Parieto-frontal Integration Theory Research Articles

Neuromodulatory effects of parietal high-definition transcranial direct-current stimulation on network-level activity serving fluid intelligence.

Fluid intelligence (Gf) involves rational thinking skills and requires the integration of information from different cortical regions to resolve novel complex problems. The effects of non-invasive brain stimulation on Gf have been studied in attempts to improve Gf, but such studies are rare and the few existing have reached conflicting conclusions. The parieto-frontal integration theory of intelligence (P-FIT) postulates that the parietal and frontal lobes play a critical role in Gf. To investigate the suggested role of parietal cortices, we applied high-definition transcranial direct current stimulation (HD-tDCS) to the left and right parietal cortices of 39 healthy adults (age 19-33years) for 20min in three separate sessions (left active, right active and sham). After completing the stimulation session, the participants completed a logical reasoning task based on Raven's Progressive Matrices during magnetoencephalography. Significant neural responses at the sensor level across all stimulation conditions were imaged using a beamformer. Whole-brain, spectrally constrained functional connectivity was then computed to examine the network-level activity. Behaviourally, we found that participants were significantly more accurate following left compared to right parietal stimulation. Regarding neural findings, we found significant HD-tDCS montage-related effects in brain networks thought to be critical for P-FIT, including parieto-occipital, fronto-occipital, fronto-parietal and occipito-cerebellar connectivity during task performance. In conclusion, our findings showed that left parietal stimulation improved abstract reasoning abilities relative to right parietal stimulation and support both P-FIT and the neural efficiency hypothesis. KEY POINTS: Abstract reasoning is a critical component of fluid intelligence and is known to be served by multispectral oscillatory activity in the fronto-parietal cortices. Recent studies have aimed to improve abstract reasoning abilities and fluid intelligence overall through behavioural training, but the results have been mixed. High-definition transcranial direct-current stimulation (HD-tDCS) applied to the parietal cortices modulated task performance and neural oscillations during abstract reasoning. Left parietal stimulation resulted in increased accuracy and decreased functional connectivity between occipital regions and frontal, parietal, and cerebellar regions. Future studies should investigate whether HD-tDCS alters abstract reasoning abilities in those who exhibit declines in performance, such as healthy ageing populations.

Healthy aging alters the oscillatory dynamics and fronto-parietal connectivity serving fluid intelligence.

Fluid intelligence (Gf) involves logical reasoning and novel problem-solving abilities. Often, abstract reasoning tasks like Raven's progressive matrices are used to assess Gf. Prior work has shown an age-related decline in fluid intelligence capabilities, and although many studies have sought to identify the underlying mechanisms, our understanding of the critical brain regions and dynamics remains largely incomplete. In this study, we utilized magnetoencephalography (MEG) to investigate 78 individuals, ages 20-65 years, as they completed an abstract reasoning task. MEG data was co-registered with structural MRI data, transformed into the time-frequency domain, and the resulting neural oscillations were imaged using a beamformer. We found worsening behavioral performance with age, including prolonged reaction times and reduced accuracy. MEG analyses indicated robust oscillations in the theta, alpha/beta, and gamma range during the task. Whole brain correlation analyses with age revealed relationships in the theta and alpha/beta frequency bands, such that theta oscillations became stronger with increasing age in a right prefrontal region and alpha/beta oscillations became stronger with increasing age in parietal and right motor cortices. Follow-up connectivity analyses revealed increasing parieto-frontal connectivity with increasing age in the alpha/beta frequency range. Importantly, our findings are consistent with the parieto-frontal integration theory of intelligence (P-FIT). These results further suggest that as people age, there may be alterations in neural responses that are spectrally specific, such that older people exhibit stronger alpha/beta oscillations across the parieto-frontal network during abstract reasoning tasks.

