Structural brain imaging correlates of general intelligence in UK Biobank

Intelligence and cortical morphometry: caveats in brain-behavior associations

It is well-established that brain size is associated with intelligence. But the relationship between cortical morphometric measures and intelligence is unclear. Studies have produced conflicting results or no significant relations between intelligence and cortical morphometric measures such as cortical thickness and peri-cortical contrast. This discrepancy may be due to multicollinearity amongst the independent variables in a multivariate regression analysis, or a failure to fully account for the relationship between brain size and intelligence in some other way. Our study shows that neither cortical thickness nor peri-cortical contrast reliably improves IQ prediction accuracy beyond what is achieved with brain volume alone. We show this in multiple datasets, with child data, developmental data, and with adult data; we show this with data acquired either at multiple sites, or at a single site; we show this with data acquired with different MRI scanner manufacturers, or with all data acquired on a single scanner; and we show this with fluid intelligence, full-scale IQ, performance IQ, and verbal IQ. But our point is not really even about IQ; rather we proffer a methodological caveat and potential explanation of the discrepancies in previous results, and which applies broadly.

Lifestyle and psychosocial associations with cognition at the cusp of midlife using twins and siblings

INTRODUCTION

This study investigates the relationship between cognitive functioning and 59 modifiable and intrinsic factors at the cusp of midlife.

METHODS

We analyzed data from 1221 participants in the Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife; Mage = 33.20, %Female = 52.74). We assessed the impact of 59 factors on cognitive functioning using regularized regression and co-twin control models, controlling for earlier-life cognitive functioning and gray matter volume.

RESULTS

Eight robust factors were identified, including education attainment, cognitive complexity, purpose-in-life, and smoking status. Twins reporting higher levels of cognitive complexity and purpose-in-life showed better cognitive performance than their cotwin, while smoking was negatively associated. Using meta-analytically derived effect size threshold, we additionally identified that twins experiencing more financial difficulty tend to perform less well compared with their cotwin.

DISCUSSION

The findings highlight the early midlife link between cognitive functioning and lifestyle/psychological factors, beyond prior cognitive performance, brain status, genetic and familial confounders. Our results further highlight the potential of established adulthood as a crucial window for dementia prevention interventions targeting lifestyle and psychosocial factors.

Highlights

Cog complexity(+), purpose-in-life(+) were associated with cognition in early midlife.Smoking(-) was also associated with cognition in early midlife.Results were consistent controlling for genetic and environmental confounds.Association between EA and cognition might be mostly genetic and familial confounded.

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.

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.

Spontaneous neural activity in the three principal networks underlying delay discounting: a resting-state fMRI study

Delay discounting, the decline in the subjective value of future rewards over time, has traditionally been understood through a tripartite neural network model, comprising the valuation, cognitive control, and prospection networks. To investigate the applicability of this model in a resting-state context, we employed a monetary choice questionnaire to quantify delay discounting and utilized resting-state functional magnetic resonance imaging (rs-fMRI) to explore the role of spontaneous brain activity, specifically regional homogeneity (ReHo), in influencing individual differences in delay discounting across a large cohort (N = 257). Preliminary analyses revealed a significant negative correlation between delay discounting tendencies and the ReHo in both the left insula and the right hippocampus, respectively. Subsequent resting-state functional connectivity (RSFC) analyses, using these regions as seed ROIs, disclosed that all implicated brain regions conform to the three principal networks traditionally associated with delay discounting. Our findings offer novel insights into the role of spontaneous neural activity in shaping individual variations in delay discounting at both regional and network levels, providing the first empirical evidence supporting the applicability of the tripartite network model in a resting-state context.

Improved estimation of general cognitive ability and its neural correlates with a large battery of cognitive tasks

Elucidating the neural mechanisms of general cognitive ability (GCA) is an important mission of cognitive neuroscience. Recent large-sample cohort studies measured GCA through multiple cognitive tasks and explored its neural basis, but they did not investigate how task number, factor models, and neural data type affect the estimation of GCA and its neural correlates. To address these issues, we tested 1,605 Chinese young adults with 19 cognitive tasks and Raven's Advanced Progressive Matrices (RAPM) and collected resting state and n-back task fMRI data from a subsample of 683 individuals. Results showed that GCA could be reliably estimated by multiple tasks. Increasing task number enhances both reliability and validity of GCA estimates and reliably strengthens their correlations with brain data. The Spearman model and hierarchical bifactor model yield similar GCA estimates. The bifactor model has better model fit and stronger correlation with RAPM but explains less variance and shows weaker correlations with brain data than does the Spearman model. Notably, the n-back task-based functional connectivity patterns outperform resting-state fMRI in predicting GCA. These results suggest that GCA derived from a multitude of cognitive tasks serves as a valid measure of general intelligence and that its neural correlates could be better characterized by task fMRI than resting-state fMRI data.

