dataesr/scientific-paragraphs-categorization huggingface dataset:
We also found associations with clinical phenotypes such as diabetes, depression score and depression symptoms. We replicated 23 of the 58 associations listed above in the UKB replication sample (p<0. 05/58; Figure  S3 , Dataset S4). We did not detect any significant association between grey-matter structure and other psychiatric variables (diagnoses and symptoms), self-reported stresses and traumas, or neuroticism (Dataset S3). The interested readers may also find the morphometricity estimates for the full UKB sample (inverse-variance weighted metaanalysis) in Dataset S3. 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 (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.
Adjustment for possible confounders The large associations between grey-matter structure and height, weight, BMI, waist and hip circumference (Figure  2 ) led us to perform a sensitivity analysis to evaluate their contribution to the brain-morphometricity of the traits studied. We repeated the analysis further controlling for height, weight and BMI, which yielded lower R 2 estimates (Figure  S5 ) and fewer significant associations with grey-matter structure. Thus, when correcting for height in the UKB, 4 of the 58 associations with grey-matter structure did not remain significant: household income, monocyte percentage, beef intake, and time spent using computer, 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 conditioning on weight and BMI (Tiredness, Anhedonia, Poor appetite-overeating, R 2 baseline+height+BMI+weight <0. 014). Yet, even after controlling for body size, we still detected a significant morphometricity for cheese intake as well as time watching TV (Dataset S3), suggesting that these behaviours are associated with brain structure irrespective of body size.
The morphometricity estimates in the UKB replication sample aligned with those from the discovery sample (cor=0. 90), except for age and sex that showed larger associations with grey-matter structure in the replication analysis (Figure  S6 ). In the HCP dataset, after controlling for body size, four of the 27 associations did not remain significant (Dataset S4) though we had limited power to detect associations smaller than R 2 of 0. 2 in this sample (see Appendix S7). In light of these results, we chose a conservative approach to control for body size variables in the main text, though the analyses using baseline covariates can be found in the supplementary. We acknowledge (see discussion) that this may be overly conservative, by implicitly making strong assumptions about body size acting as a confounding factor.
Grey-matter correlations We estimated grey-matter correlation (r GM ) between the phenotypes that showed significant brain-morphometricity in the univariate analyses (Figure  2 ). r GM can be interpreted as the correlation between the grey-matter vertices associations with each trait. We controlled for height, weight and BMI on top of the baseline covariates, leaving a conservative set of 35 UKB (18 HCP) phenotypes (Figure  3 ; Datasets S5 [UKB], S6  [HCP] ). In the UKB, we observed significant positive grey-matter correlations between cognition domains, substance use phenotypes or between measures of physical activity (Figure  3 ). In addition, we found 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 was 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 (p<0. 05/26 i. e. p<1. 9e-3, Table  S1 ). In the HCP, we also observed positive grey-matter correlations between cognition domains or between the two tobacco related phenotypes. Though, unlike in the UKB, we found a significant r GM between IQ dimensions and education level (Figure  3 , Dataset S6). For completeness, we estimated grey-matter correlations under the baseline model (Figure  S7 ), which reveals many large grey-matter correlations between measures of body size and diet, blood assay, activity levels and depression symptoms and score. These results further highlight that in the phenome, the brain-morphometricity of some traits may be accounted for by the covariation between these phenotypes and body size measurements.
6. 4. Associations with grey-matter structure of specific cortical and subcortical regions We investigated the brain-morphometricity of traits by estimating the association with grey-matter structure of specific cortical  (Desikan et al. , 2006)  and subcortical regions, correcting for multiple testing (Bonferroni significance threshold of 0. 05/(164*39)=7. 2e-6 in the UKB, 1. 2e-5 in the HCP). We found many significant ROIs associations with UKB phenotypes, including age, sex, maternal smoking around birth, fluid intelligence, diabetes or substance use (Figure  S8  and Dataset S7). In particular, the associations between greymatter structure and body size were pervasive (72/164 significant ROIs associations with height, 109 with waist circumference, 105 with BMI) (Figure  S9 , Dataset S8), suggesting that when acting as confounders height, weight or BMI could lead to false positives in many brain regions.
We replicated 633 out of the 975 significant ROI-trait associations (p<0. 05/975, see Dataset S9 for results on UKB replication sample). Most replicated 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 S7-9). Overall, some of the trait-ROIs associations were partially redundant as indicated by a sum of R 2 (over ROIs) greater than the morphometricity (see Appendix S9 for detailed results and discussion, Dataset S10 for results in HCP).
