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Intelligence is composed of a set of cognitive abilities hierarchically organized. General and specific abilities capture distinguishable, but related, facets of the intelligence construct. Here, we analyze gray matter with three morphometric indices (volume, cortical surface area, and cortical thickness) at three levels of the intelligence hierarchy (tests, first-order factors, and a higher-order general factor, g). A group of one hundred and four healthy young adults completed a cognitive battery and underwent high-resolution structural MRI. Latent scores were computed for the intelligence factors and tests were also analyzed. The key finding reveals substantial variability in gray matter correlates at the test level, which is substantially reduced for the first-order and the higher-order factors. This supports a reversed hierarchy in the brain with respect to cognitive abilities at different psychometric levels: the greater the generality, the smaller the number of relevant gray matter clusters accounting for individual differences in intelligent performance.

The Nature of Human Intelligence fated by Robert J. Sternberg. New York, NY: Cambridge University Press, 2018.335 pp. Hardcover, $99 Robert Sternberg's latest edited book surveys key topics in intelligence [...]

BackgroundPrevious studies have identified brain areas related to cognitive abilities and personality, respectively. In this exploratory study, we extend the application of modern neuroimaging techniques to another area of individual differences, vocational interests, and relate the results to an earlier study of cognitive abilities salient for vocations.FindingsFirst, we examined the psychometric relationships between vocational interests and abilities in a large sample. The primary relationships between those domains were between Investigative (scientific) interests and general intelligence and between Realistic ("blue-collar") interests and spatial ability. Then, using MRI and voxel-based morphometry, we investigated the relationships between regional gray matter volume and vocational interests. Specific clusters of gray matter were found to be correlated with Investigative and Realistic interests. Overlap analyses indicated some common brain areas between the correlates of Investigative interests and general intelligence and between the correlates of Realistic interests and spatial ability.ConclusionsTwo of six vocational-interest scales show substantial relationships with regional gray matter volume. The overlap between the brain correlates of these scales and cognitive-ability factors suggest there are relationships between individual differences in brain structure and vocations.

Abstract Background Individual differences in cognitive abilities provide information that is valuable for vocational guidance, but there is an ongoing debate about the role of ability factors, including general intelligence (g), compared to individual tests. Neuroimaging can help identify brain parameters that may account for individual differences in both factors and tests. Here we investigate how eight tests used in vocational guidance correlate to regional gray matter. We compare brain networks identified by using scores for ability factors (general and specific) to those identified by using individual tests to determine whether these relatively broad and narrow approaches yield similar results. Findings Using MRI and voxel-based morphometry (VBM), we correlated gray matter with independent ability factors (general intelligence, speed of reasoning, numerical, spatial, memory) and individual test scores from a battery of cognitive tests completed by 40 individuals seeking vocational guidance. Patterns of gray matter correlations differed between group ability factors and individual tests. Moreover, tests within the same factor showed qualitatively different brain correlates to some degree. Conclusions The psychometric factor structure of cognitive tests can help identify brain networks related to cognitive abilities beyond a general intelligence factor (g). Correlates of individual ability tests with gray matter, however, appear to have some differences from the correlates for group factors.

BackgroundNeuro-imaging studies demonstrate plasticity of cortical gray matter before and after practice for some motor and cognitive tasks in adults. Other imaging studies show functional changes after practice, but there is not yet direct evidence of how structural and functional changes may be related. A fundamental question is whether they occur at the same cortical sites, adjacent sites, or sites in other parts of a network.FindingsUsing a 3 T MRI, we obtained structural and functional images in adolescent girls before and after practice on a visual-spatial problem-solving computer game, Tetris. After three months of practice, compared to the structural scans of controls, the group with Tetris practice showed thicker cortex, primarily in two areas: left BAs 6 and 22/38. Based on fMRI BOLD signals, the Tetris group showed cortical activations throughout the brain while playing Tetris, but significant BOLD decreases, mostly in frontal areas, were observed after practice. None of these BOLD decreases, however, overlapped with the cortical thickness changes.ConclusionRegional cortical thickness changes were observed after three months of Tetris practice. Over the same period, brain activity decreases were observed in several other areas. These data indicate that structural change in one brain area does not necessarily result in functional change in the same location, at least on the levels assessed with these MRI methods.

