Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence

Neural underpinnings of individual differences in emotion regulation: A systematic review

This review synthesises individual differences in neural processes related to emotion regulation (ER). It comprises individual differences in self-reported and physiological regulation success, self-reported ER-related traits, and demographic variables, to assess their correlation with brain activation during ER tasks. Considering region-of-interest (ROI) and whole-brain analyses, the review incorporated data from 52 functional magnetic resonance imaging studies. Results can be summarized as follows: (1) Self-reported regulation success (assessed by emotional state ratings after regulation) and self-reported ER-related traits (assessed by questionnaires) correlated with brain activity in the lateral prefrontal cortex. (2) Amygdala activation correlated with ER-related traits only in ROI analyses, while it was associated with regulation success in whole-brain analyses. (3) For demographic and physiological measures, there was no systematic overlap in effects reported across studies. In showing that individual differences in regulation success and ER-related traits can be traced back to differences in the neural activity of brain regions associated with emotional reactivity (amygdala) and cognitive control (lateral prefrontal cortex), our findings can inform prospective personalised intervention models.

Life-course neighbourhood deprivation and brain structure in older adults: the Lothian Birth Cohort 1936

Neighbourhood disadvantage may be associated with brain health but the importance of exposure at different stages of the life course is poorly understood. Utilising the Lothian Birth Cohort 1936, we explored the relationship between residential neighbourhood deprivation from birth to late adulthood, and global and local neuroimaging measures at age 73. A total of 689 participants had at least one valid brain measures (53% male); to maximise the sample size structural equation models with full information maximum likelihood were conducted. Residing in disadvantaged neighbourhoods in mid- to late adulthood was associated with smaller total brain (β = -0.06; SE = 0.02; sample size[N] = 658; number of pairwise complete observations[n]=390), grey matter (β = -0.11; SE = 0.03; N = 658; n = 390), and normal-appearing white matter volumes (β = -0.07; SE = 0.03; N = 658; n = 390), thinner cortex (β = -0.14; SE = 0.06; N = 636; n = 379), and lower general white matter fractional anisotropy (β = -0.19; SE = 0.06; N = 665; n = 388). We also found some evidence on the accumulating impact of neighbourhood deprivation from birth to late adulthood on age 73 total brain (β = -0.06; SE = 0.02; N = 658; n = 276) and grey matter volumes (β = -0.10; SE = 0.04; N = 658; n = 276). Local analysis identified affected focal cortical areas and specific white matter tracts. Among individuals belonging to lower social classes, the brain-neighbourhood associations were particularly strong, with the impact of neighbourhood deprivation on total brain and grey matter volumes, and general white matter fractional anisotropy accumulating across the life course. Our findings suggest that living in deprived neighbourhoods across the life course, but especially in mid- to late adulthood, is associated with adverse brain morphologies, with lower social class amplifying the vulnerability.

Infant attachment does not depend on neonatal amygdala and hippocampal structure and connectivity

Infant attachment is an antecedent of later socioemotional abilities, which can be adversely affected by preterm birth. The structural integrity of amygdalae and hippocampi may subserve attachment in infancy. We aimed to investigate associations between neonatal amygdalae and hippocampi structure and their whole-brain connections and attachment behaviours at nine months of age in a sample of infants enriched for preterm birth. In 133 neonates (median gestational age 32 weeks, range 22.14-42.14), we calculated measures of amygdala and hippocampal structure (volume, fractional anisotropy, mean diffusivity, neurite dispersion index, orientation dispersion index) and structural connectivity, and coded attachment behaviours (distress, fretfulness, attentiveness to caregiver) from responses to the Still-Face Paradigm at nine months. After multiple comparisons correction, there were no significant associations between neonatal amygdala or hippocampal structure and structural connectivity and attachment behaviours: standardised β values - 0.23 to 0.18, adjusted p-values > 0.40. Findings indicate that the neural basis of infant attachment in term and preterm infants is not contingent on the structure or connectivity of the amygdalae and hippocampi in the neonatal period, which implies that it is more widely distributed in early life and or that network specialisation takes place in the months after hospital discharge.

Structural-functional brain network coupling predicts human cognitive ability

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

Exploring volumetric abnormalities in subcortical L-HPA axis structures in pediatric generalized anxiety disorder

BACKGROUND

Pediatric generalized anxiety disorder (GAD) is debilitating and increasingly prevalent, yet its etiology remains unclear. Some believe the disorder to be propagated by chronic dysregulation of the limbic-hypothalamic-pituitary-adrenal (L-HPA) axis, but morphometric studies of implicated subcortical areas have been largely inconclusive. Recognizing that certain subcortical subdivisions are more directly involved in L-HPA axis functioning, this study aims to detect specific abnormalities in these critical areas.

METHODS

Thirty-eight MRI scans of preschool children with (n = 15) and without (n = 23) GAD underwent segmentation and between-group volumetric comparisons of the basolateral amygdala (BLA), ventral hippocampal subiculum (vSC), and mediodorsal medial magnocellular (MDm) area of the thalamus.

