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Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional
Adora M DSouza1, Anas Zainul Abidin2, Mahesh B Nagarajan3
1Department of Electrical Engineering, University of Rochester, NY, USA.
Proceedings of Spie--The International Society for Optical Engineering
|November 25, 2017
Summary
Mutual Connectivity Analysis (MCA) effectively reveals directed functional connectivity and network structures in brain imaging data. This computational framework accurately identifies relationships in both simulated and resting-state fMRI data.
Area of Science:
- Neuroscience
- Computational Biology
- Data Science
Background:
- Functional connectivity analysis is crucial for understanding brain networks.
- Existing methods often struggle with non-linear and directed relationships.
Purpose of the Study:
- To evaluate the Mutual Connectivity Analysis (MCA) framework for directed functional connectivity.
- To assess MCA's ability to recover network structures from synthetic and real fMRI data.
Main Methods:
- Utilized Generalized Radial Basis Functions (GRBF) neural networks to assess non-linear cross-predictability between time series.
- Employed community detection algorithms, specifically the Louvain method, for network structure recovery.
- Validated the approach on synthetic datasets with known network properties and on resting-state fMRI data.
Main Results:
- MCA accurately captured directional relationships in synthetic data (AUC = 0.92 ± 0.037) and network structure (Rand index = 0.87 ± 0.063).
- Analysis of resting-state fMRI data showed strong agreement with a motor cortex network derived from stimulation (Dice coefficient = 0.45).
Conclusions:
- MCA is a robust computational framework for analyzing non-linear directed functional connectivity.
- The MCA approach effectively reveals underlying functional network structures in complex systems like the brain.
