dataesr/scientific-paragraphs-categorization huggingface dataset:
The exclusion of data after 2009-2011 however is specifically problematic for diseases with delayed diagnosis like narcolepsy and is of the major biases of previously published studies  7, 14. Delays in diagnosing narcolepsy for months or even years makes it difficult to identify the exposures that contribute to disease development. We therefore run an additional, more sensitive overall prediction model (LOESS), since it incorporates all available data both before and after the 2009-2010 influenza pandemic. The longer follow-ups (from 2012 to 2016) in our database also allow us to identify more patients with a disease onset in 2009-2011. Accordingly, a considerable proportion (35. 7%, 51 out of 143) of the patients starting EDS in 2009-2011, finally diagnosed after 2012 (see Fig. 4 ), are included in our analysis.
Limitations and Perspective We could not directly explore the pathophysiology of influenza/vaccination associated narcolepsy as the EU-NN database was not designed as a surveillance study and does not include the influenza and vaccination histories of the patients. We will further analyze in future studies, limiting to countries where vaccinations registries and individual vaccination histories are available. Second, for many reasons not all patients have been entered from all sleep centers in EU-NN database. We also lack information from some non-EU-NN member countries, such as Ireland, Norway and Sweden, where an association between Pandemrix and NT1 has been observed. Although we assume that our sample gives a representative figure about the European narcolepsy patients, a selection bias and influences by missing data are possible.
iEEG data acquisition and preprocessing Patients underwent intracerebral recordings by means of stereotactically implanted semirigid, multilead depth electrodes (sEEG). Five to seventeen electrodes were implanted in each patient. Electrodes had a diameter of 0. 8 mm and, depending on the target structure, contained 8-18 contact leads 2-mm-wide and 1. 5-mm-apart (Dixi, Besançon, France). Anatomical localizations of iEEG contacts were determined on the basis of postimplant computed tomography scans or postimplant MRI scans coregistered with preimplant scans  (Deman et al. , 2018). Electrode implantation was performed according to routine clinical procedures, and all target structures for the presurgical evaluation were selected strictly according to clinical considerations with no reference to the current study.
Computational modeling A standard Q-learning algorithm (QL) was used to model choice behavior. For each pair of cues A and B, the model estimates the expected value of choosing A (Qa) or B (Qb), according to previous choices and outcomes. The initial expected values of all cues were set at 0, which corresponded to the average of all possible outcome values. After each trial (t), the expected value of the chosen stimuli (say A) was updated according to the rule Qa(t+1) = Qa(t) + α*δ(t). as previously described  (Khamassi et al. , 2015). Bayesian information criterion (BIC) was computed for each subject and model (BIC = log(ntrials) * (ndegrees of freedom) + 2*LLmax). Outcome prediction errors (estimated with the QLr model) for each patient and trial were then Z-scored and used as statistical regressors for iEEG data analysis.
Electrophysiological analysis Collected iEEG signals were analyzed using Fieldtrip  (Oostenveld et al. , 2011)  and homemade MATLAB codes. Anatomical labeling of bipolar derivation between adjacent contact-pairs was performed with IntrAnat software  (Deman et al. , 2018). The 3D T1 preimplantation MRI gray/white matter was segmented and spatially normalized to obtain a series of cortical parcels using MarsAtlas  (Auzias et al. , 2016)  and the Destrieux atlas  (Destrieux et al. , 2010). 3D models of electrodes were then positioned on post-implantation images (MRI or CT). Each recording site (i. e. , each bipolar derivation) was thus labeled according to its position in a parcellation scheme in the patients' native space. Regions of interest definition. The vmPFC ROI (54 sites) was defined as the ventromedial Computation of envelopes in lower frequencies.