RESULTS

Children with GAD displayed significantly larger vSC compared to healthy peers, F(1, 31) = 6.50, pFDR = .048. On average, children with GAD presented with larger BLA and MDm, Fs(1, 31) ≥ 4.86, psFDR ≤ .054. Exploratory analyses revealed right-hemispheric lateralization of all measures, most notably the MDm, F(1, 31) = 8.13, pFDR = .024, the size of which scaled with symptom severity, r = .83, pFDR = .033.

CONCLUSION

The BLA, vSC, and MDm are believed to be involved in the regulation of anxiety and stress, both individually and collectively through the excitation and inhibition of the L-HPA axis. All were found to be enlarged in children with GAD, perhaps reflecting hypertrophy related to hyperexcitability, or early neuronal overgrowth. Longitudinal studies should investigate the relationship between these early morphological differences and the long-term subcortical atrophy previously observed.

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.

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

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.

Investigating robust associations between functional connectivity based on graph theory and general intelligence

Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

Structural brain correlates of non-verbal cognitive ability in 5-year-old children: Findings from the FinnBrain birth cohort study

Non-verbal cognitive ability predicts multiple important life outcomes, for example, school and job performance. It has been associated with parieto-frontal cortical anatomy in prior studies in adult and adolescent populations, while young children have received relatively little attention. We explored the associations between cortical anatomy and non-verbal cognitive ability in 165 5-year-old participants (mean scan age 5.40 years, SD 0.13; 90 males) from the FinnBrain Birth Cohort study. T1-weighted brain magnetic resonance images were processed using FreeSurfer. Non-verbal cognitive ability was measured using the Performance Intelligence Quotient (PIQ) estimated from the Block Design and Matrix Reasoning subtests from the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III). In vertex-wise general linear models, PIQ scores associated positively with volumes in the left caudal middle frontal and right pericalcarine regions, as well as surface area in left the caudal middle frontal, left inferior temporal, and right lingual regions. There were no associations between PIQ and cortical thickness. To the best of our knowledge, this is the first study to examine structural correlates of non-verbal cognitive ability in a large sample of typically developing 5-year-olds. The findings are generally in line with prior findings from older age groups, with the important addition of the positive association between volume / surface area in the right medial occipital region and non-verbal cognitive ability. This finding adds to the literature by discovering a new brain region that should be considered in future studies exploring the role of cortical structure for cognitive development in young children.

The bidirectional effects between cognitive ability and brain morphology: A life course Mendelian randomization analysis

Introduction

Little is understood about the dynamic interplay between brain morphology and cognitive ability across the life course. Additionally, most existing research has focused on global morphology measures such as estimated total intracranial volume, mean thickness, and total surface area.

Methods

Mendelian randomization was used to estimate the bidirectional effects between cognitive ability, global and regional measures of cortical thickness and surface area, estimated total intracranial volume, total white matter, and the volume of subcortical structures (N=37,864). Analyses were stratified for developmental periods (childhood, early adulthood, mid-to-late adulthood; age range: 8-81 years).

Results

The earliest effects were observed in childhood and early adulthood in the frontoparietal lobes. A bidirectional relationship was identified between higher cognitive ability, larger estimated total intracranial volume (childhood, mid-to-late adulthood) and total surface area (all life stages). A thicker posterior cingulate cortex and a larger surface area in the caudal middle frontal cortex and temporal pole were associated with greater cognitive ability. Contrary, a thicker temporal pole was associated with lower cognitive ability.

Discussion

Stable effects of cognitive ability on brain morphology across the life course suggests that childhood is potentially an important window for intervention.

Grey and white matter metrics demonstrate distinct and complementary prediction of differences in cognitive performance in children: Findings from ABCD (N= 11 876)

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either grey or white matter metrics in humans, leaving open the key question as to whether grey or white matter microstructure play distinct or complementary roles supporting cognitive performance. To compare the role of grey and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with grey and white matter measures. Specifically, we compared how grey matter (volume, cortical thickness and surface area) and white matter measures (volume, fractional anisotropy and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study, 5680 female; 6196 male) at 10 years old. We found that grey and white matter metrics bring partly non-overlapping information to predict cognitive performance. The models with only grey or white matter explained respectively 15.4% and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in we additionally found that different metrics within grey and white matter had different predictive power, and that the tracts/regions that were most predictive of cognitive performance differed across metric. These results show that studies focusing on a single metric in either grey or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

Image response regression via deep neural networks

Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it ideal for capturing complex association patterns. We establish estimation and selection consistency and derive asymptotic error bounds. We demonstrate the method's advantages through intensive simulations and analyses of two functional magnetic resonance imaging data sets.

