scholarly journals Personality and local brain structure: their shared genetic basis and reproducibility

2019 ◽  
Author(s):  
Sofie L. Valk ◽  
Felix Hoffstaedter ◽  
Julia A. Camilleri ◽  
Peter Kochunov ◽  
B.T. Thomas Yeo ◽  
...  
Keyword(s):  
Personality Traits ◽  
Surface Area ◽  
Brain Structure ◽  
Large Scale ◽  
Local Variation ◽  
Total Surface Area ◽  
Frontal Surface ◽  
Cortical Structure ◽  
Human Connectome Project ◽  
Local Brain

AbstractLocal variation in cortical architecture is highly heritable and distinct genes are associated with specific cortical regions. Total surface area has been shown to be genetically correlated with complex cognitive capacities, suggesting cortical brain structure is a viable endophenotype linking genes to behavior. However, to what extend local brain structure has a genetic association with cognitive and emotional functioning is incompletely understood. Here, we study the genetic correlation between personality traits and local cortical structure in a large-scale twin sample (Human Connectome Project, n=1106, 22-37y). We found a genetic overlap between personality traits and local cortical structure in 10 of 17 observed phenotypic associations in predominantly frontal cortices. To evaluate the robustness of observed personality-brain associations we studied two independent age-matched samples (GSP: n=926, age=19-35y, eNKI: n=210, age: 19-39y). We observed anecdotal to moderate evidence for a successful replication of the negative association between surface area in medial prefrontal cortex and Neuroticism in both samples. Quantitative functional decoding indicated this region is implicated in emotional and socio-cognitive functional processes. In sum, our observations suggest that associations between local brain structure and personality are, in part, under genetic control. However, associations are weak and only the relation between frontal surface area and Neuroticism was consistently observed across three independent samples of young adults.

scholarly journals Quantifying the Polygenic Architecture of the Human Cerebral Cortex: Extensive Genetic Overlap between Cortical Thickness and Surface Area

2020 ◽  
Vol 30 (10) ◽  
pp. 5597-5603 ◽  
Author(s):  
Dennis van der Meer ◽  
Oleksandr Frei ◽  
Tobias Kaufmann ◽  
Chi-Hua Chen ◽  
Wesley K Thompson ◽  
...  
Keyword(s):  
Surface Area ◽  
Cortical Thickness ◽  
Large Scale ◽  
Gaussian Mixture ◽  
Total Surface Area ◽  
Brain Morphology ◽  
Genetic Determinants ◽  
Genetic Predictors ◽  
Genetic Overlap ◽  
Causal Variants

Abstract The thickness of the cerebral cortical sheet and its surface area are highly heritable traits thought to have largely distinct polygenic architectures. Despite large-scale efforts, the majority of their genetic determinants remain unknown. Our ability to identify causal genetic variants can be improved by employing brain measures that better map onto the biology we seek to understand. Such measures may have fewer variants but with larger effects, that is, lower polygenicity and higher discoverability. Using Gaussian mixture modeling, we estimated the number of causal variants shared between mean cortical thickness and total surface area, as well as the polygenicity and discoverability of regional measures. We made use of UK Biobank data from 30 880 healthy White European individuals (mean age 64.3, standard deviation 7.5, 52.1% female). We found large genetic overlap between total surface area and mean thickness, sharing 4016 out of 7941 causal variants. Regional surface area was more discoverable (P = 2.6 × 10−6) and less polygenic (P = 0.004) than regional thickness measures. These findings may serve as a roadmap for improved future GWAS studies; knowledge of which measures are most discoverable may be used to boost identification of genetic predictors and thereby gain a better understanding of brain morphology.

scholarly journals The genetic architecture of the human cerebral cortex

Science ◽  
2020 ◽  
Vol 367 (6484) ◽  
pp. eaay6690 ◽  
Author(s):  
Katrina L. Grasby ◽  
Neda Jahanshad ◽  
Jodie N. Painter ◽  
Lucía Colodro-Conde ◽  
Janita Bralten ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Surface Area ◽  
Meta Analysis ◽  
Brain Magnetic Resonance Imaging ◽  
Regulatory Elements ◽  
Total Surface Area ◽  
Imaging Data ◽  
Cortical Structure ◽  
A Genome ◽  
Significant Enrichment

