The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling

The Causal Relationships Between Gut Microbiota, Brain Volume, and Intelligence: A Two-Step Mendelian Randomization Analysis

BACKGROUND

Growing evidence indicates that dynamic changes in gut microbiome can affect intelligence; however, whether these relationships are causal remains elusive. We aimed to disentangle the poorly understood causal relationship between gut microbiota and intelligence.

METHODS

We performed a 2-sample Mendelian randomization (MR) analysis using genetic variants from the largest available genome-wide association studies of gut microbiota (N = 18,340) and intelligence (N = 269,867). The inverse-variance weighted method was used to conduct the MR analyses complemented by a range of sensitivity analyses to validate the robustness of the results. Considering the close relationship between brain volume and intelligence, we applied 2-step MR to evaluate whether the identified effect was mediated by regulating brain volume (N = 47,316).

RESULTS

We found a risk effect of the genus Oxalobacter on intelligence (odds ratio = 0.968 change in intelligence per standard deviation increase in taxa; 95% CI, 0.952-0.985; p = 1.88 × 10-4) and a protective effect of the genus Fusicatenibacter on intelligence (odds ratio = 1.053; 95% CI, 1.024-1.082; p = 3.03 × 10-4). The 2-step MR analysis further showed that the effect of genus Fusicatenibacter on intelligence was partially mediated by regulating brain volume, with a mediated proportion of 33.6% (95% CI, 6.8%-60.4%; p = .014).

CONCLUSIONS

Our results provide causal evidence indicating the role of the microbiome in intelligence. Our findings may help reshape our understanding of the microbiota-gut-brain axis and development of novel intervention approaches for preventing cognitive impairment.

Cognition and Cognitive Reserve

Cognition is a mental process that provides the ability to think, know, and learn. Though cognitive skills are necessary to do daily tasks and activities, cognitive aging causes changes in various cognitive functions. Cognitive abilities that are preserved and strengthened by experience can be kept as a reserve and utilized when necessary. The concept of reserving cognition was found when people with Alzheimer's disease had differences in clinical manifestations and cognitive functions. The cognitive reserve builds resilience against cognitive decline and improves the quality of life. Also, several lines of studies have found that the plasticity between neurons has a significant impact on cognitive reserve and acts against cognitive decline. To extend the findings, the present study provides a comprehensive understanding of cognitive reserve and the variables that are involved in maintaining cognition. The study also considers reading as one of the cognitive proxies that develops and maintains cognitive reserve.

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.

Bidirectional genetic overlap between autism spectrum disorder and cognitive traits

Autism spectrum disorder (ASD) is a highly heritable condition with a large variation in cognitive function. Here we investigated the shared genetic architecture between cognitive traits (intelligence (INT) and educational attainment (EDU)), and risk loci jointly associated with ASD and the cognitive traits. We analyzed data from genome-wide association studies (GWAS) of INT (n = 269,867), EDU (n = 766,345) and ASD (cases n = 18,381, controls n = 27,969). We used the bivariate causal mixture model (MiXeR) to estimate the total number of shared genetic variants, local analysis of co-variant annotation (LAVA) to estimate local genetic correlations, conditional false discovery rate (cond/conjFDR) to identify specific overlapping loci. The MiXeR analyses showed that 12.7k genetic variants are associated with ASD, of which 12.0k variants are shared with EDU, and 11.1k are shared with INT with both positive and negative relationships within overlapping variants. The majority (59-68%) of estimated shared loci have concordant effect directions, with a positive, albeit modest, genetic correlation between ASD and EDU (rg = 0.21, p = 2e-13) and INT (rg = 0.22, p = 4e-12). We discovered 43 loci jointly associated with ASD and cognitive traits (conjFDR<0.05), of which 27 were novel for ASD. Functional analysis revealed significant differential expression of candidate genes in the cerebellum and frontal cortex. To conclude, we quantified the genetic architecture shared between ASD and cognitive traits, demonstrated mixed effect directions, and identified the associated genetic loci and molecular pathways. The findings suggest that common genetic risk factors for ASD can underlie both better and worse cognitive functioning across the ASD spectrum, with different underlying biology.

Perinatal inflammation exposure and developmental outcomes 7 years after neonatal arterial ischaemic stroke

AIM

To test the association between perinatal inflammation exposure and Full-Scale IQ (FSIQ) score 7 years after neonatal arterial ischaemic stroke (NAIS).