Lessons from complex trait genetics may help us overcome the neuroimaging replication crisis

The research fields of Complex Trait (or Statistical) Genetics and Neuroimaging face similar challenges in identifying reliable biological correlates of common traits and diseases. This Viewpoint focuses on five major lessons that allowed population-level genetics research to overcome many of its issues of replicability and may be directly applicable to inter-individual neuroimaging research. First, the failure of candidate gene studies inspires abandoning overly simplistic studies mapping individual brain regions onto traits and diseases. Second, developments in genetics research demonstrate that robust study results can be achieved by increasing sample sizes. Third and fourth, the success of genome-wide association studies motivates the use of mass-univariate testing and sharing summary-level association data to boost large-scale collaboration and meta-analysis. Finally, applying genetics methods dealing with complex data structures to vertex-wise (or voxel-wise) neuroimaging data promises more robust discoveries without the need to develop novel neuroimaging-specific methods. Those practices - that are firmly established in genetics research - should either be further endorsed, or newly adopted by the neuroimaging community, promising to accelerate the evolution of Neuroimaging through robust discovery.

Elevated CRP and TNF-α levels are associated with blunted neural oscillations serving fluid intelligence

INTRODUCTION

Inflammatory processes help protect the body from potential threats such as bacterial or viral invasions. However, when such inflammatory processes become chronically engaged, synaptic impairments and neuronal cell death may occur. In particular, persistently high levels of C-reactive protein (CRP) and tumor necrosis factor-alpha (TNF-α) have been linked to deficits in cognition and several psychiatric disorders. Higher-order cognitive processes such as fluid intelligence (Gf) are thought to be particularly vulnerable to persistent inflammation. Herein, we investigated the relationship between elevated CRP and TNF-α and the neural oscillatory dynamics serving Gf.

METHODS

Seventy adults between the ages of 20-66 years (Mean = 45.17 years, SD = 16.29, 21.4% female) completed an abstract reasoning task that probes Gf during magnetoencephalography (MEG) and provided a blood sample for inflammatory marker analysis. MEG data were imaged in the time-frequency domain, and whole-brain regressions were conducted using each individual's plasma CRP and TNF-α concentrations per oscillatory response, controlling for age, BMI, and education.

RESULTS

CRP and TNF-α levels were significantly associated with region-specific neural oscillatory responses. In particular, elevated CRP concentrations were associated with altered gamma activity in the right inferior frontal gyrus and right cerebellum. In contrast, elevated TNF-α levels scaled with alpha/beta oscillations in the left anterior cingulate and left middle temporal, and gamma activity in the left intraparietal sulcus.

DISCUSSION

Elevated inflammatory markers such as CRP and TNF-α were associated with aberrant neural oscillations in regions important for Gf. Linking inflammatory markers with regional neural oscillations may hold promise in identifying mechanisms of cognitive and psychiatric disorders.

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components : gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 41 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.15 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

Olfactory training effects in children after mild traumatic brain injury

OBJECTIVE

Mild traumatic brain injury (mTBI) might impair the sense of smell and cognitive functioning. Repeated, systematic exposure to odors, i.e., olfactory training (OT) has been proposed for treatment of olfactory dysfunctions, including post-traumatic smell loss. Additionally, OT has been shown to mitigate cognitive deterioration in older population and enhance selected cognitive functions in adults. We aimed to investigate olfactory and cognitive effects of OT in the pediatric population after mTBI, likely to exhibit cognitive and olfactory deficits.

METHODS

Our study comprised 159 children after mTBI and healthy controls aged 6-16 years (M = 9.68 ± 2.78 years, 107 males), who performed 6-months-long OT with a set of 4 either high- or low-concentrated odors. Before and after OT we assessed olfactory functions, fluid intelligence, and executive functions.

RESULTS

OT with low-concentrated odors increased olfactory sensitivity in children after mTBI. Regardless of health status, children who underwent OT with low-concentrated odors had higher fluid intelligence scores at post-training measurement, whereas scores of children performing OT with high-concentrated odors did not change.

CONCLUSION

Our study suggests that OT with low-concentrated odors might accelerate rehabilitation of olfactory sensitivity in children after mTBI and support cognitive functions in the area of fluid intelligence regardless of head trauma.

Few temporally distributed brain connectivity states predict human cognitive abilities

Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities - which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.

Structural architecture and brain network efficiency link polygenic scores to intelligence

Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

Dynamic weighted hypergraph convolutional network for brain functional connectome analysis

The hypergraph structure has been utilized to characterize the brain functional connectome (FC) by capturing the high order relationships among multiple brain regions of interest (ROIs) compared with a simple graph. Accordingly, hypergraph neural network (HGNN) models have emerged and provided efficient tools for hypergraph embedding learning. However, most existing HGNN models can only be applied to pre-constructed hypergraphs with a static structure during model training, which might not be a sufficient representation of the complex brain networks. In this study, we propose a dynamic weighted hypergraph convolutional network (dwHGCN) framework to consider a dynamic hypergraph with learnable hyperedge weights. Specifically, we generate hyperedges based on sparse representation and calculate the hyper similarity as node features. The hypergraph and node features are fed into a neural network model, where the hyperedge weights are updated adaptively during training. The dwHGCN facilitates the learning of brain FC features by assigning larger weights to hyperedges with higher discriminative power. The weighting strategy also improves the interpretability of the model by identifying the highly active interactions among ROIs shared by a common hyperedge. We validate the performance of the proposed model on two classification tasks with three paradigms functional magnetic resonance imaging (fMRI) data from Philadelphia Neurodevelopmental Cohort. Experimental results demonstrate the superiority of our proposed method over existing hypergraph neural networks. We believe our model can be applied to other applications in neuroimaging for its strength in representation learning and interpretation.

