Trends in Neurosciences
Volume 23, Issue 10, 1 October 2000, Pages 475-483
Journal home page for Trends in Neurosciences

Review
Common regions of the human frontal lobe recruited by diverse cognitive demands

https://doi.org/10.1016/S0166-2236(00)01633-7Get rights and content

Abstract

Though many neuroscientific methods have been brought to bear in the search for functional specializations within prefrontal cortex, little consensus has emerged. To assess the contribution of functional neuroimaging, this article reviews patterns of frontal-lobe activation associated with a broad range of different cognitive demands, including aspects of perception, response selection, executive control, working memory, episodic memory and problem solving. The results show a striking regularity: for many demands, there is a similar recruitment of mid-dorsolateral, mid-ventrolateral and dorsal anterior cingulate cortex. Much of the remainder of frontal cortex, including most of the medial and orbital surfaces, is largely insensitive to these demands. Undoubtedly, these results provide strong evidence for regional specialization of function within prefrontal cortex. This specialization, however, takes an unexpected form: a specific frontal-lobe network that is consistently recruited for solution of diverse cognitive problems.

Section snippets

Clustering of frontal activations

A general impression of clustering in the frontal activations associated with widely different cognitive demands is easy to obtain from the imaging literature. Fig ure 1 provides an initial illustration. To produce this figure, reported peak activation foci were taken from six studies that concerned, respectively, auditory discrimination18, visual divided attention19, self-paced response production20, task switching21, spatial problem solving22 and semantic processing of words23. Studies were

Systematic comparison of five cognitive demands

To address these problems requires some more systematic comparison of activation patterns associated with different cognitive demands. For this purpose it is especially useful to find studies in which a well-specified demand has been manipulated in the context of an otherwise identical task. In recent years, a number of reviews of imaging data have appeared in an attempt to compare regional recruitment for different cognitive demands. Often, however, results have been disappointing: combining

Finer functional specializations

Of course, the finding of frequent co-recruitment of mid-dorsolateral, mid-ventrolateral and dorsal anterior cingulate regions does not rule out finer specializations within this network. Three variants of this possibility might be considered.

The first is specialization within each of these regions at a level of scale beyond the resolution of functional imaging. For example, if several, interdigitated subregions of dorsolateral prefrontal cortex were recruited to solve problems in any given

Other prefrontal regions

What then of the large frontal regions outside the active zones revealed by our analysis – including the majority of the medial and orbital surfaces, and much of the superior frontal gyrus from the anterior limit of premotor cortex to the frontal pole? Again there are interesting hints in the imaging literature. Many authors, for example, have noted the affective and motivational changes that can follow frontal-lobe injury. In imaging studies, correspondingly, both dorsomedial and orbitomedial

Concluding remarks

Certainly functional imaging contributes something new to our understanding of regional specialization within prefrontal cortex. On the one hand, there is strong evidence for such specialization: very specific prefrontal regions are repeatedly recruited by simple cognitive demands. On the other hand, specialization takes an unexpected form: very much the same regions are recruited by different demands, suggesting a specific prefrontal network recruited in solution of diverse cognitive problems.

Acknowledgements

We are indebted to Matthew Brett for development of image creation software, and to Ian Nimmo-Smith for statistical assistance.

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