Large Volume, Behaviorally-relevant Illumination for Optogenetics in Non-human Primates

This protocol describes a large-volume illuminator, which was developed for optogenetic manipulations in the non-human primate brain. The illuminator is a modified plastic optical fiber with etched tip, such that the light emitting surface area is > 100x that of a conventional fiber. In addition to describing the construction of the large-volume illuminator, this protocol details the quality-control calibration used to ensure even light distribution. Further, this protocol describes techniques for inserting and removing the large volume illuminator. Both superficial and deep structures may be illuminated. This large volume illuminator does not need to be physically coupled to an electrode, and because the illuminator is made of plastic, not glass, it will simply bend in circumstances when traditional optical fibers would shatter. Because this illuminator delivers light over behaviorally-relevant tissue volumes (≈ 10 mm³) with no greater penetration damage than a conventional optical fiber, it facilitates behavioral studies using optogenetics in non-human primates.

Noninvasive optical inhibition with a red-shifted microbial rhodopsin

Optogenetic inhibition of the electrical activity of neurons enables the causal assessment of their contributions to brain functions. Red light penetrates deeper into tissue than other visible wavelengths. We present a red-shifted cruxhalorhodopsin, Jaws, derived from Haloarcula (Halobacterium) salinarum (strain Shark) and engineered to result in red light-induced photocurrents three times those of earlier silencers. Jaws exhibits robust inhibition of sensory-evoked neural activity in the cortex and results in strong light responses when used in retinas of retinitis pigmentosa model mice. We also demonstrate that Jaws can noninvasively mediate transcranial optical inhibition of neurons deep in the brains of awake mice. The noninvasive optogenetic inhibition opened up by Jaws enables a variety of important neuroscience experiments and offers a powerful general-use chloride pump for basic and applied neuroscience.

Semiautomatic vessel wall detection and quantification of wall thickness in computed tomography images of human abdominal aortic aneurysms

PURPOSE

Quantitative measurements of wall thickness in human abdominal aortic aneurysms (AAAs) may lead to more accurate methods for the evaluation of their biomechanical environment.

METHODS

The authors describe an algorithm for estimating wall thickness in AAAs based on intensity histograms and neural networks involving segmentation of contrast enhanced abdominal computed tomography images. The algorithm was applied to ten ruptured and ten unruptured AAA image data sets. Two vascular surgeons manually segmented the lumen, inner wall, and outer wall of each data set and a reference standard was defined as the average of their segmentations. Reproducibility was determined by comparing the reference standard to lumen contours generated automatically by the algorithm and a commercially available software package. Repeatability was assessed by comparing the lumen, outer wall, and inner wall contours, as well as wall thickness, made by the two surgeons using the algorithm.

RESULTS

There was high correspondence between automatic and manual measurements for the lumen area (r = 0.978 and r = 0.996 for ruptured and unruptured aneurysms, respectively) and between vascular surgeons (r = 0.987 and r = 0.992 for ruptured and unruptured aneurysms, respectively). The authors' automatic algorithm showed better results when compared to the reference with an average lumen error of 3.69%, which is less than half the error between the commercially available application Simpleware and the reference (7.53%). Wall thickness measurements also showed good agreement between vascular surgeons with average coefficients of variation of 10.59% (ruptured aneurysms) and 13.02% (unruptured aneurysms). Ruptured aneurysms exhibit significantly thicker walls (1.78 +/- 0.39 mm) than unruptured ones (1.48 +/- 0.22 mm), p = 0.044.

CONCLUSIONS

While further refinement is needed to fully automate the outer wall segmentation algorithm, these preliminary results demonstrate the method's adequate reproducibility and low interobserver variability.