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.

scholarly journals The genetic architecture of the human cerebral cortex

2018 ◽  
Author(s):  
Katrina L. Grasby ◽  
Neda Jahanshad ◽  
Jodie N. Painter ◽  
Lucía Colodro-Conde ◽  
Janita Bralten ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Surface Area ◽  
Multiple Testing ◽  
Meta Analysis ◽  
Brain Mri ◽  
Regulatory Elements ◽  
Total Surface Area ◽  
Multiple Testing Correction ◽  
Cortical Structure ◽  
A Genome

The cerebral cortex underlies our complex cognitive capabilities, yet we know little about the specific genetic loci influencing human cortical structure. To identify genetic variants, including structural variants, impacting cortical structure, we conducted a genome-wide association meta-analysis of brain MRI data from 51,662 individuals. We analysed the surface area and average thickness of the whole cortex and 34 regions with known functional specialisations. We identified 255 nominally significant loci (P≤ 5 × 10−8); 199 survived multiple testing correction (P≤ 8.3 × 10−10; 187 surface area; 12 thickness). We found significant enrichment for loci influencing total surface area within regulatory elements active during prenatal cortical development, supporting the radial unit hypothesis. Loci impacting regional surface area cluster near genes in Wnt signalling pathways, known to influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson’s disease, insomnia, depression and ADHD.One Sentence SummaryCommon genetic variation is associated with inter-individual variation in the structure of the human cortex, both globally and within specific regions, and is shared with genetic risk factors for some neuropsychiatric disorders.

scholarly journals Robust prediction of individual personality from brain functional connectome

2020 ◽  
Vol 15 (3) ◽  
pp. 359-369 ◽  
Author(s):  
Huanhuan Cai ◽  
Jiajia Zhu ◽  
Yongqiang Yu
Keyword(s):  
Personality Traits ◽  
Large Scale ◽  
Five Factor Model ◽  
Brain Regions ◽  
Individual Variability ◽  
Personality Factors ◽  
Imaging Features ◽  
Imaging Data ◽  
Functional Connectome ◽  
Human Connectome Project

Abstract Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual’s unique functional connectome may help advance the translation of ‘brain connectivity fingerprinting’ into real-world personality psychological settings.

scholarly journals Little Evidence for Associations Between the Big Five Personality Traits and Variability in Brain Gray or White Matter

2019 ◽  
Author(s):  
Reut Avinun ◽  
Salomon Israel ◽  
Annchen R. Knodt ◽  
Ahmad R. Hariri
Keyword(s):  
White Matter ◽  
Personality Traits ◽  
Surface Area ◽  
Big Five ◽  
Brain Structure ◽  
Superior Temporal Gyrus ◽  
Big Five Personality Traits ◽  
Small Sample ◽  
Big Five Personality ◽  
Using Data

AbstractAttempts to link the Big Five personality traits of Openness-to-Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism with variability in trait-like features of brain structure have produced inconsistent results. Small sample sizes and heterogeneous methodology have been suspected in driving these inconsistencies. Here, we tested for associations between the Big Five personality traits and multiple measures of brain structure using data from 1,107 university students (636 women, mean age 19.69±1.24 years) representing the largest attempt to date. In addition to replication analyses based on a prior study, we conducted exploratory whole-brain analyses. Four supplementary analyses were also conducted to examine 1) possible associations with lower-order facets of personality; 2) modulatory effects of sex; 3) effect of controlling for non-target personality traits; and 4) parcellation scheme effects. The analyses failed to identify any significant associations between the Big Five personality traits and variability in measures of cortical thickness, surface area, subcortical volume, or white matter microstructural integrity, except for an association between greater surface area of the superior temporal gyrus and lower scores on conscientiousness that explained 0.44% of the morphometric measure’s variance. Notably however, the latter association is largely not supported by previous studies. The supplementary analyses mirrored these largely null findings, suggesting they were not substantively biased by our choice of analytic model. Collectively, these results indicate that if there are direct associations between the Big Five personality traits and variability in brain structure, they are of likely very small effect sizes and will require very large samples for reliable detection.

