Strategies for automatic generation of information processing pathway maps

Information Processing Pathway Maps (IPPMs) are a concise way to represent the evidence for the transformation of information as it travels around the brain. However, their construction currently relies on hand-drawn maps from electrophysical recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). This is both inefficient and contains an element of subjectivity. A better approach would be to automatically generate IPPMs from the data and objectively evaluate their accuracy. In this work, we propose a range of possible strategies and compare them to select the best. To this end, we (a) provide a test dataset against which automatic IPPM creation procedures can be evaluated; (b) suggest two novel evaluation metrics-<i>causality violation</i> and <i>transform recall</i>-from which these proposed procedures can be evaluated; (c) conduct a simulation study to evaluate how well ground-truth IPPMs can be recovered using the automatic procedure; and (d) propose and evaluate a selection of different IPPM creation procedures. Our results suggest that the <i>max pooling</i> approach gives the best results on these metrics. We conclude with a discussion of the limitations of this framework, and possible future directions.