Functional brain networks involved in the Raven's standard progressive matrices task and their relation to theories of fluid intelligence

A dimensionality reduction method was used to determine the task-timing-related functional brain networks underlying the Raven's Standard Progressive Matrices (RSPM), a non-verbal estimate of fluid intelligence (Gf). We identified five macro-scale task-based blood‑oxygen-level-dependent (BOLD)-signal brain networks and interpreted their network-level task-induced BOLD changes to provide functional interpretations separately for each network. This led to new observations about the brain networks underlying the RSPM: (1) the multiple demand network (MDN) for solution searching peaked early in the trial (∼9 s peak), followed by response (RESP) for response selection (∼12 s), and re-evaluation (RE-EV) for solution checking (∼18 s peak), (2) high activity in the MDN was correlated with high activity in the later-peaking RE-EV network, proposed to underpin cooperative solution searching (MDN) and checking (RE-EV) processes, and (3) high activity in the MDN in all conditions was associated with low accuracy in the hard RSPM condition, suggesting that those with lower performance on hard problems allocate more resources into solution-searching across all conditions. These findings corroborate the MDN's significance in Gf solution searching, and add the RE-EV network as playing an important checking role, providing overlap with the proposed abstraction/elaboration and hypothesis testing phases of the Parieto-Frontal Integration Theory (P-FIT). Therefore, this set of results not only supports past theoretical work on the brain networks underlying Gf and the RSPM task, but extends it by providing more complete anatomical, temporal, and functional information based on a set of brain task-based networks which replicate over many tasks.

FMRI functional connectivity is a better predictor of general intelligence than cortical morphometric features and ICA parcellation order affects predictive performance

Intelligence, as a general cognitive ability, shows a substantial inter-subject variation. Because of its impact on our lives, there is great interest in explaining the neural substrates of these differences. We used a large set of neuroimaging and behavioral data from 805 subjects, provided by the Human Connectome Project, and applied predictive models based on elastic-net regression using functional connectivity and brain morphometric data to predict general intelligence values. Additionally, we explored the impact of brain spatial distribution of the input connectivity data in the regression model using two strategies: brain parcellation and individual components. Features derived from functional connectivity were considerably more correlated with general intelligence than cortical thickness and surface area. Considering the regularization terms in this particular prediction problem, the best performances were obtained when the impact of all the independent variables was considered in the regresion, i.e. null LASSO sparsity term. Using different parcellation schemes affected predictive performances, which indicates spatial heterogeneity in the regression. We were able to explain 17,5% of the general intelligence variance, in the best performance reached, with a brain parcellation of 25 independent components; by other hand, using only cortical morphometric features the performance reduced to 1,6% for both cortical thickness and surface area. While no component, in particular, was responsible for predicting a large portion of the variance, the spatial components with the best results comprehend parietal, frontal and occipital regions, in agreement with the Parieto-Frontal Integration Theory (P-FIT).

Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach.

Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N= 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.

Brain volumes, thicknesses, and surface areas as mediators of genetic factors and childhood adversity on intelligence.

Although genetic and environmental factors influence general intelligence (g-factor), few studies examined the neuroanatomical measures mediating environmental and genetic effects on intelligence. Here, we investigate the brain volumes, cortical mean thicknesses, and cortical surface areas mediating the effects of the g-factor polygenic score (gPGS) and childhood adversity on the g-factor in the UK Biobank. We first examined the global and regional brain measures that contribute to the g-factor. Most regions contributed to the g-factor through global brain size. Parieto-frontal integration theory (P-FIT) regions were not more associated with the g-factor than non-PFIT regions. After adjusting for global brain size and regional associations, only a few regions predicted intelligence and were included in the mediation analyses. We conducted mediation analyses on global measures, regional volumes, mean thicknesses, and surface areas, separately. Total brain volume mediated 7.04% of the gPGS' effect on the g-factor and 2.50% of childhood adversity's effect on the g-factor. In comparison, the fraction of the gPGS and childhood adversity's effects mediated by individual regional volumes, surfaces, and mean thicknesses was 10-15 times smaller. Therefore, genetic and environmental effects on intelligence may be mediated to a larger extent by other brain properties.

Capturing brain‐cognition relationship: Integrating task‐based fMRI across tasks markedly boosts prediction and test‐retest reliability

Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n = 873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction (r = 0.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=∼.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities (r = 0.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r = 0.56) while still providing excellent test-retest reliability (ICC=∼.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities.

