DMRI tractography and connectomics aim to create comprehensive maps of all neural circuitry in the brain, however, dMRI is a false-positive prone imaging modality. Yet, as it is the only non-invasive and in-vivo method to indirectly study the white matter connectivity of the brain it is heavily utilized. DMRI-Connectomic analyses often end with a “big data” problem, as there are many possible connections. Moreover, this data is likely populated by false-positive connections. By utilizing data-driven criteria to filter matrices this work attempts to narrow the “search window” by adding criteria on which we can discard “connections” or consider them potential candidates for further investigation. Such judgements may also lead to connections being evaluated as true or false-positives.