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The study of human individual differences has matured significantly, in the last decade or so owing, in part, to the notable advances in neuroimaging techniques. There are three major domains of inquiry within individual differences research: personality, creativity, and intelligence. Each has a discrete, testable definition (a new definition for intelligence is offered: rapid and accurate problem solving), and each has been associated with distinct brain regions and interactive networks. Here, we outline commonalities between these constructs, which appear to conform to two major axes: exploratory behavior and restraint. These axes, in turn, conform largely to two major brain networks dedicated to novelty generation (i.e. default mode network — DMN), and refinement of ideas (i.e. cognitive control network — CCN). Thus, human individual differences represent the expression of adaptive behaviors leading to exploratory and/or restrained action arising from brain structure and function.

Histological studies have reported microstructural hemispheric asymmetries in several cortical areas of the human brain, but reliable in vivo assessment methods have been lacking so far. Here, we used neurite orientation dispersion and density imaging (NODDI) to examine microstructural asymmetries in in vivo and determine if findings are in accordance with what has been reported in histological studies. We examined intra-neurite volume fraction (INVF), neurite orientation dispersion (ODI), and isotropic volume fraction (ISO) asymmetries in two independent samples of healthy adults (n = 269 and n = 251). Over both samples, we found greater left-hemispheric INVF in early auditory, inferior parietal and temporal-parietal-occipital areas. In contrast, we found greater right-hemispheric INVF in the fusiform and inferior temporal gyrus, reflecting what has been reported in histological studies. ODI was asymmetric towards the left hemisphere in frontal areas and towards the right hemisphere in early auditory areas. ISO showed less pronounced asymmetries. There were hardly any effects of sex or handedness on microstructural asymmetry as determined by NODDI. Taken together, these findings suggest substantial microstructural asymmetries in gray matter, making NODDI a promising marker for future genetic and behavioral studies on laterality.

The developmental roots of individual variation in general cognitive ability (GCA) are the subject of intense scientific interest. One unique perspective is offered by developmental instability theory, which suggests that variation in GCA in part reflects the ability to buffer brain development from key environmental and genetic perturbations. Support for this approach comes mainly from assessment of fluctuating asymmetry, or deviations from symmetry of body features that are symmetric at the population level. In this study of healthy young adults (N = 244) we assessed fluctuating asymmetry (FA) of total cortical surface area (CSFA) from 33 regions using automated analysis. Overall CSFA was negatively related to GCA, consistent with meta-analytic results from body FA studies. A correlated vectors analysis indicated that the CSFA-cognition relationship varied systematically with a test's g loading. Further, FA of frontoparietal regions was a significant predictor of GCA, but FA non-frontoparietal regions was not, consistent with the Parieto-Frontal Integration Theory (P-FIT) model of intelligence. Frontoparietal FA was less than non-frontoparietal FA, i.e., consistent with the hypothesis that regions linked with GCA are better buffered from perturbation, perhaps a reflection of the importance of GCA for important life outcomes.

Creativity plays an important role in numerical problem solving. Although the neural underpinnings of creativity have been studied over decades, very little is known about neural mechanisms of the creative process that relates to numerical problem solving. In the present study, we employed a numerical analogical reasoning task with functional Magnetic Resonance Imaging (fMRI) to investigate the neural correlates of divergent production of rules in numerical analogical reasoning. Participants performed two tasks: a multiple solution analogical reasoning task and a single solution analogical reasoning task. Results revealed that divergent production of rules involves significant activations at Brodmann area (BA) 10 in the right middle frontal cortex, BA 40 in the left inferior parietal lobule, and BA 8 in the superior frontal cortex. The results suggest that right BA 10 and left BA 40 are involved in the generation of novel rules, and BA 8 is associated with the inhibition of initial rules in numerical analogical reasoning. The findings shed light on the neural mechanisms of creativity in numerical processing.

An influential model of the neural mechanisms of creative thought suggests that creativity is manifested in the joint contributions of the Default Mode Network (DMN; a set of regions in the medial PFC, lateral and medial parietal cortex, and the medial temporal lobes) and the executive networks within the dorsolateral PFC. Several empirical reports have offered support for this model by showing that complex interactions between these brain systems account for individual differences in creative performance. The present study examined whether the engagement of these regions in idea generation is modulated by one’s eminence in a creativity-related field. Twenty (n ​= ​20) healthy eminent creators from diverse fields of expertise and a ‘smart’ comparison group of sixteen (n ​= ​16) age- and education-matched non-eminent thinkers were administered a creative generation task (an adaptation of the Alternative Uses Task) and a control perceptual task, while undergoing functional magnetic resonance imaging (fMRI). The participants’ verbal responses were recorded through a noise-canceling microphone and were later coded for fluency and accuracy. Behavioral and fMRI analyses revealed commonalities between groups, but also distinct patterns of activation in default mode and executive brain regions between the eminent and the non-eminent participants during creative thinking. We interpret these findings in the context of the well-documented contributions of these regions in the generation of creative ideas as modulated, in this study, by participants’ creative eminence.

