dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work was supported in part by  JSPS Grants-in-Aid for Scientific Research (S) 24224011  and (C)25340010. Katja Matthes  and  Rémi Thiéblemont  acknowledge support from the  Helmholtz University Young Investigators Group NATHAN , funded by the  Helmholtz Association through the President's Initiative and Networking Fund , and the  GEOMAR Helmholtz Centre for Ocean Research Kiel. This work has been conducted within the framework of the WCRP/SPARC SOLARIS-HEPPA activity. Edited by: P. Haynes Reviewed by: two anonymous referees.
The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using metaanalysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets.
2013; Yamashita et al. 2011) , but prior studies of subcortical structures have tended to look at age and asymmetry as separate aspects of study  (Caviness et al. 1996; Giedd et al. 1996). Left-handedness has not been robustly investigated in relation to subcortical asymmetries, as there are so few left-handers in most datasets  (Foundas et al. 1998; Kloppel et al. 2007 ). Likewise, in clinical studies, possible effects of sex, age, and handedness have not often been investigated, either as a result of restricted inclusion criteria, or otherwise not considering these factors in their analyses (e. g. Kang et al. 2015; Yamashita et al. 2011). The present study was the first by the Lateralization working-group embedded within the ENIGMA (Enhancing Imaging Genetics through Meta-Analysis) Consortium  (Thompson et al.
2014). Our goal was to detect effects of sex, handedness, and age on the normal variability in subcortical asymmetries, through a harmonized multi-site study using meta-analysis methods, based on 52 healthy control and population-based datasets which comprised a total of 15,847 participants. All brain magnetic resonance (MR) images were analyzed using a single, uniform protocol, despite inevitable heterogeneity in image acquisition  (Hibar et al. 2015; Stein et al. 2012 ). This study was by two orders of magnitude the largest ever of asymmetry with respect to subcortical structures of the human brain, and factors affecting its variability. This allowed us to establish subtle but definitive findings of sex and age-related effects on some of the structures, where previously the literature has been inconsistent and contradictory (see Discussion).
We also measured the heritabilities of subcortical and hippocampal asymmetries in a large family dataset, as previous studies have suggested these to be partially heritable  (Eyler et al. 2014; Hulshoff Pol et al. 2006; Renteria 2013). This heritability screen is a valuable precursor to future genome-wide association studies of laterality in brain traits, as well as identifying genetic overlap between asymmetries and cognitive or psychiatric disorders.
Datasets The participating sites were members of the Lateralization working-group within the ENIGMA Consortium  (Thompson et al. 2014) , who contributed data from 52 independent samples to this study comprising a total of 15,847 healthy participants (7524 males and 8323 females). Samples were drawn from the general population or comprised healthy controls from clinical studies. Table  1  and Supplemental Information S1 summarize the datasets' sample sizes and age distributions. Each dataset and its image acquisition protocols are described in Supplemental Information S2. Handedness of participants was known for a subset of the overall sample. The method of assessment varied per dataset (see Supplemental Information  S3 ). An ambidextrous category was not included and only datasets with enough lefthanders to perform statistical comparisons were considered.
Image acquisition and subcortical segmentation Image acquisition and subcortical volume measurement has been described in previous reports from the ENIGMA Consortium (e. g. (Hibar et al. 2015; Stein et al. 2012)  , and is consistent enough to detect SNP effects at a genome-wide significant level, which individually account for less than 1 % of the variance in structure volumes. To summarize, T1-weighted brain structural MRI scans were acquired at multiple different sites using scanners of mostly 1. 5 or 3 Tesla field strengths. One dataset (QTIM) was acquired with a 4 Tesla field strength scanner. See Supplemental Information S2 for detailed information on manufacturers and image acquisition parameters per dataset. All sites followed the same protocol for segmentation of subcortical structures, volume computation, and quality control.
ini. usc. edu/protocols/imaging-protocols/). The protocol consisted of visually checking individual images, plotted from a set of axial slices. Volume estimates derived from poorly segmented structures (i. e. where tissue labels were assigned incorrectly) were excluded from each site's datasets and subsequent analyses. In addition, a number of checks were performed to assess potential errors in the left-right orientation of the data (see Supplemental Information S4 for details).
