A connectome manipulation framework for the systematic and reproducible study of structure-function relationships...

Area of Science:

• Computational neuroscience
• Systems neuroscience
• Neuroinformatics

Background:

• Neuronal connectivity exhibits complex, nonrandom features crucial for brain function.
• Direct experimental manipulation of synaptic connectivity at a fine-grained scale is challenging.
• Computational models offer a powerful approach to investigate causal relationships between structure and function.

Purpose of the Study:

• To develop a computational framework for manipulating neuronal network connectomes.
• To enable systematic studies of how synaptic organization impacts neural activity.
• To facilitate the exploration of complex connectivity hypotheses in detailed neural models.

Main Methods:

• Developed Connectome-Manipulator, a Python framework for large-scale network models in SONATA format.
• Implemented tools for creating, manipulating, and fitting stochastic connectivity models.
• Applied the framework to a detailed rat somatosensory cortex model, including interneuron connectivity transplantation and excitatory connectivity simplification.

Main Results:

• Demonstrated the framework's capability for rapid and complex connectome manipulations.
• Showcased two use cases involving interneuron and excitatory connectivity modifications.
• Network simulations revealed distinct shifts in neuronal population activity causally linked to the manipulated connectivity.

Conclusions:

• Connectome-Manipulator provides a flexible platform for investigating the causal role of synaptic connectivity in neural function.
• The framework facilitates the study of structure-function relationships by enabling systematic manipulation and analysis of neural networks.
• Simulations confirm that alterations in neuronal connectomes can lead to predictable changes in network activity patterns.