Abstract There is more knowledge than ever about brain connectivity, without change in the capacity of humans to individually manage that knowledge. Non-invasive methods such as MRI and EEG generate large amounts of data related to connectivity in humans that lacks specificity at many levels of analysis. Experiments that use transcranial magnetic stimulation (TMS) generate smaller amounts of data that provide complementary information about the causal role of surface brain regions in modulating other brain areas and behaviors. While the volume and variety of all types of data regarding neuronal networks is increasing, sufficiently flexible comprehensive methodology for describing, summarizing, and modeling multimodal information about brain connectivity does not exist. For most types of brain connection data, there is no way to identify regional brain circuitry beside traditional bibliographic methods. Specifically, there is no publicly accessible knowledge base or theoretical approach for human TMS-based regional brain connectivity information. We have begun to aggregate, harmonize, and model brain connectivity information derived from transcranial magnetic stimulation (TMS) regarding motor behavior in normal, aged, and neurologically impaired humans. We have designed and tested a graph database approach to describe the results of TMS-connectivity experiments parsed from the literature. We will present a demonstration of how our system can provide an open-source knowledge base to the community of scientists. But there are numerous decisions to be made regarding the representation of varied experimental designs and results in a graph database and how to handle conflicts and gaps in the literature. Once these issues are addressed, the ability to store and retrieve data in a format that can generate models of function will aid scientists’ ability to generate theories of normal motor function to understand and ameliorate deficits when they occur. Keywords: functional connectivity, knowledge aggregation, computational methods, transcranial magnetic stimulation
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