Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - Amygdala Circuits (Amygdala Circuits for Appetitive Conditioning)

Teaser

Neuronal circuits for learning in health and diseaseThe capacity to learn from experience is an essential brain function, which drastically increases an animal’s fitness by enabling rapid, adaptive changes of behavior. Learning to associate a set of stimuli is a simple and...

Summary

Neuronal circuits for learning in health and disease

The capacity to learn from experience is an essential brain function, which drastically increases an animal’s fitness by enabling rapid, adaptive changes of behavior. Learning to associate a set of stimuli is a simple and fundamental form of memory formation, which has been under intense investigation from various angles for many decades. Associative learning allows organisms to confer significance to novel stimuli predicting aversive or appetitive experiences and to adopt appropriate actions according to the valence of expected outcomes. Understanding how activity in defined neuronal circuits mediates appetitive and aversive learning, as well as how these circuitries might be shared or different, is a central, unanswered question in systems neuroscience.

Our project addresses the fundamental question how the brain encodes and controls behavior. While we have a reasonable understanding of the role of entire brain areas in such processes, and of mechanisms at the molecular and synaptic levels, there is a big gap in our knowledge of how behavior is controlled at the level of defined neuronal circuits.

Experience-dependent changes in behavior are mediated by long-term functional modifications in brain circuits (Figure). To investigate the neurobiological basis of learning and memory, we are focusing on how changes in the structure and function of neuronal circuits relates to and drives learning at the behavioral level. In order to investigate the underlying mechanisms in great cellular details, we are using simple learning paradigms including classical (Pavlovian) conditioning and instrumental, goal-directed forms of learning. A large number of studies in animals and humans have identified the amygdala as a key structure embedded in a brain-wide neuronal network mediating multiple forms of associative and instrumental learning. Using a multidisciplinary approach in mice, we investigate the anatomical and functional logic of amygdala circuits, their computations, and their interactions with other brain areas.

Our research shows that functionally, anatomically and genetically defined types of amygdala neurons are precisely connected both within local and within larger-scale neuronal networks, and that they selectively contribute to specific aspects of associative learning about both positive and negative experiences. Using deep brain Ca2+ imaging in freely behaving mice, we have described, for the first time, the rules governing neuronal ensemble dynamics in amygdala circuits during associative learning and during explorative behavior. The results of our investigations bring us a step closer to understanding how the brain implements learning algorithms for complex computations at the level of defined neuronal circuits.

Importantly, the investigation of the neurobiological basis of learning is also key for obtaining a mechanistic understanding and developing new therapeutic strategies for debilitating brain diseases including severe psychiatric and neurological conditions. Mood and anxiety disorders, for instance, are among the greatest societal burdens in terms of impairment and disability. These diseases thus represent one of the greatest preventive and therapeutic challenges in medicine. To meet these challenges, there is an urgent need for a better understanding of the underlying neurobiology. However, the neurobiological mechanisms underlying the ethiology and pathophysiology of such mental disorders is poorly understood. One of the emerginc concepts posits that maladaptive neuronal plasticity caused by environmental and genetic factors can give rise to pathological neuronal circuit function, and that this process is key for the progression and manifestation of mental diseases.
See Figure attached: Hippocampal and amygdala neurons form distinct sub-networks for learning and memory

Work performed

In natural environments, chances for survival depend on learning about possible aversive and appetitive outcomes and on the appropriate behavioral responses. Most studies addressing the underlying mechanisms of learning and memory have focused on entire brain regions or on synaptic mechanisms. However, we have only a poor understanding of how learning and memory is implemented at the level of neuronal circuits – local circuits within a brain area as well as larger-scale circuits connecting different brain areas in a cell-type specific manner. To understand how activity in defined neuronal circuits mediates both aversive and appetitive learning, as well as how these circuitries are shared and interact, is a central question in the neuroscience of learning and memory and the focus of our project. Finally, we are also interested how learned information, once stored in brain circuits, can drive appropriate behavioral changes by tapping into downstream networks connected to the motor system.

Our research during the first phase of the project has shown that functionally, anatomically and genetically defined types of amygdala neurons are precisely connected both within local and within larger-scale neuronal networks, and that they selectively contribute to specific aspects of associative learning about both positive and negative experiences. Using deep brain Ca2+ imaging in freely behaving mice, we have described, for the first time, the rules governing neuronal ensemble dynamics in amygdala circuits during associative learning and during explorative behavior. The results of our investigations bring us a step closer to understanding how the brain implements learning algorithms for complex computations at the level of defined neuronal circuits.

Finally, we have addressed how the amygdala taps into downstream circuits in the brain stem, which drive specific behavioral aspects of conditioned passive and active defensive behaviors. We found that there exist separate output pathways driving active or passive behavioral coping strategies. Understanding the neuronal circuitry underlying the selection and regulation of distinct behavioral coping strategies will provide new insight into evolutionary conserved survival mechanisms. An imbalance between active and passive coping strategies is observed in highly prevalent psychiatric conditions.

Final results

Memories are not stored by single nerve cells, but most likely by populations of neurons. We however profoundly lack insight into how the activity of individual neurons within a neuronal circuit relates to the dynamics of the rest of the population, to what degree that underlies the encoding of information like stimulus properties or behavioral state and if that relationship is different for distinct neuronal circuits or changes during memory formation. A major technological development is the recently established miniature microscope that can be carried by a mouse. This approach has made it possible to image, and live-stream, the activity of large populations of individual neurons in deep brain structures of freely moving animals. We have used this approach for imaging neuronal activity in the amygdala. In a collaboration with a lab at Stanford University in the US, we investigated neuronal population dynamics of amygdala neurons during associative learning. Unforeseen from prior work that suggested that learning leads to increased neuronal responses to the learned stimulus, we found that a combination of up- and down-regulation of individual cells’ responses are as important for storing the learned CS-US association. In the second phase of the ongoing project, we plan to extend this analysis to different forms of learning including appetitive conditioning and instrumental learning paradigms. Thereby, ee hope to uncover general principles governing the neuronal implementation of learning and memory processes in the brain.