The role of the orbitofrontal cortex in exercise addiction and exercise motivation: A brain imaging study based on multimodal magnetic resonance imaging

BACKGROUND

Excessive exercise may also lead to exercise addiction (EXA), which is harmful to people's physical and mental health. Behavioral and neuroimaging studies have demonstrated that addictive disorders are essentially motivational problems. However, little is known about the neuropsychological mechanism of EXA and the effects of motivation on EXA.

METHODS

We investigated 130 regularly exercised participants with EXA symptoms to explore the neurobiological basis of EXA and its association with motivation. The correlation between EXA and gray matter volume (GMV) was evaluated by whole-brain regression analysis based on voxel-based morphometry. Then, regional brain function was extracted and the relationship between brain structure-function-EXA was analyzed. Finally, mediation analysis was performed to further detect the relationship between the brain, motivation, and EXA.

RESULTS

Whole-brain correlation analyses showed that the GMV of the right orbitofrontal cortex (OFC) was negatively correlated with EXA. The function of the right OFC played an indirect role in EXA and affected EXA via the GMV of the OFC. Importantly, the GMV of the right OFC played a mediating role in the relationship between ability motivation and EXA. These results remain significant even when adjusting for sex, age, body mass index, family socioeconomic status, general intelligence, total intracranial volume, and head motion.

LIMITATION

The results should be interpreted carefully because only the people with EXA symptoms were included.

CONCLUSION

This study provided evidence for the underlying neuropsychological mechanism of the important role of the right OFC in EXA and revealed that there may be a decrease in executive control function in EXA.

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.

Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data

IMPORTANCE

Neurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings.

OBJECTIVE

To identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets.

DESIGN, SETTING, AND PARTICIPANTS

This case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; data were extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study.

MAIN OUTCOMES AND MEASURES

The analyses consisted of a data-driven clustering procedure on measures derived from each participant's resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested.

RESULTS

Overall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths and Weaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets.

A General Cognitive Ability Factor for the UK Biobank

UK Biobank participants do not have a high-quality measure of intelligence or polygenic scores (PGSs) of intelligence to simultaneously examine the genetic and neural underpinnings of intelligence. We created a standardized measure of general intelligence (g factor) relative to the UK population and estimated its quality. After running a GWAS of g on UK Biobank participants with a g factor of good quality and without neuroimaging data (N = 187,288), we derived a g PGS for UK Biobank participants with neuroimaging data. For individuals with at least one cognitive test, the g factor from eight cognitive tests (N = 501,650) explained 29% of the variance in cognitive test performance. The PGS for British individuals with neuroimaging data (N = 27,174) explained 7.6% of the variance in g. We provided high-quality g factor estimates for most UK Biobank participants and g factor PGSs for UK Biobank participants with neuroimaging data.

The Evolution of Intelligence: Analysis of the Journal of Intelligence and Intelligence

What are the current trends in intelligence research? This parallel bibliometric analysis covers the two premier journals in the field: Intelligence and the Journal of Intelligence (JOI) between 2013 and 2022. Using Scopus data, this paper extends prior bibliometric articles reporting the evolution of the journal Intelligence from 1977 up to 2018. It includes JOI from its inception, along with Intelligence to the present. Although the journal Intelligence's growth has declined over time, it remains a stronghold for traditional influential research (average publications per year = 71.2, average citations per article = 17.07, average citations per year = 2.68). JOI shows a steady growth pattern in the number of publications and citations (average publications per year = 33.2, average citations per article = 6.48, total average citations per year = 1.48) since its inception in 2013. Common areas of study across both journals include cognitive ability, fluid intelligence, psychometrics-statistics, g-factor, and working memory. Intelligence includes core themes like the Flynn effect, individual differences, and geographic IQ variability. JOI addresses themes such as creativity, personality, and emotional intelligence. We discuss research trends, co-citation networks, thematic maps, and their implications for the future of the two journals and the evolution and future of the scientific study of intelligence.

Multimodal Brain Signal Complexity Predicts Human Intelligence

Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven's Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.

Robust associations between white matter microstructure and general intelligence

Few tract-based spatial statistics (TBSS) studies have investigated the relations between intelligence and white matter microstructure in healthy (young) adults, and those have yielded mixed observations, yet white matter is fundamental for efficient and accurate information transfer throughout the human brain. We used a multicenter approach to identify white matter regions that show replicable structure-function associations, employing data from 4 independent samples comprising over 2000 healthy participants. TBSS indicated 188 voxels exhibited significant positive associations between g factor scores and fractional anisotropy (FA) in all 4 data sets. Replicable voxels formed 3 clusters, located around the left-hemispheric forceps minor, superior longitudinal fasciculus, and cingulum-cingulate gyrus with extensions into their surrounding areas (anterior thalamic radiation, inferior fronto-occipital fasciculus). Our results suggested that individual differences in general intelligence are robustly associated with white matter FA in specific fiber bundles distributed across the brain, consistent with the Parieto-Frontal Integration Theory of intelligence. Three possible reasons higher FA values might create links with higher g are faster information processing due to greater myelination, more direct information processing due to parallel, homogenous fiber orientation distributions, or more parallel information processing due to greater axon density.

