dataesr/scientific-paragraphs-categorization huggingface dataset:
2018; de Kovel et al. 2019 ) could also contribute to global brain asymmetry, but this has not previously been studied in large population data. Here, we present the largest-ever analysis of global brain asymmetry, in 3 independent datasets: the UK Biobank (N = 39 678), Human Connectome Project (HCP, N = 1113) and BIL&GIN (N = 453, roughly balanced for left/right handedness). First, the 2 components of global brain asymmetry, that is, the horizontal and vertical asymmetry skews, were extracted from brain MRI data for each individual, to capture global left-right differences along the anterior-posterior and dorsalventral axes. The population distributions of these 2 measures were examined in the 3 datasets, to clarify the populationlevel average direction and variance of each asymmetry Downloaded from https://academic.
oup. com/cercor/article/31/9/4151/6218944 by Max-Planck-Institute for Psycholinguistics user on 16 August 2021 component. In addition, the reliability of these 2 measures was confirmed using a test-retest dataset from the HCP (30 subjects in the dataset had 2 scans each). Next, we investigated the relationships of horizontal and vertical skews with handedness in each of the datasets. Then, we used extensive phenotypic data in the UK Biobank, including early life factors, sociodemographic factors, regional gray and white matter measures derived from brain MRI, and variables related to cognitive functions and health, to explore other potential correlates of global brain asymmetry. Furthermore, we estimated the heritabilities of the horizontal and vertical skews using genome-wide genotype data in the UK Biobank data, and twin data from the HCP, and also investigated their genetic correlations with other traits and disorders.
Test-Retest Reliability of Global Asymmetry Measures The HCP-retest data (30 subjects with 2 scans per subject) allowed us to quantify test-retest reliability of the 2 global asymmetry metrics. Intraclass correlation coefficients (ICC) were calculated using IBM SPSS 20 (Model: Two-Way Mixed; Type: Consistency), where the ICC is conceptualized as the ratio of between-subjects variance to total variance. The ICC is a value between 0 and 1, where 1 indicates perfect reliability (i. e. , the within-subject variance is 0).
Association of Global Brain Asymmetry with Handedness Association analyses of the horizontal and vertical skew measures were performed for each dataset separately, as per the availability of specific handedness measures and co-variables. In the UK Biobank, the asymmetry differences between handedness groups (-1 = left, 0 = both, and 1 = right, treated as an ordinal variable) were assessed with linear regression models adjusted for sex, age, and nonlinear age (zage 2 , i. e. , ztransformed square of the age, i. e. , [age-age. mean/age. std] 2 , std=standard deviation), the first 10 principal components (PCs) which capture genome-wide population structure in the genotype data (as made available by the UK Biobank [PC1-10];  Bycroft et al. 2018) , and several technical variables related to imaging  (Alfaro-Almagro et al.
Python's pandas (pandas. pydata. org), scipy (www. scipy. org), and statsmodels (www. statsmodels. org) packages were used for these analyses. In the HCP dataset, asymmetry differences related to the strength of hand preference (ranging from -100 to +100, see above) were examined with linear regression models, adjusting for sex, age, and nonlinear age (zage 2 ), "acquisition" (a variable to control for possible scanner status differences across the study period of several years), and "race" (a variable given this label in the HCP data, to capture ethnicity). Analyses were repeated when additionally adjusting for brain volume ("FS_IntraCranial_Vol," intracranial volume estimated with FreeSurfer) and the brain-size related scaling factors indicated in Figure  1. The HCP dataset included twins and siblings, and we therefore used Permutation Analysis of Linear Models  (PALM, version alpha111; Winkler et al.
2014 ) from FSL (version 5. 0. 10), which has a specialized function for accounting for possible nonindependence caused by family structure. The use of permutation by PALM also means that it is suitable for analyzing the non-normal distribution of manual preference strength. We used 10 000 permutations and calculated 2-tailed P values. In the BIL&GIN dataset, skew differences related to the strength of hand preference (again ranging from -100 to +100, see above) were examined with linear regression models, adjusting for sex, age, and nonlinear age (zage 2 ). Given the non-normal distribution of hand preference strength, we further confirmed the association using rank-based Spearman correlation (after adjusting the skew measures for sex, age, and nonlinear age using linear regression). The BIL&GIN dataset also included a binary categorical handedness trait (right-or left-handed) based on a simple questionnaire, and for this trait we tested for brain skew differences between handedness groups using 2-sample t-tests, after adjusting the skew measures for sex, age, and nonlinear age.
