Circuit mechanics of emotional behaviour
Numerous studies have established the limbic system as the central hub in emotion processing (LeDoux, 2000). It integrates sensory information, encodes emotional states, and instructs other brain centres to regulate physiology and behaviour. However, resolving how emotions emerge from the several distinct and highly interconnected neuronal populations in limbic networks remains a major challenge. To gain a foothold into the complexity of emotions on one hand and limbic circuitry on the other, we set out to study – in exemplary fashion – basic emotions such as fear and reward in selected limbic hubs.
Fear and reward in limbic circuits
We are currently developing circuit genetic tools for efficiently identifying emotional hot spots and resolving their internal architecture and functional interactions.
In a first foray, we have employed pharmacogenetics, optogenetics and viral tracing, and discovered a local inhibitory network consisting of two antagonistic neural populations in the lateral central amygdala (CEl) portion of the extended amygdala (EA) that gates amygdala output from CEm (Haubensak et al., 2010). Architecture and neural modelling suggest that this network operates like a bistable switch (Figure 1). Optogenetic experiments mapped its open state to high fear/low reward states and its closed state to low fear/high reward states, indicating that, at the level of CE, opposing emotional and behavioural states are encoded in alternate and mutually exclusive neural network states.
We believe that this design will enable a person to efficiently integrate emotional stimuli in order to select the appropriate behavioural response, especially when facing fear and reward simultaneously. Neural tracing experiments revealed that CEl circuitry is closely interconnected with the Bed nucleus of the stria terminalis (BNST). Preliminary evidence suggests that this projection links neural activity in CEl to BNST, and modulates autonomous responses. Thus, emotional states in CEl assemble a behavioural state from neural activity in BNST and CEm, mediating physiological and motor components of the emotional response, respectively. What, in turn, controls neural activity in CEl? CTB retrograde tracing revealed strong cortical and subcortical inputs to CEl. Modelling and optogenetic epistasis experiments suggest that CEl integrates top-down control signals and interoceptive information from these sources.
One hallmark of emotions is that they are adaptive responses to stimuli that have acquired a meaning – best illustrated by Pavlovian fear and reward conditioning. Interestingly, CEl circuitry itself seems to learn and store Pavlovian associations. Here we aim to dissect the circuit mechanisms by which CEl circuitry shapes its responses during emotional learning. Neural tracing experiments revealed strong reciprocal connectivity with inputs relaying teaching and prediction error signals of classical learning circuits. Interestingly, site perturbation of these interactions prevented fear learning, demonstrating that the shaping of CEl circuit dynamics is crucial for emotional memory.
Psychopharmacology of emotion circuits
While the molecular mechanics by which genes and psychoactive drugs control neural activity at the cellular level have been worked out in great detail, the circuit mechanics by which this translates into behavioural changes have not yet been resolved. The circuits identified above provide an ideal substrate to study this problem. We therefore investigated drug effects (anxiolytic and anxiogenic drugs) on the activity of emotion circuits (Figure 2) and how these changes in activity modulate emotional states and behaviour. Preliminary results suggest that the benzodiazepine (BZD) anxiolytic effect results from a balance shift of neural activity in the EA network.
Taken together, we hope to disclose principles of stimulus behaviour transformations and the neural organization of emotions. Moreover, our research will provide a framework for genetics and the psychopharmacology of emotions in health as well as diseases such as anxiety disorders and addiction.
- Ganglberger, F., Kaczanowska, J., Penninger, JM., Hess, A., Bühler, K., Haubensak, W. (2017). Predicting functional neuroanatomical maps from fusing brain networks with genetic Information. Neuroimage.
- Cai, H., Haubensak, W., Anthony, TE., Anderson, DJ. (2014). Central amygdala PKC-δ(+) neurons mediate the influence of multiple anorexigenic signals. Nat Neurosci. 17(9):1240-8
- Haubensak, W., Kunwar, PS., Cai, H., Ciocchi, S., Wall, NR., Ponnusamy, R., Biag, J., Dong, HW., Deisseroth, K., Callaway, EM., Fanselow, MS., Lüthi, A., Anderson, DJ. (2010). Genetic dissection of an amygdala microcircuit that gates conditioned fear. Nature. 468(7321):270-6