Within-dataset analyses For each dataset and each of the seven bilaterally paired structures, the volumetric asymmetries, descriptive and statistical analyses were computed at each participating site using a single Table  1  List of contributing datasets (arranged alphabetically in two columns), their sample sizes split by sex, and their median ages. Each dataset is also given a suffix number code for reference to Fig. 2  script in R (R Development Core Team; 2012), on tableformatted data. Asymmetry Indices (AI) were defined as the relative volume difference between the left and right structure in relation to its total bilateral volume: (Left -Right)/(Left + Right). To exclude possible outliers in volumes or AIs we used an adaptive SD threshold (SD Thresh ) depending on each dataset's sample size (N < 150 ⇒ SD Thresh = 2.
AI heterogeneity between datasets For each of the seven AIs we assessed heterogeneity due to dataset differences through analyses of variance, with 'dataset' and 'FreeSurfer version' as the main factors. For this analysis we re-computed the total AI variance for a given structure and partitioned it into between-and within-'dataset' contributions, and between-and within-'FreeSurfer version' contributions. This allowed us to calculate estimates of etasquared (η 2 ), i. e. , the percentage of the total variance explained by each factor. Given that individual sites ran their analyses on one version of FreeSurfer only, we computed main effects but not their potential interactions.
Meta-analyses We combined the test statistics obtained across datasets by means of random-effect meta-analyses  (Borenstein et al. 2010). This method calculates and tests the significance of a pooled effect while weighting each dataset's contribution to the overall effect by the inverse of its error variance. In contrast to a fixed-effect meta-analysis test, this method also takes into account the amount of variability present between effects from different studies in its calculation, and hence does not make strong assumptions regarding equal effects underlying all datasets  (Borenstein et al. 2010). For meta-analyses of sex and handedness effects, we used the mean group differences in residualised AIs and recomputed the standard errors from 95 % confidence intervals and degrees of freedom generated by the Welch's two sample t-tests.
For meta-analyses of age-effects, we used the coefficients from the ANCOVA regressions of AIs on age, and their corresponding standard errors. Including results based on too few observations is likely to reduce reliability, therefore we chose to test with a cut-off of 15 observations per group and include assessments of fail-safe N's for any significant finding (at a corrected alpha of P = 0. 007). The method used was Rosenberg's  (Rosenberg 2005) , which takes into account the weighted nature of the meta-analysis test, and its outcome can be interpreted as the number of studies averaging null-results which would be needed to render the observed p-value non-significant (P >= 0. 007). Furthermore, effect heterogeneity was assessed by Cochran's Q and the complementary Higgins' I 2 , which both assess the contribution of dataset differences to the observed pooled effect.
All tests were performed in R using the 'metafor' library  (Viechtbauer 2010). For the sex group comparisons, a 15-observation minimum threshold resulted in totals between 6867 and 6962 males versus 7708 to 7897 females, depending on the specific structure. For handedness, the totals were from 644 to 668 left handers versus 7298 to 7667 right handers. For metaanalyses of age-effects we applied the threshold of at least 15 observations per sex group and included an extra criterion based on the age-range of each dataset. Only results from datasets with a minimum 5-year range between their 1st and 3rd quartile (50 % of the dataset) were included. To assess the pattern of statistically significant age effects across the lifespan, we performed a post-hoc weighted metaregression of the age coefficients from each dataset on the corresponding median ages.
Heritability of AIs We estimated the heritability of volumetric asymmetries using the Genetics of Brain Structure (GOBS) dataset  (McKay et al. 2014; Mitchell et al. 1996). This analysis included data from 1170 subjects of Mexican-American ancestry, belonging to 71 extended pedigrees. Heritability estimates were derived from variance-component analysis  (Almasy and Blangero 1998). The method partitions the observed phenotypic variance into sub-components based on the relationship structures within the families, in order to estimate the proportion of overall phenotypic variance due to additive genetic effects. To calculate this family-based heritability estimate, the method requires large pedigrees and accurate kinship estimates between family members. For a more detailed description of the approach, applied to brain imaging measures, see  (Chouinard-Decorte et al.