Vascular risk factors, white matter microstructure, and depressive symptoms: a longitudinal analysis in the UK Biobank

BACKGROUND

Cumulative burden from vascular risk factors (VRFs) has been associated with an increased risk of depressive symptoms in mid- and later life. It has been hypothesised that this association arises because VRFs disconnect fronto-subcortical white matter tracts involved in mood regulation, which puts older adults at higher risk of developing depressive symptoms. However, evidence for the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms from longitudinal studies is scarce.

METHODS

This preregistered study analysed longitudinal data from 6,964 middle-aged and older adults from the UK Biobank who participated in consecutive assessments of VRFs, brain imaging, and depressive symptoms. Using mediation modelling, we directly tested to what extend white matter microstructure mediates the longitudinal association between VRF burden and depressive symptoms.

RESULTS

VRF burden showed a small association with depressive symptoms at follow-up. However, there was no evidence that fractional anisotropy (FA) of white matter tracts mediated this association. Additional analyses also yielded no mediating effects using alternative operationalisations of VRF burden, mean diffusivity (MD) of single tracts, or overall average of tract-based white matter microstructure (global FA, global MD, white matter hyperintensity volume).

CONCLUSIONS

Our results lend no support to the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms, while highlighting the relevance of using longitudinal data to directly test pathways linking vascular and mental health.

A bipolar taxonomy of adult human brain sulcal morphology related to timing of fetal sulcation and trans-sulcal gene expression gradients

We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (N=34,725; 45-82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bipolar distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex; complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (N=228; 21-36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.

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.

Genetic and neural bases of the neuroticism general factor

We applied structural equation modeling to conduct a genome-wide association study (GWAS) of the general factor measured by a neuroticism questionnaire administered to ∼380,000 participants in the UK Biobank. We categorized significant genetic variants as acting either through the neuroticism general factor, through other factors measured by the questionnaire, or through paths independent of any factor. Regardless of this categorization, however, significant variants tended to show concordant associations with all items. Bioinformatic analysis showed that the variants associated with the neuroticism general factor disproportionately lie near or within genes expressed in the brain. Enriched gene sets pointed to an underlying biological basis associated with brain development, synaptic function, and behaviors in mice indicative of fear and anxiety. Psychologists have long asked whether psychometric common factors are merely a convenient summary of correlated variables or reflect coherent causal entities with a partial biological basis, and our results provide some support for the latter interpretation. Further research is needed to determine the extent to which causes resembling common factors operate alongside other mechanisms to generate the correlational structure of personality.

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

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.

C-Reactive protein and cognition: Mediation analyses with brain morphology in the UK Biobank

Cognitive impairments and abnormal immune activity are both associated with various clinical disorders. The association between C-Reactive protein (CRP), a marker associated with inflammation, and cognitive performance remains unclear. Further, mechanisms potentially linking CRP to cognition are not yet established. Brain structure may well mediate this relationship: immune processes play crucial roles in shaping and maintaining brain structure, with brain structure and function driving cognition. The United Kingdom Biobank (UKBB) is a large cohort study with extensive assessments, including high-sensitivity serum CRP levels, brain imaging, and various cognitive tasks. With data from 39,200 UKBB participants, we aimed first to determine the relationship between CRP and cognitive performance, and second, to assess metrics of brain morphology as potential mediators in this relationship. Participants were aged 40 to 70 at initial assessment and were mostly Caucasian. After accounting for potential covariates (e.g., age, sex, medical diagnoses, use of selective-serotonin reuptake inhibitors), we found CRP levels to have small, negative associations with fluid intelligence (b = -0.03, 95%CI[-0.05,-0.02], t(14381) = -3.62, p_cor = .004), and numeric memory (b = -0.03, 95%CI[-0.05,-0.01], t(14366) = -3.31, p_cor = .007). We found no evidence of brain morphology mediating these relationships (all |ab| < 0.001, all p_cor > .55). Our findings from this large sample suggest that serum-assessed CRP is of marginal importance for cognitive performance in mid-to-late aged Caucasians; the small effect sizes of statistically significant associations provide context to previous inconsistent results. The seeming lack of involvement of brain morphology suggests that other brain metrics (e.g., connectivity, functional activation) may be more pertinent to this relationship. Future work should also consider CRP levels measured in the central nervous system and/or other cytokines that may better predict cognitive performance in this population.

Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.

Anticholinergic burden in middle and older age is associated with lower cognitive function, but not with brain atrophy

AIMS

The aim of this study is to estimate the association between anticholinergic burden, general cognitive ability and various measures of brain structural MRI in relatively healthy middle-aged and older individuals.

METHODS

In the UK Biobank participants with linked health-care records (n = 163,043, aged 40-71 at baseline), of whom about 17 000 had MRI data available, we calculated the total anticholinergic drug burden according to 15 different anticholinergic scales and due to different classes of drugs. We then used linear regression to explore the associations between anticholinergic burden and various measures of cognition and structural MRI, including general cognitive ability, 9 separate cognitive domains, brain atrophy, volumes of 68 cortical and 14 subcortical areas and fractional anisotropy and median diffusivity of 25 white-matter tracts.

RESULTS

Anticholinergic burden was modestly associated with poorer cognition across most anticholinergic scales and cognitive tests (7/9 FDR-adjusted significant associations, standardised betas (β) range: -0.039, -0.003). When using the anticholinergic scale exhibiting the strongest association with cognitive functions, anticholinergic burden due to only some classes of drugs exhibited negative associations with cognitive function, with β-lactam antibiotics (β = -0.035, PFDR < 0.001) and opioids (β = -0.026, PFDR < 0.001) exhibiting the strongest effects. Anticholinergic burden was not associated with any measure of brain macrostructure or microstructure (PFDR > 0.08).

CONCLUSIONS

Anticholinergic burden is weakly associated with poorer cognition, but there is little evidence for associations with brain structure. Future studies might focus more broadly on polypharmacy or more narrowly on distinct drug classes, instead of using purported anticholinergic action to study the effects of drugs on cognitive ability.

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.

General dimensions of human brain morphometry inferred from genome-wide association data

Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2  = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg  = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.

The Misguided Veneration of Averageness in Clinical Neuroscience: A Call to Value Diversity over Typicality

Research and practice in clinical neurosciences often involve cognitive assessment. However, this has traditionally used a nomothetic approach, comparing the performance of patients to normative samples. This method of defining abnormality places the average test performance of neurologically healthy individuals at its center. However, evidence suggests that neurological 'abnormalities' are very common, as is the diversity of cognitive abilities. The veneration of central tendency in cognitive assessment, i.e., equating typicality with healthy or ideal, is, I argue, misguided on neurodiversity, bio-evolutionary, and cognitive neuroscientific grounds. Furthermore, the use of average performance as an anchor point for normal performance is unreliable in practice and frequently leads to the mischaracterization of cognitive impairments. Examples are explored of how individuals who are already vulnerable for socioeconomic reasons can easily be over-pathologized. At a practical level, by valuing diversity rather than typicality, cognitive assessments can become more idiographic and focused on change at the level of the individual. The use of existing methods that approach cognitive assessment ideographically is briefly discussed, including premorbid estimation methods and informant reports. Moving the focus away from averageness to valuing diversity for both clinical cognitive assessments and inclusion of diverse groups in research is, I argue, a more just and effective way forward for clinical neurosciences.

Cortical thickness, gray matter volume, and cognitive performance: a crosssectional study of the moderating effects of age on their interrelationships

In a sample comprising younger, middle-aged, and older cognitively healthy adults (N = 375), we examined associations between mean cortical thickness, gray matter volume (GMV), and performance in 4 cognitive domains-memory, speed, fluency, and crystallized intelligence. In almost all cases, the associations were moderated significantly by age, with the strongest associations in the older age group. An exception to this pattern was identified in a younger adult subgroup aged <23 years when a negative association between cognitive performance and cortical thickness was identified. Other than for speed, all associations between structural metrics and performance in specific cognitive domains were fully mediated by mean cognitive ability. Cortical thickness and GMV explained unique fractions of the variance in mean cognitive ability, speed, and fluency. In no case, however, did the amount of variance jointly explained by the 2 metrics exceed 7% of the total variance. These findings suggest that cortical thickness and GMV are distinct correlates of domain-general cognitive ability, that the strength and, for cortical thickness, the direction of these associations are moderated by age, and that these structural metrics offer only limited insights into the determinants of individual differences in cognitive performance across the adult lifespan.

