dataesr/scientific-paragraphs-categorization huggingface dataset:
Discovery Sample description At the time of download (July 2017), T1w images were available for 10,102 participants of the UK Biobank (UKB) project. None of the participants had withdrawn consent after the data was collected. We excluded 175 participants with T1w images labelled as unusable by the UKB, leaving 9,928 MRI scans to process. T2 FLAIR images were available for 9,755 of those. The FreeSurfer processing failed or did not complete within 48 hours for a handful of participants: 37 for cortical processing, 19 for subcortical, including 17 for whom both processing failed. For simplicity, we chose not to re-run image processing on these participants as their exclusion should have a minimal impact on the results obtained from the full sample. Excluded individuals are described in Dataset S1.
Our final sample comprised 9,890 participants with usable cortical data, 9,908 with subcortical data and 9,888 with both cortical and subcortical data. This sample consisted of 9,888 adults aged 62. 5 on average (SD=7. 5, range 44. 6-79. 6) and comprised 52. 4% of female participants. We excluded 391 participants with extreme brains (outliers) or likely to have a large effect on the analyses (see Appendix S1 for details of QC and Dataset S1 for description of the excluded participants).
Replication Sample description Replication data set was downloaded in May 2018 and consisted in an additional 4,942 participants with a T1w image. Image processing and phenotype selection were identical to that of the discovery sample. This led to the exclusion of 381 participants whose processing failed and 238 excluded from QC. The final sample (N=4,323) included in the replication analysis was on average 63. 1 years old (SD=7. 46, range 46. 1-80. 3) with 52. 1% of females. The age difference between discovery and replication sample was small but significant (p=9. 02e-7). See Dataset S1 for a full description of replication participants (final, QCed and failed processing) in addition to a comparison of the discovery and replication samples.
, 2013). We downloaded the processed images  (Marcus et al. , 2011)  and performed ENIGMA shape analysis  (Gutman et al. , 2013; Gutman et al. , 2012)  to extract vertex-wise measurements of the subcortical thickness and curvature. As for the UKB sample, a total of 654,386 vertex measurements were extracted for each individual. We excluded 24 outliers with extreme brains or likely to bias the analyses (see Appendix S1 and Dataset S2 for description of excluded participants).
Sample description As per the HCP "1200 Subjects data release" (1 st of March 2017), 1,113 participants were scanned on the 3T MRI and underwent extensive behavioural testing. Participants were mostly (54. 4%) females and were 28. 8 years old on average (SD=3. 7, range 22-37). The sample comprised 455 twins (41. 0%), 286 monozygotic twins (138 complete pairs) and 169 dizygotic twins (78 complete pairs). In addition, siblings and half siblings of twins were also recruited which resulted in only 445 distinct families in the sample.
Variables used For the HCP sample, we included 161 variables, some of which were also available in the UKB (e. g. demographics, cognition, physical assessment, blood assay or psychiatry). We also included interesting variables only present in the HCP sample: personality, emotion, in depth mental health assessment (Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) and Adult Self Report (ASR)  (Achenbach, 2009; Achenbach et al. , 2003) ), detailed cognition, Pittsburgh sleep index (PSQI)  (Buysse et al. , 1989) , or results from the urine drug tests (see Dataset S2).
2 ) with all 654,386 vertex-wise grey-matter measures extracted during image processing, as well as with covariates (acquisition, age, sex and brain/head size variablessee Methods). The R 2 may be interpreted as the proportion of variance in a phenotype captured by all grey-matter morphology. Figure  1  shows only the significant results (Bonferroni significance threshold; p UKB_discovery =0. 05/175=2. 8e-4, p HCP =0. 05/160=2. 9e-4) with the full results available in Dataset S3, S4 (see Figure  S2  for positive control associations). Grey-matter structure was strongly associated with age (R 2 UKB =0. 77, SE=0. 018; R 2 HCP =0. 88, SE=0. 10), sex (R 2 UKB =0. 66, SE=0. 012; R 2 HCP =0. 56, SE=0. 059), as well as height (R 2 UKB =0. 22, SE=0. 011; R 2 HCP =0. 47, SE=0. 060) weight (R 2 UKB =0.