AI heterogeneity between datasets We observed notable heterogeneity in the AI distributions across datasets (Table  2  and Supplemental Information S6). Except for the hippocampus and putamen, dataset heterogeneity explained over 10 % (η 2 > 0. 1) of the total observed variance per structure. Likewise, heterogeneity attributable to different versions of FreeSurfer was also substantial, with η 2 > 0. 1 for AI's of the nucleus accumbens, globus pallidus and thalamus. Independent of dataset mean differences, the nucleus accumbens showed the most variable AI estimates, and the caudate nucleus was the least variable (see Table  2 ). The average variability around AI means, as a proportion of bilateral volume (σ 2 within *100), was 7. 8 % for the nucleus accumbens and 2. 5 % for the caudate nucleus.
Meta-analysis of group differences by sex and handedness After adjusting the significance threshold to P = 0. 007 for multiple testing of seven structures, meta-analyses showed significant differences in AIs between males and females for the globus pallidus and putamen (Table  3  and Fig. 2 ), corrected for covariate effects of age and intracranial volume (ICV) within datasets. The direction of the sex difference for the putamen was negative (see Table  3 ), indicating a lower AI in males versus females, i. e. a rightwards shift in asymmetry in males. The opposite was found for the globus pallidus, where a leftward shift in AI was observed in males relative to females. Table 3 also reports the results of the studyheterogeneity and fail-safe N estimations. We observed no significant heterogeneity in sex effects across datasets for putamen and globus pallidus.
Meta-analysis of age effects on AIs After adjusting the significance threshold to P = 0. 007 for multiple testing over seven structures, meta-analysis revealed a significant effect of age on the AI of the putamen (see Table  4  and Fig. 3a ), corrected for covariate effects of sex and ICV within datasets. A positive pooled effect for the putamen indicated increasingly leftward shifts in asymmetry with increasing age. Table 4 also reports the results of the study-heterogeneity tests. While the heterogeneity tests for age effects on putamen AI were statistically significant, the estimate of a fail-safe N (n = 85) suggested that a biasing influence of study heterogeneity on the results was unlikely. In our post-hoc analysis of age effects, by means of weighted meta-regressions, the putamen showed effects that changed across the median ages of our samples.
Establishing effects of age, sex, and genetics There is an inconsistent literature regarding basic biological factors that may affect subcortical and hippocampal asymmetries, including age, handedness, and sex. Subcortical asymmetries are subtle compared to some cerebral cortical asymmetries, and have so far only been assessed in small sample sizes, often with different analysis methods across studies (see Introduction). Compared to prior reports on subcortical asymmetries, our study analyzed a large number of datasets worldwide using a harmonized protocol and  meta-analysis methods. To our knowledge, this was by far the largest ever study of healthy variation in any aspect of human brain asymmetry. The 52 datasets had technical and demographic differences, which appeared to influence the levels of asymmetry measured, but in this respect were representative of the heterogeneity that exists across cohorts worldwide.
Our data show which subcortical asymmetries are heritable and suitable for detecting subtle modulatory effects and group differences. Taken together, our heritability-and meta-analyses indicate that asymmetries of the putamen, globus pallidus, hippocampus and thalamus are the most likely structures through which genetic variation may impact lateralization for human cognition, its variability, and susceptibility to brain disorders. From a developmental perspective, some human CNS lateralizations change throughout life  (Kovalev et al. 2003). Asymmetries are detectable during fetal gestation behaviorally  (Hepper 2013 ) and anatomically  (Corballis 2013) , so differential development between the two human brain hemispheres must, at least in part, be genetically coded in utero  (Francks 2015).
