Orienting of visuospatial attention refers to reallocation of attentional focus from one target or location to another and can occur either with (overt) or without (covert) eye movement. Although it has been demonstrated that both types of orienting commonly involve frontal and parietal brain regions as the frontoparietal network (FPN), the underlying representational coding of these two types of orienting remains unclear. In this functional magnetic resonance imaging study, participants performed a task that elicited overt and covert orienting to endogenously or exogenously cued targets with eye-tracking to monitor eye movement. Although the FPN was commonly activated for both overt and covert orienting, multivariate patterns of the activation of voxels in the FPN accurately predicted whether eye movements were involved or not during orienting. These overt- and covert-preferred voxels were topologically distributed as distinct and interlaced clusters in a millimeter scale. Inclusion of the two types of clusters predicted orienting type more accurately than one type of clusters alone. These findings suggest that overt and covert orienting are represented by interdependent functional clusters of neuronal populations in regions of the FPN, which might reflect a generalizable principle in the nervous system for functional organization of closely associated processes.

Cognitive control is a capacity-limited function responsible for the resolution of conflict among competing cognitive processes. However, whether cognitive control handles multiple concurrent requests through a single bottleneck or a resource sharing mechanism remains elusive. In this functional magnetic resonance imaging study, we examined the effect of dual flanker conflict processing on behavioral performance and on activation in regions of the cognitive control network (CCN). In each trial, participants completed two flanker conflict tasks (T1 and T2) sequentially, with the stimulus onset asynchrony (SOA) varied as short (100 ms) and long (1000 ms). We found a significant conflict effect (indexed by the difference between incongruent and congruent flanker conditions) in reaction time (RT) for both T1 and T2, together with a significant interaction between SOA and T1-conflict on RT for T2 with an additive effect. Importantly, there was a small but significant SOA effect on T1 with a prolonged RT under the short SOA compared to the long SOA. Increased activation in the CCN was associated with conflict processing and the main effect of SOA. The anterior cingulate cortex and anterior insular cortex showed a significant interaction effect between SOA and T1-conflict in activation parallel with the behavioral results. The behavioral and brain activation patterns support a central resource sharing model, in which the core resources for cognitive control are shared when multiple simultaneous conflicting processes are required.

Background: Glioblastoma (GBM) is an aggressive brain tumor, with surgery being an integral part of treatment. Aggressive resections improve clinical outcomes but need to be balanced against potential functional impairment. Neuropsychological (NP) testing is an important tool neurosurgeons use to assess cognitive functioning. Importantly, associations between NP test scores and imaging biomarkers could enable a testable baseline by which to track patient outcomes over time and aid in presurgical counseling. Methods: We identified 44 patients diagnosed with primary GBM and who had detailed NP testing and presurgical imaging. Regression models for NP indices were created with tumor size, hemisphere, and lobar location as predictors. Lesion–symptom mapping (LSM) analyses were used to identify more detailed structure–function relationships. Results: Larger tumor volumes predicted worse attention, immediate memory, language, visuospatial, and overall NP performance (p < 0.05 for all). Left hemisphere involvement predicted worse attention, language, and immediate memory NP performance (p < 0.01 for all). Only visuospatial testing had lobar location significantly associated with worse scores (occipital lobe; p < 0.05). The LSM analyses identified areas around the left sagittal stratum as significantly associated with language performance (p < 0.05), with no other structure–function relationships being identified. Conclusions: These findings support the growing evidence that outside of a small number of truly critical regions, high-grade gliomas impair cognition generally, likely due to progressive tumor infiltration-associated neuroplasticity of complex parallel and interconnected networks. To investigate this, future studies should incorporate larger cohort sizes and should examine the relationship of glioma-induced network-level perturbations on cognitive decline.

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 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 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. Connector sites were concentrated primarily in temporal and inferior parietal cortices. Finally, these network features accurately predicted which sites were critical in machine learning models. These findings provide a preoperative framework for predicting cortical sites critical for speech and language function, which may ultimately help accelerate or improve brain mapping.