47, SE=0. 019; R 2 HCP =0. 81, SE=0. 099) and BMI (R 2 UKB =0. 57, SE=0. 024; R 2 HCP =0. 92, SE=0. 12). Measures of build, body fat and metabolism were also associated with grey-matter structure (R 2 UKB =0. 45, SE=0. 019 with waist circumference, R 2 UKB =0. 24, SE=0. 013 with body fat percentage, R 2 UKB =0. 19, SE=0. 009 with basal metabolic rate; corresponding measures not available in the HCP dataset). In addition, grey-matter structure was associated with measures of strength in both samples (e. g. hand grip: R 2 UKB =0. 074, SE=0. 009; R 2 HCP =0. 23, SE=0. 58) and levels of physical activity (R 2 UKB ranging between 0. 059-0. 25, not-significant in the HCP). Grey-matter structure was further associated with cognitive domains (R 2 UKB ranging in 0. 048-0. 13, R 2 HCP in 0. 34-0. 57), smoking (R 2 UKB ranging in 0.
95, excluding blood assay, Figure  S3b ). Full replication results have been added to Dataset S4. In the UKB (discovery), results and conclusions did not change regardless of fitting a single random effect or several random effects each corresponding to one of the greymatter modalities (i. e. cortical thickness, cortical surface, subcortical thickness, and subcortical area) (Figure  S4 ). In the HCP, we observed 3 extra significant associations between grey-matter structure and cocaine (urine test), self-reported number of times used cocaine or hallucinogens. Similar to the association found with opiate (urine test), the small number of positive participants warrants replication. We chose not to include these 3 variables in the subsequent analyses. 2 1  As some covariates may be correlated, the R 2 was calculated by adding progressively the covariates in that order: acquisition and processing variables (labelled "other"), age, sex and head size (ICV, total cortical thickness and surface area).
Dataset S3 ). Such finding is consistent with the reported association between body size and income or socio-economic status in the UKB  (Tyrrell et al. , 2016). When further correcting for weight and BMI another 14 associations did not remain significant including educational attainment, frequency drinking alcohol, most diet items (cereal, dried fruits, poultry, processed meat), time spent driving, red blood cell count, frequency of walks and small exercise. Notably, the brain-morphometricity of the depression score could be completely explained by differences in weight and BMI (R 2 baseline =0. 050, SE=0. 018; R 2 baseline+height =0. 048, SE=0. 017, R 2 baseline+height+BMI+weight <0. 001, SE=0. 007) and none of the associations between grey-matter structure and depression symptoms remained significant (Tiredness, Anhedonia, Poor appetite-overeating, R 2 baseline+height+BMI+weight <0.
014). The brain- morphometricity estimates (correcting for body size) in the UKB replication sample aligned with results from the discovery sample (cor=0. 90), except for age and sex showing larger associations with grey-matter structure in the replication analysis (Figure  S6 ). Similarly, 4 of the 27 associations did not remain significant in the HCP dataset: with fluid intelligence (total skipped), hand grip strength, ASR thought and attention problems and frequency of being drunk in the past year. Though we had limited power to detect associations smaller than R 2 of 0. 2 in this sample (see 2. 6). In light of these results, we chose a conservative approach to present in the main text results that include body size variables as covariates, though the analyses using baseline covariates can be found in the supplementary.
3. 3. Grey-matter correlations We estimated grey-matter and residual correlations (r GM and r E ) between the phenotypes that showed significant brain-morphometricity in the univariate analyses. r GM can be interpreted as the proportion of grey-matter vertices similarly associated with both traits, while r E offers insight into factors, shared between the traits, but that do not relate to grey-matter structure (e. g. other brain modalities, non-brain contribution). A weighted sum of r GM and r E make up the phenotypic correlation (see 2. 3. 3). In this section, we controlled for height, weight and BMI on top of the baseline covariates, which yields a conservative set of 39 phenotypes and prevents results from being confounded by body size (Figure  2 ; Datasets S5 (UKB), S6 (HCP) for point estimates).
