Multitemporal Association Tracking (MAT) is a new graph-based method for multitarget tracking in biological applications that reduces the error rate and implementation complexity compared to approaches based on bipartite matching. The data association problem is solved over a window of future detection data using a graph-based cost function that approximates the Bayesian a posteriori association probability. MAT has been applied to hundreds of image sequences, tracking organelle and vesicles to quantify the deficiencies in axonal transport that can accompany neurodegenerative disorders such as Huntington's Disease and Multiple Sclerosis and to quantify changes in transport in response to therapeutic interventions.