Graph Mapping: A novel and simple test to validly assess fluid reasoning

We present Graph Mapping - a simple and effective computerized test of fluid intelligence (reasoning ability). The test requires structure mapping - a key component of the reasoning process. Participants are asked to map a pair of corresponding nodes across two mathematically isomorphic but visually different graphs. The test difficulty can be easily manipulated - the more complex structurally and dissimilar visually the graphs, the higher response error rate. Graph Mapping offers high flexibility in item generation, ranging from trivial to extremally difficult items, supporting progressive item sequences suitable for correlational studies. It also allows multiple item instances (clones) at a fixed difficulty level as well as full item randomization, both particularly suitable for within-subject experimental designs, longitudinal studies, and adaptive testing. The test has short administration times and is unfamiliar to participants, yielding practical advantages. Graph Mapping has excellent psychometric properties: Its convergent validity and reliability is comparable to the three leading traditional fluid reasoning tests. The convenient software allows a researcher to design the optimal test variant for a given study and sample. Graph Mapping can be downloaded from: https://osf.io/wh7zv/.

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.

Default mode and fronto-parietal network associations with IQ development across childhood in autism

Background

Intellectual disability affects approximately one third of individuals with autism spectrum disorder (autism). Yet, a major unresolved neurobiological question is what differentiates autistic individuals with and without intellectual disability. Intelligence quotients (IQs) are highly variable during childhood. We previously identified three subgroups of autistic children with different trajectories of intellectual development from early (2–3½ years) to middle childhood (9–12 years): (a) persistently high: individuals whose IQs remained in the normal range; (b) persistently low: individuals whose IQs remained in the range of intellectual disability (IQ < 70); and (c) changers: individuals whose IQs began in the range of intellectual disability but increased to the normal IQ range. The frontoparietal (FPN) and default mode (DMN) networks have established links to intellectual functioning. Here, we tested whether brain regions within the FPN and DMN differed volumetrically between these IQ trajectory groups in early childhood.

Methods

We conducted multivariate distance matrix regression to examine the brain regions within the FPN (11 regions x 2 hemispheres) and the DMN (12 regions x 2 hemispheres) in 48 persistently high (18 female), 108 persistently low (32 female), and 109 changers (39 female) using structural MRI acquired at baseline. FPN and DMN regions were defined using networks identified in Smith et al. (Proc Natl Acad Sci U S A 106:13040–5, 2009). IQ trajectory groups were defined by IQ measurements from up to three time points spanning early to middle childhood (mean age time 1: 3.2 years; time 2: 5.4 years; time 3: 11.3 years).

Results

The changers group exhibited volumetric differences in the DMN compared to both the persistently low and persistently high groups at time 1. However, the persistently high group did not differ from the persistently low group, suggesting that DMN structure may be an early predictor for change in IQ trajectory. In contrast, the persistently high group exhibited differences in the FPN compared to both the persistently low and changers groups, suggesting differences related more to concurrent IQ and the absence of intellectual disability.

Conclusions

Within autism, volumetric differences of brain regions within the DMN in early childhood may differentiate individuals with persistently low IQ from those with low IQ that improves through childhood. Structural differences in brain networks between these three IQ-based subgroups highlight distinct neural underpinnings of these autism sub-phenotypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-022-09460-y.

Relationship of prefrontal brain lateralization to optimal cognitive function differs with age

There is considerable debate about whether additional fMRI-measured activity in the right prefrontal cortex readily observed in older adults represents compensatory activation that enhances cognition or whether maintenance of youthful brain activity best supports cognitive function in late adulthood. To investigate this issue, we tested a large lifespan sample of 461 adults (aged 20-89) and treated degree of left-lateralization in ventrolateral and dorsolateral prefrontal cortex during a semantic judgment fMRI task as an individual differences variable to predict cognition. We found that younger adults were highly left-lateralized, but lateralization did not predict better cognition, whereas higher left-lateralization of prefrontal cortex predicted better cognitive performance in middle-aged adults, providing evidence that left-lateralized, youth-like patterns are optimal in middle age. This relationship was reversed in older adults, with lower laterality scores associated with better cognition. The findings suggest that bilaterality in older adults facilitates cognition, but early manifestation of this pattern during middle age is characteristic of low performers. Implications of these findings for current theories of neurocognitive aging are discussed.

Intelligence and Visual Mismatch Negativity: Is Pre-Attentive Visual Discrimination Related to General Cognitive Ability?