Analyses were repeated when additionally adjusting for brain volume (again intracranial volume estimated with FreeSurfer) and the brain-size related scaling factors indicated in Figure  1. Python's pandas and statsmodels packages were used. Although left-handedness was deliberately over-represented in the BIL&GIN dataset (to achieve balance for handedness), we did not attempt to correct for sampling bias, because the findings (see Results) were in line with the other 2 datasets, in which handedness was not over-represented. The relatively small sample size of the BIL&GIN dataset meant that repeat dropping of 9/10 of left-handers to match their population prevalence would make the statistical power too low, and would also result in unequal group sizes (which can create its own statistical issues).
Phenome-wide Associations of Global Brain Asymmetry The UK Biobank dataset includes many variables, including early life factors, psychosocial factors, derived imaging traits, and variables related to cognitive functions and health. We ran Phenome-wide Association Scan (pheWAS) analysis for each global asymmetry component, to screen for other associated variables besides handedness and genetic data. We used the package PHEnome Scan Analysis Tool (PHESANT;  Millard et al. 2017) , which enables comprehensive phenome scans to be performed across all data fields in the UK Biobank. PHESANT uses a rule-based method to automatically determine how to test each variable. The decision rules start by assigning each variable as 1 of 4 types: continuous, ordered categorical, unordered categorical, or binary.
A description of PHESANT's automated rule-based method is given in detail elsewhere  (Millard et al. 2017). PHESANT then estimates the bivariate association of an independent variable of interest (in our case either horizontal or vertical brain asymmetry) with each dependent variable in the dataset. Dependent variables with continuous, binary, ordered categorical, and unordered categorical data types, are tested using linear, logistic, ordered logistic, and multinominal logistic regression, respectively. Prior to testing, an inverse normal rank transform is applied to variables of the continuous data type. All analyses were adjusted for covariates as in the handedness association analyses (see above). Downloaded from https://academic. oup. com/cercor/article/31/9/4151/6218944 by Max-Planck-Institute for Psycholinguistics user on 16 August 2021 We corrected for multiple testing using Bonferroni correction, with a significance threshold determined by dividing 0.
05 by the number of tests performed, separately for the horizontal and vertical asymmetry measures as independent variables. We also looked up the results for some variables of particular interest in relation to global brain asymmetry (see Results), which did not necessarily survive multiple testing correction over all phenotypes tested, in which case we report the nominal P values. This enables comparison of the identified associations with results for other, possibly related variables. Finally, as language is a prominently lateralized and humanspecific function  (Gazzaniga 2009; Price 2012) , we ran association analyses between the 2 global asymmetry components and 4 behavioral performance measures related to language, which were available in the HCP dataset. The tasks included were the Penn Word Memory Test, Language Task for functional magnetic resonance imaging, and the NIH Toolbox Oral Reading Recognition Test and Picture Vocabulary Test  (Barch et al.
0. 1) (https:// github. com/bulik/ldsc; Finucane et al. 2015). This approach was also used to measure genetic correlation between brain skews and left-handedness, using GWAS summary statistics for lefthandedness reported by de  Kovel and Francks (2019). Precomputed LD scores from the 1000 Genomes European data (i. e. , eur_w_ld_chr. tar. bz2) were used, and there was no constraint on the intercept of regression. We also applied causal mixture models to estimate polygenicity (estimated number of causal variants) and discoverability (proportion of phenotypic variance explained on average by a causal variant, σ β 2 ; Holland et al. 2020), using the MiXeR package (v1. 2; https://github. com/ precimed/mixer). This analysis was based on the GWAS summary statistics for the brain skew measures as generated in the present study.
Heritability could also be estimated in the HCP dataset, as it included MZ and DZ twin pairs, as well as other siblings and unrelated individuals (see above). We estimated the heritability of each skew measure using variance-component analysis implemented in SOLAR  (Almasy and Blangero 1998). Briefly, each skew measure was entered as a dependent variable into separate linear mixed-effects models, which included fixed effects of sex, age, and nonlinear age (zage 2 ), and a random effect of genetic similarity, whose covariance structure was determined by the pedigrees. Genetic similarity was coded as 1 for MZ pairs, 0. 5 for DZ twin or sibling pairs, and 0 for unrelated pairs of individuals. Maximum likelihood-based bivariate variance decomposition analysis was also applied, again using SOLAR, to estimate the genetic correlation of the 2 skew measures.