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 at different stages of the life course is poorly understood. Utilizing the Lothian Birth Cohort 1936, we explored the relationship between residential neighbourhood deprivation from birth to late adulthood, and global and regional neuroimaging measures at age 73. We found that residing in disadvantaged neighbourhoods in mid- to late adulthood was associated with smaller total brain (β=-0.06; SE=0.02; n=390) and grey matter volume (β=-0.11; SE=0.03; n=390), thinner cortex (β=-0.15; SE=0.06; n=379), and lower general white matter fractional anisotropy (β=-0.19; SE=0.06; n=388). Regional analysis identified affected focal cortical areas and specific white matter tracts. Among individuals belonging to lower occupational social classes, the brain-neighbourhood associations were stronger, with the impact of neighbourhood deprivation accumulating across the life course. Our findings suggest that living in deprived neighbourhoods is associated with adverse brain morphologies, with occupational social class adding to the vulnerability.

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

White matter, cognition and psychotic-like experiences in UK Biobank

BACKGROUND

Psychotic-like experiences (PLEs) are risk factors for the development of psychiatric conditions like schizophrenia, particularly if associated with distress. As PLEs have been related to alterations in both white matter and cognition, we investigated whether cognition (g-factor and processing speed) mediates the relationship between white matter and PLEs.

METHODS

We investigated two independent samples (6170 and 19 891) from the UK Biobank, through path analysis. For both samples, measures of whole-brain fractional anisotropy (gFA) and mean diffusivity (gMD), as indications of white matter microstructure, were derived from probabilistic tractography. For the smaller sample, variables whole-brain white matter network efficiency and microstructure were also derived from structural connectome data.

RESULTS

The mediation of cognition on the relationships between white matter properties and PLEs was non-significant. However, lower gFA was associated with having PLEs in combination with distress in the full available sample (standardized β = -0.053, p = 0.011). Additionally, lower gFA/higher gMD was associated with lower g-factor (standardized β = 0.049, p < 0.001; standardized β = -0.027, p = 0.003), and partially mediated by processing speed with a proportion mediated of 7% (p = < 0.001) for gFA and 11% (p < 0.001) for gMD.

CONCLUSIONS

We show that lower global white matter microstructure is associated with having PLEs in combination with distress, which suggests a direction of future research that could help clarify how and why individuals progress from subclinical to clinical psychotic symptoms. Furthermore, we replicated that processing speed mediates the relationship between white matter microstructure and g-factor.

Associations between right inferior frontal gyrus morphometry and inhibitory control in individuals with nicotine dependence

BACKGROUND

The hyperdirect pathway - a circuit involved in executing inhibitory control (IC) - is dysregulated among individuals with nicotine dependence. The right inferior frontal gyrus (rIFG), a cortical input to the hyperdirect circuit, has been shown to be functionally and structurally altered among nicotine-dependent people who smoke. The rIFG is composed of 3 cytoarchitecturally distinct subregions: The pars opercularis, pars triangularis, and pars orbitalis. The present study assessed the relationship between rIFG subregion morphometry and inhibitory control among individuals with nicotine dependence.

METHODS

Behavioral and magnetic resonance brain imaging (MRI) data from 127 nicotine-dependent adults who smoke (MFTND = 5.4, ± 1.9; MCPD = 18.3, ± 7.0; Myears = 25.04, ± 11.97) (Mage = 42.9, ± 11.1) were assessed. Brain morphometry was assessed from T1-weighted MRIs using Freesurfer. IC was assessed with a response-inhibition Go/Go/No-Go (GGNG) task and a smoking relapse analog task (SRT).

RESULTS AND CONCLUSIONS

Vertex-wise analyses revealed that GGNG task scores were positively associated with cortical thickness and volume in the right pars triangularis (cluster-wise p = 0.006, 90% CI = 0.003 - 0.009; cluster-wise p = 0.040, 90% CI = 0.032 - 0.048), and the ability to inhibit ad lib smoking during the SRT was positively associated with cortical thickness in the right pars orbitalis (cluster-wise p = 0.011, 90% CI = 0.007 - 0.015). Our results indicate that cortical thickness of distinct rIFG subregions may serve as biomarkers for unique forms of IC deficits.

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.

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.

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.

