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Intrinsic optical imaging (IOI) has emerged as a very powerful tool to assess neuronal function in small animals. Although it has been used extensively in the brain, its application to the spinal cord is rare. The inability of intrinsic optical techniques to resolve different depths and embedded gray matter hampers their capacity to distinguish larger vasculature contributions of hemodynamic signals originating from motoneuron and interneuron activation. Laminar optical tomography (LOT) is a recently-developed method that fills the gap left between IOI and diffuse optical imaging. With distinct source-detector separations, light that propagates deeper into tissues can be distinguished from light originating from the surface, providing depth sensitivity. In this work, LOT is investigated for the first time to image spinal cord activation with simultaneous IOI of the cortex in rats. Such proof of concept provides a powerful imaging modality to study spinal cord activation and disruption after injury.
©2010 Optical Society of America
1. Introduction
Approximately 12,000 spinal cord trauma cases are reported each year in North America [1], and the emergence of new neuronal recording techniques is crucial to understanding post-traumatic neuronal reorganization. In animal models, disruption of the descending neuronal tracts after spinal cord injuries (SCI) is known to decrease the locomotor performance of limbs caudal to the lesion site to various degrees, depending on lesion severity and location [2–6]. It has been established that functional and structural reorganization occurs after SCI [5]. Increased sprouting and synaptic reorganization are thought to combine with the reduction of supraspinal descending pathways to modulate clinical conditions associated with injuries, such as bladder dysfunction, chronic neuropathic pain, autonomic dysreflexia, locomotion capacity and spasticity. Conventional electrophysiological recordings detect heightened lumbar spinal excitability after SCI [6,7]. Recent work [8] with BOLD functional magnetic resonance imaging (fMRI) has revealed increased sensitivity to stimulation at 3 to 6 months post-SCI, thus exploring potential causes of neuropathic pain and autonomic dysreflexia.
The spatial extent of spinal cord activation is very difficult to study in vivo with conventional electrophysiological techniques. High field fMRI can image neuronal function, but the small size of the cord, physiological motion and susceptibility artefacts have hampered fMRI investigations so far, with clear and consistent results being published only recently [9–13]. The contrast mechanism for BOLD is the same as for the brain, but detection is still a challenge, even in the presence of strong stimuli [14,15]. Evidence of reorganization in the spinal cord below the injury has been obtained with BOLD fMRI [8], but vasculature disruption, caused by the lesion, may have biased the results and were not discussed by the authors.
Intrinsic optical imaging (IOI) is emerging as a very powerful tool to assess neuronal function in small animals. Although it has been used extensively in the brain, its application to the spinal cord is rare. Movements, animal preparation, the inversion of gray and white matter when compared to the cortex have led to increased experimental and methodological complexity. In particular, the inability of intrinsic optical techniques to resolve different depths has raised issues with spinal cord applications. Embedded gray matter hampers the ability to distinguish larger vasculature contributions of hemodynamic signals originating from motoneuron and interneuron activation, which may explain why the spinal cord has not been explored extensively in the optics literature, except for the cervical area [16]. Voltage sensitive dyes have been used in in-vitro preparations but not in the adult rat. Staining the embedded gray matter is an issue and the faint fluorescence signal has to travel across tissue decreasing sensitivity. Laminar optical tomography (LOT) is a recently-developed technique that fills the gap left between IOI and diffuse optical imaging. It is based on a fast scanning light source with detectors across the surface of tissues to be imaged. With distinct source-detector separations, light that propagates deeper into tissues can be distinguished from light originating from the surface, providing depth sensitivity. By combining LOT measurements with an accurate light propagation model, image reconstruction algorithms can deliver 3D images of hemodynamics with high spatial accuracy.
In the present work, LOT spinal cord imaging was investigated, for the first time, to establish whether it can be applied to study spinal cord neuronal activation in anesthetized rats. Simultaneous IOI of the brain and LOT imaging of the spinal cord were performed after hind paw stimulation. The results indicate that LOT is able to image neuronal activation in the spinal cord and that simultaneous imaging of the spinal cord and brain is possible. This proof of concept should be the starting point for further studies to assess spinal cord lesions.
