Neuroscientists increasingly use computational tools to build and simulate models of the brain. The amounts of data involved in these simulations are immense and the importance of their efficient management is primordial. One particular problem in analyzing this data is the scalable execution of range queries on spatial models of the brain. Known indexing approaches do not perform well, even on today's small models containing only few million densely packed spatial elements. The problem of current approaches is that with the increasing level of detail in the models, the overlap in the tree structure also increases, ultimately slowing down query execution. The neuroscientists' need to work with bigger and more importantly, with increasingly detailed (denser) models, motivates us to develop a new indexing approach. To this end we developed FLAT, a scalable indexing approach for dense data sets. We based the development of FLAT on the key observation that current approaches suffer from overlap in case of dense data sets. We hence designed FLAT as an approach with two phases, each independent of density. Our experimental results confirm that FLAT achieves independence from data set size as well as density and also outperforms R-Tree variants in terms of I/O overhead from a factor of two up to eight.