METHOD

We conducted a cross-sectional ancillary study nested in a multicentric longitudinal French cohort of infants born at term with NAIS between November 2003 and October 2006. Seventy-three children were included (45 males, 28 females). The a priori defined primary outcome measure was the FSIQ score assessed with the Wechsler Intelligence Scale for Children, Fourth Edition at 7 years of age.

RESULTS

Seventeen (23%) of the included children were exposed to perinatal inflammation. Exposure to perinatal inflammation was independently associated with an increase of FSIQ score (coefficient 13.4, 95% confidence interval 1.3-25.4; p = 0.03). Children exposed to perinatal inflammation had a higher median cerebral volume, a lower median lesion volume, and less extensive lesion distributions compared to non-exposed children.

INTERPRETATION

We propose the existence of two NAIS categories: arteritis-associated NAIS in children exposed to perinatal inflammation and embolism-associated NAIS in children non-exposed to perinatal inflammation. Identifying these two NAIS categories would open the possibility for specific curative strategies: anti-inflammatory strategy in arteritis-associated NAIS and recanalization strategy in embolism-associated NAIS.

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.

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.

Measures of Brain Connectivity and Cognition by Sex in US Children

IMPORTANCE

The neurobiological underpinnings underlying sex differences in cognition during adolescence are largely unknown.

OBJECTIVE

To examine sex differences in brain circuitry and their association with cognitive performance in US children.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed behavioral and imaging data from 9- to 11-year-old children from the Adolescent Brain Cognitive Development (ABCD) study between August 2017 and November 2018. The ABCD study is an open-science, multisite study following up more than 11 800 youths into early adulthood for 10 years with annual laboratory-based assessments and biennial magnetic resonance imaging (MRI). The selection of ABCD study children for the current analysis was based on the availability of functional and structural MRI data sets in ABCD Brain Imaging Data Structure Community Collection format. Five hundred and sixty participants who had excessive level of head motion (>50% of time points with framewise displacement >0.5 mm) during resting-state functional MRI were excluded from the analyses. Data were analyzed between January and August 2022.

MAIN OUTCOMES AND MEASURES

The main outcomes were the sex differences in (A) global functional connectivity density at rest and (B) mean water diffusivity (MD) and (C) the correlation of these metrics with total cognitive scores.

RESULTS

A total of 8961 children (4604 boys and 4357 girls; mean [SD] age, 9.92 [0.62] years) were included in this analysis. Girls had higher functional connectivity density in default mode network hubs than boys, predominantly in the posterior cingulate cortex (Cohen d = -0.36), and lower MD and transverse diffusivity, predominantly in the superior corticostriatal white matter bundle (Cohen d = 0.3). Age-corrected fluid and total composite scores were higher for girls than for boys (Cohen d = -0.08 [fluid] and -0.04 [total]; P = 2.7 × 10-5). Although total mean (SD) brain volume (1260 [104] mL in boys and 1160 [95] mL in girls; t = 50; Cohen d = 1.0; df = 8738) and the proportion of white matter (d = 0.4) were larger for boys than for girls, the proportion of gray matter was larger for girls than for boys (d = -0.3; P = 2.2 × 10-16).

CONCLUSIONS AND RELEVANCE

The findings of this cross-sectional study on sex differences in brain connectivity and cognition are relevant to the future creation of brain developmental trajectory charts to monitor for deviations associated with impairments in cognition or behavior, including those due to psychiatric or neurological disorders. They could also serve as a framework for studies investigating the differential contribution of biological vs social or cultural factors in the neurodevelopmental trajectories of girls and boys.

Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects

Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.

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.

A Century of Behavioral Genetics at the University of Minnesota

The University of Minnesota has played an important role in the resurgence and eventual mainstreaming of human behavioral genetics in psychology and psychiatry. We describe this history in the context of three major movements in behavioral genetics: (1) radical eugenics in the early 20th century, (2) resurgence of human behavioral genetics in the 1960s, largely using twin and adoption designs to obtain more precise estimates of genetic and environmental influences on individual differences in behavior; and (3) use of measured genotypes to understand behavior. University of Minnesota scientists made significant contributions especially in (2) and (3) in the domains of cognitive ability, drug abuse and mental health, and endophenotypes. These contributions are illustrated through a historical perspective of major figures and events in behavioral genetics.

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.