Nearly everyone has the ability for creative thought. Yet, certain individuals create works that propel their fields, challenge paradigms, and advance the world. What are the neurobiological factors that might underlie such prominent creative achievement? In this study, we focus on morphometric differences in brain structure between high creative achievers from diverse fields of expertise and a 'smart' comparison group of age-, intelligence-, and education-matched average creative achievers. Participants underwent a high-resolution structural brain imaging scan and completed a series of intelligence, creative thinking, personality, and creative achievement measures. We examined whether high and average creative achievers could be distinguished based on the relationship between morphometric brain measures (cortical area and thickness) and behavioral measures. Although participants' performance on the behavioral measures did not differ between the two groups aside from creative achievement, the relationship between posterior parietal cortex morphometry and creativity, intelligence, and personality measures depended on group membership. These results suggest that extraordinary creativity may be associated with measurable structural brain differences, especially within parietal cortex.

The refinement of localization of intelligence in the human brain is converging onto a distributed network that broadly conforms to the Parieto-Frontal Integration Theory (P-FIT). While this theory has received support in the neuroimaging literature, no functional magnetic resonance imaging study to date has conducted a whole-brain network-wise examination of the changes during engagement in tasks that are reliable measures of general intelligence (e.g., Raven's Progressive Matrices Test; RPM). Seventy-nine healthy subjects were scanned while solving RPM problems and during rest. Functional networks were extracted from the RPM and resting state data using Independent Component Analysis. Twenty-nine networks were identified, 26 of which were detected in both conditions. Fourteen networks were significantly correlated with the RPM task. The networks' spatial maps and functional connectivity measures at 3 frequency levels (low, medium, & high) were compared between the RPM and rest conditions. The regions involved in the networks that were found to be task related were consistent with the P-FIT, localizing to the bilateral medial frontal and parietal regions, right superior frontal lobule, and the right cingulate gyrus. Functional connectivity in multiple component pairs was differentially affected across all frequency levels during the RPM task. Our findings demonstrate that functional brain networks are more stable than previously thought, and maintain their general features across resting state and engagement in a complex cognitive task. The described spatial and functional connectivity alterations that such components undergo during fluid reasoning provide a network-wise framework of the P-FIT that can be valuable for further, network based, neuroimaging inquiries regarding the neural underpinnings of intelligence.

Hampshire and colleagues used factor analyses and simulations to conclude that the g-factor is not a valid construct for general intelligence because it could be accounted for by at least two independent components defined by distinct brain networks. In our view, their results depend on a number of assumptions and subjective decisions that, at best, allow for different interpretations. We also had a unique role in the review process of their paper prior to its publication when we were invited to write a Preview. We detail that role here and describe how non-transparent editorial decision-making rejected our Preview and allowed publication despite our major concerns. The main purpose of this report is to invite Hampshire and colleagues to respond to our specific scientific concerns that aim to clarify their work and contribute a constructive discussion about the meaning of their findings.

Hampshire and Owen maintain that their original paper was flawless, but doubts remain about their factor analysis methods and related assumptions. Failure to cite relevant papers, poor sampling and restricted ranges also remain problematic for the definitive conclusions they drew. The editorial review process for investigating the serious issues we raised prior to publication in Neuron remains a mystery. We stand by the opinion expressed in our preview: the Hampshire et al. paper is an interesting but flawed exercise and their conclusions are not as definitive, or original, as they believe.

