Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model

Examining the neurostructural architecture of intelligence: The Lothian Birth Cohort 1936 study

Examining underlying neurostructural correlates of specific cognitive abilities is practically and theoretically complicated by the existence of the positive manifold (all cognitive tests positively correlate): if a brain structure is associated with a cognitive task, how much of this is uniquely related to the cognitive domain, and how much is due to covariance with all other tests across domains (captured by general cognitive functioning, also known as general intelligence, or 'g')? We quantitatively address this question by examining associations between brain structural and diffusion MRI measures (global tissue volumes, white matter hyperintensities, global white matter diffusion fractional anisotropy and mean diffusivity, and FreeSurfer processed vertex-wise cortical volumes, smoothed at 20mm fwhm) with g and cognitive domains (processing speed, crystallised ability, memory, visuospatial ability). The cognitive domains were modelled using confirmatory factor analysis to derive both hierarchical and bifactor solutions using 13 cognitive tests in 697 participants from the Lothian Birth Cohort 1936 study (mean age 72.5 years; SD = .7). Associations between the extracted cognitive factor scores for each domain and g were computed for each brain measure covarying for age, sex and intracranial volume, and corrected for false discovery rate. There were a range of significant associations between cognitive domains and global MRI brain structural measures (r range .008 to .269, p < .05). Regions implicated by vertex-wise regional cortical volume included a widespread number of medial and lateral areas of the frontal, temporal and parietal lobes. However, at both global and regional level, much of the domain-MRI associations were shared (statistically accounted for by g). Removing g-related variance from cognitive domains attenuated association magnitudes with global brain MRI measures by 27.9-59.7% (M = 46.2%), with only processing speed retaining all significant associations. At the regional cortical level, g appeared to account for the majority (range 22.1-88.4%; M = 52.8% across cognitive domains) of regional domain-specific associations. Crystallised and memory domains had almost no unique cortical correlates, whereas processing speed and visuospatial ability retained limited cortical volumetric associations. The greatest spatial overlaps across cognitive domains (as denoted by g) were present in the medial and lateral temporal, lateral parietal and lateral frontal areas.

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 19-33 years) for 20 min 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.

Utilizing maternal prenatal cognition as a predictor of newborn brain measures of intellectual development

Identifying reliable indicators of cognitive functioning prior to age five has been challenging. Prior studies have shown that maternal cognition, as indexed by intellectual quotient (IQ) and years of education, predict child intelligence at school age. We examined whether maternal full scale IQ, education, and inhibitory control (index of executive function) are associated with newborn brain measures and toddler language outcomes to assess potential indicators of early cognition. We hypothesized that maternal indices of cognition would be associated with brain areas implicated in intelligence in school-age children and adults in the newborn period. Thirty-seven pregnant women and their newborns underwent an MRI scan. T2-weighted images and surface-based morphometric analysis were used to compute local brain volumes in newborn infants. Maternal cognition indices were associated with local brain volumes for infants in the anterior and posterior cingulate, occipital lobe, and pre/postcentral gyrus - regions associated with IQ, executive function, or sensori-motor functions in children and adults. Maternal education and executive function, but not maternal intelligence, were associated with toddler language scores at 12 and 24 months. Newborn brain volumes did not predict language scores. Overall, the pre/postcentral gyrus and occipital lobe may be unique indicators of early intellectual development in the newborn period. Given that maternal executive function as measured by inhibitory control has robust associations with the newborn brain and is objective, brief, and easy to administer, it may be a useful predictor of early developmental and cognitive capacity for young children.

From gifted to high potential and twice exceptional: A state-of-the-art meta-review

Despite the abundant literature on intelligence and high potential individuals, there is still a lack of international consensus on the terminology and clinical characteristics associated to this population. It has been argued that unstandardized use of diagnosis tools and research methods make comparisons and interpretations of scientific and epidemiological evidence difficult in this field. If multiple cognitive and psychological models have attempted to explain the mechanisms underlying high potentiality, there is a need to confront new scientific evidence with the old, to uproot a global understanding of what constitutes the neurocognitive profile of high-potential in gifted individuals. Another particularly relevant aspect of applied research on high potentiality concerns the challenges faced by individuals referred to as "twice exceptional" in the field of education and in their socio-affective life. Some individuals have demonstrated high forms of intelligence together with learning, affective or neurodevelopmental disorders posing the question as to whether compensating or exacerbating psycho-cognitive mechanisms might underlie their observed behavior. Elucidating same will prove relevant to questions concerning the possible need for differential diagnosis tools, specialized educational and clinical support. A meta-review of the latest findings from neuroscience to developmental psychology, might help in the conception and reviewing of intervention strategies.

EEG-based neurophysiological indices for expert psychomotor performance - a review

A primary objective of current human neuropsychological performance research is to define the physiological correlates of adaptive knowledge utilization, in order to support the enhanced execution of both simple and complex tasks. Within the present article, electroencephalography-based neurophysiological indices characterizing expert psychomotor performance, will be explored. As a means of characterizing fundamental processes underlying efficient psychometric performance, the neural efficiency model will be evaluated in terms of alpha-wave-based selective cortical processes. Cognitive and motor domains will initially be explored independently, which will act to encapsulate the task-related neuronal adaptive requirements for enhanced psychomotor performance associating with the neural efficiency model. Moderating variables impacting the practical application of such neuropsychological model, will also be investigated. As a result, the aim of this review is to provide insight into detectable task-related modulation involved in developed neurocognitive strategies which support heightened psychomotor performance, for the implementation within practical settings requiring a high degree of expert performance (such as sports or military operational settings).

