Representation and Retrieval of Brain Connectivity Information derived from TMS Experiments

BackgroundTranscranial magnetic stimulation (TMS) is a painless non-invasive method that allows focal activation or deactivation of a human brain region in order to assess effects on other brain regions. As such, it has a unique role in elucidating brain connectivity during behavior and at rest. Information regarding brain connectivity derived from TMS experiments has been published in hundreds of papers but is not accessible in aggregate. ObjectiveOur objective was to identify, extract, and represent TMS-connectivity data in a graph database. This approach uses nodes connected by edges to capture the directed nature of interregional communication in the brain while also being flexible enough to contain other information about the connections, such as the source of information and details about the experiments that produced them. MethodsData related to interregional brain connectivity is first extracted from full-text publications, with creation of a table-like structure that list data of multiple types, principally the source and target brain regions, sign (excitatory/inhibitory) and latency. While machine-reading methods were explored, so far human experts have had to extract and verify data. These data are used to populate a neo4j graph database. A graphical user interface coupled with a query system allows users to search for networks and display information about connections between any two brain regions of interest. ResultsExperiments involving two TMS stimulating coils, in which one is over a putative source region and the other is over another region with a measurable effect in the body (such as the primary motor cortex) are the most straightforward to represent in the database. Even in those experiments, differing conventions for naming regions, and differing experimental parameters such as stimulation intensity and coil position, create issues for representing data from multiple studies in the same database. Other types of experiments, such a neuromodulatory stimulation such as rTMS, can provide information regarding connectivity, but are harder to represent. But we have, thus far, stored information about 100 connections in the database and demonstrated its utility in exploring direct and indirect connections between brain regions. We have also explored adding a causal inference method to those connections, allowing information about latency to shape the connections retrieved given latency limits. ConclusionA graph database can flexibly store information about interregional brain connectivity and is particularly useful for exploring the temporal aspects of brain networks.