89, SE=0. 086; r GM-replication =-1. 0, SE=0. 12; sign due to coding of the variable, see Dataset S1), smoking status (r GM =0. 71, SE=0. 13) and past tobacco use (r GM =-0. 64, SE=0. 14). Finally, we identified unexpected large grey-matter correlations. For example, cheese intake and forced expiratory volume were both correlated (r GM =1. 0, SE=0. 11) with fluid intelligence, and waist circumference correlated with overall health rating and pulse rate (r GM >0. 67). Overall, 9 out of the 26 significant correlations replicated in the UKB replication sample; sign of the grey-matter correlation was always consistent between discovery and replication analyses (Table  S1 ). In the HCP, we observed positive grey-matter correlations between cognition domains (Figure  2  and Dataset S6) and between IQ dimensions and education level.
Using the replication UKB sample, we replicated 633 out of the 975 significant ROItrait associations (p<0. 05/975). Most associations were found with age, sex and body size variables, though we also replicated associations between subcortical volumes and hand grip strength or time spent watching TV (Dataset S8). In addition, the magnitude of the associations with age, and body size were greatly similar between discovery and replication analyses (Figure  S9 ). For sex, we observed larger ROI associations in the UKB replication sample (Figure  S9 ), consistent with the larger brain-morphometricity observed in this sample (Figure  S6 ).
05/58, See FigureS12 and DatasetS11). Predicted age correlated with chronological age (r=0. 72 in the discovery, r=0. 70 in the replication), while predicted sex also strongly associated with the observed value (AUC of 0. 90 and 0. 89). Greymatter scores of body shape (under the baseline covariates) were also significantly correlated with the observed values (r=0. 25 for height, r=0. 29 for body fat percentage, r=0. 39 for weight and hip or waist circumference, r=0. 45 for BMI). Finally, grey-matter scores of BMI correlated positively with depression symptom count (r=0. 10, p-value<1e-14), as expected from the brain-morphometricity of depression being limited the covariation with body size. It even outperformed the grey-matter score built from the depression score itself (r=0. 05, p-value<1e.
19). None of the other grey-matter scores significantly correlated with a similar HCP variable. Without correcting for body size, 19 BLUP scores correlated to corresponding variables (Dataset S11, Figure  S12 ). For example, scores for BMI, body fat percentage, hip or waist circumference also correlated positively with BMI (r=0. 21, p-value<1. 2e-3), while scores for height and weight also correlated with the observed phenotypes (r Height =0. 17, r Weight =0. 19). Finally, scores build from diet items or quantifying activity levels significantly predicted BMI in the HCP. Labels highlight some of the significant prediction having the greatest accuracy. As predicted by the theory, the prediction accuracy is capped by the total association R 2 (points below the diagonal). In addition, out of sample prediction results in a lower prediction accuracy than in-sample prediction.
3. 7. Best cortical processing We compared the brain-morphometricity estimates obtained by varying the cortical processing options: smoothing of the cortical meshes and applying coarser meshes available in FreeSurfer (see 2. 10). We performed this analysis on the UKB discovery and replication samples as the large SE of the estimates in HCP would limit the interpretation of the results. We used baseline covariates as in Figure  1. We found that applying smoothing (5-25mm) or reducing the cortical mesh complexity always led to a lower point estimate of brain morphometricity in the UKB discovery (Figure  4 ) and replication (SFigure 13, Datasets S12-13 for full tables) samples. As such, the fsaverage cortical mesh with no smoothing can be deemed "best" processing for all phenotypes considered.
In our post-hoc LMM-ROI analysis, we found those phenotypes to be associated with most cortical and subcortical regions (Figure  S8-11). Our results for sex are consistent with results from the UKB first release (N=5,216, using ROI average  (Ritchie et al. , 2018) ), while several studies have previously reported associations between BMI and several grey-matter measurements  (Cole et al. , 2013; Gupta et al. , 2015; Kurth et al. , 2013; Masouleh et al. , 2016; Medic et al. , 2016; Opel et al. , 2017). Despite such large and widespread pattern of association between body-size and grey-matter we did not observe significant brain-morphometricity for (selfreported) anorexia, bulimia or binge eating though the small numbers (<30 cases in the UKB, Dataset S1) limit the interpretability of the results.