Neuroplasticity induced by working memory training: A spatio-temporal model of decreased distribution in brain regions based on fMRI experiments

<p id="p00010">Working memory training (WMT) induces neuroplasticity, but its specific mechanism remains unclear. In order to explore the spatio-temporal characteristics of brain function changes induced by WMT, the findings of 37 fMRI papers on WMT in normal population over the last 20 years were investigated with a step-by-step approach, including 5 methods and the following 6 steps, as well as with two assumptions based on the two theories— “Extended Parieto-Frontal Integration Theory” (ExtPFIT) and “neuro efficiency hypothesis”. Step 1, the changes of activation pattern and function connectivity in various brain regions before and after WMT among the 37 papers were compared using descriptive review, frequency analysis and Chi-square test. It was found that 5 association areas, 7 macro regions and 3 sub-regions of the brain were changed by WMT. Especially, the number of articles reporting decreased activation in 3 sub-regions—superior frontal gyrus, inferior parietal lobule and cingulate gyrus was more than that reporting increased activation in them, and this difference was statistically significant. Step 2, 26 out of the 37 papers were used to conduct meta-analysis with activation likelihood estimation method, and it showed that 3 sub-regions with the statistically significant effects in their decreased activation were middle frontal gyrus (BA6 and 8), superior frontal gyrus (BA6) and anterior cingulate gyrus (BA24 and 32). Step 3, based on the comprehensive results of qualitative and quantitative research analyses, a spatio-temporal model of decreased distribution in brain regions of WMT was proposed, and there were 5 results and their discussions obtained with the said model. Step 4, 31 out of the 37 papers were analyzed using non-parametric tests to check the factors that possibly regulated the training effect of WMT, which showed that the respective effect of task type and time of the training on brain activation had statistically significant. Step 5, according to the above analyses, 3 conclusions were drawn regarding the spatio-temporal characteristics of the neuroplasticity induced by WMT in normal population, i.e. 1) WMT can either weaken or enhance the brain activities, and the weakening effect appears more prominent. In addition, updating and shorter time of WMT tend to induce more weakening effects; 2) Such changes occur mostly in the frontal-parietal association area, but also possibly in the temporal lobe, occipital lobe, cingulate gyrus, striatum and other association areas, which reflects the whole brain functional association within a certain range. It highlights the spatial characteristics of the neuroplasticity, which fits in easily with ExtPFIT; 3) The 4 sub-regions of the middle frontal gyrus, superior frontal gyrus, inferior parietal lobule and cingulate gyrus (anterior in particular) in decreased activation mainly reflect the spatial characteristics of the neuroplasticity which fits in easily with “neural efficiency hypothesis” and “smarter brains may be slightly lazier”. Step 6, it was proposed that further research of the neuroplasticity induced by WMT was to possibly focus on low brain activity on its plasticity, distinguish the temporal characteristics of the training effect reflected in 4 sub-regions of middle frontal gyrus, superior frontal gyrus, inferior parietal lobule and cingulate gyrus (anterior in particular), as well as to probe the comprehensive influencing factors that increase or decrease the brain activities.

High-definition transcranial direct current stimulation modulates performance and alpha/beta parieto-frontal connectivity serving fluid intelligence.

Fluid intelligence (Gƒ) includes logical reasoning abilities and is an essential component of normative cognition. Despite the broad consensus that parieto-prefrontal connectivity is critical for Gƒ (e.g. the parieto-frontal integration theory of intelligence, P-FIT), the dynamics of such functional connectivity during logical reasoning remains poorly understood. Further, given the known importance of these brain regions for Gƒ, numerous studies have targeted one or both of these areas with non-invasive stimulation with the goal of improving Gƒ, but to date there remains little consensus on the overall stimulation-related effects. To examine this, we applied high-definition direct current anodal stimulation to the left and right dorsolateral prefrontal cortex (DLPFC) of 24 healthy adults for 20min in three separate sessions (sham, left, and right active). Following stimulation, participants completed a logical reasoning task during magnetoencephalography (MEG). Significant neural responses at the sensor-level were imaged using a beamformer, and peak task-induced activity was subjected to dynamic functional connectivity analyses to evaluate the impact of distinct stimulation montages on network activity. We found that participants responded faster following right DLPFC stimulation vs. sham. Moreover, our neural findings followed a similar trajectory of effects such that left parieto-frontal connectivity decreased following right and left DLPFC stimulation compared to sham, with connectivity following right stimulation being significantly correlated with the faster reaction times. Importantly, our findings are consistent with P-FIT, as well as the neural efficiency hypothesis (NEH) of intelligence. In sum, this study provides evidence for beneficial effects of right DLPFC stimulation on logical reasoning. KEY POINTS: Logical reasoning is an indispensable component of fluid intelligence and involves multispectral oscillatory activity in parietal and frontal regions. Parieto-frontal integration is well characterized in logical reasoning; however, its direct neural quantification and neuromodulation by brain stimulation remain poorly understood. High-definition transcranial direct current stimulation of dorsolateral prefrontal cortex (DLPFC) had modulatory effects on task performance and neural interactions serving logical reasoning, with right stimulation showing beneficial effects. Right DLPFC stimulation led to a decrease in the response time (i.e. better task performance) and left parieto-frontal connectivity with a marginal positive association between behavioural and neural metrics. Other modes of targeted stimulation of DLPFC (e.g. frequency-specific) can be employed in future studies.