Associations between Socioeconomic Status, Cognition, and Brain Structure: Evaluating Potential Causal Pathways Through Mechanistic Models of Development

Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts themselves must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences.

A quantified comparison of cortical atlases on the basis of trait morphometricity

BACKGROUND

Many different brain atlases exist that subdivide the human cortex into dozens or hundreds of regions-of-interest (ROIs). Inconsistency across studies using one or another cortical atlas may contribute to the replication crisis across the neurosciences.

METHODS

Here, we provide a quantitative comparison between seven popular cortical atlases (Yeo, Desikan-Killiany, Destrieux, Jülich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcellation retains the most information in the analysis of behavioural traits (incl. age, sex, body mass index, and cognitive ability) in the UK Biobank sample (N∼40,000). We use linear mixed models to compare whole-brain morphometricity; the proportion of trait variance accounted for when using a given atlas.

RESULTS

Commonly-used atlases resulted in a considerable loss of information compared to vertex-wise representations of cortical structure. Morphometricity increased linearly as a function of the log-number of ROIs included in an atlas, indicating atlas-based analyses miss many true associations and yield limited prediction accuracy. Likelihood ratio tests revealed that low-dimensional atlases accounted for unique trait variance rather than variance common between atlases, suggesting that previous studies likely returned atlas-specific findings. Finally, we found that the commonly-used atlases yielded brain-behaviour associations on par with those obtained with random parcellations, where specific region boundaries were randomly generated.

DISCUSSION

Our findings motivate future structural neuroimaging studies to favour vertex-wise cortical representations over coarser atlases, or to consider repeating analyses across multiple atlases, should the use of low-dimensional atlases be necessary. The insights uncovered here imply that cortical atlas choices likely contribute to the lack of reproducibility in ROI-based studies.

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.

Brain structural parameters correlate with University Selection Test outcomes in Chilean high school graduates

How well students learn and perform in academic contexts is a focus of interest for the students, their families, and the entire educational system. Although evidence has shown that several neurobiological factors are involved in scholastic achievement (SA), specific brain measures associated with academic outcomes and whether such associations are independent of other factors remain unclear. This study attempts to identify the relationship between brain structural parameters, and the Chilean national University Selection Test (PSU) results in high school graduates within a multidimensional approach that considers socio-economic, intellectual, nutritional, and demographic variables. To this end, the brain morphology of a sample of 102 students who took the PSU test was estimated using Magnetic Resonance Imaging. Anthropometric parameters, intellectual ability (IA), and socioeconomic status (SES) were also measured. The results revealed that, independently of sex, IA, gray matter volume, right inferior frontal gyrus thickness, and SES were significantly associated with SA. These findings highlight the role of nutrition, health, and socioeconomic variables in academic success.

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.

Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging

Background

Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures.

Methods

MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD.

Findings

Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala.

Conclusion

Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.

Brain aging differs with cognitive ability regardless of education

Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal cognitively healthy brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20-88 years, followed-up for up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan.

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, we report an integrated DNA methylation and phenotypic study of the circulating proteome in relation to brain health. Methylome-wide association studies of 4058 plasma proteins are performed (N = 774), identifying 2928 CpG-protein associations after adjustment for multiple testing. These are independent of known genetic protein quantitative trait loci (pQTLs) and common lifestyle effects. Phenome-wide association studies of each protein are then performed in relation to 15 neurological traits (N = 1,065), identifying 405 associations between the levels of 191 proteins and cognitive scores, brain imaging measures or APOE e4 status. We uncover 35 previously unreported DNA methylation signatures for 17 protein markers of brain health. The epigenetic and proteomic markers we identify are pertinent to understanding and stratifying brain health.

Empirical facts from search for replicable associations between cortical thickness and psychometric variables in healthy adults

The study of associations between inter-individual differences in brain structure and behaviour has a long history in psychology and neuroscience. Many associations between psychometric data, particularly intelligence and personality measures and local variations of brain structure have been reported. While the impact of such reported associations often goes beyond scientific communities, resonating in the public mind, their replicability is rarely evidenced. Previously, we have shown that associations between psychometric measures and estimates of grey matter volume (GMV) result in rarely replicated findings across large samples of healthy adults. However, the question remains if these observations are at least partly linked to the multidetermined nature of the variations in GMV, particularly within samples with wide age-range. Therefore, here we extended those evaluations and empirically investigated the replicability of associations of a broad range of psychometric variables and cortical thickness in a large cohort of healthy young adults. In line with our observations with GMV, our current analyses revealed low likelihood of significant associations and their rare replication across independent samples. We here discuss the implications of these findings within the context of accumulating evidence of the general poor replicability of structural-brain-behaviour associations, and more broadly of the replication crisis.

