Shared genetic aetiology between cognitive performance and brain activations in language and math tasks

A behavioural exploration of language aptitude and experience, cognition and more using Graph Analysis

Language aptitude has recently regained interest in cognitive neuroscience. Traditional language aptitude testing included phonemic coding ability, associative memory, grammatical sensitivity and inductive language learning. Moreover, domain-general cognitive abilities are associated with individual differences in language aptitude, together with factors that have yet to be elucidated. Beyond domain-general cognition, it is also likely that aptitude and experience in domain-specific but non-linguistic fields (e.g. music or numerical processing) influence and are influenced by language aptitude. We investigated some of these relationships in a sample of 152 participants, using exploratory graph analysis, across different levels of regularisation, i.e. sensitivity. We carried out a meta cluster analysis in a second step to identify variables that are robustly grouped together. We discuss the data, as well as their meta-network groupings, at a baseline network sensitivity level, and in two analyses, one including and the other excluding dyslexic readers. Our results show a stable association between language and cognition, and the isolation of multilingual language experience, musicality and literacy. We highlight the necessity of a more comprehensive view of language and of cognition as multivariate systems.

Language Dominance in Left-Handers: Unveiling Left Hemisphere Global Dominance With Specific Right Hemisphere Regional Dominance

Introduction The degree to which each human brain hemisphere governs specific cognitive processes, such as language and handedness (the preference or dominance of one hand over the other), varies across individuals. Research has explored the nature of language laterality in left-handed (LH) individuals, indicating that left-hemisphere dominance for language is commonly observed across both left- and right-handed populations. Advanced imaging techniques, including functional transcranial Doppler sonography and fMRI, have revealed subtle differences in language lateralization between LH and right-handed (RH) individuals, particularly in semantic processing tasks. These findings underscore the complex relationship between handedness and language lateralization. This study investigates the spatial patterns of language lateralization in LH and RH individuals using high-resolution fMRI data and the Human Connectome Project (HCP) multimodal parcellation (MMP).  Method We utilized pre-processed MRI scans from the HCP database, comprising 140 healthy young adults, with 70 individuals in each of the RH and LH groups. The language task includes two contrasts: the STORY contrast, where participants listened to brief auditory stories compared to a baseline, and the STORY-MATH contrast, where participants listened to stories versus solving addition and subtraction problems. Data processing involved the HCP Pipelines and the MMP atlas was applied for analysis. The Edinburgh Handedness Inventory categorized participants as either LH or RH. For analysis, we focused on the number of brain surface elements (3D surface vertices) with positive elements (PEs) within each brain region, indicating blood-oxygen-level-dependent (BOLD) activity. The study's methodology aimed to quantify and compare PEs across the hemispheres (paired sample) and handedness groups (independent sample), providing insights into language lateralization. Statistical analysis involved Mann-Whitney U tests for differences across gender and handedness groups and robust t-tests for hemispheric dominance. Results were visualized by projecting mean and effect size values onto a 3D brain surface. Results The analysis of hemispheric mean differences in PEs revealed robust left hemisphere dominance in both the STORY and STORY-MATH contrasts among the RH group, while the LH group exhibited more balanced activity. Significant variations in PEs were observed across numerous MMP regions, with LH individuals showing pronounced asymmetry in 67 and 76 MMP regions (out of 180 regions) in the STORY and STORY-MATH contrasts, respectively, compared to 83 and 99 regions in RH individuals. Additionally, when comparing LH and RH groups, significant differences in PEs were identified within 14 MMP regions (out of 360 regions), all demonstrating significant asymmetry in LH individuals and primarily located in the right hemisphere (12 regions), notably in the inferior parietal lobule (Brodmann 39 and 40). No differences were found in the STORY-MATH contrast. Conclusion We identified hemispheric left-lateralization dominance in brain areas associated with language processing, irrespective of handedness. However, employing multimodal brain parcellation with fMRI language tasks unveiled notable differences in specific regions. Particularly striking was the heightened activity observed in certain right hemisphere regions among LH individuals.

