nat. Med. 17, 223 (2011). [CrossRef]
8. M. J. Levene, D. A. Dombeck, K. A. Kasischke, R. P. Molloy, and W. W. Webb, J. Neurophysiol. 91, 1908 (2004). [CrossRef]
9. Q. Guo, J. Zhou, Q. Feng, R. Lin, H. Gong, Q. Luo, S. Zeng, M. Luo, and L. Fu, Biomed. Opt. Express 6, 3919 (2015). [CrossRef]
10. R. P. Barretto, B. Messerschmidt, and M. J. Schnitzer, Nat. Methods 6, 511 (2009). [CrossRef]
11. Y. Ziv, L. D. Burns, E. D. Cocker, E. O. Hamel, K. K. Ghosh, L. J. Kitch, A. El Gamal, and M. J. Schnitzer, Nat. Neurosci. 16, 264 (2013). [CrossRef]
12. E. J. Hamel, B. F. Grewe, J. G. Parker, and M. J. Schnitzer, Neuron 86, 140 (2015). [CrossRef]
13. A. Monfared, N. H. Blevins, E. L. Cheung, J. C. Jung, G. Popelka, and M. J. Schnitzer, Otol. Neurotol. 27, 144 (2006). [CrossRef]
14. A. Ponticorvo, D. Cardenas, A. K. Dunn, D. Ts’o, and T. Q. Duong, J. Biomed. Opt. 18, 090501 (2013). [CrossRef]
15. T. H. Kong, S. Yu, B. Jung, J. S. Choi, and Y. J. Seo, PLoS ONE 13, e0191978 (2018). [CrossRef]
16. A. Y. Neganova, D. D. Postnov, O. Sosnovtseva, and J. C. Jacobsen, PLoS ONE 12, e0173805 (2017). [CrossRef]
17. Y. Li, R. Liu, Y. Wang, D. Wen, L. Meng, J. Lu, and P. Li, Opt. Express 24, 8382 (2016). [CrossRef]
18. P. C. Li, S. L. Ni, L. Zhang, S. Q. Zeng, and Q. M. Luo, Opt. Lett. 31, 1824 (2006). [CrossRef]
19. Y. Wang, W. Lv, X. Chen, J. Lu, and P. Li, Opt. Lett. 42, 4655 (2017). [CrossRef]
20. O. Thompson, M. Andrews, and E. Hirst, Biomed. Opt. Express 2, 1021 (2011). [CrossRef]
21. J. C. Ramirez-San-Juan, R. Ramos-Garcia, G. Martinez-Niconoff, and B. Choi, Opt. Lett. 39, 678 (2014). [CrossRef]
22. K. A. Tennant, S. L. Taylor, E. R. White, and C. E. Brown, Nat. Commun. 8, 15879 (2017). [CrossRef]
23. A. Mayevsky, Mitochondrion 9, 165 (2009). [CrossRef]
24. H. Shi, N. Sun, A. Mayevsky, Z. Zhang, and Q. Luo, J. Innov. Opt. Heal. Sci. 07, 1450033 (2014). [CrossRef]
25. C. Kleinschnitz, M. Pozgajova, M. Pham, M. Bendszus, B. Nieswandt, and G. Stoll, Circulation 115, 2323 (2007). [CrossRef]
26. N. Matsumiya, R. C. Koehler, and R. J. Traystman, Stroke 21, 908 (1990). [CrossRef]
27. A. Livnat, E. Barbiro-Michaely, and A. Mayevsky, J. Neurosci. Meth. 188, 76 (2010). [CrossRef]
28. P. Frykholm, J. L. R. Andersson, J. Valtysson, H. C. S. Silander, L. Hillered, L. Persson, Y. Olsson, W. R. Yu, G. Westerberg, Y. Watanabe, B. Långström, and P. Enblad, Acta Neurol. Scand. 102, 18 (2000). [CrossRef]