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Psychologist J. P. Guildford issued a challenge to study creativity nearly 70 years ago. How well have we done and what might the next steps be in our endeavors to understand creativity? The field of creativity research has examined the internal thinking process of creativity, largely through measures of divergent thinking and remote associates. We have tracked the creative successes of people assumed to be of high talent and high intelligence. And, we have evaluated creative products. More recently, we have correlated various cognitive and personality measures, purported to be linked to creativity, with brain structure and function. In spite of this flurry of activity, there are some problems. These assessments do not provide specific or significant predictive validity of creative achievement. Similarly, while Guilford desired to separate creativity from the purview of intelligence in his challenge, these constructs appear to be as entangled today as they were in the 1950's. When viewing all predictive variables of creative achievement across person, process, and product, we found that the personality trait of openness to experience -- provides the best combination of specificity and predictive power. Finally, we join others in calling for more study of imagination as a mental tool that individuals at all skill levels use to predict outcomes, visualize scenarios, and engage in counterfactual thinking -- in service of creative achievement.

ObjectiveWe propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia.MethodsIt uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa.ResultsFindings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively.ConclusionN-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications.SignificanceThe use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.

Genetic factors are known to influence both risk for schizophrenia (SZ) and variation in brain structure. A pressing question is whether the genetic underpinnings of brain phenotype and the disorder overlap. Using multivariate analytic methods and focusing on 1,402 common single-nucleotide polymorphisms (SNPs) mapped from the Psychiatric Genomics Consortium (PGC) 108 regions, in 777 discovery samples, we identified 39 SNPs to be significantly associated with SZ-discriminating gray matter volume (GMV) reduction in inferior parietal and superior temporal regions. The findings were replicated in 609 independent samples. These 39 SNPs in chr6:28308034-28684183 (6p22.1), the most significant SZ-risk region reported by PGC, showed regulatory effects on both DNA methylation and gene expression of postmortem brain tissue and saliva. Furthermore, the regulated methylation site and gene showed significantly different levels of methylation and expression in the prefrontal cortex between cases and controls. In addition, for one regulated methylation site we observed a significant in vivo methylation-GMV association in saliva, suggesting a potential SNP-methylation-GMV pathway. Notably, the risk alleles inferred for GMV reduction from in vivo imaging are all consistent with the risk alleles for SZ inferred from postmortem data. Collectively, we provide evidence for shared genetic risk of SZ and regional GMV reduction in 6p22.1 and demonstrate potential molecular mechanisms that may drive the observed in vivo associations. This study motivates dissecting SZ-risk variants to better understand their associations with focal brain phenotypes and the complex pathophysiology of the illness.

Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development.

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.

Proton magnetic resonance spectroscopy ([to the first power]H-MRS) is a technique for the assay of brain neurochemistry "in vivo." N-acetylaspartate (NAA), the most prominent metabolite visible within the [to the first power]H-MRS spectrum, is found primarily within neurons. The current study was designed to further elucidate NAA-cognition relationships, particularly whether such relationships are moderated by sex, or tissue type (gray or white matter). We administered standard measures of intelligence to 63 young, healthy subjects and obtained spectroscopic imaging data within a slab of tissue superior to the lateral ventricles. We found that "lower" NAA within right anterior gray matter predicted better performance VIQ (F = 6.83, p = 0.011, r[superscript 2] = 0.10), while "higher" NAA within the right posterior gray matter region predicted better PIQ (F = 8.175, p = 0.006, r[superscript 2] = 0.12). These findings add to the small but growing body of literature linking brain biochemistry to intelligence in normal healthy subjects using [to the first power]H-MRSI. (Contains 2 figures and 2 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.)

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 less than 0.001), there were correlations with gray matter in Brodmann areas (BA) 20 and 9 involving the rotation-verbal dimension and in BA 18 involving the focus-diffusion dimension. There were white matter correlations involving the rotation-verbal dimension near BA 18, 40, 39, 10, 8, and 4 and involving the focus-diffusion dimension near BA 5 and the right sub-lobar amygdala. These correlations may have implications for understanding individual differences in the manifestation of intelligence.

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.

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)