Imaging genetics of language network functional connectivity reveals links with language-related abilities, dyslexia and handedness

Language is supported by a distributed network of brain regions with a particular contribution from the left hemisphere. A multi-level understanding of this network requires studying its genetic architecture. We used resting-state imaging data from 29,681 participants (UK Biobank) to measure connectivity between 18 left-hemisphere regions involved in multimodal sentence-level processing, as well as their right-hemisphere homotopes, and interhemispheric connections. Multivariate genome-wide association analysis of this total network, based on genetic variants with population frequencies  >1%, identified 14 genomic loci, of which three were also associated with asymmetry of intrahemispheric connectivity. Polygenic dispositions to lower language-related abilities, dyslexia and left-handedness were associated with generally reduced leftward asymmetry of functional connectivity. Exome-wide association analysis based on rare, protein-altering variants (frequencies <1%) suggested 7 additional genes. These findings shed new light on genetic contributions to language network organization and related behavioural traits.

Processing Language Partly Shares Neural Genetic Basis with Processing Tools and Body Parts

Language is an evolutionarily salient faculty for humans that relies on a distributed brain network spanning across frontal, temporal, parietal, and subcortical regions. To understand whether the complex language network shares common or distinct genetic mechanisms, we examined the relationships between the genetic effects underlying the brain responses to language and a set of object domains that have been suggested to coevolve with language: tools, faces (indicating social), and body parts (indicating social and gesturing). Analyzing the twin datasets released by the Human Connectome Project that had functional magnetic resonance imaging data from human twin subjects (monozygotic and dizygotic) undergoing language and working memory tasks contrasting multiple object domains (198 females and 144 males for the language task; 192 females and 142 males for the working memory task), we identified a set of cortical regions in the frontal and temporal cortices and subcortical regions whose activity to language was significantly genetically influenced. The heterogeneity of the genetic effects among these language clusters was corroborated by significant differences of the human gene expression profiles (Allen Human Brain Atlas dataset). Among them, the bilateral basal ganglia (mainly dorsal caudate) exhibited a common genetic basis for language, tool, and body part processing, and the right superior temporal gyrus exhibited a common genetic basis for language and tool processing across multiple types of analyses. These results uncovered the heterogeneous genetic patterns of language neural processes, shedding light on the evolution of language and its shared origins with tools and bodily functions.

The heritability and structural correlates of resting-state fMRI complexity

The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders.

Are numerical abilities determined at early age? A brain morphology study in children and adolescents with and without developmental dyscalculia

The intraparietal sulcus (IPS) has been associated with numerical processing. A recent study reported that the IPS sulcal pattern was associated with arithmetic and symbolic number abilities in children and adults. In the present study, we evaluated the link between numerical abilities and the IPS sulcal pattern in children with Developmental Dyscalculia (DD) and typically developing children (TD), extending previous analyses considering other sulcal features and the postcentral sulcus (PoCS). First, we confirm the longitudinal sulcal pattern stability of the IPS and the PoCS. Second, we found a lower proportion of left sectioned IPS and a higher proportion of a double-horizontal IPS shape bilaterally in DD compared to TD. Third, our analyses revealed that arithmetic is the only aspect of numerical processing that is significantly related to the IPS sulcal pattern (sectioned vs not sectioned), and that this relationship is specific to the left hemisphere. And last, correlation analyses of age and arithmetic in children without a sectioned left IPS indicate that although they may have an inherent disadvantage in numerical abilities, these may improve with age. Thus, our results indicate that only the left IPS sulcal pattern is related to numerical abilities and that other factors co-determine numerical abilities.

Genetic and environmental influence on resting state networks in young male and female adults: a cartographer mapping study

We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low-frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically-influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low-level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long-term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high-level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex-specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task-oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5-19.1 times more GIF connections in males than females. These preliminary (young adult cohort-specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex-specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort-specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain-wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.].

Meta-analysis of age-related cognitive decline reveals a novel locus for the attention domain and implicates a COVID-19-related gene for global cognitive function

INTRODUCTION

Cognitive abilities have substantial heritability throughout life, as shown by twin- and population-based studies. However, there is limited understanding of the genetic factors related to cognitive decline in aging across neurocognitive domains.