Three prior reports have suggested genetic contributions to variability in subcortical asymmetries based on twin-based heritability estimates. One found evidence for amygdala volumes being under strong genetic control, with higher heritability for the left than the right hemisphere (h 2 = 0. 80 and 0. 55, respectively;  (Hulshoff Pol et al. 2006) ). Another found that genetic contributions to left and right volume variability were partly distinct for the nucleus accumbens and globus pallidus in particular  (Eyler et al. 2014). A third found significant heritabilities of asymmetry indexes for the caudate nucleus and putamen, h2 = 0. 17 and 0. 32, respectively  (Renteria 2013). Fig. 2  Forest plots of the mean sex differences in AIs per dataset, for the structures that showed significant sex effects in metaanalysis.
For each structure, the datasets are ordered top-tobottom by their estimated sex difference. The identities of the datasets are given by the numbers in the left-hand columns, with reference to Table  1. The size of a square is proportional to the weights assigned in metaanalysis. The confidence intervals are shown, as well as dashed vertical lines to indicate the point of no mean sex difference In terms of developmental biology and molecular genetics, the best studied model organism for CNS lateralization is the zebrafish. During the zebrafish's development, there is a leftbiased migration of a midline structure (the parapineal organ) that results in differential innervation of the bilateral epithalamus into the surrounding tissue, which later affects other brain regions  (Concha et al. 2009).
2013). We tested only linear effects of age at the dataset level, and these coefficients were meta-analyzed. Non-linear changes in AI with age might have gone undetected in our analysis, and may affect the measured linear effects. However, these metaanalyses were restricted to age effects observed in datasets with at least a 5-year age-range between the first and third quartile of participants. Most of our datasets had median ages between 20 and 60 years, so a linear regressor would have captured main effects of age on AIs, in these datasets, even if there were subtle non-linear effects. However, these factors should be considered when interpreting the pooled effect sizes reported. Perhaps surprisingly, handedness had no detectable effect on subcortical asymmetries. However, as there are fewer lefthanders than right-handers, the effective sample size was roughly one sixth for this analysis than for our analysis of sex differences.
Dataset heterogeneity Studies of subcortical structure have been greatly advanced by in vivo imaging. Even so, findings of population-level mean lateralizations of subcortical structures have been inconsistently reported. For example, there have been reports of the putamen being leftward lateralized on average (i. e. the left volume larger than the right  (Giedd et al. 1996; Kang et al. 2015) , as well as rightward lateralized  (Abedelahi et al. 2013). Likewise the globus pallidus has been reported as leftward lateralized  (Kang et al. 2015) , as well as rightward lateralized  (Wyciszkiewicz and Pawlak 2014). Similar discrepancies have also been found for the hippocampus  (Kang et al. 2015; Niemann et al. 2000; Shi et al. 2009) , amygdala  (Makris et al. 2004; Niu et al. 2004; Szeszko et al.
1999)  and the caudate nucleus  (Abedelahi et al. 2013; Glenthoj et al. 2007; Raz et al. 1995; Vernaleken et al. 2007 ). Here we used uniform image processing protocols, but our analysis showed substantial differences in mean AIs across datasets, which were partly attributable to different versions of FreeSurfer (see Table  2  and Supplemental Information S3 ). However, the majority of datasets (39 out of 52) were processed using version 5. 3, so that our ability to assess the The ability to distinguish different structures using MRI depends on the contrast achieved between different tissues. Subcortical structures and the surrounding tissue are often imperfectly contrasted, so that automated methods of image analysis must rely to some extent on atlas-derived information. These are often based on manual segmentations of existing datasets, which will reflect any mean asymmetries present in those datasets  (Han and Fischl 2007; Patenaude et al.
2015). However, given the caveats outlined above, we are cautious about interpreting the mean population AIs at face value. Different AI means across datasets may indicate which structures are more or less susceptible to methodological biases. The mean AIs for the hippocampus, amygdala, and putamen differed the least between datasets. The mean AI of the thalamus, on the other hand, showed the highest heterogeneity attributable to dataset heterogeneity (including FreeSurfer versions), and at the same time showed one of the strongest population-level AI lateralizations. This pattern is in line with our previous report that the hippocampus AI showed the highest scan-rescan correlation of all structures quantified with FreeSurfer (among the seven structures studied here), while the thalamus showed the second lowest scan-rescan correlation, in subjects scanned twice using varying protocols, and sometimes using different scanners with different field strengths  (Guadalupe et al.