The envelopes of theta, alpha and beta bands were extracted in a similar manner as the broadband gamma frequency except that steps were 1 Hz for theta and alpha and 5 Hz for beta. The ranges corresponding to the different frequency bands were as follows: broadband gamma was defined as 50-150 Hz, beta as 13-33 Hz, alpha as 8-13 Hz and theta as 4-8 Hz. Time-frequency decomposition. Time-frequency analyses were performed with the FieldTrip toolbox for MATLAB. A multitapered time-frequency transform allowed the estimation of spectral powers (Slepian tapers; lower frequency range: 4-32Hz, 6 cycles and 3 tapers per window; higher frequency range: 32-200Hz, fixed time-windows of 240ms, 4 to 31 tapers per window). This approach uses a steady number of cycles across frequencies up to 32 Hz (time window durations therefore decrease as frequency increases) whereas for frequencies above 32Hz, the time window duration is fixed with an increasing number of tapers to increase the precision of power estimation by increasing smoothing at higher frequencies.
The model comparison was conducted using the VBA toolbox (Variational Bayesian Analysis toolbox; available at http://mbb-team. github. io). Log-model evidence obtained in each recording site was taken to a group-level, random-effect, Bayesian model selection (RFX-BMS) procedure  (Rigoux et al. , 2014). RFX-BMS provides an exceedance probability (Xp) that measures how likely it is that a given model is more frequently implemented, relative to all the others considered in the model space, in the population from which samples are drawn. For the separate investigation of prediction error components, two separate analyses were conducted for reward and punishment PE. For each analysis, power time-series Y was regressed against both outcome (R or P) and expectation (Qr or Qp): Y= α + β 1 x R + β 2 x Q With β 1 and β 2 corresponding to the outcome (R or P) and expectation (Qr or Qp) regression estimates.
Acknowledgments This work benefited from the program from  University Grenoble Alpes , within the  program 'Investissements d'Avenir'  ( ANR-17-CE37-0018 ;  ANR-18-CE28-0016 ;  ANR-13-TECS-0013 ) and from the  European Union   Seventh Framework Program  ( FP7/2007-2013)  under (grant  604102 ,  Human Brain Project ). MG received a PhD fellowship from  Region Rhône-Alpes  (grant  ARC-15-010226801 ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank all patients; the sta of the  Grenoble Neurological Hospital epilepsy unit ; and  Patricia Boschetti ,  Virginie Cantale ,  Marie Pierre Noto ,  Dominique Ho mann ,  Anne Sophie Job  and  Chrystelle Mosca  for their support.
Supervised classi cation was performed using a support vector machine. Support vector machines are universal approximators: when good care is taken to control their complexity, they can provide better tting than classical polynomial regressions  (Hammer et al. , 2003). Support vector machines constructs a hyperplane in a high-dimensional space, which can be used for classi cation. The original nitedimensional space is mapped into a higher-dimensional space, with non-linearities obtained by rede ning dot products in terms of a nonlinear kernel function. Subsets of of the best features selected using OFR were used to compute and estimate the output vector. The support vector machine was optimized using sequential minimal optimization. Performances were estimated using a leave-one-out approach: One sample was taken out of the database.
The network was then trained on the remaining samples, and afterwards tested on the excluded sample. The same estimation was performed iteratively for all samples of the database. The overall classi cation of the excluded samples is the leave-one-out error, which is a good estimate of the generalization error. We tuned the support vector machines hyperparameters (Kernel choice, Kernel parameters and soft margins) according to the leave-one-out error (minimizing the classi cation error between and ). Once optimal hyperparameters were estimated, the model was retrained on the whole training/validation set. It was then applied to the test set without any retraining in order to assess the generalization capability of the model and estimate the test error on independent new data.
Acknowledgements The study was promoted by  INSERM (VISA-AR ,  2010-A00149-30 ). This study was conducted as part of PhD thesis of  Simona Caldani  ( Paris Sud-Orsay University ). We thank the practitioners from the C'JAAD team ( Drs M Kazes  and  J Bourgin ,  G Martinez ,  O Gay ), and from  Service Hospitalo-Universitaire ,  S14   Centre Hospitalier Sainte-Anne , for their role in participants' enrolment. We also thank the  Clinical Research Center team (M Cuenca, C Bergot) , the  URC Paris Centre Descartes , and DRCI. We thank the  Paris Descartes University's Centre de Langues  for revising the English version of the manuscript.