Interindividual differences in matrix reasoning are linked to functional connectivity between brain regions nominated by Parieto-Frontal Integration Theory

The Parieto-Frontal Integration Theory (P-FIT) predicts that human intelligence is closely linked to structural and functional properties of several brain regions mainly located in the parietal and frontal cortices. It also proposes that solving abstract reasoning tasks involves multiple processing stages and thus requires the harmonic interplay of these brain regions. However, empirical studies directly investigating the relationship between intellectual performance and the strength of individual functional connections related to the P-FIT network are scarce. Here we demonstrate, in two independent samples comprising a total of 1489 healthy individuals, that fMRI resting-state connectivity, especially between P-FIT regions, is associated with interindividual differences in matrix reasoning performance. Interestingly, respective associations were only present in the overall samples and the female subsamples but not in the male subsamples, indicating a sex-specific effect. We found five statistically significant connections which replicated across both samples. These were constituted by BAs 8, 10, 22, 39, 46, and 47 in the left as well as BAs 44 and 45 in the right hemisphere. Given that many of these brain regions are predominantly involved in language processing, we hypothesized that our results reflect the importance of inner speech for solving matrix reasoning tasks. Complementary to previous research investigating the association between intelligence and functional brain connectivity by means of comprehensive network metrics, our study is the first to identify specific connections from the P-FIT network whose functional connectivity strength at rest can be considered an indicator of intellectual capability.

Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample.

The parieto-frontal integration theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional magnetic resonance imaging measures of the amplitude of low frequency fluctuations (ALFFs) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo, and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic, and cerebellar regions showed comparable effects, hence PFIT needs expansion into an extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining a low resting energy state.

Το νευροβιολογικό υπόστρωμα της νοημοσύνης: Ανατομικά και λειτουργικά χαρακτηριστικά του εγκεφάλου που συνδέονται με γνωστικές ικανότητες

The modern era of brain imaging has enabled the direct investigation of the neurobiological substrate of intelligence thus contributing significantly to our understanding of individual differences in cognitive functions. This is a review of recent findings on the anatomical and functional underpinnings of intelligence. Specifically, the anatomical features of the brain that are associated with intelligence are brain volume, cortical thickness, white matter microstructure, and corpus callosum thickness. The functional characteristics associated with intelligence are the degree of activation of the prefrontal cortex and other areas of the parieto-frontal network, nerve conduction velocity, rate of glucose metabolism, and direction of cerebral laterality. Two recent neurobiological theories of intelligence, namely the neural efficiency hypothesis and the parieto-frontal integration theory (P-FIT), are also presented.

Relationship Between Anterior Commissure and Corpus Callosum Size and Human Intelligence in Brain MRI of Healthy Volunteers

Background: The parieto-frontal integration theory of intelligence revealed a novel scope of investigation examining how performance networks are related to structural networks in the brain. Objectives: These investigations highlighted the role of brain commissures, but no evaluation has been performed examining the anterior commissure (AC) in determining the level of intelligence. Patients and Methods: A total of 100 healthy volunteers were selected, and their brain MRIs were acquired. Measurements were performed for the AC and the corpus callosum (CC) on MRI. Intelligence quotient (IQ) was calculated using the Wechsler Adult Intelligence Scale-R (WAIS-R). P < 0.05 was considered significant. Results: The average age of men was 32.58 ± 8.84 years, and the average age of women was 32.88 ± 7.83 years. The average IQ of volunteers in the study was 103.57 ± 4.178. Partitioning the subjects by gender did not lead to significant results. No significant correlation was determined between the dimensions of AC and IQ. The height of the CC had a positive correlation with IQ. However, the thickness of the genu, body, and splenium of the CC demonstrated no significant relation to IQ. Conclusion: The AC had no clear relationship with IQ, although it may have a role in women. The height parameter of the CC had a significant correlation with IQ, suggesting the interference between the anterior and posterior parts of the brain in the human intelligence process.