METHODS

We conducted a meta-analysis on 3045 individuals aged ≥65, derived from three population-based cohorts, to identify genetic variants associated with the decline of five neurocognitive domains (attention, memory, executive function, language, visuospatial function) and global cognitive decline. We also conducted gene-based and functional bioinformatics analyses.

RESULTS

Apolipoprotein E (APOE)4 was significantly associated with decline of memory (p = 5.58E-09) and global cognitive function (p = 1.84E-08). We identified a novel association with attention decline on chromosome 9, rs6559700 (p = 2.69E-08), near RASEF. Gene-based analysis also identified a novel gene, TMPRSS11D, involved in the activation of SARS-CoV-2, to be associated with the decline in global cognitive function (p = 4.28E-07).

DISCUSSION

Domain-specific genetic studies can aid in the identification of novel genes and pathways associated with decline across neurocognitive domains.

HIGHLIGHTS

rs6559700 was associated with decline of attention. APOE4 was associated with decline of memory and global cognitive decline. TMPRSS11D, a gene involved in the activation of SARS-CoV-2, was implicated in global cognitive decline. Cognitive domain abilities had both unique and shared molecular pathways across the domains.

A topography-based predictive framework for naturalistic viewing fMRI

Functional magnetic resonance imaging (fMRI) during naturalistic viewing (NV) provides exciting opportunities for studying brain functions in more ecologically valid settings. Understanding individual differences in brain functions during NV and their behavioural relevance has recently become an important goal. However, methods specifically designed for this purpose remain limited. Here, we propose a topography-based predictive framework (TOPF) to fill this methodological gap. TOPF identifies individual-specific evoked activity topographies in a data-driven manner and examines their behavioural relevance using a machine learning-based predictive framework. We validate TOPF on both NV and task-based fMRI data from multiple conditions. Our results show that TOPF effectively and stably captures individual differences in evoked brain activity and successfully predicts phenotypes across cognition, emotion and personality on unseen subjects from their activity topographies. Moreover, TOPF compares favourably with functional connectivity-based approaches in prediction performance, with the identified predictive brain regions being neurobiologically interpretable. Crucially, we highlight the importance of examining individual evoked brain activity topographies in advancing our understanding of the brain-behaviour relationship. We believe that the TOPF approach provides a simple but powerful tool for understanding brain-behaviour relationships on an individual level with a strong potential for clinical applications.

Language network lateralization is reflected throughout the macroscale functional organization of cortex

Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While the majority of people exhibit left-hemispheric language dominance, a substantial minority of the population shows reverse lateralization. Using twin and family data from the Human Connectome Project, we provide evidence that atypical language dominance is associated with global shifts in cortical organization. Individuals with atypical language organization exhibit corresponding hemispheric differences in the macroscale functional gradients that situate discrete large-scale networks along a continuous spectrum, extending from unimodal through association territories. Analyses reveal that both language lateralization and gradient asymmetries are, in part, driven by genetic factors. These findings pave the way for a deeper understanding of the origins and relationships linking population-level variability in hemispheric specialization and global properties of cortical organization.

Determinants of Cognitive Performance in Children and Adolescents: A Populational Longitudinal Study

We evaluated the determinants of cognitive performance in children and adolescents. This is a longitudinal study, secondary analysis of the Physical Activity and Nutrition in Children (PANIC) study. We assessed 502 children (51.6% girls) at middle childhood (range: 6.6 to 9.0 years), at late childhood, 437 children (51.0% girls, range: 8.8 to 11.2 years), and in 277 adolescents (54.5% girls, range: 15.0 to 17.4 years). Raven's progressive matrices tests estimated the participants' cognitive performance (outcome variable) at all time points. In total, we evaluated 29 factors from various dimensions (prenatal, neonatal, child fitness, lifestyle and anthropometrics). None of the neonatal and anthropometric parameters were associated with cognitive performance. Preeclampsia (prenatal) and listening to music, writing, arts and craft and watching TV (lifestyle) were negatively associated with cognitive performance. Shuttle run and box and block tests (fitness), and playing music, reading and time at the computer (lifestyle) were positive determinants of cognitive performance in children and adolescents. Fitness and lifestyle factors during childhood and adolescence diminished the importance of prenatal factors on cognitive performance and lifestyle factors were especially relevant in regard to cognitive performance. Reading was positively associated with cognitive performance, regardless of age and time dedicated, and should be promoted.

