| Literature DB >> 26578901 |
Erica A Boschin1, Mark J Buckley1.
Abstract
The ability to maintain and manipulate information across temporal delays is a fundamental requirement to bridge the gap between perception and action. In the case of higher-order behavior, the maintenance of rules and strategies is particularly helpful in bridging this gap. The prefrontal cortex (PFC) has long been considered critical for such processes, and research has focused on different subdivisions of PFC to gain an insight into their diverse contributions to these mechanisms. Substantial evidence indicates that dorsolateral PFC (dlPFC) is an important structure for maintaining information across delays, with cells actively firing across delays and lesions to this region causing deficits in tasks involving delayed responses and maintenance of rules online. Frontopolar cortex (FP), on the other hand, appears to show the opposite pattern of results, with cells not firing across delays and lesions to this region not affecting the same rule-based, delayed response tasks that are impaired following dlPFC lesions. The body of evidence therefore suggests that dlPFC and FP's contributions to working memory differ. In this article, we will provide a perspective on how these regions might implement distinct but complementary and interactive functions that contribute to more general temporally-extended processes and support flexible, dynamic behavior.Entities:
Keywords: delay; dorsolateral prefrontal; frontopolar cortex; prefrontal cortex; valuation
Year: 2015 PMID: 26578901 PMCID: PMC4624853 DOI: 10.3389/fnsys.2015.00144
Source DB: PubMed Journal: Front Syst Neurosci ISSN: 1662-5137
Figure 1Anatomy and connectivity of prefrontal cortex (PFC) in the human and monkey brain. (A) Lateral view of human brain (adapted from Brodmann (1909), pp. 108, Figure 85, with permission from Springer): frontopolar cortex (FP) (red) is visible at the most anterior portion of the frontal lobe, identified approximately as Brodmann area 10, with dlPFC (yellow) occupying the area immediately posterior and superior to FP. (B) Lateral, medial and inferior view of the macaque’s PFC (adapted from Walker (1940), with permission from Wiley): FP (red) is visible at the tip of the macaque’s frontal lobe and dlPFC (yellow) is visible in the tissue above and surrounding the principal sulcus. (C–E) Medial view of the human (C) (adapted from Ongür et al. (2003), with permission from Wiley) and macaque (D,E) (adapted from Ongür et al. (2003) and Petrides and Pandya (1999), respectively, with permission from Wiley) PFC: FP (red) extends rostrally into the medial surface of the PFC according to some cytoarchitectonical subdivisions (C,D—areas 10r and 10m). (F) Mapping of resting-state functional connectivity of FP (medial—left, in purple—and lateral—right, in red) with more posterior areas, comparing connectivity in the macaque brain (top) with the human brain (bottom). Spider plots illustrate the intensities of the coupling patterns between FP (location of the seed regions are illustrated in the central column, following the same color scheme) and the target regions of interest. The connectivity profile of human medial FP (FPm) closely resembles that of medial area 10 (10m) the macaque brain. Human lateral FP (FPl), on the other hand, appears to resemble macaque area 46, here shown in yellow (adapted from Neubert et al., 2014, with permission from Elsevier).
Figure 2Patterns of spared and impaired performance following FP lesion in the macaque (adapted from Boschin et al., Delayed-Matching/Delayed-Non-Matching-to-Sample: FP animals are not impaired compared to controls across several different delays. (B) Objects-in-scenes: in this task, animals learn about which of a pair of foreground objects (alphanumeric characters, indicated by the red arrows) presented within a complex scene is associated with reward. They are presented with 20 novel problems every day and in each daily session they are tested on that set of problems eight times. Animals are tested for 15 days pre-operatively and post-operatively. For control animals, the greatest improvement in performance (measured as decrease in percent error) was observed between the first and second run, indicating rapid learning. FP animals, on the other hand, did not show such substantial improvement between the first and second run, indicating a deficit in rapidly learning about the relative values of novel stimuli. (C) Successive single-problem learning. The animals learn about which of a single pair objects (clipart images) is associated with reward with problems presented successively. In the first run they are given forced-choice trials where the rewarded and unrewarded item are presented individually (order counter-balanced across trials), then they are tested on that problem 10 times successively. A session comprises 10 such problems and each animal completes 10 sessions pre- and post-operatively. FP animals were again impaired on rapid, one-trial learning about the relative value of novel stimuli, (here measured as the decrease in percent error between the forced-choice phase and the first presentation of a problem between the two stimuli). (D) Acquisition of a new abstract rule: animals are trained to perform a simultaneous matching-to-sample task requiring them to choose a stimulus on the basis of two concurrent abstract rules (“matching” and “smaller than”). As an intermediate phase they are trained on the new “smaller-than” rule for 3 days, which is depicted in this figure. Control animals showed a significant decrease in percent error from the first to the second day of learning to apply the new “smaller than” rule. This is indicative of rapid learning about the value of the novel abstract rule. FP animals, however, did not display such an improvement.