2. Methodology
2.1 Animal preparation
Surgical procedures, performed according to the recommendations of the Canadian Council on Animal Care, were approved by the Animal Research Ethics Committee of Hôpital du Sacré-Coeur de Montréal. Two female Sprague-Dawley rats (300 and 360 g) were tested. They were anesthetized with isoflurane (5%), and body temperature was maintained at 37°C with a heating blanket (50-7053-F, Harvard Apparatus, Holliston, MA). Stimulation was provided by bipolar stainless steel electrodes implanted in the left hind paw. Both heart rate (AT-601G, Nihon Kohden, Tokyo, Japan) and expired CO2 level (Capstar 100, CWE Inc., Ardmore, PA) were monitored. A tracheotomy was undertaken, and the rats were artificially ventilated (TOPO, Kent Scientific, Torrington, CT) with ambient air. Their breathing rate was maintained between 60 and 80 cycles/min, with approximately 2-ml tidal volume and set to obtain an end tidal expired CO2 concentration of 3%. The rats were then positioned on a custom-made stereotaxic frame to fix the spinal cord and minimize movement artefacts due to both respiration and hind paw stimulation. Lumbar spinal cord segments from the thoracic (T10) to the sacral (S1) area were exposed by laminectomy. Isoflurane administration was stopped after surgery; the animals were then anesthetized with alpha-chloralose (first by a 50 mg/kg bolus and then by 40 mg/kg/h). Thirty min were allowed for animal stabilization. Mineral oil was added to prevent drying of the spinal cord, and 2 sets of clamps were applied on the vertebrae to avoid any longitudinal spinal cord movement induced by breathing. The area surrounding the spinal cord was covered with Gelfoam (Pharmacia & Upjohn, Sumerset County, NJ) to prevent bleeding. For concurrent brain imaging, the skull was thinned to around 100 µm over the somatosensory cortex.
2.2 Acquisition system
The LOT experimental set-up in this study was similar to that described in detail in other papers [17–22], but its main features are briefly mentioned here. Figure 1 gives the schematics of the LOT system. Illuminated by a laser diode (690 nm, HL6738, Opnext Inc., Fremont, CA), it outputs a beam that is collimated, linearly polarized and then sent into the beam splitter (50:50, 25.4 mm diameter). The beam emerging from the beam splitter is incident into the scanning system that consists of 2 scanning mirrors. To achieve a high frame rate and an adequate number of pixels in the digitized images, the scanning device deploys a resonant scanner (SC-10 with PLD-XYG driver, Electro-Optical Products Corp., Fresh Meadows, NY) that oscillates sinusoidally at a resonant frequency of 1.5 kHz. The second scanner, controlled by the same driver, is a galvanometer mirror (6810P, Cambridge Technology, Inc., Lexington, MA) operating at 15 Hz and providing a linear frame-scan with an effective rate of 7.5 frames/s. The magnitude of scanning angles of the mirrors can, in turn, be controlled by adjusting the system’s field of view (FOV). The beam reflects from the galvanometer mirror and is focused by scan lens (f = 25.4 mm, 25.4 mm diameter) on an intermediate focal plane. This plane is then imaged directly into the sample by objective lens (f = 50 mm, 25.4 mm diameter). Light re-emitted from the imaged object passes back up through the objective and scan lens and onto the scanning system, where it is de-scanned and reflected towards the detection plane by the beam splitter. A second polarizer is placed before the detector array to reduce the effect of specular reflections [22]. An 4 × 8 avalanche photodiode detector (APD) array (S8550, Hamamatsu Photonics K.K., Hamamatsu city, Shizuoka Pref., Japan) is positioned in this plane, capturing light at different radial positions relative to the centre of the scanning spot, which is focused on the first element, although it is not employed for acquiring data, because the signal contains some specular reflections despite the presence of crossed polarizers. In the experiments reported below, only one line of 8 elements was used. Each APD element was recorded by an analog input channel in the data acquisition card (16 bit, 8 channels, 250 kS/s per channel, PCI-6143, National Instruments, Austin, TX). Data acquisition was synchronized with galvanometer movement by a custom-made graphics interface developed in LabView (National Instruments). The formed images were equivalent to tomographic reflectance measurements from 192 × 80 = 15,360 source positions (over a 5.5 × 8 mm2 maximal FOV) and 192 × 80 × 7 = 107,520 detector positions, even though these acquired images are later downsampled to a smaller grid to perform 3-D reconstruction. The size of the detection spot on the sample was 0.64 mm2. The minimum and maximum source-detector distances were respectively 575 and 4,025 µm (see Fig. 2 ), allowing the detection of absorption variations at depths of approximately 2.5 mm according to Monte Carlo simulations.