who used a combination of self-report and clustering based on PCs that capture genome-wide diversity in the dataset ("in. white. British. ancestry. subset"; Bycroft et al. 2018). We randomly excluded one individual from each pair with a kinship coefficient > 0. 0442, as defined within the UK Biobank relatedness file included with the downloaded genotype data. At the SNP level, we excluded SNPs with a minor allele frequency below 1%, Hardy-Weinberg P value below 1 × 10 -7 or imputation quality INFO scores below 0. 7 (the latter as provided by the UK Biobank with the imputed data;  Bycroft et al. 2018) , which resulted in 9 904 141 SNPs genomewide. GWAS was performed with BGENIE (v. 1. 2; Bycroft et al. 2018)  for each of the residualized global asymmetry measures separately (after accounting for the same covariate effects as for SNP heritability analysis, above), using imputed genotype dosages and an additive model.
The gene-level association statistics were then used to perform gene-set enrichment analysis, again using MAGMA, for gene ontology (GO) terms for biological processes, cellular components, and molecular functions  (Ashburner et al. 2000 ; minimum set size of 10 genes, maximum size 1000 genes, and total n = 6576 GO sets meeting these criteria) obtained from the latest MsigDB (v6. 2) database (http://software. broadinstitu te. org/gsea/msigdb), and Bonferroni correction was performed. This approach tests whether the genes in a given set show, on average, more evidence for association with the trait in question than the rest of the genes in the genome for which scores could be calculated, while accounting for nonindependence of SNPs due to LD.
Global Brain Asymmetry in the UK Biobank, HCP, and BIL&GIN Datasets We extracted 2 global asymmetry components for each individual. A positive score for horizontal skew indicates a global pattern in which the right hemisphere is shifted anteriorly relative to the left, whereas a negative score indicates that the left hemisphere is shifted anteriorly relative to the right (see Fig. 2  and Supplementary Fig. S1  for examples). Note that the skew is calculated as a global feature, not a feature of any particular slice (Supplementary Fig. S1 ). Similarly, a positive score for vertical skew indicates a global shift downwards of the left hemisphere relative to the right, whereas a negative vertical skew indicates the opposite pattern (Fig. 2  and see Supplementary Fig. S2 ). Both asymmetry components showed almost perfect reliability (horizontal skew: ICC = 0.
989; vertical skew: ICC = 0. 977) as indicated in test-retest analysis of data from 30 HCP subjects who underwent 2 scans each (Fig. 2 ). The average values of both asymmetry components were positive in the 3 independent datasets (Fig. 2   Notwithstanding the average asymmetry patterns, the distributions of the vertical and horizontal skews showed considerable individual differences, with for example, 31. 8% of participants showing a reversal compared with the average horizontal pattern, and 24. 6% showing a reversal compared with the average vertical pattern, in the UK Biobank dataset (Fig. 2 ). The horizontal and vertical skews showed low correlations with brain-sizerelated scaling factors in the 3 datasets (Fig. 2 ): UK Biobank (|r|s < 0. 07), HCP (|r|s < 0. 12), and BIL&GIN (|r|s < 0. 097; see Supplementary Table  S1 ), such that the skews appear to be largely independent of brain size.
e. , more positive horizontal skew scores). However, on the dorsal-ventral axis, left-handers are more likely to show skew in the same direction as the population average pattern (i. e. , more positive vertical skew scores). In addition, a difference was found when comparing vertical skew between left-handers and ambidextrous participants (t = -3. 24, P = 0. 0012, and Cohen's d = 0. 14), whereas no other comparisons between handedness groups showed significant effects (Ps > 0. 15). The HCP dataset confirmed that the 2 skews were related to hand preference. Here, a continuous index of the strength of hand preference was available, from -100 (left) to 100 (right), and the analysis accounted for family structure (see Materials and Methods). Again, horizontal skew was positively associated with right-hand preference (Z = 3.
30, P = 0. 0021), and vertical skew was positively associated with left-hand preference (Z = -3. 83, P = 0. 0002; Fig. 3 ). The BIL&GIN dataset further confirmed the finding with respect to vertical skew, that is, this was again positively correlated with increased left-hand preference, t = -3. 33, P = 0. 00095 (rho = -0. 15, P = 0. 0020) when using the hand preference scale from -100 to 100, and t = -3. 52, P = 0. 00048 when using a binary handedness assessment, see Materials and Methods (Left: N = 205; Right: N = 248). However, in this dataset, the association of hand preference with horizontal skew was not significant, t = 0. 77, P = 0. 44 (rho = 0. 02, P = 0. 73) for the continuous hand preference scale, and t = 0. 79, P = 0. 43 for binary handedness. Nonetheless, the direction of this nonsignificant association in BIL&GIN was consistent with the UK Biobank and HCP datasets.