Head circumference trajectories during the first two years of life and cognitive development, emotional, and behavior problems in adolescence: a cohort study

The associations of early-life head circumference (HC) with child neurodevelopmental and mental health among generally healthy population remain unclear. We aimed to examine the associations of early-life HC trajectories with cognitive development and emotional and behavioral problems in adolescence and to identify the HC growth-sensitive period. We conducted a prospective, community-based birth cohort study in rural western China, and 745 adolescents aged 10-14 years were followed between June and December 2016. We assessed their HC eight times during the first 2 years of life and their adolescent cognitive, emotional, and behavioral outcomes using the Wechsler Intelligence Scale for Children-IV and Youth Self-Report-2001, respectively. We applied group-based trajectory modeling to identify the HC trajectories and conditional growth to derive the HC growth-sensitive periods. We identified five distinct HC trajectories characterized as Start below average-then decrease (7.8% of the sample), Start below average-then increase (6.8%), Start average-then decrease (33%), Consistently average (38%), and Consistently above average (14%). Infants in the trajectory of consistently above average had higher cognitive scores in adolescence compared to those from suboptimal trajectories, with adjusted mean differences ranging from 2.84 to 8.99 points. The conditional gains showed that the HC growth-sensitive period was between 0 and 18 months for child cognition. We found null associations between HC measures and adolescent emotional and behavioral problem scores.

CONCLUSION

Early-life HC trajectories were associated with adolescent cognitive development. HC may serve as an inexpensive screening tool to monitor child development at risk during the first 18 months, particularly in resource-limited settings.

WHAT IS KNOWN

• Postnatal head circumference (HC) has been shown to be associated with cognitive development in infants who were born premature and/or fetal growth restriction, while inconsistent associations were reported among generally healthy populations, especially in low- and middle- income countries, challenging its utility in public health practices.

WHAT IS NEW

• Adolescents in the HC growth trajectory of consistently above average had higher cognitive scores compared to those with other suboptimal trajectories, while null findings were observed for adolescent emotional and behavioral health. • HC may serve as an inexpensive screening tool to monitor child development at risk during the first 18 months of life, particularly in resource-limited settings.

Cognition contra camouflage: How the brain mediates predator-driven crypsis evolution

While crypsis is a prominent antipredator adaptation, the role of the brain in predator-driven evolution remains controversial. Resolving this controversy requires contextualizing the brain with established antipredator traits and predation pressure. We hypothesize that the reduced predation risk through crypsis relaxes predation-driven selection on the brain and provide comparative evidence across 102 Chinese frog species for our hypothesis. Specifically, our phylogenetic path analysis reveals an indirect relationship between predation risk and crypsis that is mediated by brain size. This result suggests that at a low predation risk, frogs can afford to be conspicuous and use their large brain for cognitive predator evasion. This strategy may become less efficient or energetically costlier under higher predation pressure, favoring smaller brains and instead increasing crypsis.

Convergence and Divergence of Rare Genetic Disorders on Brain Phenotypes: A Review

IMPORTANCE

Rare genetic disorders modulating gene expression-as exemplified by gene dosage disorders (GDDs)-represent a collectively common set of high-risk factors for neuropsychiatric illness. Research on GDDs is rapidly expanding because these variants have high effect sizes and a known genetic basis. Moreover, the prevalence of recurrent GDDs (encompassing aneuploidies and certain copy number variations) enables genetic-first phenotypic characterization of the same GDD across multiple individuals, thereby offering a unique window into genetic influences on the human brain and behavior. However, the rapid growth of GDD research has unveiled perplexing phenotypic convergences and divergences across genomic loci; while phenotypic profiles may be specifically associated with a genomic variant, individual behavioral and neuroimaging traits appear to be nonspecifically influenced by most GDDs.

OBSERVATIONS

This complexity is addressed by (1) providing an accessible survey of genotype-phenotype mappings across different GDDs, focusing on psychopathology, cognition, and brain anatomy, and (2) detailing both methodological and mechanistic sources for observed phenotypic convergences and divergences. This effort yields methodological recommendations for future comparative phenotypic research on GDDs as well as a set of new testable hypotheses regarding aspects of early brain patterning that might govern the complex mapping of genetic risk onto phenotypic variation in neuropsychiatric disorders.

CONCLUSIONS AND RELEVANCE

A roadmap is provided to boost accurate measurement and mechanistic interrogation of phenotypic convergence and divergence across multiple GDDs. Pursuing the questions posed by GDDs could substantially improve our taxonomical, neurobiological, and translational understanding of neuropsychiatric illness.