Atypical brain structure mediates reduced IQ in young adults born preterm with very low birth weight

Preterm birth with very low birth weight (VLBW) confers heightened risk for perinatal brain injury and long-term cognitive deficits, including a reduction in IQ of up to one standard deviation. Persisting gray and white matter aberrations have been documented well into adolescence and adulthood in preterm born individuals. What has not been documented so far is a plausible causal link between reductions in cortical surface area or subcortical brain structure volumes, and the observed reduction in IQ. The NTNU Low Birth Weight in a Lifetime Perspective study is a prospective longitudinal cohort study, including a preterm born VLBW group (birthweight ≤1500 g) and a term born control group. Structural magnetic resonance imaging data were obtained from 38 participants aged 19, born preterm with VLBW, and 59 term-born peers. The FreeSurfer software suite was used to obtain measures of cortical thickness, cortical surface area, and subcortical brain structure volumes. Cognitive ability was estimated using the Wechsler Adult Intelligence Scale, 3rd Edition, including four IQ-indices: Verbal comprehension, Working memory, Perceptual organization, and Processing speed. Statistical mediation analyses were employed to test for indirect effects of preterm birth with VLBW on IQ, mediated by atypical brain structure. The mediation analyses revealed negative effects of preterm birth with VLBW on IQ that were partially mediated by reduced surface area in multiple regions of frontal, temporal, parietal and insular cortex, and by reductions in several subcortical brain structure volumes. The analyses did not yield sufficient evidence of mediation effects of cortical thickness on IQ. This is, to our knowledge, the first time a plausible causal relationship has been established between regional cortical area reductions, as well as reductions in specific subcortical and cerebellar structures, and general cognitive ability in preterm born survivors with VLBW.

Multifactorial structure of cognitive assessment tests in the UK Biobank: A combined exploratory factor and structural equation modeling analyses

Introduction

The UK Biobank cognitive assessment data has been a significant resource for researchers looking to investigate predictors and modifiers of cognitive abilities and associated health outcomes in the general population. Given the diverse nature of this data, researchers use different approaches - from the use of a single test to composing the general intelligence score, g, across the tests. We argue that both approaches are suboptimal - one being too specific and the other one too general - and suggest a novel multifactorial solution to represent cognitive abilities.

Methods

Using a combined Exploratory Factor (EFA) and Exploratory Structural Equation Modeling Analyses (ESEM) we developed a three-factor model to characterize an underlying structure of nine cognitive tests selected from the UK Biobank using a Cattell-Horn-Carroll framework. We first estimated a series of probable factor solutions using the maximum likelihood method of extraction. The best solution for the EFA-defined factor structure was then tested using the ESEM approach with the aim of confirming or disconfirming the decisions made.

Results

We determined that a three-factor model fits the UK Biobank cognitive assessment data best. Two of the three factors can be assigned to fluid reasoning (Gf) with a clear distinction between visuospatial reasoning and verbal-analytical reasoning. The third factor was identified as a processing speed (Gs) factor.

Discussion

This study characterizes cognitive assessment data in the UK Biobank and delivers an alternative view on its underlying structure, suggesting that the three factor model provides a more granular solution than g that can further be applied to study different facets of cognitive functioning in relation to health outcomes and to further progress examination of its biological underpinnings.

Evolution of cortical neurons supporting human cognition

Human cognitive abilities are generally thought to arise from cortical expansion over the course of human brain evolution. In addition to increased neuron numbers, this cortical expansion might be driven by adaptations in the properties of single neurons and their local circuits. We review recent findings on the distinct structural, functional, and transcriptomic features of human cortical neurons and their organization in cortical microstructure. We focus on the supragranular cortical layers, which showed the most prominent expansion during human brain evolution, and the properties of their principal cells: pyramidal neurons. We argue that the evolutionary adaptations in neuronal features that accompany the expansion of the human cortex partially underlie interindividual variability in human cognitive abilities.

Predictive models demonstrate age-dependent association of subcortical volumes and cognitive measures

Whether brain matter volume is correlated with cognitive functioning and higher intelligence is controversial. We explored this relationship by analysis of data collected on 193 healthy young and older adults through the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON) study. Our analysis involved four cognitive measures: fluid intelligence, crystallized intelligence, cognitive flexibility, and working memory. Brain subregion volumes were determined by magnetic resonance imaging. We normalized each subregion volume to the estimated total intracranial volume and conducted training simulations to compare the predictive power of normalized volumes of large regions of the brain (i.e., gray matter, cortical white matter, and cerebrospinal fluid), normalized subcortical volumes, and combined normalized volumes of large brain regions and normalized subcortical volumes. Statistical tests showed significant differences in the performance accuracy and feature importance of the subregion volumes in predicting cognitive skills for young and older adults. Random forest feature selection analysis showed that cortical white matter was the key feature in predicting fluid intelligence in both young and older adults. In young adults, crystallized intelligence was best predicted by caudate nucleus, thalamus, pallidum, and nucleus accumbens volumes, whereas putamen, amygdala, nucleus accumbens, and hippocampus volumes were selected for older adults. Cognitive flexibility was best predicted by the caudate, nucleus accumbens, and hippocampus in young adults and caudate and amygdala in older adults. Finally, working memory was best predicted by the putamen, pallidum, and nucleus accumbens in the younger group, whereas amygdala and hippocampus volumes were predictive in the older group. Thus, machine learning predictive models demonstrated an age-dependent association between subcortical volumes and cognitive measures. These approaches may be useful in predicting the likelihood of age-related cognitive decline and in testing of approaches for targeted improvement of cognitive functioning in older adults.

Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: a DTI and MEG study

Brain network analysis represents a powerful technique to gain insights into the connectivity profile characterizing individuals with different levels of fluid intelligence (Gf). Several studies have used diffusion tensor imaging (DTI) and slow-oscillatory resting-state fMRI (rs-fMRI) to examine the anatomical and functional aspects of human brain networks that support intelligence. In this study, we expand this line of research by investigating fast-oscillatory functional networks. We performed graph theory analyses on resting-state magnetoencephalographic (MEG) signal, in addition to structural brain networks from DTI data, comparing degree, modularity and segregation coefficient across the brain of individuals with high versus average Gf scores. Our results show that high Gf individuals have stronger degree and lower segregation coefficient than average Gf participants in a significantly higher number of brain areas with regards to structural connectivity and to the slower frequency bands of functional connectivity. The opposite result was observed for higher-frequency (gamma) functional networks, with higher Gf individuals showing lower degree and higher segregation across the brain. We suggest that gamma oscillations in more intelligent individuals might support higher local processing in segregated subnetworks, while slower frequency bands would allow a more effective information transfer between brain subnetworks, and stronger information integration.

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 20 min 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.

Verbal and General IQ Associate with Supragranular Layer Thickness and Cell Properties of the Left Temporal Cortex

The left temporal lobe is an integral part of the language system and its cortical structure and function associate with general intelligence. However, whether cortical laminar architecture and cellular properties of this brain area relate to verbal intelligence is unknown. Here, we addressed this using histological analysis and cellular recordings of neurosurgically resected temporal cortex in combination with presurgical IQ scores. We find that subjects with higher general and verbal IQ scores have thicker left (but not right) temporal cortex (Brodmann area 21, BA21). The increased thickness is due to the selective increase in layers 2 and 3 thickness, accompanied by lower neuron densities, and larger dendrites and cell body size of pyramidal neurons in these layers. Furthermore, these neurons sustain faster action potential kinetics, which improves information processing. Our results indicate that verbal mental ability associates with selective adaptations of supragranular layers and their cellular micro-architecture and function in left, but not right temporal cortex.

Estimates of brain age for gray matter and white matter in younger and older adults: Insights into human intelligence

Aging entails a multifaceted complex of changes in macro- and micro-structural properties of human brain gray matter (GM) and white matter (WM) tissues, as well as in intellectual abilities. To better capture tissue-specific brain aging, we combined volume and distribution properties of diffusivity indices to derive subject-specific age scores for each tissue. We compared age-related variance between younger and older adults for GM and WM age scores, and tested whether tissue-specific age scores could explain different effects of aging on fluid (Gf) and crystalized (Gc) intelligence in younger and older adults. Chronological age was strongly associated with GM (R2 = 0.73) and WM (R2 = 0.57) age scores. The GM age score accounted for significantly more variance in chronological age in younger relative to older adults (p < 0.001), whereas the WM age score accounted for significantly more variance in chronological age in older compared to younger adults (p < 0.025). Consistent with existing literature, younger adults outperformed older adults in Gf while older adults outperformed younger adults in Gc. The GM age score was negatively associated with Gf in younger adults (p < 0.02), whereas the WM age score was negatively associated with Gc in older adults (p < 0.02). Our results provide evidence for differences in the effects of age on GM and WM in younger versus older adults that may contribute to age-related differences in Gf and Gc.

Distinct Regionalization Patterns of Cortical Morphology are Associated with Cognitive Performance Across Different Domains

Cognitive performance in children is predictive of academic and social outcomes; therefore, understanding neurobiological mechanisms underlying individual differences in cognition during development may be important for improving quality of life. The belief that a single, psychological construct underlies many cognitive processes is pervasive throughout society. However, it is unclear if there is a consistent neural substrate underlying many cognitive processes. Here, we show that a distributed configuration of cortical surface area and apparent thickness, when controlling for global imaging measures, is differentially associated with cognitive performance on different types of tasks in a large sample (N = 10 145) of 9-11-year-old children from the Adolescent Brain and Cognitive DevelopmentSM (ABCD) study. The minimal overlap in these regionalization patterns of association has implications for competing theories about developing intellectual functions. Surprisingly, not controlling for sociodemographic factors increased the similarity between these regionalization patterns. This highlights the importance of understanding the shared variance between sociodemographic factors, cognition and brain structure, particularly with a population-based sample such as ABCD.

Neural correlates of individual variation in two-back working memory and the relationship with fluid intelligence

Working memory has been examined extensively using the N-back task. However, less is known about the neural bases underlying individual variation in the accuracy rate (AR) and reaction time (RT) as metrics of N-back performance. Whereas AR indexes the overall performance, RT may more specifically reflect the efficiency in updating target identify. Further, studies have associated fluid intelligence (Gf) with working memory, but the cerebral correlates shared between Gf and N-back performance remain unclear. We addressed these issues using the Human Connectome Project dataset. We quantified the differences in AR (critical success index or CSI) and RT between 2- and 0-backs (CSI_2–0 and RT_2–0) and identified the neural correlates of individual variation in CSI_2–0, RT_2–0, and Gf, as indexed by the number of correct items scored in the Raven’s Standard Progressive Matrices (RSPM) test. The results showed that CSI_2–0 and RT_2–0 were negatively correlated, suggesting that a prolonged response time did not facilitate accuracy. At voxel p < 0.05, FWE-corrected, the pre-supplementary motor area (preSMA), bilateral frontoparietal cortex (biFPC) and right anterior insula (rAI) showed activities in negative correlation with CSI_2–0 and positive correlation with RT_2–0. In contrast, a cluster in the dorsal anterior cingulate cortex (dACC) bordering the SMA showed activities in positive correlation with CSI_2–0 and negative correlation with RT_2–0. Further, path analyses showed a significant fit of the model dACC → RT_2–0 → CSI_2–0, suggesting a critical role of target switching in determining performance accuracy. Individual variations in RT_2–0 and Gf were positively correlated, although the effect size was small (f² = 0.0246). RT_2–0 and Gf shared activities both in positive correlation with the preSMA, biFPC, rAI, and dorsal precuneus. These results together suggest inter-related neural substrates of individual variation in N-back performance and highlight a complex relationship in the neural processes supporting 2-back and RSPM performance.