Better cortical processing We compared the brain-morphometricity estimates obtained by varying the cortical processing options: smoothing of the cortical meshes and applying coarser FreeSurfer meshes. 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  5 ) and replication (Figure  S10 , Datasets S11-12 for full tables) samples. These differences were significant for a handful of variables (incl. age, sex, maternal smoking or body size) using a stringent definition of significance based on overlapping confidence intervals (Table  S3 ). Thus, the fsaverage cortical mesh with no smoothing may be deemed a better processing approach for at least some of the phenotypes considered.
26). We found similar prediction results in the UKB replication sample, with 29 associations reaching significance at p<1. 2e-3 (Table  1, S3 ). Prediction accuracy into the UKB replication sample was on par for most traits, though slightly greater for age and sex compared to the cross-validation results (Figure  4 , Table  1, S3 ). This is consistent with a larger training sample being used and larger morphomometricity observed in the replication set (Figure  S6 ). When not correcting for body size, 56/58 BLUP scores significantly correlated with the observed values in the 10-fold cross validation and 42 associations replicated using the UKB replication sample (p<0. 05/58, See FigureS12 and DatasetS13). 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 by the covariation with body size. It even outperformed the grey-matter score built from the depression score itself (r=0. 05, p-value<1e. 5). BLUP achieved similar to superior prediction accuracy (R 2 ) compared to LASSO brain scores across the 58 phenotypes (Figure  S13 , Dataset S13). BLUP significantly outperformed LASSO (Wilcoxon test on absolute errors, p<0.
BMI). Grey matter scores for age, sex, and being a twin significantly correlated with the observed values (r age =0. 15, r sex =0. 25, r twin-status =0. 31, Table  1 , Table  S3  and Figure  4 ). Greymatter score for maternal smoking around birth correlated with smoking status (r=0. 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 S13, 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.
The model is equivalent to fitting all vertices as a set of random effects, constraining the association effect sizes to be normally distributed (Figure  1 ), which can be seen as an extension of multiple regression when the number of variables exceeds the number of participants. This framework allows estimating new sample characteristics such as the total association (morphometricity  (Sabuncu et al. , 2016))  between a phenotype and vertex-wise brain data or grey-matter correlations that quantify how much phenotypes may be similarly associated with grey-matter. In addition, it offers to build performant brain-based predictors that do not require hyper-parameter estimation. Our analyses replicated and extended previous morphometricity reports  (Sabuncu et al. , 2016)  (Figure  2 , Dataset S3, S4).
We have demonstrated that our methods produce robust, replicable results (Figure  S3 , S6, S10, Table  S1, S3 ) that were partly transferrable on a completely independent sample (the HCP) despite large differences between the samples (Table  S2 , Dataset S13). We have shown additional utilities of this LMM framework such as the ROI based association test that retained the vertex-wise complexity of a brain region (Fig S8  -S8 , Appendix S9, Dataset S7-S10), rather than summarising them by a single average measure, effectively bridging the gap between ROI/atlas based and vertex-wise analyses. Our results aligned with previously published associations with sex  (Ritchie et al. , 2018) , BMI  (Cole et al. , 2013; Gupta et al. , 2015; Kurth et al. , 2013; Masouleh et al. , 2016; Medic et al. , 2016; Opel et al.
To finish on processing, in the UKB we combined vertex-wise data estimated from T1w and T1w+T2w which is meant to improve grey-matter segmentation, though few studies quantified it  (Lindroth et al. , 2019). Here, we confirmed a difference in cortical thickness between processing groups  (Lindroth et al. , 2019 )(Figure  S2 ), though our data driven QC (Appendix S2) excluded 80% of the 400-odd participants processed using T1w only (flagged as outliers). We corrected for processing type in the analyses and the good replication of the UKB associations (Figure  S3 , S6, Table  S1 ) in addition to the out of sample prediction (Figure  4 ) suggest that our results are robust. Beyond the large morphometricity estimates found for age and sex, BMI, weight, waist and hip circumference, and both passive and active smoking (Figure  2 , Dataset S3), we found many small(er) associations, with a wide array of phenotypes, including some more unexpected ones (e.
g. self-reported diet, being a twin, happiness with one's health, blood assay results (Figure  2 , Dataset S3)). Such findings may echo the concerns raised by Smith and Nichols about the presence of many (small) confounded associations in big-data neuroimaging  (Smith & Nichols, 2018). The fact that we replicated the morphometricity in another UKB sample, does not completely rule out a confounding effect, as the same bias (e. g. healthy bias in recruitment  (Fry et al. , 2017) ) may be present. We illustrated this concern using the example of body size (BMI, weight, height), which showed large, replicated morphometricity and was available in both cohorts. We evaluated its contribution to the reported morphometricity by performing conditional analyses and bivariate LMMs. Both approaches yielded the same conclusions: a large fraction of the morphometricity detected was attributable to body size (Figure  S7 , Dataset  S3 ).
