Development Of An Optical Spectroscopic Method To Detect Neurohemodynamic Changes Induced By Pain In Rats

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2010-03-03T23:30:45Z

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Biomedical Engineering

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In my optical spectroscopy study, multi-channel, thin optical probes with short source-detector separation have been implemented, calibrated, and used along with a multi-channel spectrometer as a quantitative technique to observe changes in vascular hemoglobin concentration and hemoglobin oxygen saturation as well as light scattering of tissues. Each of the four needle probes used in my study consists of two bifurcated fibers; one fiber is connected to a halogen light source and the other fiber to a CCD-array spectrometer having a spectral window from 450 nm to 900 nm. Each fiber is 100 micron in diameter. As we utilized the four probes individually and simultaneously, a source multiplexer and a multi-channel spectrometer were used during the measurement. For quantification of tissue physiological parameters, each probe needed to be calibrated to remove the instrumentation effects. The laboratory experiments using liquid tissue phantoms were conducted to calibrate the probes and to validate the algorithm that was developed to quantify oxygenated [HbO] and deoxygenated [Hb] hemoglobin concentrations and scattering coefficients. These calibrated probes were then applied for the pain study in rats. Two probes were placed on the spinal cord and two on the primary somatosensory (S1) region of the rat brain, each on the ipsilateral and contralateral sides of the rat. While the paw of the rat was stimulated using electrical stimulations, hemodynamic responses were acquired at the four locations on the rat. The results showed a significant increase in [HbO] and a decrease in [Hb] at the ipsilateral side of the spinal cord and contrlateral side of the S1 region of the brain, caused by the electrical stimulation at the paw. My study demonstrates that multi-channel reflectance spectroscopy is a useful tool to study pain mechanism at different central nervous sites using animal models.

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