EEG has been used for decades to identify neurocognitive processes related to intelligence. Evidence is accumulating for associations with neural markers of higher-order cognitive processes (e.g., working memory); however, whether associations are specific to complex processes or also relate to earlier processing stages remains unclear. Addressing these issues has implications for improving our understanding of intelligence and its neural correlates. The MMN is an ERP that is elicited when, within a series of frequent standard stimuli, rare deviant stimuli are presented. As stimuli are typically presented outside the focus of attention, the MMN is suggested to capture automatic pre-attentive discrimination processes. However, the MMN and its relation to intelligence has largely only been studied in the auditory domain, thus preventing conclusions about the involvement of automatic discrimination processes in humans' dominant sensory modality-vision. EEG was recorded from 50 healthy participants during a passive visual oddball task that presented simple sequence violations and deviations within a more complex hidden pattern. Signed area amplitudes and fractional area latencies of the visual MMN were calculated with and without Laplacian transformation. Correlations between visual MMN and intelligence (Raven's Advanced Progressive Matrices) were of negligible to small effect sizes, differed critically between measurement approaches, and Bayes Factors provided anecdotal to substantial evidence for the absence of an association. We discuss differences between the auditory and visual MMN, the implications of different measurement approaches, and offer recommendations for further research in this evolving field.

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.

Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity

Medication and other therapies for psychiatric disorders show unsatisfying efficacy, in part due to the significant clinical/ biological heterogeneity within each disorder and our over-reliance on categorical clinical diagnoses. Alternatively, dimensional transdiagnostic studies have provided a promising pathway toward realizing personalized medicine and improved treatment outcomes. One factor that may influence response to psychiatric treatments is cognitive function, which is reflected in one's intellectual capacity. Intellectual capacity is also reflected in the organization and structure of intrinsic brain networks. Using a large transdiagnostic cohort (n = 1721), we sought to discover neuroimaging biomarkers by developing a resting-state functional connectome-based prediction model for a key intellectual capacity measure, Full-Scale Intelligence Quotient (FSIQ), across the diagnostic spectrum. Our cross-validated model yielded an excellent prediction accuracy (r = 0.5573, p < 0.001). The robustness and generalizability of our model was further validated on three independent cohorts (n = 2641). We identified key transdiagnostic connectome signatures underlying FSIQ capacity involving the dorsal-attention, frontoparietal and default-mode networks. Meanwhile, diagnosis groups showed disorder-specific biomarker patterns. Our findings advance the neurobiological understanding of cognitive functioning across traditional diagnostic categories and provide a new avenue for neuropathological classification of psychiatric disorders.

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=.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=.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r=.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.

Resting-state functional connectivity does not predict individual differences in the effects of emotion on memory

Emotion-laden events and objects are typically better remembered than neutral ones. This is usually explained by stronger functional coupling in the brain evoked by emotional content. However, most research on this issue has focused on functional connectivity evoked during or after learning. The effect of an individual’s functional connectivity at rest is unknown. Our pre-registered study addresses this issue by analysing a large database, the Cambridge Centre for Ageing and Neuroscience, which includes resting-state data and emotional memory scores from 303 participants aged 18–87 years. We applied regularised regression to select the relevant connections and replicated previous findings that whole-brain resting-state functional connectivity can predict age and intelligence in younger adults. However, whole-brain functional connectivity predicted neither an emotional enhancement effect (i.e., the degree to which emotionally positive or negative events are remembered better than neutral events) nor a positivity bias effect (i.e., the degree to which emotionally positive events are remembered better than negative events), failing to support our pre-registered hypotheses. These results imply a small or no association between individual differences in functional connectivity at rest and emotional memory, and support recent notions that resting-state functional connectivity is not always useful in predicting individual differences in behavioural measures.

Intelligenzdiagnostik der Zukunft

Zusammenfassung. Die menschliche Intelligenz ist eines der am besten erforschten und validierten Konstrukte innerhalb der Psychologie. Dennoch wird die Validität von Intelligenztests im gruppen- und insbesondere kulturvergleichenden Kontext regelmäßig und berechtigterweise kritisch hinterfragt. Obwohl verschiedene Alternativen und Weiterentwicklungen der Intelligenzdiagnostik vorgeschlagen wurden (z. B. kulturfaire Tests), sind fundamentale Probleme in der vergleichenden Intelligenzdiagnostik noch immer ungelöst und die Validitäten entsprechender Verfahren unklar. In dem vorliegenden Positionspapier wird diese Thematik aus der Perspektive der Kognitionspsychologie und der kognitiven Neurowissenschaften beleuchtet und eine prozessorientierte und biologisch inspirierte Form der Intelligenzdiagnostik als potentieller Lösungsansatz vorgeschlagen. Wir zeigen die Bedeutung elementarer kognitiver Prozesse auf (insbesondere Arbeitsgedächtniskapazität, Aufmerksamkeit, Verarbeitungsgeschwindigkeit), die individuellen Leistungsunterschieden zu Grunde liegen, und betonen, dass der Unterscheidung zwischen Inhalten und Prozessen eine zentrale, jedoch oft vernachlässigte Rolle in der Diagnostik allgemeiner kognitiver Leistungsunterschiede zukommt. Während aus kognitions- und neuropsychologischer Sicht davon ausgegangen werden kann, dass sich insbesondere Prozesse für interkulturelle Vergleiche eignen, sollten Inhalte als stärker kulturspezifisch verstanden werden. Darauf aufbauend diskutieren wir drei verschiedene Ansätze zur Verbesserung interkultureller Vergleichbarkeit der Intelligenzdiagnostik sowie deren Grenzen. Wir postulieren, dass sich die Intelligenzforschung im Austausch mit verschiedenen Disziplinen stärker auf die Identifikation von generellen kognitiven Prozessen fokussieren sollte und diskutieren das Potenzial zukünftiger Forschung hin zu einer prozessorientierten und biologisch inspirierten Intelligenzdiagnostik. Schließlich zeigen wir derzeitige Möglichkeiten auf, gehen aber auch auf etwaige Herausforderungen ein und beleuchten Implikationen für die zukünftige Intelligenzdiagnostik und -forschung.

