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We propose a method for estimating axonal fiber connectivity pathways (cerebral connectivity fibers) from Multi-Tensor Diffusion Magnetic Resonance Imaging (MTD-MRI). Our method uses multiple local orientation information provided by MTD-MRI for leading stochastic walks of particles. We perform stochastic walks on particles with mass which introduce inertia and gravitational forces that result in filtered trajectories. Afterwards, the fiber bunches are estimated with a clustering procedure based on terminal points that allows us to eliminate outliers. The method΄s performance is evaluated on MTD-MRI from realistic synthetic data, a diffusion phantom and demonstrated in real human brain data. [ABSTRACT FROM AUTHOR]