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Statistical Analysis of Tract-Tracing Experiments Demonstrates a Dense, Complex Cortical Network in the Mouse
Rolf J F Ypma1,2, Edward T Bullmore1,3,4,5
1Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
Plos Computational Biology
|September 13, 2016
Summary
A new statistical framework accurately estimates brain connectivity weights, revealing significantly higher connection densities in the mouse cortex than previously known. This method reliably detects even single-axon connections, offering insights into network development and function.
Area of Science:
- Neuroscience
- Computational Biology
- Statistical Modeling
Background:
- Anatomical tract tracing is crucial for mapping brain connectivity.
- Automated methods enable large-scale studies but introduce noise, obscuring weak connections.
- Recent studies highlight the importance of weak connections in cortical networks.
Purpose of the Study:
- To develop a statistical framework for estimating connectivity weights and credibility intervals from multiple tract-tracing experiments.
- To accurately quantify the density of intra-hemispheric and inter-hemispheric cortical networks in the mouse brain.
- To investigate the topological properties and biological significance of weak axonal connections.
Main Methods:
- Modeled observed tract-tracing signals using a log-normal distribution to account for tracer fluorescence and positive-mean noise.
- Applied a statistical framework to anterograde viral tract-tracing data from the Allen Institute for Brain Sciences.
- Estimated connectivity weights and 95% credibility intervals for intra- and inter-hemispheric cortical networks.
Main Results:
- Estimated mouse intra-hemispheric cortical network density at 73% (95% CI: 71%, 75%), significantly higher than prior estimates (40%).
- Estimated inter-hemispheric density at 59% (95% CI: 54%, 62%).
- Demonstrated the ability to estimate connections representing as few as one or a few axons, revealing distinct topological properties of weak connections (random, long-distance) versus strong connections (clustered, short-distance).
Conclusions:
- The proposed statistical framework enhances the detection and quantification of weak brain connections.
- Weak connections, though not dominant in weighted graphs, increase topological integration in binary graphs.
- The findings suggest weak connections may play a role in integrative information processing or serve as stochastic factors in connectome development.