However, co-variation does not necessarily imply confounding (which requires establishing direction of effects) but may instead point to intermediate phenotypes or arise from the pervasive pleiotropy across the human phenome  (Solovieff, Cotsapas, Lee, Purcell, & Smoller, 2013). In addition, we are dealing with associations, meaning that (except for exposures such as age or sex) the trait-vertex associations responsible for the morphometricity may be a cause and/or a consequence of the phenotypes. For example, there is a known association between BMI and depression, with evidence of pleiotropy, but also of a causal effect of BMI on depression  (Wray et al. , 2018) , though we do not know which of BMI and grey-matter structure cause the other and cannot label body size a confounder. Thus, a conservative interpretation is that morphometricity of the depression score is limited to the shared variation with body size in the UKB (Dataset S3, Figure  S7 ).
Finally, the possible confounding effects of body size are exacerbated in small case-control samples, leading to increased chances of false positive associations  (Button et al. , 2013; Ioannidis, 2005). Note that body size being associated to many brain regions (Figure  S9 ), its confounding effect could lead to widespread cortical or subcortical false positives. A different example may be that of cheese intake, previously given as an absurd example of putative association likely confounded by socio-economic status  (Smith & Nichols, 2018) , and for which we found a significant (replicated) morphometricity, even after correcting for body size (Figure  2 , Dataset S3). Consistent with the hypothesis of a confounded association, our bivariate analysis identified large r GM (r GM =1) between cheese intake and household income level, or fluid intelligence (Figure  S7 ), though the latter was significant only when controlling for body-size (Figure  3 ).
However, we detected significant morphometricity for pulse rate, bone mineral density and indirect measures of breathing rate/depth  (Smith & Nichols, 2018) , even after controlling for body size (Dataset S3). In addition, the large grey-matter correlations between pulse rate and overall health rating, and between forced expiratory volume and fluid intelligence (Figure  3 ), suggest they might indeed act as confounders. More work is needed to extend the list of acquisition confounders studied (e. g. head motion), and more power is needed to detect finer grained r GM with the phenotypes of interest (Appendix S7). Finally, note that r GM would also capture correlated measurement errors between traits (e. g. when 2 traits are associated with head motion). Next, we constructed BLUP scores that estimate the random effects of the LMM and demonstrated their predictive abilities in independent samples (UKB replication and to a lower extend in the HCP, [Figure  4 , S12, Table  1 , S2, Dataset S11], which differs in term of scanner and sample composition).
However, they open the way to new analyses on samples already collected, for which information was not or could not be collected. Further application of our BLUP grey-matter scores include studying correlates of brain age or predicted age difference (difference between predicted and chronological age)  (Cole, 2017; Cole et al. , 2017; Liem et al. , 2017). Despite good statistical power in theory (Appendix S7), the low numbers for some of the binary variables may explain the lack of associations found with psychiatry (e. g. schizophrenia, ADHD), stresses, traumas (Dataset S3), which would have to be confirmed using a larger UKB sample or case-control samples (see results in  (Sabuncu et al. , 2016) ). Similarly, a lot of the trait variance remains unaccounted for by the grey-matter structure variation (Figure  2 ) which calls to study brain regions not extracted here (e.
We applied Bonferroni correcting to account for multiple testing in each sample. The black bars represent the 95% confidence intervals of the morphometricity estimates. For context, we also present the association R 2 between phenotypes and covariates of the baseline model, as per the legend under the barplot. 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). Age and sex were not included as covariates when studying them as phenotypes. See Dataset S3-4 for full results. See Figure  S1  for positive control associations. Labels highlight some of the significant prediction having the greatest accuracy.
This research was supported by the  Australian National Health and Medical Research Council  ( 1078037 ,  1078901 ,  1113400 ,  1161356  and  1107258 ), the  Australian Research Council  ( FT180100186  and  FL180100072 ), the  Sylvia & Charles Viertel Charitable Foundation , as well as the  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute)  and reference  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche - 10-IA Institut Hospitalo-Universitaire-6 ). Informed consent was obtained from all  UK  Biobank participants. Procedures are controlled by a dedicated Ethics and  Guidance Council  (http://www. ukbiobank. ac. uk/ethics), with the Ethics and  Governance Framework  available at http://www. ukbiobank.
