Bridging the gap in connectomic studies: A particle filtering framework for estimating structural connectivity at...

Area of Science:

• Neuroscience
• Computational Biology
• Biophysics

Background:

• Understanding brain function necessitates mapping structural connectivity across scales, from micro- to macro-connectomics.
• The mesoscale level of neuronal network topology remains challenging to reconstruct.
• Current methods struggle with the complexity of large-scale neuronal networks.

Purpose of the Study:

• To develop a probabilistic approach for reconstructing complex topologies of large neuronal networks at the mesoscale.
• To introduce directional features and a particle filtering framework for spatial tracking of neurites.
• To enable dissection of structural connectivity in inhibitory and excitatory subnetworks.

Main Methods:

• Designed directional features to model local neuritic architecture.
• Proposed a feature-based particle filtering framework for spatial neurite tracking in microscopy images.
• Applied the method to neuronal cultures of increasing complexity on High-Density Micro Electrode Arrays.

Main Results:

• Demonstrated good stability and performance compared to expert annotations.
• Successfully reconstructed complex topologies of large neuronal networks.
• Showcased the method's ability to dissect inhibitory and excitatory subnetworks.

Conclusions:

• The developed probabilistic approach effectively reconstructs mesoscale neuronal network connectivity.
• This method offers new perspectives for investigating functional interactions among cellular populations.
• Advances the understanding of structural connectivity in the brain.