Fluctuating asymmetry in brain structure and general intelligence in 73-year-olds

Fluctuating body asymmetry is theorized to indicate developmental instability, and to have small positive associations with low socioeconomic status (SES). Previous studies have reported small negative associations between fluctuating body asymmetry and cognitive functioning, but relationships between fluctuating brain asymmetry and cognitive functioning remain unclear. The present study investigated the association between general intelligence (a latent factor derived from a factor analysis on 13 cognitive tests) and the fluctuating asymmetry of four structural measures of brain hemispheric asymmetry: cortical surface area, cortical volume, cortical thickness, and white matter fractional anisotropy. The sample comprised members of the Lothian Birth Cohort 1936 (LBC1936, N = 636, mean age = 72.9 years). Two methods were used to calculate structural hemispheric asymmetry: in the first method, regions contributed equally to the overall asymmetry score; in the second method, regions contributed proportionally to their size. When regions contributed equally, cortical thickness asymmetry was negatively associated with general intelligence (β = −0.18,p < .001). There was no association between cortical thickness asymmetry and childhood SES, suggesting that other mechanisms are involved in the thickness asymmetry-intelligence association. Across all cortical metrics, asymmetry of regions identified by the parieto-frontal integration theory (P-FIT) was not more strongly associated with general intelligence than non-P-FIT asymmetry. When regions contributed proportionally, there were no associations between general intelligence and any of the asymmetry measures. The implications of these findings, and of different methods of calculating structural hemispheric asymmetry, are discussed.

Time-lagged associations between cognitive and cortical development from childhood to early adulthood.

Throughout childhood and adolescence, humans experience marked changes in cortical structure and cognitive ability. Cortical thickness and surface area, in particular, have been associated with cognitive ability. Here we ask the question: What are the time-related associations between cognitive changes and cortical structure maturation. Identifying a developmental sequence requires multiple measurements of these variables from the same individuals across time. This allows capturing relations among the variables and, thus, finding whether (a) developmental cognitive changes follow cortical structure maturation, (b) cortical structure maturation follows cognitive changes, or (c) both processes influence each other over time. Four hundred and thiry children and adolescents (age range = 6.01-22.28 years) completed the Wechsler Abbreviated Scale of Intelligence battery and were MRI scanned at 3 time points separated by ≈2 years (Mage T1 = 10.60, SD = 3.58; Mage T2 = 12.63, SD = 3.62; Mage T3 = 14.49, SD = 3.55). Latent change score models were applied to quantify age-related relationships among the variables of interest. Our results indicate that cortical and cognitive changes related to each other reciprocally. Specifically, the magnitude or rate of the change in each variable at any occasion-and not the previous level-was predictive of later changes. These results were replicated for brain regions selected according to the coordinates identified in the Basten et al.'s (2015) meta-analysis, to the parieto-frontal integration theory (Jung & Haier, 2007) and to the whole cortex. Potential implications regarding brain plasticity and cognitive enhancement are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Fluid intelligence and gross structural properties of the cerebral cortex in middle-aged and older adults: A multi-occasion longitudinal study

According to Parieto-Frontal Integration Theory (P-FIT, Jung and Haier, 2007), individual differences in a circumscribed set of brain regions account for variations in general intelligence (g). The components of g, fluid (Gf) and crystallized (Gc) reasoning, exhibit distinct trajectories of age-related change. Because the brain also ages differentially, we hypothesized that age-related cognitive and neural changes would be coupled. In a sample of healthy middle-aged and older adults, we examined changes in Gf (operationalized by Cattell Culture Fair Test) and Gc (indexed by two vocabulary tests) as well as in structural properties of 19 brain regions. We fitted linear mixed models to the data collected on 73 healthy adults who participated in baseline assessment, with 43 returning for at least one follow-up, and 16 of them contributing four repeated assessments over seven years. We observed age differences as well as longitudinal decline in Gf, contrasted to a lack of age differences and stability in Gc. Cortical thickness and cortical volume exhibited significant age differences and longitudinal declines, which were accelerated in P-FIT regions. Gf (but not Gc) was associated with cortical thickness, but no such relationship was found for cortical volume. Uniformity of cognitive change (lack of reliable individual differences) precluded examination of the coupling between cognitive and brain changes. Cortical shrinkage was greater in high-Gc individuals, whereas in participants with higher Gf cortical volume slower volume shrinkage was observed.

