Abstract
<jats:p>Direct electrocortical stimulation (ECS) is a well-established brain mapping technique that helps achieve safe and effective resection of epileptic foci, tumors or vascular malformations. Recent studies using electrocorticography (ECoG) suggest that ECS' exerted effects on brain sites are determined by the roles of those sites in larger networks. However, ECoG has limited spatial coverage. Here, we used functional magnetic resonance imaging from eighteen participants during performance of five distinct language tasks to assess the functional network signatures of cortical sites defined as critical for speech and language by ECS. We found that critical sites causing speech arrests (SA) and language errors (LE) exhibited distinct network properties. Both types of critical sites exhibited lower local and global connectivity than non-critical sites. LE sites showed greater connectivity across sub-networks (communities) than both non-critical and SA sites, indicating their role as connectors across functional networks. This connector profile of LE sites was most robust when considering network connectivity across the entire brain; notably, the distinctiveness of these sites diminished when metrics were computed only on connections in smaller regions. Connector sites were concentrated primarily in temporal regions — including the temporal pole and superior/middle temporal gyri — as well as inferior parietal cortices. Finally, we leveraged network connectivity features to train machine learning models that accurately predicted which sites were critical. Our results extend prior ECoG results and imply that the criticality of a cortical site is determined by its functional connectivity patterns across the whole brain. These findings provide a framework for predicting cortical sites critical for speech and language function, which may ultimately help accelerate or improve brain mapping.</jats:p>