In this study we employed joint independent component analysis (jICA) to perform a novel multivariate integration of magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data to investigate the link between function and structure. This model-free approach allows one to identify covariation across modalities with different temporal and spatial scales [temporal variation in MEG and spatial variation in fractional anisotropy (FA) maps]. Healthy controls (HC) and patients with schizophrenia (SP) participated in an auditory/visual multisensory integration paradigm to probe cortical connectivity in schizophrenia. To allow direct comparisons across participants and groups, the MEG data were registered to an average head position and regional waveforms were obtained by calculating the local field power of the planar gradiometers. Diffusion tensor images obtained in the same individuals were preprocessed to provide FA maps for each participant. The MEG/FA data were then integrated using the jICA software (http://mialab.mrn.org/software/fit). We identified MEG/FA components that demonstrated significantly different (p < 0.05) covariation in MEG/FA data between diagnostic groups (SP vs. HC) and three components that captured the predominant sensory responses in the MEG data. Lower FA values in bilateral posterior parietal regions, which include anterior/posterior association tracts, were associated with reduced MEG amplitude (120-170 ms) of the visual response in occipital sensors in SP relative to HC. Additionally, increased FA in a right medial frontal region was linked with larger amplitude late MEG activity (300-400 ms) in bilateral central channels for SP relative to HC. Step-wise linear regression provided evidence that right temporal, occipital and late central components were significant predictors of reaction time and cognitive performance based on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) cognitive assessment battery. These results point to dysfunction in a posterior visual processing network in schizophrenia, with reduced MEG amplitude, reduced FA and poorer overall performance on the MATRICS. Interestingly, the spatial location of the MEG activity and the associated FA regions are spatially consistent with white matter regions that subserve these brain areas. This novel approach provides evidence for significant pairing between function (electrophysiology) and structure (white matter integrity) and demonstrates the sensitivity of this multivariate, multimodal integration technique to group differences in function and structure.

Many studies of creative cognition with a neuroimaging component now exist; what do they say about where and how creativity arises in the brain? We reviewed 45 brain-imaging studies of creative cognition. We found little clear evidence of overlap in their results. Nearly as many different tests were used as there were studies; this test diversity makes it impossible to interpret the different findings across studies with any confidence. Our conclusion is that creativity research would benefit from psychometrically informed revision, and the addition of neuroimaging methods designed to provide greater spatial localization of function. Without such revision in the behavioral measures and study designs, it is hard to see the benefit of imaging. We set out eight suggestions in a manifesto for taking creativity research forward.

When both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) data are collected they are typically analyzed separately and the joint information is not examined. Techniques that examine joint information can help to find hidden traits in complex disorders such as schizophrenia. The brain is vastly interconnected, and local brain morphology may influence functional activity at distant regions. In this paper we introduce three methods to identify inter-correlations among sMRI and fMRI voxels within the whole brain. We apply these methods to examine sMRI gray matter data and fMRI data derived from an auditory sensorimotor task from a large study of schizophrenia. In Method 1 the sMRI-fMRI cross-correlation matrix is reduced to a histogram and results show that healthy controls (HC) have stronger correlations than do patients with schizophrenia (SZ). In Method 2 the spatial information of sMRI-fMRI correlations is retained. Structural regions in the cerebellum and frontal regions show more positive and more negative correlations, respectively, with functional regions in HC than in SZ. In Method 3 significant sMRI-fMRI inter-regional links are detected, with regions in the cerebellum showing more significant positive correlations with functional regions in HC relative to SZ. Results from all three methods indicate that the linkage between gray matter and functional activation is stronger in HC than SZ. The methods introduced can be easily extended to comprehensively correlate large data sets.

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.

There is increasing evidence relating psychometric measures of general intelligence and reasoning to regional brain structure and function assessed with a variety of neuroimaging techniques. Cognitive dimensions independent of general intelligence can also be identified psychometrically and studied for any neuroanatomical correlates. Here we investigated two such dimensions, rotation–verbal and focus–diffusion. We used structural MRI and voxel-based morphometry (VBM) in two independent samples to identify gray and white matter correlates of both dimensions. Based on statistical conjunction of both samples, (N = 45; p

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.

We examined the relationship between structural brain variation and general intelligence using voxel-based morphometric analysis of MRI data in men and women with equivalent IQ scores. Compared to men, women show more white matter and fewer gray matter areas related to intelligence. In men IQ/gray matter correlations are strongest in frontal and parietal lobes (BA 8, 9, 39, 40), whereas the strongest correlations in women are in the frontal lobe (BA10) along with Broca's area. Men and women apparently achieve similar IQ results with different brain regions, suggesting that there is no singular underlying neuroanatomical structure to general intelligence and that different types of brain designs may manifest equivalent intellectual performance.

Total brain volume accounts for about 16% of the variance in general intelligence scores (IQ), but how volumes of specific regions-of-interest (ROIs) relate to IQ is not known. We used voxel-based morphometry (VBM) in two independent samples to identify substantial gray matter (GM) correlates of IQ. Based on statistical conjunction of both samples (N = 47; P < 0.05 corrected for multiple comparisons), more gray matter is associated with higher IQ in discrete Brodmann areas (BA) including frontal (BA 10, 46, 9), temporal (BA 21, 37, 22, 42), parietal (BA 43 and 3), and occipital (BA 19) lobes and near BA 39 for white matter (WM). These results underscore the distributed neural basis of intelligence and suggest a developmental course for volume--IQ relationships in adulthood.