Brain Structural Networks Associated with Intelligence and Visuomotor Ability

Increasing evidence indicates that multiple structures in the brain are associated with intelligence and cognitive function at the network level. The association between the grey matter (GM) structural network and intelligence and cognition is not well understood. We applied a multivariate approach to identify the pattern of GM and link the structural network to intelligence and cognitive functions. Structural magnetic resonance imaging was acquired from 92 healthy individuals. Source-based morphometry analysis was applied to the imaging data to extract GM structural covariance. We assessed the intelligence, verbal fluency, processing speed, and executive functioning of the participants and further investigated the correlations of the GM structural networks with intelligence and cognitive functions. Six GM structural networks were identified. The cerebello-parietal component and the frontal component were significantly associated with intelligence. The parietal and frontal regions were each distinctively associated with intelligence by maintaining structural networks with the cerebellum and the temporal region, respectively. The cerebellar component was associated with visuomotor ability. Our results support the parieto-frontal integration theory of intelligence by demonstrating how each core region for intelligence works in concert with other regions. In addition, we revealed how the cerebellum is associated with intelligence and cognitive functions.

Regional Variations in Brain Gyrification Are Associated with General Cognitive Ability in Humans

Searching for a neurobiological understanding of human intellectual capabilities has long occupied those very capabilities. Brain gyrification, or folding of the cortex, is as highly evolved and variable a characteristic in humans as is intelligence. Indeed, gyrification scales with brain size, and relationships between brain size and intelligence have been demonstrated in humans [1-3]. However, gyrification shows a large degree of variability that is independent from brain size [4-6], suggesting that the former may independently contribute to cognitive abilities and thus supporting a direct investigation of this parameter in the context of intelligence. Moreover, uncovering the regional pattern of such an association could offer insights into evolutionary and neural mechanisms. We tested for this brain-behavior relationship in two separate, independently collected, large cohorts-440 healthy adults and 662 healthy children-using high-resolution structural neuroimaging and comprehensive neuropsychometric batteries. In both samples, general cognitive ability was significantly associated (pFDR< 0.01) with increasing gyrification in a network of neocortical regions, including large portions of the prefrontal cortex, inferior parietal lobule, and temporoparietal junction, as well as the insula, cingulate cortex, and fusiform gyrus, a regional distribution that was nearly identical in both samples (Dice similarity coefficient= 0.80). This neuroanatomical pattern is consistent with an existing, well-known proposal, the Parieto-Frontal Integration Theory of intelligence [7], and is also consistent with research in comparative evolutionary biology showing rapid neocortical expansion of these regions in humans relative to other species. These data provide a framework for understanding the neurobiology of human cognitive abilities and suggest a potential neurocellular association.

Multivariate Associations of Fluid Intelligence and NAA.

Understanding the neural and metabolic correlates of fluid intelligence not only aids scientists in characterizing cognitive processes involved in intelligence, but it also offers insight into intervention methods to improve fluid intelligence. Here we use magnetic resonance spectroscopic imaging (MRSI) to measure N-acetyl aspartate (NAA), a biochemical marker of neural energy production and efficiency. We use principal components analysis (PCA) to examine how the distribution of NAA in the frontal and parietal lobes relates to fluid intelligence. We find that a left lateralized frontal-parietal component predicts fluid intelligence, and it does so independently of brain size, another significant predictor of fluid intelligence. These results suggest that the left motor regions play a key role in the visualization and planning necessary for spatial cognition and reasoning, and we discuss these findings in the context of the Parieto-Frontal Integration Theory of intelligence.

General cognitive ability and fluctuating asymmetry of brain surface area

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.