2014b). In contrast to the substantial heterogeneity across datasets in mean AIs for some structures, there was less evidence for dataset heterogeneity in the effects of sex on mean AIs. We detected stable sex differences in AIs regardless of differences in age or ICV between and within datasets, and the sex differences were highly significant in our meta-analyses. The structures for which we detected sex differences in AIs showed L > R population-level asymmetry. For the globus pallidus this was more pronounced in males, while the opposite was observed for the putamen.
Implications for future studies Our study underlines the utility, and indeed the necessity, of analyzing subtle subcortical asymmetries in vast samples. Regarding clinical studies, some brain disorders may be associated with larger alterations in subcortical asymmetries than variables such as sex, handedness, and age. Nonetheless future studies linking subcortical asymmetries to disorders should be better powered if they analyze larger samples than used previously. Such studies will be possible within the ENIGMA Consortium. It is reassuring that consistent sex differences could be measured in our study, even when AI means varied across cohorts. Some AIs were also heritable, based on studying relative-pair similarities. It is therefore clear that automated segmentation methods can measure meaningful individual differences in subcortical and hippocampal volumetric Table  5  Heritability estimates for the AIs, their corresponding standard errors and P-values, based on a large family dataset (GOBS).
Meta-analyses results for the age coefficients on AIs, corrected for sex and ICV. The significance threshold was Bonferroni-adjusted to 0. 007 for the seven comparisons. Cochran's Q and Higgins' I 2 are the statistics for the heterogeneity of effects. Fail-safe N estimates are also given for the putamen. The statistically significant results are highlighted in bold Results from meta-analysis of age effects. aForest plot of the age coefficients for each dataset on putamen AI. The datasets are ordered topto-bottom by their estimated age coefficient. The identities of the datasets are given by the numbers in the left-hand columns, with reference to Table1. The size of a square is proportional to the weights assigned in meta-analysis. The confidence intervals are also depicted, as well as dashed vertical lines to indicate the point of an age coefficient with value zero.
Acknowledgments Open access funding provided by  Max Planck Society. The QTIM study thanks research assistants  Marlene Grace ,  Ann Eldridge ,  Richard Parker ,  Lenore Sullivan ,  Lorelle Nunn ,  Kerrie Mcaloney ,  Kori Johnson ,  Aaron Quiggle , and  Natalie Garden , radiographers  Matthew Meredith ,  Peter Hobden ,  Kate Borg ,  Aiman Al Najjar , and  Anita Burns  for acquisition of the scans, and  David Smyth ,  Anthony Conciotrorre ,  Daniel Park , and  David Butler  for IT support. The IDIVAL-PAFIP study thanks the  IDIVAL Neuroimaging Unit  for its help in the technical execution of this work. Both Sydney MAS  and OATS thank their respective research teams. The LBC1936 study would like to thank  Catherine Murray  for recruitment of the participants, the radiographers and other staff at the  Brain Research Imaging Centre.
The Zurich OCD study thanks  Reto Iannaccone  for supporting the data collection. BIG would also like to thank  Hans van Bokhoven  for his contributions to the  Cognomics initiative. Finally, all sites within the ENIGMA-Lateralization working group are very grateful to the involved participants for their generosity of time and willingness to participate in each of the collaborating studies. All authors have no conflict of interests to declare for this study. Compliance with ethical standards ENIGMA protocol development is supported by the  NIH   BD2K  BBig Data to Knowledge^initiative ( U54 020403 ; PI:  Paul Thompson ) which is funded by a cross- NIH  partnership. The AMC OCD dataset was supported by grants from  ZonMW  (grant numbers:  3160007 ,  91676084 ,  31160003 ,  31180002 ,  31000056 ,  2812412 ,  100001002 ,  100002034 ),  NWO  (grant numbers:  90461193 ,  40007080 ,  48004004 ,  40003330 ), and grants from the  Amsterdam Brain Imaging Platform, Neuroscience Campus Amsterdam  and the  Dutch Brain foundation.