, 2018). Dioxin molecules were parametrized according to procedure described in  Mayne et al. (2013) , using the graphical force-field toolkit available in VMD package  (Humphrey et al. , 1996). The procedure included geometry optimization at MP2/6-31G* level of theory, fitting of atomic charges, based on the energies of interaction with water molecules, and fitting of the missing dihedral angle parameter between oxygen atoms (1,4dioxin bridge). The charges obtained for chlorine atoms were split into the core charge and the lone pair region, corresponding to the sigma hole  (Soteras Gutierrez et al. , 2016). The sigma hole charge was fixed at 0. 05 e and positioned at 0. 164 nm from the chlorine atom. The remaining charge of the carbon-chlorine group was distributed in such way that reproduced the molecular dipole moment, derived from the original molecular geometry and remaining atomic charges.
The quantum-chemical calculations were performed with Gaussian 09 package  (Frisch et al. , 2009). In all simulations periodic boundary conditions were applied in all three directions. In order to eliminate unphysical interactions between replicas in the direction perpendicular to the monolayers (z) the box length in this direction was increased to 100 nm. In preparatory simulations, Langevin thermostat and barostat  (Feller et al. , 1995)  were used to control the temperature and pressure. Production simulations were carried out in NVT ensemble, with velocity rescaling applied to control the temperature. A time-step of 1 fs was used in preparatory simulations. In production runs it was increased to 2 fs and all bond lengths involving hydrogen atoms were frozen at equilibrium values with the use of LINCS algorithm  (Hess et al.
, 1997). In all simulations 1. 2 nm cut-off was used for van der Waals interactions and full electrostatics was calculated with PME method  (Darden et al. , 1993). Trajectories were visualized with VMD package  (Humphrey et al. , 1996). Trajectory analysis was performed with homedeveloped programs, VMD, and Gromacs tools. All computed properties were averaged over the two monolayers present in each system. The only exception were the density profiles along monolayer normal. Deuterium order parameters were calculated with a home developed program according to the formula: S mol ¼ À 2S CD (3) S CD ¼ 1 2 3 < cos 2 q > À 1 ( 4 ) where q is the angle between CeH bond and monolayer normal and <> denote averaging over time, molecules, and both CeH bonds formed in each CH 2 group. rdf, density, gangle and msd tools from Gromacs were applied to calculate radial pair distribution functions, g(r), density profiles along monolayer normal, tilt angles between various system components and monolayer normal, and diffusion coefficients, respectively.
Acknowledgements This work was supported by the  Polish National Science Centre , Project No. UMO-2014/13/B/ST4/04995. Calculations were formed at  Faculty of Chemistry of Jagiellonian University  on a computer cluster purchased with the financial support from the  European Regional Development Fund  in the framework of the  Polish Innovation Economy Operational Program  (Contract No. OIG. 02. 01. 00-12-023/08 ) and on  PL-Grid infrastructure at ACK CYFRONET  with the support of the " HPC Infrastructure for Grand Challenges of Science and Engineering " project.
B. Proximity and Tactile Signal Conditioning With a Single Integrated Circuit An off-the-shelf PSoC from Cypress is used to implement both force and proximity sensing signal conditioning. The specific device of this article is the CY8C28445  [21]. The user module "SmartSense2X_EMC" is exploited to measure proximity. It is based on Capsense, a capacitive touch sensing technology from Cypress that uses a sigma-delta converter to perform capacitive-to-digital conversion  [28]. Fig. 6 (a) and (b) shows the connection diagram. As the array of capacitors is scanned, the selected capacitor is connected and disconnected alternately through the switches at a certain frequency f φ. From a well-known theory on switched-capacitor circuits, the value of the capacitor and the frequency determines the current from the analog bus AMUXBUS as  electrode T1 and the ground associated with the i th FSR and V AMUXBUS is the voltage at the analog bus.