We observed moderate to small associations (R 2 <0. 4) between grey-matter and substance use (tobacco and alcohol), maternal smoking around birth, blood assay results, education and income level, diet, depression score and symptoms, twin-status as well as cognition domains (Figure  1 ). The latter replicated and expanded the result of an analogous analysis on an early release of the HCP (N=150) and the ADNI dataset  (Sabuncu et al. , 2016). We note that handedness was only weakly (R 2 UKB =0. 04, R 2 HCP <0. 001, not significant) associated with cortical or subcortical grey-matter coherent with the conflicting results reviewed in (Jin  Kang et al. , 2017). Our results indicate that individuals that display similar grey-matter structure tend to also be similar in term of age, sex, body size, cognition, activity levels, substance use and lifestyle.
We did not detect significant association between grey-matter morphometry and psychiatric diagnoses (lifetime self-reported), sleep phenotypes or lifetime stress/traumas (Dataset S3) despite previous morphometricity reports from case-control samples of autism, schizophrenia and ADHD  (Sabuncu et al. , 2016). When controlling for height, weight and BMI in the analyses, many of the associations became non-significant: such as those between grey-matter and diet, activity levels or depression score/symptoms (R 2 <0. 04; Dataset S3). Furthermore, we did not detect any significant association between grey-matter structure and other depression related phenotypes (e. g. self-reported diagnosis by a doctor, MDD case-control status as used by the Psychiatric Genetic Consortium  (Wray et al. , 2018) , and neuroticism; Dataset S3).
This meant that we likely reported conservative estimates of brain-morphometricity and fewer significant grey-matter correlations, predictive grey-matter scores or trait-ROI associations (see Figures S7, S12 and Dataset S3 for uncorrected results). The large covariation of body-size with the phenome (at least with the variables we selected) is still of interest but it may be more powerful to study directly BMI for example. This is exemplified by the greater prediction accuracy achieved by a BMI grey-matter score (vs. depression specific score) when predicting depression score. Such behaviour can be anticipated based on the large r GM between BMI and depression score (Figure  S7 ), combined to the larger brain-morphometricity of BMI  (Dudbridge, 2013). Finally, our conservative approach should remind us to be careful when interpreting associations.
For example, one should not conclude about actionable links between diet and depression based on the large and significant grey-matter correlations, as it might be mediated by body size. Though, directionality of the associations will need to be established to conclude in this case. We estimated between-trait grey-matter correlation (Figure  2 , Dataset S5, S6) that quantifies the proportion of brain markers shared between two traits and found significant relationships between cognition domains, between tobacco and alcohol consumption or between measures of fitness. Large grey-matter correlations between seemingly unrelated traits (e. g. fluid IQ and cheese intake [replicated], waist circumference and pulse rate or overall health rating) raise questions about the nature of the relationships between those variables (causality, true positive or confounded association?).
, 2006; Hanlon et al. , 2016; Prom-Wormley et al. , 2015). Alcohol intake was also associated with left thalamus thickness in the UKB, consistent with the significant grey-matter correlation (Figure  2 ) between the two traits. The thalamus has been implicated in alcohol-related neurological complications (e. g. Korsakoff's syndrome)  (Pitel et al. , 2015)  but may also be associated with regular alcohol usage  (Cardenas et al. , 2007; Pitel et al. , 2015)  or alcohol use disorder  (van Holst et al. , 2012). Maternal smoking around birth was further associated with the thalamus, putamen, hippocampus and pallidum, as well as temporal and occipital ROIs. In addition, diagnosis of diabetes was associated with area of the left superior-frontal cortex (Dataset S8, Figure  S8 ). Nervous system complications of diabetes (sometimes labelled diabetic encephalopathy) are widely accepted  (Mijnhout et al.
