Modeling the Spatiotemporal Dynamics of Light and Heat Propagation for In Vivo Optogenetics

Abstract

Despite the increasing use of optogenetics in vivo, the effects of direct light exposure to brain tissue are understudied. Of particular concern is the potential for heat induced by prolonged optical stimulation. We demonstrate that high-intensity light, delivered through an optical fiber, is capable of elevating firing rate locally, even in the absence of opsin expression. Predicting the severity and spatial extent of any temperature increase during optogenetic stimulation is therefore of considerable importance. Here, we describe a realistic model that simulates light and heat propagation during optogenetic experiments. We validated the model by comparing predicted and measured temperature changes in vivo. We further demonstrate the utility of this model by comparing predictions for various wavelengths of light and fiber sizes, as well as testing methods for reducing heat effects on neural targets in vivo.

Figure 1. Monte Carlo simulations can predict light spread through the brain in 3-dimensions

(A) Depiction of the difference between models with idealized light spread (left) and light spread simulating absorption and scattering in media (right). (B) Model for coupling between a laser emitting collimated light and a fiber optic cable (left) and the method for incorporating this model into photon initiation in the Monte Carlo simulation (right). (C) Fluence rate (intensity) predicted for 532 nm light out of an optical fiber (62 μm, NA .22) by Monte Carlo Simulation as a function of distance from the fiber. Inset, intensity predicted for an idealized model as in (A). See also Figure S1.

Figure 2. Realistic bio-heat models can predict light-induced temperature change

(A) Depiction of the combination of Monte Carlo simulation with the Pennes’ Bio-Heat equation for modeling light-induced heat changes in a homogenous block of brain. (B) Heat changes predicted by the bio-heat model for 532 nm light from an optical fiber (62 μm, NA .22), plotted as a function of time and depth. Heat was calculated as the average heat change in circles of 250 μm radius, concentric with optical fibers. (C) Temperature change for the bio-heat model with (black) and without (gray) heat diffusion as a function of time at the depth of 400 μm, as marked by the stippled line in (B). See also Figure S2-4.

Figure 3. Optical parameters measured in vivo yield accurate predictions of temperature change

(A) Fluence rate using optical parameters from Johansson, 2010 (left) and Yaroslavsky et al., 2002 (right). (B) Temperature change for model parameters, as in (A). (C) Temperature changes measured for various distances between an optical fiber (532 nm, 62 μm, NA .22) and a thermistor for both 10 mW (gray circles) and 20 mW (black circles) power output. Error bars indicate the range of all measured temperatures across 5 repetitions. On the same axes, predicted temperature change as a function of depth is plotted for both sets of model parameters with 10 mW and 20 mW light power. The effects of direct light were also included and plotted for the first model (dashed lines). (D) Temperature change as a function of time from light onset for 20 mW light power recorded at a thermistor 400 μm away from an optical fiber as in figure 3. Super-imposed are the predictions for the model with (green, dashed) and without (green) a compensatory delay measured for the thermistor. Error bars indicate the entire range of recorded values. (E) Left, schema for single unit recordings with an optrode (fiber and stereotrode bundle) in the PFC. During ipsilateral stimulation, light (532 nm through 200 μm, .22 NA fiber) was delivered on the same side that the single units were recorded, while during contralateral stimulation, light was delivered on the opposite side. Right, Predicted peak temperature changes (after 30 seconds of illumination) and intensity values at the location of the stereotrode bundle (400 μm below the fiber tip) are plotted in the box above for the three light powers tested: 1, 5, and 10 mW. Firing rate of single units in the prefrontal cortex during 30 second periods of light illumination are plotted for these light powers (averaged across 5 repetitions) for ipsilateral (green) and contralateral (black) illumination. Firing rate was calculated as percent change from before illumination. * p < .05, ** p < .01, Wilcoxon signed-rank test between ipsilateral and contralateral conditions. n = 23 single units from 3 mice.

Figure 4. The effect of fiber size on light propagation and heat induction

(A) Left, iso-contour lines for light intensity predicted by the Monte Carlo simulation as a function of distance from the fiber (.22 NA) for 10 mW of various wavelengths of light out of a 62 μm (top) and 200 μm (bottom) fiber. Right, predicted temperature changes for 10 mW, 532 nm light for a 62 μm (top) and 200 μm (bottom) fiber. (B) Peak temperature change (maximum temperature change in a voxel) as a function of power output for 62 μm and 200 μm fibers as in (D). (C) Temperature change as a function of depth, quantified as in (A) for the 62 μm and 200 μm fiber of (D). See also Figure S5-6.

Figure 5. The effect of wavelength on light propagation and heat induction

(A) Predicted temperature change as a function of distance from the optical fiber (62 μm, .22 NA) for 473, 532, 561 and 593 nm light. All plots have the same color scale. Text indicates peak temperature in a single voxel. (B) Predicted temperature change as a function of depth (as in A) for the same wavelengths as in (A).

Figure 6. Light-induced temperature change drops linearly with duty cycle

Predicted temperature change is plotted in gray as a function of time for 10 mW power light out of an optical fiber (473 nm, 62 μm, .22 NA) at duty cycles of 100% (top), 50% (middle), and 10% (bottom). Superimposed blue lines indicate predicted temperature changes for continuous 10 mW (top), 5 mW (middle), and 1 mW (bottom) light power.