The BIL&GIN dataset provided only 24. 4% power to detect the association of horizontal skew with handedness at alpha 0. 05, according to the effect size of this association in the UK Biobank (Cohen's d = 0. 090). The nonsignificant association between horizontal skew and handedness in BIL&GIN is therefore likely to be a power issue, due to a relatively limited sample size (whereas for the association with vertical skew, BIL&GIN provided 54% power at alpha 0. 05, in relation the UK Biobank effect size d = 0. 165). When adjusting vertical skew for horizontal skew, and vice versa, the associations of both asymmetry variables with hand preference remained very similar (UK Biobank, 3 handedness groups, horizontal skew, t = 5. 70, P = 1. 23e-08; vertical skew, t = -9. 50, P = 2. 23e-21; HCP, horizontal skew, Z = 2.
69, P = 0. 011; vertical skew, Z = -3. 32, P = 0. 0006; and BIL&GIN, hand preference strength, horizontal skew, t = 0. 67, P = 0. 50; vertical skew, t = -3. 57, P = 0. 00039). This indicates that handedness is primarily associated independently with the 2 asymmetry variables, rather than with any shared variance between them. In the UK Biobank, there was a significant association between handedness (including left, ambidextrous, and right groups) and one of the brain-size-related scaling factors (i. e. , ScalesY: scaling in the anterior-posterior direction, t = -3. 12, and P = 0. 00174). However, this association did not replicate in the HCP (ts < 1) or BIL&GIN datasets (ts < 1 except for ScalesZ: scaling in the superior-inferior direction, t = 1. 75, and P = 0. 08). Moreover, when controlling for these scaling factors related to brain size, associations between hand preference and the global asymmetries remained unchanged (UK Biobank, 3 handedness groups, horizontal skew, t = 5.
Phenome-wide Associations of Brain Skew Measures In the UK Biobank dataset, we ran pheWAS to search for other variables associated with the horizontal or vertical components of global brain asymmetry. The variables were those included in our approved UK Biobank project 16 066 "genetics of brain asymmetry and language-related disorders," and consisted mainly of measures related to cognitive functions, sociodemographics, mental health, physical measures, medical information, and brain imaging measures. In total, the pheWAS analysis included 3562 tests for each of the 2 skew measures. Note that some variables might only be considered as "phenotypes" in a very broad sense, such as country of birth, and home area population density. For each of the 2 skew measures, the pheWAS QQ plot is shown in Supplementary Fig.
S3. There were 464 associations below the Bonferroni corrected threshold of 1. 40e-05 (0. 05/3562) for horizontal skew, and 293 associations for vertical skew. Horizontal skew showed significant associations with variables of various categories, including cognitive functions (e. g. , "fluid intelligence score": P = 2. 74e-06), sociodemographics (e. g. , "age completed full time education": P = 6. 19e-08), physical measures (e. g. , "body mass index" [BMI]: P = 6. 05e-07), and mental health ("recent changes in speed/amount of moving or speaking" from the "depression" test: P < 1. 00e-155; Fig. 4A  and see Supplementary Dataset S1). The overwhelming majority (440 out of 464) were associations with brain imaging variables (Fig. 4 ). Interestingly, these latter associations showed a global anterior-posterior "torque" pattern for both gray matter volume (Fig.
4B ) and white matter metrics (Fig. 4C ). In terms of gray matter, the volumes of right frontal and left occipital regions positively correlated with horizontal skew, whereas left frontal and right occipital regional volumes negatively correlated with horizontal skew (Fig. 4B ). Similarly, microstructural measures of homologous white matter tracts showed significant associations with skew measures in opposite directions in the 2 hemispheres (Fig. 4C ). This confirms that torque manifests not only as a relative shifting of the hemispheres overall, but also as interhemispheric regional asymmetries affecting both gray and white matter  (Kong et al. 2018). More details can be seen in Supplementary Figures  S5A, S6 , and Dataset S1. Regarding the vertical skew, we found significant associations with variables of various categories, including cognitive functions (e.