Fig. 2 Representative slices from the volume at 1 mm (A) Numerical complex phantom with 100% contrast, (B) reconstruction from simulated data (C) Reconstruction from experimental data, showing a completely absorbing wire of 150μm diameter placed along x-axis.
Intrinsic optical images were acquired with a 12-bit CCD camera (CS3960DCL, Toshiba Teli, Tokyo, Japan) at 1,392 x 1,040 resolution and 6.45-μm pixel size. A separate custom-made LabView interface (National Instruments) served to control the camera, record images, synchronize acquisition and electrical stimulation, and change the illumination. A Nikkor Macro lens (f = 50 mm) with small focal depth (350 μm) was used. Functional images of the brain were recorded under multiple hashing illumination (525, 590, 637 nm) produced by high-power LEDs (Optek Technologies, Carrollton, TX). Illumination was set so that no part of the brain was under- or over-saturated by any of the wavelengths.
2.3 LOT image reconstruction
The image reconstruction problem is ill-posed and regularisation is necessary to get appropriate solutions. To validate the LOT image reconstruction process, a simulation framework with identical source-detector geometry as the experimental system was used. Both simple and complex numerical phantoms were simulated in the turbid media at different depths. Figure 2(A) shows the simulation of a complex phantom located at 1mm depth. To generate realistic measures, Monte-Carlo simulations were used and 1 percent noise was added corresponding to the level of noise obtained in experiments. To avoid inverse-crime issues, the sensitivity matrix for the inverse problem was generated with Monte-Carlo simulations at a different spatial resolution than that used for the forward problem. As described in ref [17], a Tikhonov regularisation scheme was used and the regularisation parameter was chosen by using a L-curve technique [23]. Results of these simulations are provided in Fig. 2(A) and 2(B). Other phantoms were simulated with similar results (data not shown).
The system was also validated experimentally on liquid phantoms, which consisted of a completely absorbing wire (150 μm in diameter) placed in an intralipid-india ink dilution (μa = 0.01mm−1, μs = 10mm−1 at 690nm) whose optical properties resemble those of gray matter [24]. Using a millimetric stage, the wire was placed at different depths and the image reconstruction algorithm optimized above was used to recover a 3D volumetric image. The wire could be imaged up to 1.5mm depth establishing the system limit in this configuration. The results for the case of 1mm depth are presented in Fig. 2(C). The regularization parameters used were the same as those optimized on the numerical phantom for this level of noise. The same parameters are used for the in-vivo reconstructions below.
2.4 Imaging protocol
This experimental set-up served to image functional activity in the spinal cord of 2 uninjured rats. They were both stimulated with N = 29 blocks of 60 s. Each stimulation block was of 1-s duration with a 20-s inter-stimulus interval. The stimulus consisted of 3-Hz repetition of 3-ms current pulse delivered by an electrical current stimulator (Model 2200, A-M Systems, Carlsborg, WA). Intensity was adjusted to the previously-measured muscular threshold. The typical muscular threshold value was 1 mA, and stimulation intensities of 0.9X, 1.2X and 1.5X threshold were interlaced. IOI was performed simultaneously on the somatosensory cortex to compare and correlate functional activity in both parts of the central nervous system.
With illumination wavelength of 690 nm, deoxy-hemoglobin (HbR) was the principal chromophore providing absorption contrast. The scanning zone was located in the lumbar section with a length of 7.5 mm and a width of 2.5 mm along the dorsal vein. The formed images are equivalent to tomographic reflectance measurements from 192 × 80 = 15,360 source positions and 192 × 80 × 7 = 107,520 detector positions, even though these acquired images are later downsampled to a smaller grid to perform 3-D reconstruction.
The system was adjusted so that the scan covers both sides of the cord. Two configurations were studied separately in the 2 animals to investigate the best orientation for LOT imaging in the spinal cord. In the first configuration, the source and detectors were lined perpendicular to the rostral-caudal axis. In this configuration, the detectors and sources had a limited scanning area on the spinal cord, and while the results confirmed a hemodynamic response, it was not as robust as the second configuration. In the second configuration, used below to describe the results, the source-detectors were parallel to the rostral-caudal axis.
