Human brain & Intelligence
Intelligence can be defined as a general mental ability for reasoning, problem solving, and learning. Because of its general nature, intelligence integrates cognitive functions such as perception, attention, memory, language, or planning. On the basis of this definition, intelligence can be reliably measured by standardized tests with obtained scores predicting several broad social outcomes such as educational achievement, job performance, health, and longevity. A detailed understanding of the brain mechanisms underlying this general mental ability could provide significant individual and societal benefits. Structural and functional neuroimaging studies have generally supported a frontoparietal network relevant for intelligence. This same network has also been found to underlie cognitive functions related to perception, short-term memory storage, and language. The distributed nature of this network and its involvement in a wide range of cognitive functions fits well with the integrative nature of intelligence. A new key phase of research is beginning to investigate how functional networks relate to structural networks, with emphasis on how distributed brain areas communicate with each other.Reasoning, problem solving, and learning are crucial facets of human intelligence. People can reason about virtually any issue, and many problems may be solved. Simple and highly complex behavioral repertoires can be learned throughout the lifespan. Importantly, there are widespread individual differences in the ability to reason, solve problems, and learn which lead to human differences in the general ability to cope with challenging situations.
Exploring the relationships between human intelligence and the brain requires a careful consideration of the structure of human intelligence. As evident from above, when researchers state that they are measuring intelligence by means of the Standard Progressive Matrices Test (SPM - as another example) they are telling an imprecise story because the SPM measures g plus spatial and reasoning abilities plus SPM specificity. The exact combination of these “ingredients” for the analyzed sample must be computed before saying something clear about the measured performance. This requires that studies use a battery of tests rather than just one test. Although this was not usually done for the early functional imaging studies of intelligence, it is now more common.Results from the older and the newer studies, however, point to the importance of both whole brain and specific brain networks.
“There is no longer any doubt that a larger brain predicts greater intelligence. Several research teams, using differing scan protocols, populations, and cognitive measures, have all shown that IQ and brain volume correlate at about the 0.40 level ( ...) obviously replication of this effect is no longer required. What is required now is a more fine-grained analysis of why it is that a larger brain predicts greater intelligence, and what it is about intelligence that is most directly related to brain volume” (p 1096, emphasis added).The meta-analysis by McDaniel studied the relationship between in vivo brain volume and intelligence. Thirty-seven samples comprising a total of 1530 participants were considered simultaneously. These were the main findings:
(i) the average correlation is 0.33;
(ii) subsets of the 37 studies that allow partitioning by gender revealed that the correlation is higher for females (0.40) than for males (0.34); and
(iii) the correlation does not change across age (0.33). The report concludes that these results resolve a 169-year-old debate:
it is clear that intelligence and brain volumes are positively related.
Going one step further, several studies measured the volume of regions of interest (ROIs) showing the most significant correlations (controlling for total brain volumes) in frontal, parietal, and temporal brain regions, along with the hippocampus and the cerebellum.Nevertheless, regional correlations are moderate (ranging from 0.25 to 0.50) which implies that measures of total or local brain size are far from telling the whole story.
From this perspective, gray and white matter must be distinguished. In keeping with this, voxel-by-voxel (a voxel is a volume element analogous to a pixel) analyses also showed specific areas where the amount of gray and white matter was correlated with intelligence scores.The amount of gray matter is considered to reflect number and density of neuronal bodies and dendritic arborization, whereas the amount of white matter is considered to capture number and thickness of axons and their degree of myelination. Gray matter could support information processing capacity, while white matter might support the efficient flow of information in the brain. Available reports are consistent with the statement that both gray and white matter volumes are positively related to intelligence, but that the latter relationship is somewhat greater (unweighted mean correlation values =.27 and .31 respectively).34 It is noteworthy that new studies using diffusion tensor imaging (DTI), which is the best method to date for assessing white matter, have reported DTI correlations with intelligence scores (see white matter section below).
A distributed brain network for human intelligenceJung and Haier reviewed 37 structural and functional neuroimaging studies published between 1988 and 2007. Based on the commonalities found in their analysis, they proposed the Parieto-Frontal Integration Theory (PFIT), identifying several brain areas distributed across the brain.