A medical data processing method for determining a weight of a trajectory between a starting point and a target within a brain of a patient using a vulnerability field (VUL(x,y)) of the brain, the steps of the method being executed by a computer and comprising: a) acquiring a nerve-indicating dataset comprising information about the brain of the patient suitable for identifying neural fibres in the brain of the patient; b) determining nodes (A a, B a, C a; A b, B b, C b, D b, E b, F b) within the brain preferably being neuron-rich grey matter parts of the brain; c) determining the axonal linkage of the nodes (A a, B a, C a; A b, B b, C b, D b, E b, F b) based on the nerve-indicating dataset to obtain edges connecting the nodes (A a, B a, C a; A b, B b, C b, D b, E b, F b), the nodes (A a, B a, C a; A b, B b, C b, D b, E b, F b) and edges (1, 2, 3, 4, 5) constituting a connectivity graph; d) determining a weight for each of the edges (1, 2, 3, 4, 5) depending on a graph theoretical statistical measure of the respective edge (1, 2, 3, 4, 5) in the connectivity graph; e) determining, for each of the edges (1, 2, 3, 4, 5), which voxels in a dataset of the brain of the patient belong to the edges (1, 2, 3, 4, 5) or are passed by the edges (1, 2, 3, 4, 5) and assigning or adding the determined weight of the respective edges (1, 2, 3, 4, 5) to all of the voxels belonging to the respective edge to obtain a weighted voxel-based dataset of the brain of the patient defining the vulnerability field (VUL(x, y)) of the brain; f) acquiring a number of potential starting points of the trajectory; g) determining, for each of the number of starting points, at least one trajectory connecting the respective starting point with the target, the at least one trajectory passing through a number of respective adjacent voxels of the weighted voxel-based dataset; h) adding the weights of all voxels being passed by the respective trajectory to obtain a respective weighted trajectory. a) acquiring a nerve-indicating dataset comprising information about the brain of the patient suitable for identifying neural fibres in the brain of the patient; b) determining nodes (A a, B a, C a; A b, B b, C b, D b, E b, F b) within the brain preferably being neuron-rich grey matter parts of the brain; c) determining the axonal linkage of the nodes (A a, B a, C a; A b, B b, C b, D b, E b, F b) based on the nerve-indicating dataset to obtain edges connecting the nodes (A a, B a, C a; A b, B b, C b, D b, E b, F b), the nodes (A a, B a, C a; A b, B b, C b, D b, E b, F b) and edges (1, 2, 3, 4, 5) constituting a connectivity graph; d) determining a weight for each of the edges (1, 2, 3, 4, 5) depending on a graph theoretical statistical measure of the respective edge (1, 2, 3, 4, 5) in the connectivity graph; e) determining, for each of the edges (1, 2, 3, 4, 5), which voxels in a dataset of the brain of the patient belong to the edges (1, 2, 3, 4, 5) or are passed by the edges (1, 2, 3, 4, 5) and assigning or adding the determined weight of the respective edges (1, 2, 3, 4, 5) to all of the voxels belonging to the respective edge to obtain a weighted voxel-based dataset of the brain of the patient defining the vulnerability field (VUL(x, y)) of the brain; f) acquiring a number of potential starting points of the trajectory; g) determining, for each of the number of starting points, at least one trajectory connecting the respective starting point with the target, the at least one trajectory passing through a number of respective adjacent voxels of the weighted voxel-based dataset; h) adding the weights of all voxels being passed by the respective trajectory to obtain a respective weighted trajectory.