The genetic architecture of language functional connectivity

Language is a unique trait of the human species, of which the genetic architecture remains largely unknown. Through language disorders studies, many candidate genes were identified. However, such complex and multifactorial trait is unlikely to be driven by only few genes and case-control studies, suffering from a lack of power, struggle to uncover significant variants. In parallel, neuroimaging has significantly contributed to the understanding of structural and functional aspects of language in the human brain and the recent availability of large scale cohorts like UK Biobank have made possible to study language via image-derived endophenotypes in the general population. Because of its strong relationship with task-based fMRI (tbfMRI) activations and its easiness of acquisition, resting-state functional MRI (rsfMRI) have been more popularised, making it a good surrogate of functional neuronal processes. Taking advantage of such a synergistic system by aggregating effects across spatially distributed traits, we performed a multivariate genome-wide association study (mvGWAS) between genetic variations and resting-state functional connectivity (FC) of classical brain language areas in the inferior frontal (pars opercularis, triangularis and orbitalis), temporal and inferior parietal lobes (angular and supramarginal gyri), in 32,186 participants from UK Biobank. Twenty genomic loci were found associated with language FCs, out of which three were replicated in an independent replication sample. A locus in 3p11.1, regulating EPHA3 gene expression, is found associated with FCs of the semantic component of the language network, while a locus in 15q14, regulating THBS1 gene expression is found associated with FCs of the perceptual-motor language processing, bringing novel insights into the neurobiology of language.

Estimating the heritability of psychological measures in the Human Connectome Project dataset

The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral and genotypic measures, as well as a biologically verified family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times (fingerprinting), and to identify identical twins significantly above chance. Standard heritability estimates of 37 behavioral measures were derived from twin correlations, and machine-learning models (univariate linear model, Ridge classifier and Random Forest model) were trained to classify monozygotic twins and dizygotic twins. Correlations between the standard heritability metric and each set of model weights ranged from 0.36 to 0.7, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further explored the heritability of a smaller number of latent factors extracted from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.05 to 0.43. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present a thorough investigation of the heritabilities of the behavioral measures in the HCP as a resource for other investigators, and illustrate the utility of machine-learning methods for qualitative characterization of the differential heritability across diverse measures.

Discovering the shared biology of cognitive traits determined by genetic overlap

Investigating the contribution of biology to human cognition has assumed a bottom-up causal cascade where genes influence brain systems that activate, communicate, and ultimately drive behavior. Yet few studies have directly tested whether cognitive traits with overlapping genetic underpinnings also rely on overlapping brain systems. Here, we report a step-wise exploratory analysis of genetic and functional imaging overlaps among cognitive traits. We used twin-based genetic analyses in the human connectome project (HCP) dataset (N ​= ​486), in which we quantified the heritability of measures of cognitive functions, and tested whether they were driven by common genetic factors using pairwise genetic correlations. Subsequently, we derived activation maps associated with cognitive tasks via functional imaging meta-analysis in BrainMap (N ​= ​4484), and tested whether cognitive traits that shared genetic variation also exhibited overlapping brain activation. Our genetic analysis determined that six cognitive measures (cognitive flexibility, no-go continuous performance, fluid intelligence, processing speed, reading decoding and vocabulary comprehension) were heritable (0.3 ​< ​h² ​< ​0.5), and genetically correlated with at least one other heritable cognitive measure (0.2 ​< ​ρ_g ​< ​0.35). The meta-analysis showed that two genetically-correlated traits, cognitive flexibility and fluid intelligence (ρ_g ​= ​0.24), also had a significant brain activation overlap (ρ_perm ​= ​0.29). These findings indicate that fluid intelligence and cognitive flexibility rely on overlapping biological features, both at the neural systems level and at the molecular level. The cross-disciplinary approach we introduce provides a concrete framework for data-driven quantification of biological convergence between genetics, brain function, and behavior in health and disease.