g. , "time to answer"/prospective memory: P = 1. 91e-08), sociodemographics (e. g. , "transport type for commuting to job workplace: car/motor vehicle": P = 6. 19e-08), physical measures (e. g. , BMI: P = 1. 20e-05), and mental health (recent changes in speed/amount of moving or speaking from the depression test: P < 4. 66e-77; Fig. 5A  and see Supplementary Dataset S2). Again these significant associations were mostly (274 out of 293) with brain imaging variables. As is shown in Fig. 5B , gray matter volumes of the left inferior, medial temporal, and occipital regions correlated positively with vertical skew, whereas the homologous regions in the right hemisphere showed negative correlations. The microstructural Downloaded from https://academic. oup. com/cercor/article/31/9/4151/6218944 by Max-Planck-Institute for Psycholinguistics user on 16 August 2021 metrics of white matter tracts also showed a similar complementary interhemispheric pattern (Fig.
5C ). More details can be seen in Supplementary Figures  S5B, S7 , and Dataset S2. Certain early life factors have been shown to influence handedness in the UK Biobank, including birthweight, multiple birth, breastfeeding, and country of birth within the United Kingdom (de Kovel et al. 2019). Maternal smoking was not found to influence handedness in the UK Biobank  (de Kovel et al. 2019) , although has been implicated in handedness by other studies (e. g. ,  Dragovic et al. 2013). We looked specifically into the pheWAS results for these early life factors, for the 2 brain skew measures. Both skew measures showed significant associations with variables related to place of birth ("country of birth," P = 3. 53e-15 for horizontal skew, see Supplementary Dataset S1; "place of birth in United Kingdom-north co-ordinate," P = 2.
56e-29 and "place of birth in United Kingdom-east coordinate," P = 8. 32e-17 for vertical skew, see Supplementary Dataset S2). In addition, breastfeeding (P = 0. 0030) and birth weight (P = 0. 025) showed nominally significant associations with vertical skew, whereas other associations were not significant (Ps > 0. 10). In the HCP dataset, analysis of 4 behavioral performance measures related to language (see Materials and Methods) showed a correlation between horizontal skew and oral reading recognition performance (Z = 3. 37, permuted P = 0. 0023, which survived Bonferroni correction for 8 tests, i. e. , 2 skew measures times 4 performance measures), such that individuals with more positive horizontal skew showed better oral reading recognition ability. We found no associations with other behavioral measures in the HCP (unadjusted Ps > 0.
Heritability and Gene Mapping for Brain Skews In the UK Biobank, significant but low SNP-based heritabilities were found for horizontal skew (h 2 = 3. 85%, 95% confidence interval [CI] = [0. 70%, 7. 00%], P = 0. 024 using GCTA  [Yang et al. 2011] ; h 2 = 4. 67%, 95% CI = [0. 63%, 8. 89%], P = 0. 029 using LDSC  [Finucane et al. 2015] ) and vertical skew (h 2 = 7. 64%, 95% CI = [4. 42%, 10. 86%], P = 3. 01e-05 using GCTA  [Yang et al. 2011] , h 2 = 12. 95%, 95% CI = [8. 94%, 16. 96%], P = 5. 56e-08 using LDSC  [Finucane et al. 2015] ). In the HCP dataset, which included twins, both skew measures also showed evidence for low heritability: horizontal skew h 2 = 9. 1%, P = 0. 041; vertical skew h 2 = 10. 1%, P = 0. 030. Vertical skew was slightly more heritable than horizontal skew in both datasets.
Genetic Correlations of Brain Skews with Other Traits Handedness has shown a low but significant heritability in the UK Biobank in 2 previous studies based on > 330 000 individuals: h 2 = 1. 8%, 95% CI = (1. 79%, 1. 81%; de Kovel and Francks 2019); h 2 = 1. 2%, 95% CI = (1. 197%, 1. 202%; de  Kovel et al. 2019; Wiberg et al. 2019). In another study based on several datasets including the UK Biobank, the SNP heritability of handedness was reported to be between 3% and 6% (Cuellar-Partida et al. 2020). (Note that the sample sizes for those studies were much larger than the present study, because the present study is limited to participants who also have brain MRI data available). Using LD score regression with the GWAS summary statistics for handedness (N > 330 000) from de Kovel and Francks (2019) and the summary statistics from our GWAS for brain skew measures, we found no significant genetic correlations of hand preference with the skews (Ps > 0.