3. Results
Figure 3(B) presents a reflectance image of the scanning zone obtained with the LOT system. This image, generated by the first detector which was closest to the source, displays features corresponding to the superficial vasculature of the exposed spinal cord. Three regions of interest were chosen to plot the time course of absorption variations, the first on the ipsilateral side with respect to stimulation, the second on the contralateral side, and the third in the dorsal vein. After electric nerve stimulation of the left hind paw, neural activation led to increased blood flow and blood volume, reducing local HbR concentration. Local absorption of light at 690 nm decreased correspondingly, so an increment of reflected light was expected. Figure 3 displays the recorded signals on all 7 detectors after stimulation; signal intensity in the ipsilateral region [Fig. 3(A)] presented a slight dip, followed by a notable increase on each detector. Stimulations of 0.9X, 1.2X and 1.5X muscle threshold were interlaced, and the curves clearly show the heightened hemodynamic response with stimulation intensity over the 60-s period that was averaged. In the case of higher stimulation thresholds (1.2X and 1.5X), stronger systemic variations were observed and the data was not used for image reconstruction below to focus on the specific response. The signal to noise ratio (SNR) was high, as confirmed by the standard deviations displayed in gray.
Fig. 3 LOT hemodynamic response with left hind paw stimulus intensity at 0.9 × , 1.2 × and 1.5 × muscle threshold in normal rat, vertical lines indicate stimulus onset and intensity. Black line represents the block-averaged mean signal and shaded area indicates ± 1 standard deviation, N = 29 blocks of 60 seconds. (A) Averaged time course of region 1, which is ipsilateral to stimuli. (B) Top: Imaged area of the exposed spinal cord, the bright spot located at the lower part is due to specular reflection artefacts. Bottom: Time course (red dotted line) of region 2, corresponding to the superficial blood vessel shows ~1 s delayed activation with regard to ipsilateral activation. (C) Time course of region 3, contralateral to stimuli. In this region smaller amplitude and greater deviations are observed when compared to ipsilateral response.
While the magnitude of the response depended on the intensity of stimulation, it also depended on the source-detector pair, reflecting depth of activation. Absorption variations were observed to be more significant on the second, third, fourth and sixth detectors with the fourth detector being the strongest. The sensitivity of a given detector pair to absorption changes is described by a banana shape extending deeper in tissue as source-detector distance is increased. The distance between the source and detector for the fourth pair, 2300μm, leads to a sensitivity shape overlapping the location of gray matter located at depths between 200 and 2000μm inside the spinal cord and suggests that LOT is able to distinguish the different layers. However this reasoning is based on a template spinal cord section taken from histology (see below) and should be taken with caution.
In Fig. 3(C), contralateral activation was also observed but at a lower magnitude (about half) than ipsilateral activation [Fig. 3(A)] and with greater variance, confirming the predominance of ipsilateral activation in the spinal cord. This was not unexpected as measurement sensitivity with separated source and detectors follows a banana-shaped pattern that also extends in the ipsi-contralateral axis. The blood then drained through the venous system, particularly though the dorsal vein. At the bottom of Fig. 3(B), the time course of reflectance in a region of interest in the vein indeed shows a coherent time delay of approximately 1 s between the ipsilateral response and the venous response.
Given that the main goal of LOT system development was to study partial lesions, the feasibility of concurrent IOI of the somatosensory cortex was investigated, while the spinal cord was being imaged by LOT. Figure 4 displays simultaneously-quantified activation in the cortex and spinal cord, confirming the ability of our system to perform such measurements. The exact location where the afferent signal was the highest was 3 mm lateral and 1 mm caudal to the bregma. Panel A illustrates a thresholded time course of LOT signals from the first detector in the spinal cord after stimulation. The expected behaviour, described previously by the curves in Fig. 3, could be localized spatially. An initial slight dip was seen locally in the dorsal vein at 3.6 s, then an increase appeared on the left side of the cord at 7.20 s. Blood is finally drained through the dorsal vein at 10.80 s. In contrast to the cortex, where the hemodynamic response after hind paw stimulation was contralateral, the spinal cord response was ipsilateral. Panel B shows the intrinsic optical signal of the somatosensory cortex which appears to be mostly contralateral to stimulation, as expected.
Fig. 4 (A) Time course of LOT signals, induced by left hind paw stimulation, collected over 15 s at the 0.9 × muscle threshold (detector 1 with a source-detector separation of 575 µm). (B) Photo of the exposed cortex (left) and maximum intrinsic optical signal acquired simultaneously on the somatosensory cortex (right).