Mechanisms linking obesity and its metabolic comorbidities with cerebral grey and white matter changes

Obesity is a preventable risk factor for cerebrovascular disorders and it is associated with cerebral grey and white matter changes. Specifically, individuals with obesity show diminished grey matter volume and thickness, which seems to be more prominent among fronto-temporal regions in the brain. At the same time, obesity is associated with lower microstructural white matter integrity, and it has been found to precede increases in white matter hyperintensity load. To date, however, it is unclear whether these findings can be attributed solely to obesity or whether they are a consequence of cardiometabolic complications that often co-exist with obesity, such as low-grade systemic inflammation, hypertension, insulin resistance, or dyslipidemia. In this narrative review we aim to provide a comprehensive overview of the potential impact of obesity and a number of its cardiometabolic consequences on brain integrity, both separately and in synergy with each other. We also identify current gaps in knowledge and outline recommendations for future research.

Extended gene set analysis of human neuro-psychiatric traits shows enrichment in brain-expressed human accelerated regions across development

Human neuropsychiatric disorders are associated with genetic and environmental factors affecting the brain, which has been subjected to strong evolutionary pressures resulting in an enlarged cerebral cortex and improved cognitive performance. Thus, genes involved in human brain evolution may also play a role in neuropsychiatric disorders. We test whether genes associated with 7 neuropsychiatric phenotypes are enriched in genomic regions that have experienced rapid changes in human evolution (HARs) and importantly, whether HAR status interacts with developmental brain expression to predict associated genes. We used the most recent publicly available GWAS and gene expression data to test for enrichment of HARs, brain expression, and their interaction. These revealed significant interactions between HAR status and whole-brain expression across developmental stages, indicating that the relationship between brain expression and association with schizophrenia and intelligence is stronger among HAR than non-HAR genes. Follow-up regional analyses indicated that predicted HAR-expression interaction effects may vary substantially across regions and developmental stages. Although depression indicated significant enrichment of HAR genes, little support was found for HAR enrichment among bipolar, autism, ADHD, or Alzheimer's associated genes. Our results indicate that intelligence, schizophrenia, and depression-associated genes are enriched for those involved in the evolution of the human brain. These findings highlight promising candidates for follow-up study and considerations for novel drug development, but also caution careful assessment of the translational ability of animal models for studying neuropsychiatric traits in the context of HARs, and the importance of using humanized animal models or human-derived tissues when researching these traits.

General factors of white matter microstructure from DTI and NODDI in the developing brain

Preterm birth is closely associated with diffuse white matter dysmaturation inferred from diffusion MRI and neurocognitive impairment in childhood. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are distinct dMRI modalities, yet metrics derived from these two methods share variance across tracts. This raises the hypothesis that dimensionality reduction approaches may provide efficient whole-brain estimates of white matter microstructure that capture (dys)maturational processes. To investigate the optimal model for accurate classification of generalised white matter dysmaturation in preterm infants we assessed variation in DTI and NODDI metrics across 16 major white matter tracts using principal component analysis and structural equation modelling, in 79 term and 141 preterm infants at term equivalent age. We used logistic regression models to evaluate performances of single-metric and multimodality general factor frameworks for efficient classification of preterm infants based on variation in white matter microstructure. Single-metric general factors from DTI and NODDI capture substantial shared variance (41.8-72.5%) across 16 white matter tracts, and two multimodality factors captured 93.9% of variance shared between DTI and NODDI metrics themselves. General factors associate with preterm birth and a single model that includes all seven DTI and NODDI metrics provides the most accurate prediction of microstructural variations associated with preterm birth. This suggests that despite global covariance of dMRI metrics in neonates, each metric represents information about specific (and additive) aspects of the underlying microstructure that differ in preterm compared to term subjects.

Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study

Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.