Overlapping and dissociable brain activations for fluid intelligence and executive functions

Cognitive enhancement interventions aimed at boosting human fluid intelligence (gf) have targeted executive functions (EFs), such as updating, inhibition, and switching, in the context of transfer-inducing cognitive training. However, even though the link between EFs and gf has been demonstrated at the psychometric level, their neurofunctional overlap has not been quantitatively investigated. Identifying whether and how EFs and gf might share neural activation patterns could provide important insights into the overall hierarchical organization of human higher-order cognition, as well as suggest specific targets for interventions aimed at maximizing cognitive transfer. We present the results of a quantitative meta-analysis of the available fMRI and PET literature on EFs and gf in humans, showing the similarity between gf and (i) the overall global EF network, as well as (ii) specific maps for updating, switching, and inhibition. Results highlight a higher degree of similarity between gf and updating (80% overlap) compared with gf and inhibition (34%), and gf and switching (17%). Moreover, three brain regions activated for both gf and each of the three EFs also were identified, located in the left middle frontal gyrus, left inferior parietal lobule, and anterior cingulate cortex. Finally, resting-state functional connectivity analysis on two independent fMRI datasets showed the preferential behavioural correlation and anatomical overlap between updating and gf. These findings confirm a close link between gf and EFs, with implications for brain stimulation and cognitive training interventions.

Individual Differences in Reward-Based Learning Predict Fluid Reasoning Abilities

The ability to reason and problem-solve in novel situations, as measured by the Raven's Advanced Progressive Matrices (RAPM), is highly predictive of both cognitive task performance and real-world outcomes. Here we provide evidence that RAPM performance depends on the ability to reallocate attention in response to self-generated feedback about progress. We propose that such an ability is underpinned by the basal ganglia nuclei, which are critically tied to both reward processing and cognitive control. This hypothesis was implemented in a neurocomputational model of the RAPM task, which was used to derive novel predictions at the behavioral and neural levels. These predictions were then verified in one neuroimaging and two behavioral experiments. Furthermore, an effective connectivity analysis of the neuroimaging data confirmed a role for the basal ganglia in modulating attention. Taken together, these results suggest that individual differences in a neural circuit related to reward processing underpin human fluid reasoning abilities.

Correlates of intelligence via resting-state functional connectivity of the amygdala in healthy adults

Intelligence is a form of advanced cognition that includes reasoning, problem solving, pattern recognition, and establishing relationships among items. The amygdala plays an important role in cognitive processing, but the relationship between amygdalar function and intelligence has rarely been explored directly. Here, we investigated the relationship between resting-state functional connectivity (RSFC) of the amygdala and intelligence test performance in a large sample of healthy adults (N = 197). We found that two pairs of RSFCs were significantly increased in the high IQ group compared with that of the general IQ group. One of these RSFCs consisted of the right amygdala and the right superior parietal lobule, whereas the other RSFC consisted of the right amygdala and the left middle cingulum. In addition, we found that the brain regions in which the strength of RSFC significantly correlated with full IQ (FIQ) were mainly distributed in the parietal and limbic lobes. What's more, a further mediation analysis indicated that the functional connectivity of the right amygdala and the right superior parietal lobule significantly mediated the correlation between comprehension and object assembly, whereas the functional connectivity of the right amygdala and the left middle cingulum mediated the association between similarities and digit symbol. These findings suggest that amygdalar RSFC may reflect individual differences in intelligence and mediate specific relationships among different intellectual abilities.

Toward a "treadmill test" for cognition: Improved prediction of general cognitive ability from the task activated brain

General cognitive ability (GCA) refers to a trait-like ability that contributes to performance across diverse cognitive tasks. Identifying brain-based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole-brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N-back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2-back versus 0-back contrast achieved a 0.50 correlation with GCA scores in 10-fold cross-validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation-a brain activation pattern associated with executive processing and higher cognitive demand-are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain-based prediction of GCA.

Neuroimaging contrast across the cortical hierarchy is the feature maximally linked to behavior and demographics

An essential task of neuroscience is to elucidate the relationship between brain activity, brain structure, and human behavior. This study aims to understand this 3-way relationship by studying the population covariance of resting-state functional connectivity, cortical thickness, and behavioral/demographic measures in a large cohort of individuals. Using a data-driven canonical correlation analysis, we found that maximal pairwise correlations between the three modalities are approximately along the same direction across subjects, which is characterized by the change of the overall positive-negative trait of human behavior. More importantly, this behavioral change is associated with a divergent modulation of both resting-state connectivity and cortical thickness across cortical hierarchies between the higher-order cognitive networks and lower-order sensory/motor regions. The findings suggest that the cross-hierarchy contrast of structural and functional brain measures is tightly linked to the overall positive-negative trait of human behavior/demographics.