scholarly journals Quantifying the polygenic architecture of the human cerebral cortex: Extensive genetic overlap between cortical thickness and surface area

2019 ◽  
Author(s):  
Dennis van der Meer ◽  
Oleksandr Frei ◽  
Tobias Kaufmann ◽  
Chi-Hua Chen ◽  
Wesley K. Thompson ◽  
...  
Keyword(s):  
Surface Area ◽  
Cortical Thickness ◽  
Large Scale ◽  
Gaussian Mixture ◽  
Total Surface Area ◽  
Brain Morphology ◽  
Genetic Determinants ◽  
Genetic Predictors ◽  
Genetic Overlap ◽  
Causal Variants

ABSTRACTIntroductionThe thickness of the cerebral cortical sheet and its surface area are highly heritable traits thought to have largely distinct polygenic architectures. Despite large-scale efforts, the majority of their genetic determinants remains unknown. Our ability to identify causal genetic variants can be improved by employing better delineated, less noisy brain measures that better map onto the biology we seek to understand. Such measures may have fewer variants but with larger effects, i.e. lower polygenicity and higher discoverability.MethodsUsing Gaussian mixture modeling, we estimated the number of causal variants shared between mean cortical thickness and total surface area. We further determined the polygenicity and discoverability of regional cortical measures from five often-employed parcellation schemes. We made use of UK Biobank data from 31,312 healthy White European individuals (mean age 55.5, standard deviation (SD) 7.4, 52.1% female).ResultsContrary to previous reports, we found large genetic overlap between total surface area and mean thickness, sharing 4427 out of 7150 causal variants. Regional surface area was more discoverable (p=4.1×10−6) and less polygenic (p=.007) than regional thickness measures. We further found that genetically-informed and less granular parcellation schemes had highest discoverability, with no differences in polygenicity.ConclusionsThese findings may serve as a roadmap for improved future GWAS studies; Knowledge of which measures or parcellations are most discoverable, as well as the genetic overlap between these measures, may be used to boost identification of genetic predictors and thereby gain a better understanding of brain morphology.

Borderline personality traits are not correlated with brain structure in two large samples.

2019 ◽  
Author(s):  
David A Baranger ◽  
Lauren R. Few ◽  
Daniel H Sheinbein ◽  
Arpana Agrawal ◽  
Thomas Oltmanns ◽  
...  
Keyword(s):  
Personality Traits ◽  
Brain Structure ◽  
Meta Analysis ◽  
Positive Control ◽  
Borderline Personality ◽  
Brain Morphology ◽  
Human Connectome Project ◽  
Borderline Personality Traits ◽  
Structure Metrics ◽  
Suicidal Thoughts And Behaviors

Background. Borderline personality disorder (BPD) is associated with severe psychiatric presentations and has been linked to variability in brain structure. Dimensional models of borderline personality traits (BPT) have grown influential; however, associations between BPT and brain structure remain poorly understood.Methods. We tested whether BPT are associated with regional cortical thickness, cortical surface area, and subcortical volumes (n=152 brain structure metrics) in the Duke Neurogenetics Study (DNS; n=1,299), and Human Connectome Project (HCP; n=1,099). Positive control analyses tested whether BPT are associated with related behaviors (e.g., suicidal thoughts and behaviors, psychiatric diagnoses) and experiences (e.g., adverse childhood experiences).Results. While BPT were robustly associated with all positive control measures, they were not significantly associated with any brain structure metrics in the DNS or HCP, or in a meta-analysis of both samples. The strongest findings from the meta-analysis showed a positive association between BPT scores and volumes of the left ventral diencephalon and thalamus (ps<0.005 uncorrected, pFDRs>0.1). Contrasting high and low BPT decile groups (N=552) revealed no FDR-significant associations with brain structure. Conclusions. We find replicable evidence that BPT are not associated with brain structure, despite being correlated with independent behavioral measures. Prior reports linking brain morphology to BPD may be driven by factors other than traits (e.g., severe presentations, comorbid conditions, severe childhood adversity, or medication) or reflect false positives. The etiology and/or consequences of BPT may not be attributable to MRI-measured brain structure. Future studies of BPT will require much larger sample sizes to detect these very small effects.