Response time (RT) generally slows with aging, but the contribution of structural brain changes to this slowing is unknown. We used voxel-based morphometry (VBM) to determine gray matter (GM) and white matter (WM) brain volumes in 9 middle-aged adults (38-58 years old) and 9 seniors (66-82 years old). We correlated brain volumes with RT assessed in both a simple visual stimulus-response task and a visual continuous recognition memory task. No GM correlations with simple RT were significant; there was one WM correlation in the right fusiform gyrus. In the memory task, faster RT was correlated (p < .05, corrected) with less GM in the globus pallidus, the parahippocampus, and the thalamus for both groups. Several Brodmann areas (BA) differed between the groups such that in each area, less GM was correlated with slower RTs in the middle-aged group but with faster RTs in the senior group (BAs 19, 37, 46, 9, 8, 6, 13, 10, 41, and 7). The results suggest that individual differences in specific brain structure volumes should be considered as potential moderating factors in cognitive brain imaging studies.

Brain size and cerebral glucose metabolic rate (GMR) were determined with magnetic resonance imaging (MRI) and positron emission tomography (PET) in individuals with mild mental retardation (MR), individuals with Down syndrome (DS) without dementia and in matched controls. The MRI data showed that the MR and the DS groups both had brain volumes of about 80% of controls; variance was greatest within the MR group. PET was obtained with [18F]-fluorodeoxyglucose (FDG) as the tracer during a test of attention (Continuous Performance Test; CPT). Whole brain cortex GMR was higher than the controls in both the MR and the DS groups. For all subjects combined, the correlation between brain size and IQ was .65 (p < .005). © 1995.

The parieto-frontal integration theory (P-FIT) nominates several areas distributed throughout the brain as relevant for intelligence. This theory was derived from previously published studies using a variety of both imaging methods and tests of cognitive ability. Here we test this theory in a new sample of young healthy adults (N = 100) using a psychometric battery tapping fluid, crystallized, and spatial intelligence factors. High resolution structural MRI scans (3T) were obtained and analyzed with Voxel-based Morphometry (VBM). The main findings are consistent with the P-FIT, supporting the view that general intelligence (g) involves multiple cortical areas throughout the brain. Key regions include the dorsolateral prefrontal cortex, Broca's and Wernicke's areas, the somato-sensory association cortex, and the visual association cortex. Further, estimates of crystallized and spatial intelligence with g statistically removed, still share several brain areas with general intelligence, but also show some degree of uniqueness. (Contains 6 figures and 6 tables.)

Heterogeneous results among neuro-imaging studies using psychometric intelligence measures may result from the variety of tests used. The g-factor may provide a common metric across studies. Here we derived a g-factor from a battery of eight cognitive tests completed by 6929 young adults, 40 of whom also completed structural MRI scans. Regional gray matter (GM) was determined using voxel-based-morphometry (VBM) and correlated to g-scores. Results showed correlations distributed throughout the brain, but there was limited overlap with brain areas identified in a similar study that used a different battery of tests to derive g-scores. Comparable spatial scores (with g variance removed) also were derived from both batteries, and there was considerable overlap in brain areas where GM was correlated to the respective spatial scores. The results indicate that g-scores derived from different test batteries do not necessarily have equivalent neuro-anatomical substrates, suggesting that identifying a "neuro-g" will be difficult. The neuro-anatomical substrate of a spatial factor, however, appears more consistent and implicates a distributed network of brain areas that may be involved with spatial ability. Future imaging studies directed at identifying the neural basis of intelligence may benefit from using a psychometric test battery chosen with specific criteria. (Contains 3 figures and 6 tables.)