Summary-level data (BLUP weights): https://cnsgenomics. com/content/data and https://cloudstor. aarnet. edu. au/plus/s/T1gyJyQsF6wTMjF; Code used for the analyses and plots is downloadable at https://github. com/baptisteCD/Brain-LMM and viewable at https://baptistecd. github. io/Brain-LMM/index. html ; OSCA: http://cnsgenomics. com/software/osca/ ; ENIGMA processing protocols: http://enigma. ini. usc. edu/protocols/imaging-protocols/ ;.
The quantitative PCR analysis was done using CFX ConnectTM BIORAD and analyzed with Biorad CFX Maestro software. Relative levels of mRNA were calculated according to the ∆∆CT method, relative to the housekeeping gene HPRT (hypoxanthine phosphoribosyltransferase) and TBP (TATA-binding protein). Primers used for quantitative real time PCR are available in Table  S1.
Statistical Analysis Statistical analyses were carried out with StatView, version 5. 0, software (Abacus Concepts, Berkeley, CA, USA). Comparison between groups was performed using the χ2 test for categorical data and nonparametric Mann-Whitney U test or Kruskall-Wallis test for continuous data. The relationship between 2 continuous variables was evaluated using the nonparametric Spearman rank correlation test. Recurrence after treatment was analyzed by Kaplan-Meier method, and the curves were compared using the log-rank test. p-values < 0. 05 were considered significant.
Methods Animals and ethical statement. All data integrated in the study were taken from the database of the mouse lemur colony of Brunoy (MECADEV, MNHN/CNRS, IBISA Platform, agreement F 91. 114. 1, DDPP, Essonne, France). These data have been collected between 1996 and 2013. All experiments were performed in accordance with the Principles of Laboratory Animal Care (National Institutes of Health publication 86-23, revised 1985) and the European Communities Council Directive (86/609/EEC). The research was conducted under the approval of the Cuvier Ethical Committee (Committee number 68 of the "Comité National de Réflexion Ethique sur l'Expérimentation Animale") under authorization number 12992-2018011613568518 v4. Animals were raised in the laboratory under identical nutritional and social conditions.
Ltd, Minnesota, USA). Endogenous period measurement. In order to measure the endogenous period tau, individuals included in the survey were isolated and submitted to free-running conditions i. e. total darkness during 7-30 days. Feeding and weighing were planned at random times of the day, so that no bias could be introduced by human activity in the animals' facility. After circadian activity measurements, the light regimen was returned to 10:14 light-dark cycles. Endogenous periods were assessed using Clocklab software (Actimetrics, Evanston, IL, USA).
Data analysis All analyses were conducted in Matlab (MathWorks Inc). For the primary analysis of data from Exp. 1, choice patterns were analyzed with probit regressions, separately for stimOFF trials and stimON trials (Eq. 1). For each group of trials, we derived measures for the relative value of the juices (ρ), the sigmoid steepness (η) and the order bias (ε). The effects of electrical stimulation on each parameter were assessed using Wilcoxon signed-rank tests and paired t tests (Figs. 1-2), Fig. 2 ). For data from Exp. 2, we first ran two independent probit regressions for stimON trials and stimOFF trials. We found that electrical stimulation did not systematically alter the sigmoid steepness (Fig. S4 ). Thus we ran a probit regression assuming equal steepness for the two groups of trials (Eq.
Stimuli A total of 14 sinusoidal tones were generated using Sound Forge Pro 10. 0 (Sony Creative Software Inc. ). The duration of each tone was 50 ms (including 5 ms rise/fall times). The frequency of each tone was within the range of 261. 626-987. 767 Hz, matching the absolute frequency of a series of 14 natural keys on a modern piano (i. e. , C4 D4 E4 F4 G4 A4 B4 C5 D5 E5 F5 G5 A5 B5) (Table  2 ). Unlike previous research using 2 tones in the auditory localglobal paradigm  (Bekinschtein et al. 2009; Wacongne et al. 2011; Chennu et al. 2013; El Karoui et al. 2015)  (i. e. , blocks of frequent repetition and infrequent change: 80% AAAAA + 20% AAAAB or 80% BBBBB + 20% BBBBA; blocks of infrequent repetition and frequent change: 20% AAAAA + 80% AAAAB or 20% BBBBB + 80% BBBBA), here we introduced variation in pitch using 14 tones.