Here we review 37 modern neuroimaging studies in an attempt to address this question posed by Halstead (1947) as he and other icons of the last century endeavored to understand how brain and behavior are linked through the expression of intelligence and reason. Reviewing studies from functional (i.e., functional magnetic resonance imaging, positron emission tomography) and structural (i.e., magnetic resonance spectroscopy, diffusion tensor imaging, voxel-based morphometry) neuroimaging paradigms, we report a striking consensus suggesting that variations in a distributed network predict (...) individual differences found on intelligence and reasoning tasks. We describe this network as the Parieto-Frontal Integration Theory (P-FIT). The P-FIT model includes, by Brodmann areas (BAs): the dorsolateral prefrontal cortex (BAs 6, 9, 10, 45, 46, 47), the inferior (BAs 39, 40) and superior (BA 7) parietal lobule, the anterior cingulate (BA 32), and regions within the temporal (BAs 21, 37) and occipital (BAs 18, 19) lobes. White matter regions (i.e., arcuate fasciculus) are also implicated. The P-FIT is examined in light of findings from human lesion studies, including missile wounds, frontal lobotomy/leukotomy, temporal lobectomy, and lesions resulting in damage to the language network (e.g., aphasia), as well as findings from imaging research identifying brain regions under significant genetic control. Overall, we conclude that modern neuroimaging techniques are beginning to articulate a biology of intelligence. We propose that the P-FIT provides a parsimonious account for many of the empirical observations, to date, which relate individual differences in intelligence test scores to variations in brain structure and function. Moreover, the model provides a framework for testing new hypotheses in future experimental designs. (shrink)

Evidence from functional neuroimaging of the human brain indicates that information about salient properties of an object¿such as what it looks like, how it moves, and how it is used¿is stored in sensory and motor systems active when that information was acquired. As a result, object concepts belonging to different categories like animals and tools are represented in partially distinct, sensory- and motor property-based neural networks. This suggests that object concepts are not explicitly represented, but rather emerge from weighted activity (...) within property-based brain regions. However, some property-based regions seem to show a categorical organization, thus providing evidence consistent with category-based, domain-specific formulations as well.Acronyms and DefinitionsBiological motion: motion of animate agents characterized by highly flexible, fully articulated motion vectors, in contrast to the rigid, unarticulated motion vectors associated with most tools.Category-specific disorder: a relatively greater impairment in retrieving information about members of one superordinate object category (e.g., animals) as compared with other categories following brain injury or diseaseIPS: intraparietal sulcusLO: lateral occipital cortexObject concept: memory representations of a class or category of objects. Necessary for numerous cognitive functions including identifying an object as a member of a specific category and drawing inferences about object propertiespMTG: posterior middle temporal gyruspSTS: posterior superior temporal sulcusRepetition suppression: decreased neural response associated with repeated presentation of an identical, or a semantically/conceptually related, stimulusSD: semantic dementiaSemantic memory: a large division of long-term memory containing knowledge about the world including facts, ideas, beliefs, and conceptsSemantic priming: a short-lasting facilitation in processing a stimulus due to the prior presentation of a semantically related stimulusTMS: transcranial magnetic stimulationVPMC: ventral premotor cortex. (shrink)

Although patient data have traditionally implicated the left prefrontal cortex in hypothesis generation, recent lesion data implicate right PFC in hypothesis generation tasks that involve set shifts. To test the involvement of the right prefrontal cortex in a hypothesis generation task involving set shifts, we scanned 13 normal subjects with fMRI as they completed Match Problems and a baseline task. In Match Problems subjects determined the number of possible solutions for each trial. Successful solutions are indicative of set shifts. In (...) the baseline condition subjects evaluated the accuracy of hypothetical solutions to match problems. A comparison of Match Problems versus baseline trials revealed activation in right ventral lateral PFC and left dorsal lateral PFC. A further comparison of successfully versus unsuccessfully completed Match Problems revealed activation in right ventral lateral PFC, left middle frontal gyrus and left frontal pole, thus identifying the former as a critical component of the neural mechanisms of set-shift transformation. By contrast, activation in right dorsal lateral PFC covaried as a function of the number of solutions generated in Match Problems, possibly due to increased working memory demands to maintain multiple solutions ‘on-line’, conflict resolution, or progress monitoring. These results go beyond the patient data by identifying the ventral lateral aspect of right PFC as being a critical component of the neural systems underlying lateral transformations, and demonstrate a dissociation between right VLPFC and DLPFC in hypotheses generation and maintenance. (shrink)