The goal of this article is to summarize current brain research on intelligence and creativity that may be relevant to education in the near future. Five issues are addressed: (a) Why is there a neuroscience interest in intelligence? (b) Can intelligence be located in the brain? (c) Why are some brains smarter than others? (d) What do we know about creativity and the brain? and (e) Can information about an individual's brain structure and function be useful to benefit his or her education? As we enter the 21st century, old controversies about measurement of intelligence are less relevant. Integrating neuroscience findings into education practices is a daunting challenge that will require educators to reexamine old ideas and acquire fundamental backgrounds in new areas. (Contains 1 figure.)

It is unclear whether brain mechanisms underlying human intelligence are distributed throughout the brain or mainly concentrated in the frontal lobes. Data are inconsistent possibly due, at least in part, to the different ways the construct of intelligence is measured. Here we apply the method of correlated vectors to determine how the general factor of intelligence ("g") is related to regional gray matter and white matter volumes. This is a re-analysis of an earlier study showing regional gray matter and white matter volume is correlated to Full Scale IQ (FSIQ). However, it is well-known that FSIQ taps several cognitive abilities and skills in addition to "g." The results now show that the "g" factor accounts for several but not all FSIQ/gray matter correlations distributed throughout the brain and these areas may differ for young and older adults.

Administered Raven's Advanced Progressive Matrices to 22 adults and measured cerebral glucose activity as subjects viewed videos on 2 occasions. Data provide evidence that individual differences in intelligence correlate with brain function even when the brain is engaged in non-reasoning tasks. (SLD)

A study of eight normal right-handed men demonstrates widespread significant decreases in brain glucose metabolic rate (GMR) following learning a complex computer task, a computer game. Correlations between magnitude of GMR change and intelligence scores are also demonstrated. (SLD)

The commentaries address conceptual issues ranging from our narrow focus on neuroimaging to the various definitions of intelligence. The integration of the P-FIT and data from cognitive neuroscience is particularly important and considerable consistency is found. Overall, the commentaries affirm that advances in neuroscience techniques have caused intelligence research to enter a new phase. The P-FIT is recognized as a reasonable empirical framework to test hypotheses about the relationship of brain structure and function with intelligence and reasoning.

'Is there a biology of intelligence which is characteristic of the normal human nervous system?' 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. Keywords: diffusion tensor imaging (DTI); functional magnetic resonance imaging (fMRI); g; genomics; intelligence; IQ; magnetic resonance spectroscopy (MRS); positron emission tomography (PET); reasoning; structural magnetic resonance imaging (sMRI); voxel-based morphometry (VBM)

INTRODUCTION It is certainly a cause for concern that the use of alcohol and drugs is widespread among adolescents and young adualts, and that 6% to 10% of the users [...]

High aptitude is directly proportional to high cortical glucose use. The relation of cerebral glucose use and cognitive ability depends upon the participant and task selection. Positron emission tomography data collected using the Raven's Advanced Progressive Matrices detected cortical glucose usage during problem solving. Results reveal a low use of glucose during difficult problem solving by average aptitude participants. Greater right hemisphere activation took place during difficult problem solving for the high aptitude group.

Abstract Background Individual differences in cognitive abilities provide information that is valuable for vocational guidance, but there is an ongoing debate about the role of ability factors, including general intelligence (g), compared to individual tests. Neuroimaging can help identify brain parameters that may account for individual differences in both factors and tests. Here we investigate how eight tests used in vocational guidance correlate to regional gray matter. We compare brain networks identified by using scores for ability factors (general and specific) to those identified by using individual tests to determine whether these relatively broad and narrow approaches yield similar results. Findings Using MRI and voxel-based morphometry (VBM), we correlated gray matter with independent ability factors (general intelligence, speed of reasoning, numerical, spatial, memory) and individual test scores from a battery of cognitive tests completed by 40 individuals seeking vocational guidance. Patterns of gray matter correlations differed between group ability factors and individual tests. Moreover, tests within the same factor showed qualitatively different brain correlates to some degree. Conclusions The psychometric factor structure of cognitive tests can help identify brain networks related to cognitive abilities beyond a general intelligence factor (g). Correlates of individual ability tests with gray matter, however, appear to have some differences from the correlates for group factors.