Faculty Opinions recommendation of Synapses with inhibitory neurons differentiate anterior cingulate from dorsolateral prefrontal pathways associated with cognitive control.

Author(s):  
Kevan A Martin
2018 ◽  
Author(s):  
Liya Ma ◽  
Jason L. Chan ◽  
Kevin Johnston ◽  
Stephen G. Lomber ◽  
Stefan Everling

SUMMARYIn primates, both the dorsal anterior cingulate cortex (dACC) and the dorsolateral prefrontal cortex (dlPFC) are key regions of the frontoparietal cognitive control network. To study the role of the dACC and its communication with the dlPFC in cognitive control, we recorded the local field potentials from the dlPFC before and during the reversible deactivation of the dACC, in macaque monkeys engaging in uncued switches between two stimulus-response rules. Cryogenic dACC deactivation impaired response accuracy during rule-maintenance, but not rule-switching, which coincided with a reduction in the correct-error difference in dlPFC beta activities specifically during maintenance of the more challenging rule. During both rule switching and maintenance, dACC deactivation prolonged the animals’ reaction time and reduced task-related theta/alpha activities in the dlPFC; it also weakened dlPFC theta-gamma phase-amplitude modulation. Thus, the dACC and its interaction with the dlPFC plays a critical role in the maintenance of a new, challenging rule.


2010 ◽  
Vol 22 (12) ◽  
pp. 2758-2767 ◽  
Author(s):  
Chris Blais ◽  
Silvia Bunge

How cognitive control is recruited and implemented has become a major focus of researchers in cognitive psychology and neuroscience. Current theories posit that cognitive control operates at the level of general rules—for example, in a Stroop task, “attend to the color of the stimulus.” Here we report behavioral evidence suggesting that cognitive control is implemented much more locally, operating at the level of specific stimuli appearing in a task block. In addition, we report neural evidence that many of the regions implicated in cognitive control on the Stroop task, including anterior cingulate cortex and dorsolateral prefrontal cortex, operate at a local level.


eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Hanlin Tang ◽  
Hsiang-Yu Yu ◽  
Chien-Chen Chou ◽  
Nathan E Crone ◽  
Joseph R Madsen ◽  
...  

Rapid and flexible interpretation of conflicting sensory inputs in the context of current goals is a critical component of cognitive control that is orchestrated by frontal cortex. The relative roles of distinct subregions within frontal cortex are poorly understood. To examine the dynamics underlying cognitive control across frontal regions, we took advantage of the spatiotemporal resolution of intracranial recordings in epilepsy patients while subjects resolved color-word conflict. We observed differential activity preceding the behavioral responses to conflict trials throughout frontal cortex; this activity was correlated with behavioral reaction times. These signals emerged first in anterior cingulate cortex (ACC) before dorsolateral prefrontal cortex (dlPFC), followed by medial frontal cortex (mFC) and then by orbitofrontal cortex (OFC). These results disassociate the frontal subregions based on their dynamics, and suggest a temporal hierarchy for cognitive control in human cortex.


2019 ◽  
Vol 14 (11) ◽  
pp. 1219-1232 ◽  
Author(s):  
Lucía Magis-Weinberg ◽  
Ruud Custers ◽  
Iroise Dumontheil

Abstract Cognitive control allows the coordination of cognitive processes to achieve goals. Control may be sustained in anticipation of goal-relevant cues (proactive control) or transient in response to the cues themselves (reactive control). Adolescents typically exhibit a more reactive pattern than adults in the absence of incentives. We investigated how reward modulates cognitive control engagement in a letter-array working memory (WM) task in 30 adolescents (12–17 years) and 20 adults (23–30 years) using a mixed block- and event-related functional magnetic resonance imaging design. After a Baseline run without rewards, participants performed a Reward run where 50% trials were monetarily rewarded. Accuracy and reaction time (RT) differences between Reward and Baseline runs indicated engagement of proactive control, which was associated with increased sustained activity in the bilateral anterior insula (AI), right dorsolateral prefrontal cortex (PFC) and right posterior parietal cortex (PPC). RT differences between Reward and No reward trials of the Reward run suggested additional reactive engagement of cognitive control, accompanied with transient activation in bilateral AI, lateral PFC, PPC, supplementary motor area, anterior cingulate cortex, putamen and caudate. Despite behavioural and neural differences during Baseline WM task performance, adolescents and adults showed similar modulations of proactive and reactive control by reward.


2015 ◽  
Vol 27 (11) ◽  
pp. 2354-2410 ◽  
Author(s):  
William H. Alexander ◽  
Joshua W. Brown

Anterior cingulate and dorsolateral prefrontal cortex (ACC and dlPFC, respectively) are core components of the cognitive control network. Activation of these regions is routinely observed in tasks that involve monitoring the external environment and maintaining information in order to generate appropriate responses. Despite the ubiquity of studies reporting coactivation of these two regions, a consensus on how they interact to support cognitive control has yet to emerge. In this letter, we present a new hypothesis and computational model of ACC and dlPFC. The error representation hypothesis states that multidimensional error signals generated by ACC in response to surprising outcomes are used to train representations of expected error in dlPFC, which are then associated with relevant task stimuli. Error representations maintained in dlPFC are in turn used to modulate predictive activity in ACC in order to generate better estimates of the likely outcomes of actions. We formalize the error representation hypothesis in a new computational model based on our previous model of ACC. The hierarchical error representation (HER) model of ACC/dlPFC suggests a mechanism by which hierarchically organized layers within ACC and dlPFC interact in order to solve sophisticated cognitive tasks. In a series of simulations, we demonstrate the ability of the HER model to autonomously learn to perform structured tasks in a manner comparable to human performance, and we show that the HER model outperforms current deep learning networks by an order of magnitude.


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