Resting State Glucose Utilization and Adult Reading Test Performance

Adult reading tests (ART) have been widely used in both research and clinical settings as a measure of premorbid cognitive abilities or cognitive reserve. However, the neural substrates underlying ART performance are largely unknown. Furthermore, it has not yet been examined whether the neural substrates of ART performance reflect the cortical regions associated with premorbid intelligence or cognitive reserve. The aim of the study is to identify the functional neural correlates of ART performance using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging in the cognitively normal (CN) middle- and old-aged adults. Voxel-wise analyses revealed positive correlations between glucose metabolism and ART performance in the frontal and primary somatosensory regions, more specifically the lateral frontal cortex, anterior cingulate cortex and postcentral gyrus (PCG). When conducted again only for amyloid-β (Aβ)-negative individuals, the voxel-wise analysis showed significant correlations in broader areas of the frontal and primary somatosensory regions. This is the first neuroimaging study to directly demonstrate the cerebral resting-state glucose utilization associated with ART performance. Our findings provide important evidence at the neural level that ART predicts premorbid general intelligence and cognitive reserve, as brain areas that showed significant correlations with ART performance correspond to regions that have been associated with general intelligence and cognitive reserve.

Accessing the development and heritability of the capacity of cognitive control

Cognitive control serves as a core construct, with limited capacity, to support executive functions and other higher-level mental processes such as intellectual activity. Although previous studies have investigated the development of executive functions during specific age periods, the development of the capacity of cognitive control (CCC) from early childhood to late adolescence and the heritability of the CCC have yet to be delineated. In this study, we estimated the CCC based on the performance of a perceptual decision-making task in monozygotic (n = 95) and dizygotic (n = 81) twin pairs with an age range from 6 to 18 years and in a reference young adult group (n = 41, mean age = 26.15 years). In addition, the intelligence quotient (IQ) of these participants was assessed using the Wechsler Intelligence Scales. We found an increase in the CCC from 1.55 bits per second (bps) at age 6 years to its 95% capacity of 3.87 bps at age 21 years, with a reduced growth rate as a function of age. The estimated heritability of the CCC was 0.66, and shared and non-shared environmental influences on the CCC were 0.18 and 0.16, respectively. The CCC was significantly correlated to IQ (r = 0.34). These findings indicate that the CCC is developed throughout the school years, is highly heritable, and is associated with higher-level cognition.

A cross-task comparison on visuospatial processing in autism spectrum disorders

This study aimed to draw a cross-task comparison on visuospatial processing in autism spectrum disorder without intellectual disability. Participants with autism spectrum disorder were matched with typically developing individuals on general intelligence and perceptual reasoning index. The two groups were subsequently compared on visuospatial processing speed, visuo-perceptual, visuo-constructive, and visuospatial working memory tasks. Our results revealed similar performances between autism spectrum disorder and typically developing individuals on measures of visuospatial processing speed and visuospatial working memory. The autism spectrum disorder group showed slower reaction times than the typically developing group in the visuo-perceptual task, when stimuli were characterized by a minimum level of perceptual cohesiveness, revealing weaker spatial integration abilities. Concerning the visuo-constructive domain, no differences between the autism spectrum disorder and the typically developing group emerged for the unsegmented condition, revealing that our participants with autism spectrum disorder were similar to the typically developing group in the local analysis of the stimuli. The discussion takes into account the role of individual differences on visuospatial intelligence, task requirements, and cognitive domains to clarify the visuospatial processing skills of individuals with autism spectrum disorder.

The neural code of intelligence: From correlation to causation

Research into the neural underpinning of intelligence has mainly adopted a construct perspective: trying to find structural and functional brain characteristics that would accommodate the psychological concept of g. Few attempts have been made to explain intelligence exclusively based on brain characteristics - the brain perspective. From a methodological viewpoint the brain intelligence relation has been studied by means of correlational and interventional studies. The later providing a causal elucidation of the brain - intelligence relation. The best neuro-anatomical predictor of intelligence is brain volume showing a modest positive correlation with g, explaining between 9 to 16% of variance. The most likely explanation was that larger brains, containing more neurons, have a greater computational power and in that way allow more complex cognitive processing. Correlations with brain surface, thickness, convolution and callosal shape showed less consistent patterns. The development of diffusion tensor imaging has allowed researchers to look also into the microstructure of brain tissue. Consistently observed was a positively correlation between white matter integrity and intelligence, supporting the idea that efficient information transfer between hemispheres and brain areas is crucial for higher intellectual competence. Based on functional studies of the brain intelligence relationship three theories have been put forward: the neural efficiency, the P-FIT and the multi demand (MD) system theory. On the other hand, The Network Neuroscience Theory of g, based on methods from mathematics, physics, and computer science, is an example for the brain perspective on neurobiological underpinning of intelligence. In this framework network flexibility and dynamics provide the foundation for general intelligence. With respect to intervention studies the most promising results have been achieved with noninvasive brain stimulation and behavioral training providing tentative support for findings put forward by the correlational approach. To date the best consensus based on the diversity of results reported would be that g is predominantly determined by lateral prefrontal attentional control of structured sensory episodes in posterior brain areas. The capacity of flexible transitions between these network states represents the essence of intelligence - g.