Causal effects of cingulate morphology on executive functions in healthy young adults

In this study, we want to explore evidence for the causal relationship between the anatomical descriptors of the cingulate cortex (surface area, mean curvature-corrected thickness, and volume) and the performance of cognitive tasks such as Card Sort, Flanker, List Sort used as instruments to measure the executive functions of flexibility, inhibitory control, and working memory. We have performed this analysis in a cross-sectional sample of 899 healthy young subjects of the Human Connectome Project. To the best of our knowledge, this is the first study using causal inference to explain the relationship between cingulate morphology and the performance of executive tasks in healthy subjects. We have tested the causal model under a counterfactual framework using stabilized inverse probability of treatment weighting and marginal structural models. The results showed that the posterior cingulate surface area has a positive causal effect on inhibition (Flanker task) and cognitive flexibility (Card Sort). A unit increase (+1 mm² ) in the posterior cingulate surface area will cause a 0.008% and 0.009% increase from the National Institute of Health (NIH) normative mean in Flankers (p-value <0.001), and Card Sort (p-value 0.005), respectively. Furthermore, a unit increase (+1 mm² ) in the anterior cingulate surface area will cause a 0.004% (p-value <0.001) and 0.005% (p-value 0.001) increase from the NIH normative mean in Flankers and Card Sort. In contrast, the curvature-corrected-mean thickness only showed an association for anterior cingulate with List Sort (p = 0.034) but no causal effect.

Using resting thalamic connectivity to identify the relationship between Eysenck personality traits and intelligence in healthy adults

Personality refers to a set of relatively stable psychological characteristics of individuals and has been associated with intelligence. It is well known that the thalamus plays an important role in cognitive processes and personality traits, but the relationship between personality traits, thalamic function, and intelligence has rarely been directly explored. Hence, we investigated the relationship between Eysenck personality traits, resting-state functional connectivity (rsFC) of the thalamus, and intelligence in a large sample of healthy adults (N = 176). We found that the trait of psychoticism was negatively associated with intelligence. The high intelligence group showed significantly lower psychoticism and demonstrated enhanced thalamic connectivity to the amygdala, inferior parietal lobules, pallidum, medial superior/middle frontal gyrus, and precuneus. Furthermore, a mediation analysis indicated that the FC between the left thalamus and left amygdala significantly mediated the correlation between psychoticism and full IQ (FIQ). These findings suggest that intelligent people may be less prone to psychoticism. Meanwhile, thalamic rsFC may reflect individual differences in intelligence and play a key role in the relationship between personality traits and intellectual abilities.

Is Early Bilingual Experience Associated with Greater Fluid Intelligence in Adults?

Emerging evidence suggests that early bilingual experience constrains the development of attentional processes in infants, and that some of these early bilingual adaptations could last into adulthood. However, it is not known whether the early adaptations in the attentional domain alter more general cognitive abilities. If they do, then we would expect that bilingual adults who learned their second language early in life would score more highly across cognitive tasks than bilingual adults who learned their second language later in life. To test this hypothesis, 170 adult participants were administered a well-established (non-verbal) measure of fluid intelligence: Raven’s Advanced Progressive Matrices (RAPM). Fluid intelligence (the ability to solve novel reasoning problems, independent of acquired knowledge) is highly correlated with numerous cognitive abilities across development. Performance on the RAPM was greater in bilinguals than monolinguals, and greater in ‘early bilinguals’ (adults who learned their second language between 0–6 years) than ‘late bilinguals’ (adults who learned their second language after age 6 years). The groups did not significantly differ on a proxy of socioeconomic status. These results suggest that the difference in fluid intelligence between bilinguals and monolinguals is not a consequence of bilingualism per se, but of early adaptive processes. However, the finding may depend on how bilingualism is operationalized, and thus needs to be replicated with a larger sample and more detailed measures.