Beetle Antennae Search: Using Biomimetic Foraging Behaviour of Beetles to Fool a Well-Trained Neuro-Intelligent System

Deep Convolutional Neural Networks (CNNs) represent the state-of-the-art artificially intelligent computing models for image classification. The advanced cognition and pattern recognition abilities possessed by humans are ascribed to the intricate and complex neurological connection in human brains. CNNs are inspired by the neurological structure of the human brain and show performance at par with humans in image recognition and classification tasks. On the lower extreme of the neurological complexity spectrum lie small organisms such as insects and worms, with simple brain structures and limited cognition abilities, pattern recognition, and intelligent decision-making abilities. However, billions of years of evolution guided by natural selection have imparted basic survival instincts, which appear as an “intelligent behavior”. In this paper, we put forward the evidence that a simple algorithm inspired by the behavior of a beetle (an insect) can fool CNNs in image classification tasks by just perturbing a single pixel. The proposed algorithm accomplishes this in a computationally efficient manner as compared to the other adversarial attacking algorithms proposed in the literature. The novel feature of the proposed algorithm as compared to other metaheuristics approaches for fooling a neural network, is that it mimics the behavior of a single beetle and requires fewer search particles. On the contrary, other metaheuristic algorithms rely on the social or swarming behavior of the organisms, requiring a large population of search particles. We evaluated the performance of the proposed algorithm on LeNet-5 and ResNet architecture using the CIFAR-10 dataset. The results show a high success rate for the proposed algorithms. The proposed strategy raises a concern about the robustness and security aspects of artificially intelligent learning systems.

Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method

Regional human brain volumes including total area, average thickness, and total volume are heritable and associated with neurological disorders. However, the genetic architecture of brain structure and function is still largely unknown and worthy of exploring. The Pediatric Imaging, Neurocognition, and Genetics (PING) data set provides an excellent resource with genome-wide genetic data and related neuroimaging data. In this study, we perform genome-wide association studies (GWAS) of 315 brain volumetric phenotypes from the PING data set including 1036 samples with 539,865 single-nucleotide polymorphisms (SNPs). We introduce a nonparametric test based on K-sample Ball Divergence (KBD) to identify genetic risk variants that influence regional brain volumes. We carry out simulations to demonstrate that KBD is a powerful test for identifying significant SNPs associated with multivariate phenotypes although controlling the type I error rate. We successfully identify nine SNPs below a significance level of 5 × 10^-5 for the PING data. Among the nine identified genetic variants, two SNPs rs486179 and rs562110 are located in the ADRA1A gene that is a well-known risk factor of mental illness, such as schizophrenia and attention deficit hyperactivity disorder. Our study suggests that the nonparametric test KBD is an effective method for identifying genetic variants associated with complex diseases in large-scale GWAS of multiple phenotypes.

In Vivo Amygdala Nuclei Volumes in Schizophrenia and Bipolar Disorders

Abnormalities in amygdala volume are well-established in schizophrenia and commonly reported in bipolar disorders. However, the specificity of volumetric differences in individual amygdala nuclei is largely unknown. Patients with schizophrenia disorders (SCZ, N = 452, mean age 30.7 ± 9.2 [SD] years, females 44.4%), bipolar disorders (BP, N = 316, 33.7 ± 11.4, 58.5%), and healthy controls (N = 753, 34.1 ± 9.1, 40.9%) underwent T1-weighted magnetic resonance imaging. Total amygdala, nuclei, and intracranial volume (ICV) were estimated with Freesurfer (v6.0.0). Analysis of covariance and multiple linear regression models, adjusting for age, age2, ICV, and sex, were fitted to examine diagnostic group and subgroup differences in volume, respectively. Bilateral total amygdala and all nuclei volumes, except the medial and central nuclei, were significantly smaller in patients relative to controls. The largest effect sizes were found for the basal nucleus, accessory basal nucleus, and cortico-amygdaloid transition area (partial η2 > 0.02). The diagnostic subgroup analysis showed that reductions in amygdala nuclei volume were most widespread in schizophrenia, with the lateral, cortical, paralaminar, and central nuclei being solely reduced in this disorder. The right accessory basal nucleus was marginally smaller in SCZ relative to BP (t = 2.32, P = .05). Our study is the first to demonstrate distinct patterns of amygdala nuclei volume reductions in a well-powered sample of patients with schizophrenia and bipolar disorders. Volume differences in the basolateral complex (lateral, basal, and accessory basal nuclei), an integral part of the threat processing circuitry, were most prominent in schizophrenia.