Cognitive and neural architecture of decision making competence

Although cognitive neuroscience has made remarkable progress in understanding the neural foundations of goal-directed behavior and decision making, neuroscience research on decision making competence, the capacity to resist biases in human judgment and decision making, remain to be established. Here, we investigated the cognitive and neural mechanisms of decision making competence in 283 healthy young adults. We administered the Adult Decision Making Competence battery to assess the respondent's capacity to resist standard biases in decision making, including: (1) resistance to framing, (2) recognizing social norms, (3) over/under confidence, (4) applying decision rules, (5) consistency in risk perception, and (6) resistance to sunk costs. Decision making competence was assessed in relation to core facets of intelligence, including measures of crystallized intelligence (Shipley Vocabulary), fluid intelligence (Figure Series), and logical reasoning (LSAT). Structural equation modeling was applied to examine the relationship(s) between each cognitive domain, followed by an investigation of their association with individual differences in cortical thickness, cortical surface area, and cortical gray matter volume as measured by high-resolution structural MRI. The results suggest that: (i) decision making competence is associated with cognitive operations for logical reasoning, and (ii) these convergent processes are associated with individual differences within cortical regions that are widely implicated in cognitive control (left dACC) and social decision making (right superior temporal sulcus; STS). Our findings motivate an integrative framework for understanding the neural mechanisms of decision making competence, suggesting that individual differences in the cortical surface area of left dACC and right STS are associated with the capacity to overcome decision biases and exhibit competence in decision making.

Testing a Cognitive Control Model of Human Intelligence

The definition of human intelligence and its underlying psychological constructs have long been debated. Although previous studies have investigated the fundamental cognitive functions determining intellectual abilities, such as the broadly defined executive functions including working memory, the core process has yet to be identified. A potential candidate for such a role might be cognitive control, a psychological construct for the coordination of thoughts and actions under conditions of uncertainty. In this study, we tested a cognitive control model of intellectual ability by examining the association between cognitive control, measured by a perceptual decision-making task and by the attention network test, and general intelligence including components of fluid intelligence (Gf, concerning the ability to solve problems by abstraction) and crystalized intelligence (Gc, related to learning from prior knowledge and experience) measured by the Wechsler Adult Intelligence Scale. We also examined the potential role of cognitive control as a core process involved in another determinant of intellectual abilities, the working memory, measured by the N-back tasks and the working memory complex span tasks. The relationship among intelligence, cognitive control, and working memory was examined using structural equation modeling. Results showed that cognitive control shared a large amount of variance with working memory and both measures were strongly associated with Gf and Gc, with a stronger association with Gf than Gc. These findings suggest that cognitive control, serving as a core construct of executive functions, contributes substantially to general intellectual ability, especially fluid intelligence.

Genes, Cells and Brain Areas of Intelligence

What is the neurobiological basis of human intelligence? The brains of some people seem to be more efficient than those of others. Understanding the biological foundations of these differences is of great interest to basic and applied neuroscience. Somehow, the secret must lie in the cells in our brain with which we think. However, at present, research into the neurobiology of intelligence is divided between two main strategies: brain imaging studies investigate macroscopic brain structure and function to identify brain areas involved in intelligence, while genetic associations studies aim to pinpoint genes and genetic loci associated with intelligence. Nothing is known about how properties of brain cells relate to intelligence. The emergence of transcriptomics and cellular neuroscience of intelligence might, however, provide a third strategy and bridge the gap between identified genes for intelligence and brain function and structure. Here, we discuss the latest developments in the search for the biological basis of intelligence. In particular, the recent availability of very large cohorts with hundreds of thousands of individuals have propelled exciting developments in the genetics of intelligence. Furthermore, we discuss the first studies that show that specific populations of brain cells associate with intelligence. Finally, we highlight how specific genes that have been identified generate cellular properties associated with intelligence and may ultimately explain structure and function of the brain areas involved. Thereby, the road is paved for a cellular understanding of intelligence, which will provide a conceptual scaffold for understanding how the constellation of identified genes benefit cellular functions that support intelligence.

Inhibitory control mediates a negative relationship between body mass index and intelligence: A neurocognitive investigation

The structure and function of the human brain is closely related to cognitive processes of the mind and physiological processes of the body, suggesting that an intricate relationship exists between cognitive health, body health, and underlying neural architecture. In the current study, morphometric differences in cortical and subcortical gray matter regions, white matter integrity, and resting-state functional connectivity was assessed to determine what combinations of neural variables best explain an interconnected behavioral relationship between body mass index (BMI), general intelligence, and specific measures of executive function. Data for 82 subjects were obtained from the Nathan Kline Institute Rockland Sample. Behavioral results indicated a negative relationship between BMI and intelligence, which exhibited mediation by an inhibitory measure of executive function. Neural analyses further revealed generally contrasting associations of BMI, intelligence, and executive function with cortical morphometric regions important for inhibitory control and directed attention. Moreover, BMI related to morphometric alterations in components of a frontolimbic network, namely reduced thickness in the anterior cingulate cortex and ventromedial prefrontal cortex, whereas intelligence and inhibitory control primarily related to increased thickness and volume in parietal regions, as well as significantly increased across-network connectivity of visual and default mode resting-state networks. These results propose that medial prefrontal structure and interconnected frontolimbic and frontoparietal networks are important to consider in the relationship between BMI, intelligence, and executive function.

Intelligence and uncertainty: Implications of hierarchical predictive processing for the neuroscience of cognitive ability

Hierarchical predictive processing (PP) has recently emerged as a candidate theoretical paradigm for neurobehavioral research. To date, PP has found support through its success in offering compelling explanations for a number of perceptual, cognitive, and psychiatric phenomena, as well as from accumulating neurophysiological evidence. However, its implications for understanding intelligence and its neural basis have received relatively little attention. The present review outlines the key tenets and evidence for PP, and assesses its implications for intelligence research. It is argued that PP suggests indeterminacy as a unifying principle from which to investigate the cognitive hierarchy and brain-ability correlations. The resulting framework not only accommodates prominent psychometric models of intelligence, but also incorporates key findings from neuroanatomical and functional activation research, and motivates new predictions via the mechanisms of prediction-error minimization. Because PP also suggests unique neural signatures of experience-dependent activity, it may also help clarify environmental contributions to intellectual development. It is concluded that PP represents a plausible, integrative framework that could enhance progress in the neuroscience of intelligence.

