By Yatang Li
Neural pathways for defensive behaviors elicited by non-visual cues include hypothalamus, amygdala and periaqueductal gray (PAG) (Silva et al., 2016). Although visual cues play an important role in the defensive behaviors of animals, the underlying neural circuit mechanism remain poorly understood. In the mouse, an overhead expanding dark disc, which mimics an aerial approaching predator, elicits robust freezing or flight behaviors (Yilmaz and Meister, 2013). Previous studies indicate that the superior colliculus (SC) occupies a key node in the neural pathway controlling looming-evoked behaviors. Freezing behaviors appear to be mediated via the retina-SC-thalamus-amygdala pathway (Wei et al., 2015) or the retina-SC-PBGN-amygdala pathway (Shang et al., 2015). In a recent journal club, we discussed a paper from Tiago Branco’s lab at UCL about the neural circuit for the flight behavior (Evans et al., 2018). This paper, titled “A synaptic threshold mechanism for computing escape decisions”, was posted on February 27, 2018 in BioRxiv.
In this study, Evans et al. demonstrated a monosynaptic excitatory connection from the deep layers of medial superior colliculus (dmSC) to dorsal PAG (dPAG) neurons that is necessary for initiating escape behavior via a threshold mechanism. Although the connection is weak and unreliable, the synaptic drive to dPAG can be strongly amplified by recurrent excitation and short-term synaptic facilitation in the dmSC.
The authors started with an exploration of how the behaviors of mice were modulated by looming stimuli with different contrasts. They showed that both the response time and running speed were correlated with the contrast. This relationship was modeled using a variant of drift-diffusion model, a popular model in perceptual decision making in primates. The key variable of the model is the threat level which increases with sensory evidence and decays over time. The authors found this model fit well with their behavioral data. Nevertheless, the interpretation deserves more thought. How is the internal threat reflected by behavioral measurements? Does the running speed reflect the threat level above the threshold? If the authors monitor the animal’s autonomous responses or muscle activity, they may have a better estimation about the quality of their model below the threshold for flight. In addition, an alternative model should be provided for fitting the behavioral data. One such hypothesis is that the mouse escapes simply because it recognizes an overhead looming stimulus. To test this hypothesis, the authors need also include the posture and orientation of the mouse, which determine the visual field of the looming stimulus.
Then, the authors aimed to define the critical nodes for the behavior. They focused on mSC, V1, amygdala, and dPAG; but they did not provide rationales for why they only chose these four regions. They found that inactivation of V1 or amygdala with muscimol had a modulatory effect on the behavior. Interestingly, inactivation of mSC and dPAG chemically or optogenetically affected the behavior in different manners. Specifically, inactivation of the dPAG led to a switch from escape to freezing behavior while inactivation of the mSC eliminated the defensive responses. However, this is not well supported by their data as shown below. Clearly, inactivation of either region decreased the running speed. However, it’s not clear whether the mouse entered a freezing state or just took a rest. This ambiguity may be primarily attributed to the unclear definition of freezing.
Calcium imaging revealed that dmSC neurons encode both visual and behavioral information while dPAG neurons encode behaviors only. We think it would be interesting if the authors can show the neuronal responses to stimuli of different contrasts during escape behavior and fit that to the drift-diffusion model. If a dmSC neuron’s activity can be fitted as consistently as the behavioral data, this neuron is likely to encode the threat level. In addition, the different roles of dmSC and dPAG were demonstrated by optogenetic stimulation of mSC and dPAG with different laser power. When the light intensity was increased gradually, dmSC neurons showed a progressive increase in the probability of escape and a decrease of the variability of the responses. In contrast, dPAG neurons showed a steep increase in the probability of escape and stereotyped responses for each intensity.
How does the SC pass the signal to the PAG? Retrograde monosynaptic rabies tracing showed that the dPAG receives inputs mainly from deep and intermediate layers of the SC with a 11:1 convergence ratio. In vitro whole-cell patch-clamp data showed that optogenetic stimulation of the mSC with a single light pulse resulted in very small postsynaptic currents in the dPAG and that the statistics of neurotransmitter release were not different from a Poisson model. However, the postsynaptic currents were significantly increased with 5 light pulses at 20Hz, which are attributed to short-term facilitation among the light pulses and a long lasting increase in sEPSCs frequency after the pulses. Further experiments indicated that the light-train-evoked neuronal responses were amplified by the strong recurrent excitatory connection found in the dmSC. The authors also confirmed that this 20 Hz-light-evoked response is in the range of sensory-evoked response. Lastly, the authors tested the necessity of the dPAG by co-expressing the synaptically targeted inhibitory designer receptor hM4D-neurexin5 and ChR2 in VGluT2+ dmSC neurons. When the synaptic inputs to dPAG were suppressed, the animals stopped showing escape behaviors. In contrast, inhibition of the projections to the lateral posterior nucleus of the thalamus (LP) slightly increased the escape probability. The authors provide a circuit model in which evidence about threats is integrated in the dmSC network and passed through a threshold at the dPAG level to initiate defensive escape.
In sum, this is a nice study on an interesting question using a variety of techniques. It demonstrated a novel neural mechanism for looming-evoked escape behavior in which the SC, not the visual cortex or the amygdala, plays an important role.
Evans, D.A., Stempel, V., Vale, R., Ruehle, S., Lefler, Y., and Branco, T. (2018). A synaptic threshold mechanism for computing escape decisions. BioRxiv 272492.
Shang, C., Liu, Z., Chen, Z., Shi, Y., Wang, Q., Liu, S., Li, D., and Cao, P. (2015). A parvalbumin-positive excitatory visual pathway to trigger fear responses in mice. Science 348, 1472–1477.
Silva, B.A., Gross, C.T., and Gräff, J. (2016). The neural circuits of innate fear: detection, integration, action, and memorization. Learn. Mem. 23, 544–555.
Wei, P., Liu, N., Zhang, Z., Liu, X., Tang, Y., He, X., Wu, B., Zhou, Z., Liu, Y., Li, J., et al. (2015). Processing of visually evoked innate fear by a non-canonical thalamic pathway. Nat. Commun. 6, 6756.
Yilmaz, M., and Meister, M. (2013). Rapid Innate Defensive Responses of Mice to Looming Visual Stimuli. Curr. Biol. 23, 2011–2015.