The Personal Trait of Spiritual Growth Is Correlated With the White Matter Integrity of the Brain

Determining the relationship between the entire brain structure and individual differences is important in extending healthy life expectancy, which can be affected by brain atrophy. The entire brain structure has been gradually known to be correlated not only with age but also with individual differences, such as quality of life, general intelligence, and lifestyle. However, little attention has been paid to the relationship between the entire brain structure and personal traits. We herein focused on one personal trait, namely spiritual growth, and examined its relationship with the entire brain structure using two neuroimaging-derived measures, namely the gray matter Brain Healthcare Quotient (GM-BHQ), a measure of GM volume, and the fractional anisotropy Brain Healthcare Quotient (FA-BHQ), a measure of white matter (WM) integrity, in 229 healthy participants (53 female, 176 male). The results indicated no significant relationship between the GM-BHQ and spiritual growth, but there was a significant positive correlation between the FA-BHQ and spiritual growth after controlling for age, sex, and body mass index (BMI) with partial correlation analysis. Furthermore, multiple regression analysis revealed a significant positive correlation between the FA-BHQ and spiritual growth after controlling for physical characteristics, such as age, sex, and BMI, as well as other variables related to lifestyle that were collected using the Health-Promoting Lifestyle Profile. These results support the idea that there is a relationship between the entire WM brain structure and spiritual growth. Further studies are required to clarify the causal relationship between the entire WM brain structure and spiritual growth with some interventions to improve spiritual growth. Such studies will help extend healthy life expectancy from a new perspective of personal trait.

Of differing methods, disputed estimates and discordant interpretations: the meta-analytical multiverse of brain volume and IQ associations

Brain size and IQ are positively correlated. However, multiple meta-analyses have led to considerable differences in summary effect estimations, thus failing to provide a plausible effect estimate. Here we aim at resolving this issue by providing the largest meta-analysis and systematic review so far of the brain volume and IQ association (86 studies; 454 effect sizes from k = 194 independent samples; N = 26 000+) in three cognitive ability domains (full-scale, verbal, performance IQ). By means of competing meta-analytical approaches as well as combinatorial and specification curve analyses, we show that most reasonable estimates for the brain size and IQ link yield r-values in the mid-0.20s, with the most extreme specifications yielding rs of 0.10 and 0.37. Summary effects appeared to be somewhat inflated due to selective reporting, and cross-temporally decreasing effect sizes indicated a confounding decline effect, with three quarters of the summary effect estimations according to any reasonable specification not exceeding r = 0.26, thus contrasting effect sizes were observed in some prior related, but individual, meta-analytical specifications. Brain size and IQ associations yielded r = 0.24, with the strongest effects observed for more g-loaded tests and in healthy samples that generalize across participant sex and age bands.

Linking interindividual variability in brain structure to behaviour

What are the brain structural correlates of interindividual differences in behaviour? More than a decade ago, advances in structural MRI opened promising new avenues to address this question. The initial wave of research then progressively led to substantial conceptual and methodological shifts, and a replication crisis unveiled the limitations of traditional approaches, which involved searching for associations between local measurements of neuroanatomy and behavioural variables in small samples of healthy individuals. Given these methodological issues and growing scepticism regarding the idea of one-to-one mapping of psychological constructs to brain regions, new perspectives emerged. These not only embrace the multivariate nature of brain structure-behaviour relationships and promote generalizability but also embrace the representation of the relationships between brain structure and behavioural data by latent dimensions of interindividual variability. Here, we examine the past and present of the study of brain structure-behaviour associations in healthy populations and address current challenges and open questions for future investigations.

The Resting-State Neural Network of Delay Discounting

Delay discounting is a common phenomenon in daily life, which refers to the subjective value of a future reward decreasing as a function of time. Previous studies have identified several cortical regions involved in delay discounting, but the neural network constructed by the cortical regions of delay discounting is less clear. In this study, we employed resting-state functional magnetic resonance imaging (RS-fMRI) to measure the spontaneous neural activity in a large sample of healthy young adults and used the Monetary Choice Questionnaire to directly measure participants’ level of delay discounting. To identify the neural network of delay discounting at rest, we used an individual difference approach to explore brain regions whose spontaneous activities were related to delay discounting across the whole brain. Then, these brain regions served as seeds to identify the neural network of delay discounting. We found that the fractional amplitude of low-frequency fluctuations (fALFF) of the left insula were positively correlated to delay discounting. More importantly, its connectivity to the anterior cingulate cortex was read out for participants’ behavioral performance in the task of delay discounting. In short, our study provides empirical evidence that insula-anterior cingulate cortex connectivity may serve as a part of the neural network for delay discounting.

Structural brain differences do not mediate the relations between sex and personality or psychopathology

INTRODUCTION

Males and females tend to exhibit small but reliable differences in personality traits and indices of psychopathology that are relatively stable over time and across cultures. Previous work suggests that sex differences in brain structure account for differences in domains of cognition.