The Relationship Between General Intelligence and Cortical Structure in Healthy Individuals

Considerable work in recent years has examined the relationship between cortical thickness (CT) and general intelligence (IQ) in healthy individuals. It is not known whether specific IQ variables (i.e., perceptual reasoning [PIQ], verbal comprehension IQ [VIQ], and full-scale IQ [FSIQ]) are associated with multiple cortical measures (i.e., CT, cortical volume (CV), cortical surface area (CSA) and cortical gyrification (CG)) within the same individuals. Here we examined the association between these neuroimaging metrics and IQ in 56 healthy adults. At a cluster-forming threshold (CFT) of p < 0.05, we observed significant positive relationships between CT and all three IQ variables in regions within the posterior frontal and superior parietal lobes. Regions within the temporal and posterior frontal lobes exhibited positive relationships between CV and two IQ variables (PIQ and FSIQ) and regions within the inferior parietal lobe exhibited positive relationships between CV and PIQ. Additionally, CV was positively associated with VIQ in the left insula and with FSIQ within the inferior frontal gyrus. At a more stringent CFT (p < 0.01), the CT-PIQ, CT-VIQ, CT-FSIQ, and CV-PIQ relationships remained significant within the posterior frontal lobe, as did the CV-PIQ relationship within the temporal and inferior parietal lobes. We did not observe statistically significant relationships between IQ and either CSA or CG. Our findings suggest that the neural basis of IQ extends beyond previously observed relationships with fronto-parietal regions. We also conclude that CT and CV may be more useful metrics than CSA or CG in the study of intellectual abilities.

Different brain networks underlying intelligence in autism spectrum disorders

There has been sustained clinical and cognitive neuroscience research interest in how network correlates of brain-behavior relationships might be altered in Autism Spectrum Disorders (ASD) and other neurodevelopmental disorders. As previous work has mostly focused on adults, the nature of whole-brain connectivity networks underlying intelligence in pediatric cohorts with abnormal neurodevelopment requires further investigation. We used network-based statistics (NBS) to examine the association between resting-state functional Magnetic Resonance Imaging (fMRI) connectivity and fluid intelligence ability in male children (n = 50) with Autism Spectrum Disorders (ASD; M = 10.45, SD = 1.58 years and in controls (M = 10.38, SD = 0.96 years) matched on fluid intelligence performance, age and sex. Repeat analyses were performed in independent sites for validation and replication. Despite being equivalent on fluid intelligence ability to strictly matched neurotypical controls, boys with ASD displayed a subnetwork of significantly increased associations between functional connectivity and fluid intelligence. Between-group differences remained significant at higher edge thresholding, and results were validated in independent-site replication analyses in an equivalent age and sex-matched cohort with ASD. Regions consistently implicated in atypical connectivity correlates of fluid intelligence in ASD were the angular gyrus, posterior middle temporal gyrus, occipital and temporo-occipital regions. Development of fluid intelligence neural correlates in young ASD males is aberrant, with an increased strength in intrinsic connectivity association during childhood. Alterations in whole-brain network correlates of fluid intelligence in ASD may be a compensatory mechanism that allows equal task performance to neurotypical peers.

Macular Xanthophylls Are Related to Intellectual Ability among Adults with Overweight and Obesity

Excess adiposity or obesity has been inversely related to cognitive function and macular xanthophyll status. However, whether the neuroprotective effects of macular xanthophylls on cognitive function are independent of excess adiposity is unclear. We investigated the relationship between macular xanthophylls and intellectual ability among adults (N = 114) between 25 and 45 years with overweight and obesity (≥25 kg/m²). Dual energy X-ray absorptiometry and heterochromatic flicker photometry were used to assess whole body adiposity (%Fat) and macular pigment optical density (MPOD), respectively. Dietary xanthophylls (lutein and zeaxanthin) were assessed using 7-day diet records. The Kaufman Brief Intelligence Test-2 (KBIT-2) was used to assess general intelligence (IQ) as well as fluid and crystallized intelligence. Bivariate correlations revealed that MPOD was inversely related to %Fat and positively associated with IQ and fluid intelligence. Although %Fat was inversely correlated to IQ and fluid intelligence, this relationship did not persist following adjustment for sex and MPOD. Further, MPOD was an independent predictor of IQ and fluid intelligence. However, no significant relationships were observed between MPOD and crystalized intelligence. These results suggest that macular xanthophylls are selectively related to fluid intelligence, regardless of degree of adiposity among adults with overweight and obesity.

Enhancing Intelligence: From the Group to the Individual

Research aimed at testing whether short-term training programs can enhance intelligence is mainly concentrated on behavior. Expected positive effects are found sometimes, but the evidence is far from conclusive. It is assumed that training must evoke changes in the brain for observing genuine improvements in behavior. However, behavioral and brain data are seldom combined in the same study. Here we present one example of this latter type of research summarizing, discussing, and integrating already published results. The training program was based on the adaptive dual n-back task, and participants completed a comprehensive battery measuring fluid and crystallized ability, along with working memory and attention control, before and after training. They were also submitted to MRI scanning at baseline and post-training. Behavioral results revealed positive effects for visuospatial processing across cognitive domains. Brain imaging data were analyzed by longitudinal voxel-based morphometry, tensor-based morphometry, surface-based morphometry, and structural connectivity. The integration of these multimodal brain results provides clues about those observed in behavior. Our findings, along with previous research and current technological advances, are considered from the perspective that we now live in ideal times for (a) moving from the group to the individual and (b) developing personalized training programs.

