Circuit Analysis of the Drosophila Brain using Connectivity-based Neuronal Classification Reveals Organization of Key Communication Pathways

Abstract

We present a functionally relevant, quantitative characterization of the neural circuitry of Drosophila melanogaster at the mesoscopic level of neuron types as classified exclusively based on network connectivity. Starting from a large neuron-to-neuron brain-wide connectome of the fruit fly, we use stochastic block modeling and spectral graph clustering to group neurons together into a common 'cell class' if they connect to neurons of other classes according to the same probability distributions. Thus, nodes of the identified circuit represent cell classes, while the directed edges represent the connection probabilities between neurons in those respective classes. We then characterize the connectivity-based cell classes with standard neuronal biomarkers, including neurotransmitters, developmental birthtimes, morphological features, spatial embedding, and functional anatomy. Mutual information indicates that connectivity-based classification reveals aspects of neurons that are not adequately captured by traditional classification schemes. Next, using graph-theoretic and random walk analyses to identify neuron classes as hubs, sources, or destinations, we detect patterns of directional connectivity and information flow that potentially underpin specific functional interactions in the Drosophila brain. We uncover a core of highly interconnected dopaminergic cell classes functioning as the backbone information highway for multisensory integration. We also recognize glutamatergic-dominant connections between the motor and optic regions, possibly facilitating circadian rhythmic activity. Additional predicted pathways pertain to spatial orientation, fight-or-flight response, and olfactory learning. Mapping the developmental birthtimes of neurons also pinpoints the temporal change of the circuit blueprint over the fly lifespan, revealing distinct critical growth periods of individual pathways. Our analysis provides experimentally testable hypotheses critically deconstructing complex brain function from organized connectomic architecture. We present a functionally relevant, quantitative characterization of the neural circuitry of Drosophila melanogaster at the mesoscopic level of neuron types as classified exclusively based on network connectivity. Starting from a large neuron-to-neuron brain-wide connectome of the fruit fly, we use stochastic block modeling and spectral graph clustering to group neurons together into a common 'cell class' if they connect to neurons of other classes according to the same probability distributions. Thus, nodes of the identified circuit represent cell classes, while the directed edges represent the connection probabilities between neurons in those respective classes. We then characterize the connectivity-based cell classes with standard neuronal biomarkers, including neurotransmitters, developmental birthtimes, morphological features, spatial embedding, and functional anatomy. Mutual information indicates that connectivity-based classification reveals aspects of neurons that are not adequately captured by traditional classification schemes. Next, using graph-theoretic and random walk analyses to identify neuron classes as hubs, sources, or destinations, we detect patterns of directional connectivity and information flow that potentially underpin specific functional interactions in the Drosophila brain. We uncover a core of highly interconnected dopaminergic cell classes functioning as the backbone information highway for multisensory integration. We also recognize glutamatergic-dominant connections between the motor and optic regions, possibly facilitating circadian rhythmic activity. Additional predicted pathways pertain to spatial orientation, fight-or-flight response, and olfactory learning. Mapping the developmental birthtimes of neurons also pinpoints the temporal change of the circuit blueprint over the fly lifespan, revealing distinct critical growth periods of individual pathways. Our analysis provides experimentally testable hypotheses critically deconstructing complex brain function from organized connectomic architecture.