scholarly journals The Evolutionary History of Common Genetic Variants Influencing Human Cortical Surface Area

2020 ◽  
Author(s):  
Amanda K Tilot ◽  
Ekaterina A Khramtsova ◽  
Dan Liang ◽  
Katrina L Grasby ◽  
Neda Jahanshad ◽  
...  
Keyword(s):  
Human Brain ◽  
Surface Area ◽  
Brain Structure ◽  
Visual Processing ◽  
Large Scale ◽  
Structural Changes ◽  
Homo Sapiens ◽  
Regulatory Element ◽  
Regulatory Elements ◽  
Common Genetic Variants

Abstract Structural brain changes along the lineage leading to modern Homo sapiens contributed to our distinctive cognitive and social abilities. However, the evolutionarily relevant molecular variants impacting key aspects of neuroanatomy are largely unknown. Here, we integrate evolutionary annotations of the genome at diverse timescales with common variant associations from large-scale neuroimaging genetic screens. We find that alleles with evidence of recent positive polygenic selection over the past 2000–3000 years are associated with increased surface area (SA) of the entire cortex, as well as specific regions, including those involved in spoken language and visual processing. Therefore, polygenic selective pressures impact the structure of specific cortical areas even over relatively recent timescales. Moreover, common sequence variation within human gained enhancers active in the prenatal cortex is associated with postnatal global SA. We show that such variation modulates the function of a regulatory element of the developmentally relevant transcription factor HEY2 in human neural progenitor cells and is associated with structural changes in the inferior frontal cortex. These results indicate that non-coding genomic regions active during prenatal cortical development are involved in the evolution of human brain structure and identify novel regulatory elements and genes impacting modern human brain structure.

scholarly journals Multidimensional Heritability Analysis of Neuroanatomical Shape

2015 ◽  
Author(s):  
Tian Ge ◽  
Martin Reuter ◽  
Anderson M. Winkler ◽  
Avram J. Holmes ◽  
Phil H. Lee ◽  
...  
Keyword(s):  
Brain Structure ◽  
Large Scale ◽  
Brain Mri ◽  
Brain Structures ◽  
Biomedical Data ◽  
Genetic Determinants ◽  
Genetic Studies ◽  
Genome Wide ◽  
Human Connectome Project ◽  
Mri Scans

In the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behavior and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure.

The relationship between brain structure and general psychopathology in preadolescents

2020 ◽  
Author(s):  
Louise Mewton ◽  
Briana Lees ◽  
Lindsay Squeglia ◽  
Miriam K. Forbes ◽  
Matthew Sunderland ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Surface Area ◽  
Brain Structure ◽  
Parental Education ◽  
Mental Illnesses ◽  
General Psychopathology ◽  
Control Approach ◽  
Cortical Volume ◽  
Subcortical Regions ◽  
The Relationship

Categorical mental disorders are being recognized as suboptimal targets in clinical neuroscience due to poor reliability as well as high rates of heterogeneity within, and comorbidity between, mental disorders. As an alternative to the case-control approach, recent studies have focused on the relationship between neurobiology and latent dimensions of psychopathology. The current study aimed to investigate the relationship between brain structure and psychopathology in the critical preadolescent period when psychopathology is emerging. This study included baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study® (n = 11,721; age range = 9-10 years; male = 52.2%). General psychopathology, externalizing, internalizing, and thought disorder dimensions were based on a higher-order model of psychopathology and estimated using Bayesian plausible values. Outcome variables included global and regional cortical volume, thickness, and surface area. Higher levels of psychopathology across all dimensions were associated with lower volume and surface area globally, as well as widespread and pervasive alterations across the majority of cortical and subcortical regions studied, after adjusting for sex, race/ethnicity, and parental education. The relationships between general psychopathology and brain structure were attenuated when adjusting for cognitive functioning. There was evidence of a relationship between externalizing psychopathology and frontal regions of the cortex that was independent of general psychopathology. The current study identified lower cortical volume and surface area as transdiagnostic biomarkers for general psychopathology in preadolescence. The widespread and pervasive relationships between general psychopathology and brain structure may reflect cognitive dysfunction that is a feature across a range of mental illnesses.

Sign in / Sign up

Export Citation Format

Share Document