Allergic tendencies are associated with larger gray matter volumes

Allergic tendencies are associated with important cognitive and physiological factors, such as intelligence and mathematical abilities. Allergies are widely prevalent, especially in modern life, and the reason for its association with important cognitive variables is an intriguing scientific question. However, despite the unique characteristics of cognitive correlates of allergy, the anatomical correlates of allergy remain unknown. The aim of this study was to identify the associations between regional gray matter volume (rGMV) and allergic tendencies in young adults. In a study cohort of 1,219 healthy, educated young adults, we identified a positive correlation between total allergic tendency and rGMV in large anatomical clusters that mainly encompassed the dorsal part of the cerebral neocortex, right anterior insula, and cerebellum. Furthermore,both mean rGMV of the entire part of these clusters and total allergenic tendency showed a significant positive correlation with spatial ability. These results suggest the link among allergic tendencies, larger rGMV, and the better spatial ability in healthy, educated young adults.

The Relationship between Regional Gray Matter Volume of Social Exclusion Regions and Personal Self-Esteem Is Moderated by Collective Self-Esteem

According to sociometer theory, self-esteem is an internal monitor of positive social bonds to others. Social exclusion can break or threaten social bonds, which might be reflected by the brain structure of social exclusion regions. Thus, self-esteem might be influenced by structurally individual differences in these regions. It has been suggested that self-esteem can be divided into personal (PSE) and collective (CSE) self-esteem and CSE can bring individuals many benefits, such as acceptance, belonging, and social support, which could further maintain or increase their PSE. Based on this, we hypothesized that CSE might moderate the relationship between structurally individual differences in social exclusion regions and PSE. Therefore, in the present study, the moderating effect of CSE on the relationships between PSE and individual differences in regional gray matter volume (rGMV) of 10 social exclusion regions from previous meta-analysis of social exclusion were investigated using voxel-based morphometry. The results showed that CSE played a moderating role in the relationship between PSE and rGMV of the left posterior cingulate cortex (PCC). Specifically, PSE was positively associated with rGMV of left PCC in lower CSE, while there was no significant relationship between PSE and rGMV of left PCC in higher CSE. Therefore, we believe that compared with a higher CSE, because of lack of acceptance, belonging, and social support from valued groups, lower CSE individuals might be more prone to be influenced by social exclusion with decreased rGMV of the left PCC, which makes them more prone to develop lower PSE.

Distinct neural substrates of visuospatial and verbal-analytic reasoning as assessed by Raven's Advanced Progressive Matrices

Recent studies revealed spontaneous neural activity to be associated with fluid intelligence (gF) which is commonly assessed by Raven's Advanced Progressive Matrices, and embeds two types of reasoning: visuospatial and verbal-analytic reasoning. With resting-state fMRI data, using global brain connectivity (GBC) analysis which averages functional connectivity of a voxel in relation to all other voxels in the brain, distinct neural correlates of these two reasoning types were found. For visuospatial reasoning, negative correlations were observed in both the primary visual cortex (PVC) and the precuneus, and positive correlations were observed in the temporal lobe. For verbal-analytic reasoning, negative correlations were observed in the right inferior frontal gyrus (rIFG), dorsal anterior cingulate cortex and temporoparietal junction, and positive correlations were observed in the angular gyrus. Furthermore, an interaction between GBC value and type of reasoning was found in the PVC, rIFG and the temporal lobe. These findings suggest that visuospatial reasoning benefits more from elaborate perception to stimulus features, whereas verbal-analytic reasoning benefits more from feature integration and hypothesis testing. In sum, the present study offers, for different types of reasoning in gF, first empirical evidence of separate neural substrates in the resting brain.

Brain grey and white matter predictors of verbal ability traits in older age: The Lothian Birth Cohort 1936

Cerebral grey and white matter MRI parameters are related to general intelligence and some specific cognitive abilities. Less is known about how structural brain measures relate specifically to verbal processing abilities. We used multi-modal structural MRI to investigate the grey matter (GM) and white matter (WM) correlates of verbal ability in 556 healthy older adults (mean age = 72.68 years, s.d. = .72 years). Structural equation modelling was used to decompose verbal performance into two latent factors: a storage factor that indexed participants' ability to store representations of verbal knowledge and an executive factor that measured their ability to regulate their access to this information in a flexible and task-appropriate manner. GM volumes and WM fractional anisotropy (FA) for components of the language/semantic network were used as predictors of these verbal ability factors. Volume of the ventral temporal cortices predicted participants' storage scores (β = .12, FDR-adjusted p = .04), consistent with the theory that this region acts as a key substrate of semantic knowledge. This effect was mediated by childhood IQ, suggesting a lifelong association between ventral temporal volume and verbal knowledge, rather than an effect of cognitive decline in later life. Executive ability was predicted by FA fractional anisotropy of the arcuate fasciculus (β = .19, FDR-adjusted p = .001), a major language-related tract implicated in speech production. This result suggests that this tract plays a role in the controlled retrieval of word knowledge during speech. At a more general level, these data highlight a basic distinction between information representation, which relies on the accumulation of tissue in specialised GM regions, and executive control, which depends on long-range WM pathways for efficient communication across distributed cortical networks.