Periodic Reporting for period 1 - EvolvingCircuits (The evolution of neural circuits for navigational decisions - from synapses to behavior)
Période du rapport: 2022-09-01 au 2025-02-28
Of the 1.2 million animal species described on Earth, more than 80% are insects. As for mammals, each insect is equipped with a brain that has evolved to optimally control the species’ behavior in the context of its environment. In more than 450 million years of evolution the neural circuits guiding behavioral decisions have diverged from an ancestral version to enable insects to conquer every terrestrial habitat on the planet and to equip many species with behavioral strategies that rival even mammals in complexity. These circuits thus have to be rigid enough to maintain their ancestral, core functions, while also being sufficiently adaptable to enable the addition of novel functions. The mechanisms of how this is achieved across vast evolutionary timescales are unknown. While this is true for all animals, insects with their numerically simpler brains and a comparably rigid neuroarchitecture offer the unique chance to unravel the evolution of decision making circuits at the level of identified neurons and synapses. Aided by recent technological breakthroughs and the establishment of rich ground-truth data in the fruit fly, I will combine whole brain anatomy, connectomics and computational modeling with electrophysiology and behavior to dissect the evolution of the central decision making center of insect brains, the central complex - across the entire insect phylogeny. I aim to reveal how this region has evolved in the context of the entire brain, how its intrinsic circuits have changed with increasing evolutionary distance (circuit phylogeny), and which changes in its circuitry are linked to specialized behavioral abilities (circuit adaptation). Finally, I will directly establish behavioral correlates of identified circuit features (circuit function), attaching relevance to connectomics data in a way that is achievable only by a wide, comparative approach - raising our understanding of structure-function relations in animal nervous systems to a new level.
Over the first two years of the project we have advanced towards all three main research objectives. Firstly, we have studied insect brains at the level of entire brains. To that aim, we have obtained samples of over 50 insect species from across the phylogenetic tree and have used X-ray based imaging to obtain 3D image stacks of the brains of a subset of those. We used these data to optimize our sample preparation and scanning procedures and have started to quantitatively analyze the different regions comprising their brains, illuminating evolutionary trends at the level of entire brains, covering over 450 million years of evolution. Second, we have pioneered a novel approach in comparative connectomics to advance our understanding of how neural circuits evolve. For that purpose we have established an imaging regime to obtain multi resolution 3D electron microscopical image volumes. Five large insect species have already been imaged in this way, representing the first such data outside of the fruit fly Drosophila. To analyze the many terabytes of image data resulting from these scans, we have developed a new image processing and analysis pipeline. This pipeline combines complex image alignment protocols with subsequent extraction of neural morphologies. These are either manually traced as neural skeletons (in low resolution overview data stacks) or via machine learning based image segmentation (in synaptic resolution data). Together with automatically detected synaptic contacts, we combine all neural data into synaptic level connectivity graphs, i.e. connectomes. This has been completed for the neural circuits that encode head direction in bees and revealed surprising levels of conservation across hundreds of million of years of evolution. Importantly, we have also identified highly evolvable circuit components - evolutionary hotspots - as well as completely unique circuits that correlate with bee’s complex navigational abilities. Both now serve as foundation for the third line of enquiry, namely attaching functional significance to neural circuit data. This is achieved in three ways: Firstly by translating the circuit data directly into computational models of the brain. Second, by probing the identified neurons with electrophysiological methods, and third by directly observing and quantifying the behavioral abilities of our insect species. In the first two years of this project we have generated an overarching model of the brain region of interest (the central complex), that is now used as foundation to generate species specific models in which to directly generate predictions for links between circuits and behavior. Behaviorally we have developed novel approaches to quantitatively dissect insect behavior using walking arenas of different sizes in a controlled laboratory based setting. Filming their behavior and using machine learning to extract the fine structure of movements and their movements strategies we can now compare diverse species with reproducible methods. This way we have identified complex navigation abilities in walking bumblebees that perfectly reflect specialization identified at the level of connectomes. Those are first examples of how specializations in neural machinery could generate novel behavioral traits and showcases that the strategy pursued by this project can indeed deliver groundbreaking results.
For the first time in insects other than very small species like Drosophila, we have obtained electron microscopic image volumes of the decision making center of their brains, allowing to extract neural circuit information at synaptic resolution from species that have been long standing model organisms in the context of advanced navigation behavior (bees, ants, locusts, dung beetles) or have been models for other purposes (cockroaches, praying mantids). These datasets are the first seven out of a minimum of 16 to be imaged in this project and already provide the richest ultrastructural dataset of any comparative brain study to date.
To analyze these massive amounts of image data we have developed a novel pipeline that combines several state of the art software tools developed by the Drosophila connectomics community and adapts them for usage on our data from large insects. Via a resource sharing approach we have now enabled research groups from around the world to analyze their EM image volumes using our newly established IT infrastructure and thus have contributed to democratizing the field of connectomics, which traditionally had been focused on very few large research consortia located at highly specialized facilities.
Finally, we have shown that bumblebees possess complex memories of navigational vectors that they can use to achieve mammalian like navigation behavior, without the need for cognitive maps. These findings were achieved via a novel, laboratory based behavioral paradigm that now allows us to probe these sophisticated abilities at the level of neural circuits and thus link them directly to our ongoing connectomics work.
To analyze these massive amounts of image data we have developed a novel pipeline that combines several state of the art software tools developed by the Drosophila connectomics community and adapts them for usage on our data from large insects. Via a resource sharing approach we have now enabled research groups from around the world to analyze their EM image volumes using our newly established IT infrastructure and thus have contributed to democratizing the field of connectomics, which traditionally had been focused on very few large research consortia located at highly specialized facilities.
Finally, we have shown that bumblebees possess complex memories of navigational vectors that they can use to achieve mammalian like navigation behavior, without the need for cognitive maps. These findings were achieved via a novel, laboratory based behavioral paradigm that now allows us to probe these sophisticated abilities at the level of neural circuits and thus link them directly to our ongoing connectomics work.