The LOT data from all detectors were combined with a Monte Carlo model of light propagation and a Rytov approximation to reconstruct 3D images of neural activation of whole spinal cord volume. The data on each source-detector pair were initially averaged between 4.5 and 9 s post-stimulation to generate measures for the inverse problem at maximum activation. A baseline measure was constructed by averaging the intensity for the 3 second period prior to stimulation. A differential measurement with respect to that taken 3 s prior to stimulation was constructed. A 3D mesh with voxel size of 150 µm was used. Measures corresponding to specular reflections (in the first detector, localized spatially) were not used for the inversion process. The mesh for reconstruction was 16 x 48 x 10 in x, y and z, respectively thus extending to 1.5mm depth consistent with previous phantom work. To account for tissue heterogeneity, a template histological cut of the spinal cord was used to create the medium for photon propagation. Figure 5(B) displays the histological image that was segmented into gray and white matter. Each tissue type was then assigned optical properties according to the literature [24], that is: μa = 0.09 mm−1 and μs’ = 4 mm−1 for white matter, μa = 0.02 mm−1 and μs’ = 0.9 mm−1 for gray matter. Monte-Carlo simulations were used to simulate photon propagation in the cord. Figure 5(A) shows the sensitivity matrix for a given source-detector pair. The inverse problem was performed with Tikhonov regularization. As mentioned above, the regularization parameter was optimized for a similar noise level to that measured separately for the same system in liquid phantoms. Figures 5(C) and 5(D) show the results of the reconstruction.
Fig. 5 (A) Depiction of the sensitivity matrix for a given source-detector pair. (B) Histology based segmented model of lumbar spinal cord of the rat, utilized for Monte Carlo simulation of light propagation. Dotted red lines indicate the extent of field of view, (C) 3D map of neural activation in the spinal cord induced by left hind paw stimulation at the 0.9 × muscle threshold. Ipsilateral activation around z = 0.4 mm is consistent with interneuron activation.(D) Reconstruction viewed across the segmented volume along the line in (C).
On superficial layers, most of the signal is ipsilateral [Fig. 5(C)] and correspond to the location of gray matter in the cord [Fig. 5(D)]. It is consistent along the slice and located around L4-L5 (see Fig. 3). The signal from the dorsal half of the spinal cord was representative of interneuron activation [25], and in accordance with anatomical expectations for afferences from the sciatic nerve. The ipsilateral signal has been validated by electrophysiology previously in separate work. Deeper in the cord the signal is more diffuse but contralateral activation is observed in the volume that may originate from interneuron connections. Further studies are needed to establish the ability of the system to measure motoneurons activation due to their deeper localisation.
4. Discussion
In this work LOT has been employed for spinal cord imaging in rats. The preliminary results obtained, showed higher SNR than previous measures taken by our group with intrinsic optical imaging in the cord. Spatial resolution was adequate to be indicative of neural activation and its localization. Temporal resolution was sufficient to establish the time course of response to stimuli. Moreover, the system allowed the recovery of depth-resolved signals. Indeed, in all detectors, a reproducible response was measured in LOT with intensity proportional to stimulus strength but also to source-detector distance. When aligning images on histological cuts from a rat spinal cord, the reconstructed signal was observed to be stronger in the gray matter of the cord which is consistent with spinal cord anatomy. Previous IOI approaches have also provided neural activation maps but only the first hundred microns of the spinal cord could be imaged by such a technique. The origin of the optical signal in the latter case was thus more difficult to interpret due to the presence of a large vasculature at the surface. Here, LOT lifts the ambiguity left by IOI and is expected to become a useful tool to investigate the spinal cord in small animals. However depth limitations remain and phantom results indicated that only the first 1.5mm could reasonably be imaged.
The combination of IOI and LOT has been demonstrated to image both the cortex and spinal cord without cross-contamination of optical signals. The proof of principle provided here confirms that simultaneous brain and spinal cord measurements can be compared and correlated. It is expected to open new horizons for the study of SCI, particularly partial lesions. Since it is known that vasculature organization and neural activation of the spinal cord change after a SCI [26], it may be relevant to explore how the cortex adapts to this reorganization. While 3D activation maps recovered in this work provide a volumetric view of activation, it is difficult to fully interpret the results since they only convey amplitude. For example, the observation of contralateral activation deeper in the spinal cord should be investigated in view of the sensitivity analysis of reconstructed solutions as a function of depth. To make LOT a bona fide neuronal technique, statistical measures akin to those in fMRI (e.g. SPM, http://www.fil.ion.ucl.ac.uk/spm/) should be developed to provide statistical maps of activation rather than simple amplitude reconstructions. The statistical information would provide a more accurate assessment of activation, and this will be the subject of future work.
