Improving Structural Brain Connectomes through Statistical Evaluation via Model Optimization

Accurate mapping of the structural brain connectomes is fundamental to understanding the role of white matter in health and disease. Diffusion-weighted magnetic resonance imaging (dMRI) and fiber tractography provide the only way to map brain connectomes in living human brains. Several studies have shown technical gaps in robustly mapping brain connectomes. The lack of connectome evaluation methods is evident from the recent findings. The present work focuses on developing methods for the statistical evaluation of brain connectomes. We present a new method that builds on LiFE and COMMIT2 methods to reduce a candidate tractography to an optimized one by identifying the brain connections that best model the dMRI signal. We used sparse group regularization, which requires finding a parameter (λ) for the trade-off between better fitting the signal with individual streamlines while maintaining the bundle's cohesion. Previous methods using regularizations to evaluate connectomes set fixed λs, refitting the model for several values of λ. We propose an efficient approach to selecting the optimal λ value. We performed experiments to test the complexity and efficacy of the approach using two datasets: simulated and real datasets. The simulated data were generated using Phantomas, with simple bundles and tissue factors. In addition, we used diffusion data from the Human Connectome Project (HCP). Results show that our approach can identify the optimal λ in a reliable amount of time. The full λ optimization process for 100 different λ took 17 min on a standard desktop computer, while it takes 4x more time than COMMIT 2 to select the optimal λ. In addition, the model's mean squared error is 0.0036 for the HCP dataset and 3.89e-5 for the simulated dataset. This is 14.78x less than COMMIT 2 (0.0544). The reduction in error is due precisely to the optimized selection of λ.