A bias due to twins is unlikely as our results on the full HCP sample yielded always similar (e. g. Fluid IQ) or lower (e. g. attention) brain-morphometricity estimates than reported by Sabuncu et al. , who selected 1 subject per family  (Sabuncu et al. , 2016). Finally, the grey-matter similarity of twins was greater than average but in line with the similarity seen between unrelated individuals (Appendix S2), which led us not to exclude twin pairs from the analyses (contrary to what is seen/done in genetics). Due to recruitment choices, the UKB and HCP samples do not contain many psychiatric cases (outside of the highly prevalent MDD, see  (Fry et al. , 2017)  on the healthy volunteer bias in the UKB) and cannot replace the large case-control initiatives (e. g. ENIGMA disease groups). Despite using some of the largest imaging samples available, our ROI based and grey-matter score analyses suffered from limited statistical power, though more data is currently being collected by the UKB.
28-0. 36 range). No other association survived multiple testing correction (Figure S10 and Dataset S9). Body size variables were strongly associated with subcortical structure under the baseline model for the others). The associations between grey-matter structure and body size were pervasive (72/164 significant ROIs associations with height, 109 with waist circumference, 105 with BMI) (Figure S11, Dataset S10), suggesting that when acting as confounders height, weight or BMI could lead to false positives in many brain regions.
Next, significant ROI associations included sex, associated with all subcortical volumes (R 2 in the 0. 0049-0. 024 range for thickness, 0. 0058-0. 027 for area) and with many cortical regions (R 2 in the 0. 0011-0. 0076 range for cortical thickness, 0. 0019-0. 014 for cortical surface area) (Figure S8 and Dataset S7). Maternal smoking around.
2 vel of the CM-iFT junction was correlated with the length of the iFT, which is consistent with the relatively low variability of the iFT-DS junction level. This correlation had already been demonstrated by Pinto et al  12  but is not consensual. 2  Besides, we do not confirm the significant correlation between the level of the iFT-DS junction and the length of the eFT 2 , nor the correlation between the levels of the CM-iFT and the iFT-DS-eFT junctions. 12  These discordant results could be related to statistical power and a larger sample may be required to conclude. Ethnical differences may also have contributed as not all previously published data were obtained in European specimens. Ne ertheless, no correlation has been constantly found in the other studies. S bsequently, the clinical application of these results requires caution because of several biases including measurements after removal, in old specimens and overestimation of nervous structures in cadavers.
Data Processing We performed whole brain deterministic tractography and data processing using a validated processing pipeline  28, 29. T1-weighted and DW images were processed using BrainVISA 4. 2 (http://www. brainvisa. info) and Connectomist 2. 0, respectively. Data were assessed for movement, susceptibility and noise artefacts for both T1 and DW acquisitions. For DW data, an orientation distribution function at each voxel was computed, containing information about the angular profile of diffusion within each voxel, which in turn allows detection of principal directions of diffusivity similar to the main eigenvector of DTI models. We used the Q-ball imaging (QBI) model which better models diffusivity in WM areas of complex architecture and organisation (i. e. crossing fibers)  30  than the classical diffusion tensor model.
Funding This work was supported by the  French-German ANR/DFG  " FUNDO " project, the  French ANR  under the  "VIP" (MNP 2008 ) project, the Investissements d'Avenir programs managed by the  ANR  under references  ANR-11-IDEX-004-02  ( Labex BioPsy ) and  ANR-10-COHO-10-01  and  CONICYT FONDECYT 1161427  y  CONICYT PIA/Anillo de Investigación en Ciencia y Tecnología   ACT172121. This project has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement No  720270  ( HBP SGA1 ).
Understanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale brain organization and function can be described in a compact manner with multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. Here, we present BrainSpace, a Python/Matlab toolbox for (i) the identification of gradients, (ii) their alignment, and (iii) their visualization. Our toolbox furthermore allows for controlled association studies between gradients with other brain-level features, adjusted with respect to null models that account for spatial autocorrelation.
To offer flexibility in implementation, we provide our toolbox in both Python and Matlab, two languages widely used in the neuroimaging and network neuroscience communities. Associated functions are freely available for download (http://github. com/MICA-MNI/ BrainSpace) and complemented with an expandable online documentation (http://brainspace. readthedocs. io). We anticipate that our toolbox will assist researchers interested in studying gradients of cortical organization, and propel further work that establishes the overarching principles through which structural and functional organization of human and nonhuman brains gives rise to key aspects of cognition.