Understanding how individual neurons contribute to network functions is fundamental to neuroscience. Recent years have seen exciting progresses in the reconstructions of single-neuron morphologies and wiring diagrams at the level of individual synapses. Although these progresses offer promises of understanding neuronal networks, such understandings would not be reached if we do not understand how the structural details of single neurons contribute to the network connectivity. Neuronal network connectivity, which is an emergent property generated by the connections among single neurons, has been studied in depth using graph theory and other mathematical approaches. However, most computational models have disregarded fine morphological features involved in network connectivity. For the few that did, the methods developed are either unavailable to the broad neuroscience community or not user-friendly, preventing further investigations of the link between experimental structural data and network modelling. The objective of this proposed study is to develop and validate a user-friendly toolset for discovering the rules that link neuronal morphology to network connectivity, which will allow to extract and predict neural network properties from single-neuron morphologies. This open-source computational tool will include methods for visualization and data analysis for neuronal populations derived from whole brain imaging data. It will also provide an innovative generative model for interrogation of the organizational principles underlying brain networks? architecture exploring potentially relevant network properties.
In Specific Aim 1, we will develop new models and methods for analyzing the impact of single-cell morphology on network connectivity.
In Specific Aim 2, we will validate the use of the toolset to predict network connectivity and pathological deviations. The contribution of the proposed research will be significant because it will: (1) provide new computational tools that allow users to fill the gap between single-cell and network properties; (2) introduce the concept of neuronal structural variation; (3) yield a toolset that can be used to generate testable predictions and new biomarkers for developing therapeutic interventions for brain disorders. The research is innovative because it will (1) develop an open-source model to generate in- silico neuronal circuits capable of incorporating neuronal reconstructions, brain region segmentations and whole-brain fluorescence imaging datasets; (2) apply the concept of multi-objective optimality to network topology at the cellular scale; (3) analyze and model within-class morphological variations; 4) use intellectual disability models to validate our tools. Accomplishing the specific aims will yield a tool for linking descriptions of neuronal structures with network modeling, allowing the exploration of multi-objective optimality theoretical frameworks and improved methodologies for circuit classification based on network topology and the discovery of fundamental wiring laws in the brain.
The proposed research is relevant to public health because it will develop novel computational tools to enable researchers to investigate how the shapes of individual cells contribute to the connectivity of nervous system in both health and diseases. Thus, the proposed research is relevant to the part of NIH's mission that seeks fundamental knowledge of living systems to reduce the burdens of mental and neurological disorders.
