A realtime marker of objectbased attention in the human brain. A possible component of a xe2x80x9cgatekeeping mechanismxe2x80x9d performing late attentional selection in the VentroLateral Prefrontal Cortex Abstract   The decision to process an incoming stimulus attentively  and to trigger a followup cascade of highlevel processes  is strategic for the human brain as it becomes transiently unavailable to subsequent stimulus processing. In this study, we set to identify brain networks that carry out such evaluations. We therefore assessed the timecourse of neural responses with intracerebral EEG in human patients during an attentional reading task, contrasting tobeattended vs. tobeignored items. We measured HighFrequency Activity 50xe2x80x93150xe2x80xafHz as a proxy of populationlevel spiking activity and we identified a crucial component of a GateKeeping Mechanism bilateral in the midVentroLateral Prefrontal Cortex (VLPFC), at the interplay of the Ventral and Dorsal Attention Networks, that selectively reacts before domain specialized cortical regions that engage in full stimulus analysis according to task demands.

Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention? Abstract   Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological components. Nevertheless, some brain networks support multiple attentional functions. In this study, we used the visual attentional blink (VAB) as a test of the functional generalizability of the brainu0027s attentional networks. In a VAB task, attention devoted to a target often causes a subsequent item to be missed. Although frequently attributed to limitations in attentional capacity or selection, VAB deficits attenuate when participants are distracted or deploy attention diffusely. The VAB is also behaviorally and theoretically dissociable from other attention tasks. Here we used Connectomebased Predictive Models (CPMs), which associate individual differences in task performance with functional connectivity patterns, to test their ability to predict performance for multiple attentional tasks. We constructed visual attentional blink (VAB) CPMs, and then used them and a sustained attention network model (saCPM; Rosenberg et al., 2016a,b) to predict performance. The latter model had been previously shown to successfully predict performance across tasks involving selective attention, inhibitory control, and even reading recall. Participants (nxe2x80xafxe2x80xaf73; 24 males) underwent fMRI while performing the VAB task and while resting. Outside the scanner, they completed other cognitive tasks over several days. A vabCPM constructed from VAB task data (behavior and fMRI) successfully predicted VAB performance. Strikingly, the network edges that predicted better VAB performance (positive edges) predicted worse performance for selective and sustained attention tasks, and vice versa. Predictions from applying the saCPM to the data mirrored these results, with the networku0027s negative edges predicting better VAB performance. The vabCPMu0027s positive edges partially yet significantly overlapped with the saCPMu0027s negative edges, and vice versa. Many positive edges from the vabCPM involved the default mode network, whereas many negative edges involved the salienceventral attention network. We conclude that the vabCPM and saCPM networks reflect general attentional functions that influence performance on many tasks. The networks may indicate an individualu0027s propensity to deploy attention in a more diffuse or a more focused manner.