METHODS

We used data from the Human Connectome Project (N = 1098) to test whether sex differences in brain morphometry account for observed differences in the personality traits neuroticism and agreeableness, as well as symptoms of internalizing and externalizing psychopathology. We operationalized brain morphometry in three ways: omnibus measures (e.g., total gray matter volume), Glasser regions defined through a multi-modal parcellation approach, and Desikan regions defined by structural features of the brain.

RESULTS

Most expected sex differences in personality, psychopathology, and brain morphometry were observed, but the statistical mediation analyses were null: sex differences in brain morphometry did not account for sex differences in personality or psychopathology.

CONCLUSIONS

Men and women tend to exhibit meaningful differences in personality and psychopathology, as well as in omnibus morphometry and regional morphometric differences as defined by the Glasser and Desikan atlases, but these morphometric differences appear unrelated to the psychological differences.

Age- and Sex-Related Topological Organization of Human Brain Functional Networks and Their Relationship to Cognition

Age and sex associated with changes in the functional brain network topology and cognition in large population of older adults have been poorly understood. We explored this question further by examining differences in 11 resting-state graph theory measures with respect to age, sex, and their relationships with cognitive performance in 17,127 United Kingdom Biobank participants (mean = 62.83 ± 7.41 years). Age was associated with an overall decrease in the effectiveness of network communication (i.e., integration) and loss of functional specialization (i.e., segregation) of specific brain regions. Sex differences were also observed, with women showing more efficient networks, which were less segregated than in men (FDR adjusted p < 0.05). The age-related changes were also more apparent in men than in women, which suggests that men may be more vulnerable to cognitive decline with age. Interestingly, while network segregation and strength of limbic network were only nominally associated with cognitive performance, the network measures collectively were significantly associated with cognition (FDR adjusted p ≤ 0.002). This may imply that individual measures may be inadequate to capture much of the variance in the neural activity or its output and need further refinement. The complexity of the organization of the functional brain may be shaped by the age and sex of an individual, which ultimately may influence the cognitive performance of older adults. Age and sex stratification may be used to inform clinical neuroscience research to identify older adults at risk of cognitive dysfunction.

Brain and cognitive ageing: The present, and some predictions (…about the future)

Experiencing decline in one's cognitive abilities is among the most feared aspects of growing old [53]. Age-related cognitive decline carries a huge personal, societal, and financial cost both in pathological ageing (such as dementias) and also within the non-clinical majority of the population. A projected 152 million people worldwide will suffer from dementia by 2050 [3]. The early stages of cognitive decline are much more prevalent than dementia, and can still impose serious limitations of performance on everyday activities, independence, and quality of life in older age [5], [60], [80]. Cognitive decline also predicts poorer health, adherence to medical regimens, and financial decision-making, and can herald dementia, illness, and death [6], [40]. Of course, when seeking to understand why some people experience more severe cognitive ageing than others, researchers have turned to the organ of thinking for clues about the nature, possible mechanisms, and determinants that might underpin more and less successful cognitive agers. However, that organ is relatively inaccessible, a limitation partly alleviated by advances in neuroimaging. Here we discuss lessons for cognitive and brain ageing that have come from neuroimaging research (especially structural brain imaging), what neuroimaging still has left to teach us, and our views on possible ways forward in this multidisciplinary field.

Genetic variation, brain, and intelligence differences

Individual differences in human intelligence, as assessed using cognitive test scores, have a well-replicated, hierarchical phenotypic covariance structure. They are substantially stable across the life course, and are predictive of educational, social, and health outcomes. From this solid phenotypic foundation and importance for life, comes an interest in the environmental, social, and genetic aetiologies of intelligence, and in the foundations of intelligence differences in brain structure and functioning. Here, we summarise and critique the last 10 years or so of molecular genetic (DNA-based) research on intelligence, including the discovery of genetic loci associated with intelligence, DNA-based heritability, and intelligence's genetic correlations with other traits. We summarise new brain imaging-intelligence findings, including whole-brain associations and grey and white matter associations. We summarise regional brain imaging associations with intelligence and interpret these with respect to theoretical accounts. We address research that combines genetics and brain imaging in studying intelligence differences. There are new, though modest, associations in all these areas, and mechanistic accounts are lacking. We attempt to identify growing points that might contribute toward a more integrated 'systems biology' account of some of the between-individual differences in intelligence.