The ability to classify visual objects into discrete categories ("friend" versus "foe"; "edible" versus "poisonous") is essential for survival and is a fundamental cognitive function. The cortical substrates that mediate this function, however, have not been identified in humans. To identify brain regions involved in stimulus categorization, we developed a task in which subjects classified stimuli according to a variable categorical boundary. Psychophysical functions were used to define a decision variable, categorization uncertainty, which was systematically manipulated. Using event-related functional MRI, we discovered that activity in a fronto-striatal-thalamic network, consisting of the medial frontal gyrus, anterior insula, ventral striatum, and dorsomedial thalamus, was modulated by categorization uncertainty. We found this network to be distinct from the frontoparietal attention network, consisting of the frontal and parietal eye fields, where activity was not correlated with categorization uncertainty.

BACKGROUND AND PURPOSE:The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.

The dorsal medial frontal cortex (dMFC) is highly active during choice behavior. Though many models have been proposed to explain dMFC function, the conflict monitoring model is the most influential. It posits that dMFC is primarily involved in detecting interference between competing responses thus signaling the need for control. It accurately predicts increased neural activity and response time (RT) for incompatible (high-interference) vs. compatible (low-interference) decisions. However, it has been shown that neural activity can increase with time on task, even when no decisions are made. Thus, the greater dMFC activity on incompatible trials may stem from longer RTs rather than response conflict. This study shows that (1) the conflict monitoring model fails to predict the relationship between error likelihood and RT, and (2) the dMFC activity is not sensitive to congruency, error likelihood, or response conflict, but is monotonically related to time on task.

In neuroimaging research on attention, cognitive control, decision-making, and other areas where response time (RT) is a critical variable, the temporal variability associated with the decision is often assumed to be inconsequential to the hemodynamic response (HDR) in rapid event-related designs. On this basis, the majority of published studies model brain activity lasting less than four seconds with brief impulses representing the onset of neural or cognitive events, which are then convolved with the hemodynamic impulse response function (HRF). However, electrophysiological studies have shown that decision-related neuronal activity is not instantaneous, but in fact, often lasts until the motor response. It is therefore possible that small differences in neural processing durations, similar to human RTs, will produce noticeable changes in the HDR, and therefore in the results of regression analyses. In this study we compare the effectiveness of traditional models that assume no temporal variance with a model that explicitly accounts for the duration of very brief epochs of neural activity. Using both simulations and fMRI data, we show that brief differences in duration are detectable, making it possible to dissociate of the effects of stimulus intensity from stimulus duration, and that optimizing the model for the type of activity being detected improves the statistical power, consistency, and interpretability of results.

Performance on traditional selective attention tasks, like the Stroop and flanker protocols, is subject to modulation by trial history, whereby the magnitude of congruency (or conflict) effects is often found to decrease following an incongruent trial compared to a congruent one. These “congruency sequence effects” (CSEs) typically appear to reflect a mesh of memory- and attention-based processes. The current study aimed to shed new light on the nature of the attention-based contribution to CSEs, by characterizing the shape of the CSE time-course while controlling for mnemonic influences. Existing attention-based accounts of CSEs are either ambiguous in their predictions of CSE time-courses, or predict CSEs to persist or grow over the post-stimulus/response interval in anticipation of an upcoming stimulus. We gauged CSE time-courses by systematically varying inter-stimulus (Experiment 1) and response-to-stimulus (Experiment 2) intervals across a wide temporal range, in a face–word Stroop task. In spite of an exponential increase in the likelihood of stimulus appearance with increasing interval duration (i.e., an exponential hazard function), results from both experiments showed CSEs to be most pronounced at the shortest intervals, to quickly decay in magnitude with increasing interval length, and to be absent at longer intervals. These data refute the idea that attentional contributions to CSEs remain static over post-stimulus/response intervals and are incompatible with the notion that CSEs reflect expectation-guided preparatory biasing in anticipation of a forthcoming stimulus. The data are compatible, however, with the notion that attentional contributions to CSEs reflect a short-lived, phasic enhancement of attentional set in reaction to processing conflict.

A customized deep learning tool is accurate in the detection and quantification of hemorrhage on NCCT. Demonstrated high performance on prospective NCCTs ordered from the emergency department suggests the clinical viability of the proposed deep learning tool.

clinicaltrials.gov Identifier: NCT02134951.

Using localized biopsies, we have generated a model that illustrates a quantitative and significant relationship between MR signal and cell density. Projecting this relationship over the entire tumor volume allows mapping of the intratumoral heterogeneity in both the contrast-enhancing tumor core and nonenhancing margins of glioblastoma and may be used to guide extended surgical resection, localized biopsies, and radiation field mapping.