Mitochondrial Functions, Cognition, and the Evolution of Intelligence: Reply to Commentaries and Moving Forward

In response to commentaries, I address questions regarding the proposal that general intelligence (g) is a manifestation of the functioning of intramodular and intermodular brain networks undergirded by the efficiency of mitochondrial functioning (Geary 2018). The core issues include the relative contribution of mitochondrial functioning to individual differences in g; studies that can be used to test associated hypotheses; and, the adaptive function of intelligence from an evolutionary perspective. I attempt to address these and related issues, as well as note areas in which other issues remain to be addressed.

Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (rg) between BV and intelligence to be 0.24. We show that the rg is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV's shared genetic etiology with intelligence.

The Relevance of Ecological Transitions to Intelligence in Marine Mammals

Macphail's comparative approach to intelligence focused on associative processes, an orientation inconsistent with more multifaceted lay and scientific understandings of the term. His ultimate emphasis on associative processes indicated few differences in intelligence among vertebrates. We explore options more attuned to common definitions by considering intelligence in terms of richness of representations of the world, the interconnectivity of those representations, the ability to flexibly change those connections, and knowledge. We focus on marine mammals, represented by the amphibious pinnipeds and the aquatic cetaceans and sirenians, as animals that transitioned from a terrestrial existence to an aquatic one, experiencing major changes in ecological pressures. They adapted with morphological transformations related to streamlining the body, physiological changes in respiration and thermoregulation, and sensory/perceptual changes, including echolocation capabilities and diminished olfaction in many cetaceans, both in-air and underwater visual focus, and enhanced senses of touch in pinnipeds and sirenians. Having a terrestrial foundation on which aquatic capacities were overlaid likely affected their cognitive abilities, especially as a new reliance on sound and touch, and the need to surface to breath changed their interactions with the world. Vocal and behavioral observational learning capabilities in the wild and in laboratory experiments suggest versatility in group coordination. Empirical reports on aspects of intelligent behavior like problem-solving, spatial learning, and concept learning by various species of cetaceans and pinnipeds suggest rich cognitive abilities. The high energy demands of the brain suggest that brain-intelligence relationships might be fruitful areas for study when specific hypotheses are considered, e.g., brain mapping indicates hypertrophy of specific sensory areas in marine mammals. Modern neuroimaging techniques provide ways to study neural connectivity, and the patterns of connections between sensory, motor, and other cortical regions provide a biological framework for exploring how animals represent and flexibly use information in navigating and learning about their environment. At this stage of marine mammal research, it would still be prudent to follow Macphail's caution that it is premature to make strong comparative statements without more empirical evidence, but an approach that includes learning more about how animals flexibly link information across multiple representations could be a productive way of comparing species by allowing them to use their specific strengths within comparative tasks.

Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment

Genetic factors and socioeconomic status (SES) inequalities play a large role in educational attainment, and both have been associated with variations in brain structure and cognition. However, genetics and SES are correlated, and no prior study has assessed their neural associations independently. Here we used a polygenic score for educational attainment (EduYears-PGS), as well as SES, in a longitudinal study of 551 adolescents to tease apart genetic and environmental associations with brain development and cognition. Subjects received a structural MRI scan at ages 14 and 19. At both time points, they performed three working memory (WM) tasks. SES and EduYears-PGS were correlated (r = 0.27) and had both common and independent associations with brain structure and cognition. Specifically, lower SES was related to less total cortical surface area and lower WM. EduYears-PGS was also related to total cortical surface area, but in addition had a regional association with surface area in the right parietal lobe, a region related to nonverbal cognitive functions, including mathematics, spatial cognition, and WM. SES, but not EduYears-PGS, was related to a change in total cortical surface area from age 14 to 19. This study demonstrates a regional association of EduYears-PGS and the independent prediction of SES with cognitive function and brain development. It suggests that the SES inequalities, in particular parental education, are related to global aspects of cortical development, and exert a persistent influence on brain development during adolescence.

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

Mitochondria as the Linchpin of General Intelligence and the Link between g, Health, and Aging

In a recent theoretical article, I proposed that the efficiency of mitochondrial functioning is the most fundamental biological mechanism contributing to individual differences in general intelligence (g; Geary, 2018). The hypothesis accommodates other contributing mechanisms at higher levels of analysis (e.g., brain networks), and is attractive because mitochondrial energy production undergirds the developmental, maintenance, and expression of these other mechanisms and provides a means to link individual differences in g to individual differences in health and successful aging in adulthood. I provide a brief summation here and a few clarifications to the original article.