Another limitation of our study is that the actual 3D reconstructions performed involved a template cord model for light propagation. While within a species and at fixed age, the cord is very similar, this procedure is not optimal and improvements will include reconstructions in individualized models. With the new model and the addition of a second illumination source, it will also be possible to discriminate HbO and HbR concentrations and distinguish between contributions of the venous, arteriolar and capillaries systems.
5. Conclusion
Our preliminary study confirmed that LOT can image the spinal cord with adequate SNR and spatial accuracy. Concurrent intrinsic imaging of the brain and the coherent activations observed open new avenues to investigate spinal cord lesions. We hope to pursue lesion studies in the near future.
Acknowledgments
This work was supported by grants from the Centre de recherche, Hôpital du Sacré-Coeur de Montréal and the Natural Sciences and Engineering Research Council of Canada (NSERC) to F. Lesage and E. Beaumont. E. Guevara acknowledges financial support from the Mexican National Science and Technology Council (CONACYT) through scholarship No. 304501 and the Secretariat of Public Education (SEP).
References and links
1. “SPINALCORD: Facts & Figures at a Glance,” http://www.spinalcord.uab.edu.
2. D. M. Basso, M. S. Beattie, and J. C. Bresnahan, “Graded histological and locomotor outcomes after spinal cord contusion using the NYU weight-drop device versus transection,” Exp. Neurol. 139(2), 244–256 (1996). [CrossRef] [PubMed]
3. D. M. Basso, M. S. Beattie, and J. C. Bresnahan, “Descending systems contributing to locomotor recovery after mild or moderate spinal cord injury in rats: experimental evidence and a review of literature,” Restor. Neurol. Neurosci. 20(5), 189–218 (2002).
4. D. S. Magnuson, T. C. Trinder, Y. P. Zhang, D. Burke, D. J. Morassutti, and C. B. Shields, “Comparing deficits following excitotoxic and contusion injuries in the thoracic and lumbar spinal cord of the adult rat,” Exp. Neurol. 156(1), 191–204 (1999). [CrossRef] [PubMed]
5. P. Schucht, O. Raineteau, M. E. Schwab, and K. Fouad, “Anatomical correlates of locomotor recovery following dorsal and ventral lesions of the rat spinal cord,” Exp. Neurol. 176(1), 143–153 (2002). [CrossRef] [PubMed]
6. L. M. Mendell, “Modifiability of spinal synapses,” Physiol. Rev. 64(1), 260–324 (1984). [PubMed]
7. A. Valero-Cabré, J. Forés, and X. Navarro, “Reorganization of reflex responses mediated by different afferent sensory fibers after spinal cord transection,” J. Neurophysiol. 91(6), 2838–2848 (2004). [CrossRef] [PubMed]
8. T. Endo, C. Spenger, E. Westman, T. Tominaga, and L. Olson, “Reorganization of sensory processing below the level of spinal cord injury as revealed by fMRI,” Exp. Neurol. 209(1), 155–160 (2008). [CrossRef]
9. M. Maieron, G. D. Iannetti, J. Bodurka, I. Tracey, P. A. Bandettini, and C. A. Porro, “Functional responses in the human spinal cord during willed motor actions: evidence for side- and rate-dependent activity,” J. Neurosci. 27(15), 4182–4190 (2007). [CrossRef] [PubMed]
10. P.W. Stroman, V. Krause, K.L. Malisza, U.N. Frankenstein, and B. Tomanek, “Extravascular proton-density changes as a non-BOLD component of contrast in fMRI of the human spinal cord,” Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, vol. 48, Jul. 2002, pp. 122–127.
11. P.W. Stroman, B. Tomanek, V. Krause, U.N. Frankenstein, and K.L. Malisza, “Functional magnetic resonance imaging of the human brain based on signal enhancement by extravascular protons (SEEP fMRI),” Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, vol. 49, Mar. 2003, pp. 433–439.