Evidence of discontinuity between psychosis-risk and non-clinical samples in the neuroanatomical correlates of social function

Objective

Social dysfunction is a major feature of clinical-high-risk states for psychosis (CHR-P). Prior research has identified a neuroanatomical pattern associated with impaired social function outcome in CHR-P. The aim of the current study was to test whether social dysfunction in CHR-P is neurobiologically distinct or in a continuum with the lower end of the normal distribution of individual differences in social functioning.

Methods

We used a machine learning classifier to test for the presence of a previously validated brain structural pattern associated with impaired social outcome in CHR-P (CHR-outcome-neurosignature) in the neuroimaging profiles of individuals from two non-clinical samples (total n = 1763) and examined its association with social function, psychopathology and cognition.

Results

Although the CHR-outcome-neurosignature could be detected in a subset of the non-clinical samples, it was not associated was adverse social outcomes or higher psychopathology levels. However, participants whose neuroanatomical profiles were highly aligned with the CHR-outcome-neurosignature manifested subtle disadvantage in fluid (P_FDR = 0.004) and crystallized intelligence (P_FDR = 0.01), cognitive flexibility (P_FDR = 0.02), inhibitory control (P_FDR = 0.01), working memory (P_FDR = 0.0005), and processing speed (P_FDR = 0.04).

Conclusions

We provide evidence of divergence in brain structural underpinnings of social dysfunction derived from a psychosis-risk enriched population when applied to non-clinical samples. This approach appears promising in identifying brain mechanisms bound to psychosis through comparisons of patient populations to non-clinical samples with the same neuroanatomical profiles.

Longitudinal changes in the neural oscillatory dynamics underlying abstract reasoning in children and adolescents

Fluid reasoning is the ability to problem solve in the absence of prior knowledge and is commonly conceptualized as “non-verbal” intelligence. Importantly, fluid reasoning abilities rapidly develop throughout childhood and adolescence. Although numerous studies have characterized the neural underpinnings of fluid reasoning in adults, there is a paucity of research detailing the developmental trajectory of this neural processing. Herein, we examine longitudinal changes in the neural oscillatory dynamics underlying fluid intelligence in a sample of typically developing youths. A total of 34 participants age 10 to 16 years-old completed an abstract reasoning task during magnetoencephalography (MEG) on two occasions set one year apart. We found robust longitudinal optimization in theta, beta, and gamma oscillatory activity across years of the study across a distributed network commonly implicated in fluid reasoning abilities. More specifically, activity tended to decrease longitudinally in additional, compensatory areas such as the right lateral prefrontal cortex and increase in areas commonly utilized in mature adult samples (e.g., left frontal and parietal cortices). Importantly, shifts in neural activity were associated with improvements in task performance from one year to the next. Overall, the data suggest a longitudinal shift in performance that is accompanied by a reconfiguration of the functional oscillatory dynamics serving fluid reasoning during this important period of development.

Spindle-Shaped Neurons in the Human Posteromedial (Precuneus) Cortex

The human posteromedial cortex (PMC), which includes the precuneus (PC), represents a multimodal brain area implicated in emotion, conscious awareness, spatial cognition, and social behavior. Here, we describe the presence of Nissl-stained elongated spindle-shaped neurons (suggestive of von Economo neurons, VENs) in the cortical layer V of the anterior and central PC of adult humans. The adapted "single-section" Golgi method for postmortem tissue was used to study these neurons close to pyramidal ones in layer V until merging with layer VI polymorphic cells. From three-dimensional (3D) reconstructed images, we describe the cell body, two main longitudinally oriented ascending and descending dendrites as well as the occurrence of spines from proximal to distal segments. The primary dendritic shafts give rise to thin collateral branches with a radial orientation, and pleomorphic spines were observed with a sparse to moderate density along the dendritic length. Other spindle-shaped cells were observed with straight dendritic shafts and rare branches or with an axon emerging from the soma. We discuss the morphology of these cells and those considered VENs in cortical areas forming integrated brain networks for higher-order activities. The presence of spindle-shaped neurons and the current discussion on the morphology of putative VENs address the need for an in-depth neurochemical and transcriptomic characterization of the PC cytoarchitecture. These findings would include these spindle-shaped cells in the synaptic and information processing by the default mode network and for general intelligence in healthy individuals and in neuropsychiatric disorders involving the PC in the context of the PMC functioning.

Personality Neuroscience: An Emerging Field with Bright Prospects

Personality neuroscience is the study of persistent psychological individual differences, typically in the general population, using neuroscientific methods. It has the potential to shed light on the neurobiological mechanisms underlying individual differences and their manifestation in ongoing behavior and experience. The field was inaugurated many decades ago, yet has only really gained momentum in the last two, as suitable technologies have become widely available. Personality neuroscience employs a broad range of methods, including molecular genetics, pharmacological assays or manipulations, electroencephalography, and various neuroimaging modalities, such as magnetic resonance imaging and positron emission tomography. Although exciting progress is being made in this young field, much remains unknown. In this brief review, we discuss discoveries that have been made, methodological challenges and advances, and important questions that remain to be answered. We also discuss best practices for personality neuroscience research and promising future directions for the field.