12. P. W. Stroman, J. Kornelsen, J. Lawrence, and K. L. Malisza, “Functional magnetic resonance imaging based on SEEP contrast: response function and anatomical specificity,” Magn. Reson. Imaging 23(8), 843–850 (2005). [CrossRef] [PubMed]
13. P. W. Stroman and L. N. Ryner, “Functional MRI of motor and sensory activation in the human spinal cord,” Magn. Reson. Imaging 19(1), 27–32 (2001). [CrossRef] [PubMed]
14. N. Govers, J. Béghin, J. W. M. Goethem, J. Michiels, L. Hauwe, E. Vandervliet, and P. M. Parizel, “Functional MRI of the cervical spinal cord on 1.5 T with fingertapping: to what extent is it feasible?” Neuroradiology 49(1), 73–81 (2007). [CrossRef]
15. F. Zhao, M. Williams, X. Meng, D. C. Welsh, A. Coimbra, E. D. Crown, J. J. Cook, M. O. Urban, R. Hargreaves, and D. S. Williams, “BOLD and blood volume-weighted fMRI of rat lumbar spinal cord during non-noxious and noxious electrical hindpaw stimulation,” Neuroimage 40(1), 133–147 (2008). [CrossRef] [PubMed]
16. S. Sasaki, I. Yazawa, N. Miyakawa, H. Mochida, K. Shinomiya, K. Kamino, Y. Momose-Sato, and K. Sato, “Optical imaging of intrinsic signals induced by peripheral nerve stimulation in the in vivo rat spinal cord,” Neuroimage 17(3), 1240–1255 (2002). [CrossRef] [PubMed]
17. E. M. C. Hillman, D. A. Boas, A. M. Dale, and A. K. Dunn, “Laminar optical tomography: demonstration of millimeter-scale depth-resolved imaging in turbid media,” Opt. Lett. 29(14), 1650–1652 (2004). [CrossRef] [PubMed]
18. E. Hillman, A. Devor, and D. Boas, “High-resolution functional optical imaging of living tissues,” Biomedical Imaging: Nano to Macro,2006. 3rd IEEE International Symposium, 2006, pp. 1192–1195.
19. E. M. C. Hillman, “Laminar optical tomography: high-resolution 3D functional imaging of superficial tissues,” Proceedings of SPIE, San Diego, CA, USA: 2006, pp. 61431M–61431M–14.
20. E. M. C. Hillman, A. Devor, M. B. Bouchard, A. K. Dunn, G. W. Krauss, J. Skoch, B. J. Bacskai, A. M. Dale, and D. A. Boas, “Depth-resolved optical imaging and microscopy of vascular compartment dynamics during somatosensory stimulation,” Neuroimage 35(1), 89–104 (2007). [CrossRef] [PubMed]
21. B. Yuan, S. A. Burgess, A. Iranmahboob, M. B. Bouchard, N. Lehrer, C. Bordier, and E. M. C. Hillman, “A system for high-resolution depth-resolved optical imaging of fluorescence and absorption contrast,” Rev. Sci. Instrum. 80(4), 043706 (2009). [CrossRef] [PubMed]
22. E. M. C. Hillman, M. Bouchard, A. Devor, A. de Crespigny, and D. A. Boas, “Functional optical imaging of brain activation: a multi-scale, multi-modality approach,” Life Science Systems and Applications Workshop,2006. IEEE/NLM, 2006, pp. 1–2.
23. C. R. Vogel, “Computational methods for inverse problems,” in Frontiers in Applied Mathematics (Society for Industrial and Applied Mathematics, 2002), pp. 106–108.
24. A. N. Yaroslavsky, P. C. Schulze, I. V. Yaroslavsky, R. Schober, F. Ulrich, and H. J. Schwarzmaier, “Optical properties of selected native and coagulated human brain tissues in vitro in the visible and near infrared spectral range,” Phys. Med. Biol. 47(12), 2059–2073 (2002). [CrossRef] [PubMed]
25. W.D. Willis and R.E. Coggeshall, Sensory Mechanisms of the Spinal Cord, 1991.
26. F. Lesage, N. Brieu, S. Dubeau, and E. Beaumont, “Optical imaging of vascular and metabolic responses in the lumbar spinal cord after T10 transection in rats,” Neurosci. Lett. 454(1), 105–109 (2009). [CrossRef] [PubMed]