This elastomer is recommended to improve the sensitivity in the force measurement  [19]. Fig. 11(b)  shows the response of the sensor when the contact is made between the metallic piece and the sensor, and an increasing force is exerted on it. Fifteen measurements were taken and the average as well as the error (standard deviation with respect to the full-scale output) are given. The response is almost linear, as expected for the implemented signal conditioning and the data provided in the datasheet  [22]. Another test was made where the reading of both proximity and force measurements was registered as the metallic piece approached the sensor, made contact, exerted force on it, and returned to its original position following the inverse sequence [see Fig. 11(c) ]. The speed of the prism is 142.
As expected, the response of the proposal of this article is better for conductive objects, though the PLA cylinder is detected at a distance larger than 3 mm. Regarding the dynamic response, the test setup was not optimized for a high scan rate but for flexibility and the data are obtained at 20 Hz. Nevertheless, the complete scan loop for 32 proximity and tactile data was measured by means of a timer in the PSoC and it was found that it takes less than 5 ms. Therefore, the whole tactile and proximity array can be scanned at a rate of 200 Hz without considering the communications nor other processing that the application could do in the loop. Another simple test was performed related to the sensitivity of the proximity sensing to electromagnetic interferences (EMIs). A signal generator Rohde&Schwarz SML03S together with a λ/4 monopole antenna was used to carry out two frequency sweeps between 1 and 500 MHz at 11 dBm and between 500 and 3 GHz at 15 dBm with the antenna at 3 cm from the sensor and the metallic prism on the sensor, but without exerting any force.
ACKNOWLEDGEMENTS We acknowledge support from  INN-CNEA, UNCuyo  ( 06/C455 ),  ANPCyT  ( PICT2014-1382 ,  PICT2016-0867 ,  PICT2017-1836 ,  PICT 2017-0984 ),  CONICET  ( PIP 11220150100653CO ) and  Univ. Buenos Aires (UBACyT 20020170100284BA ). M. A. also acknowledges financial support of  H2020-MSCA-RISE-2016  SPICOLOST Grant No. 734187  to perform TEM studies at  LMA-INA, University of Zaragoza. S. B. thanks technical support from  N. Cortes.
Acknowledgements This work was supported by the INTERREG Upper Rhine program from the ERDF (European Regional Development Fund), SPIRITS project (interreg-spirits. eu), and also by Investissements d'Avenir program (Robotex ANR-10-EQPX-44, Labex CAMI ANR-11-LABX-0004). The authors thank Sensoptic SA for the work on needle with tip force sensing capabilities and Help Tech GmbH for providing the Braille display.
The recent availability of large-scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex-wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N=9,497 for discovery, N=4,323 for replication) and the Human Connectome Project (N=1,110). We used a linear mixed model with the brain measures of individuals fitted as random effects with covariance relationships estimated from the imaging data. We tested 167 behavioural, cognitive, psychiatric or lifestyle phenotypes and found significant morphometricity for 58 phenotypes (spanning substance use, blood assay results, education or income level, diet, depression, and cognition domains), 23 of which replicated in the UKB replication set or the HCP.
Introduction The field of MRI studies is at a turning point owing to the recent availability of large data sets to researchers, including the UKB  (Miller et al. , 2016)  and HCP  (Van Essen et al. , 2013; Van Essen, Ugurbil, et al. , 2012)  samples. These datasets promote the replication of previous findings, but also the identification of small(er) associations and the expansion of the range of phenotypes available for study (e. g. psychiatric symptoms and lifestyle factors). Furthermore, the boost in statistical power may allow the simultaneous use of all the brain complexity data of current MRI acquisitions rather than relying on data reduction techniques (e. g. the region-of-interest [ROI] approach). In addition, these community samples can complement the typical case-control paradigm by identifying confounders of MRI analyses or by studying related traits (e.
g. cognition domains relevant in Alzheimer's disease). However, "big-data" neuroimaging offers a number of statistical challenges (on top of the obvious computing ones)  (Smith & Nichols, 2018) : i) the curse of dimensionality (the number of tests may increase faster than the sample size) which requires efficient methods and appropriate control of multiple testing; ii) the possibility that (small) associations result from confounding (via another variable, or acquisition noise); iii) the difficulty to generate prediction from complex datasets. Here, we propose a linear mixed model (LMM), efficiently implemented to tackle several of these big-data neuroimaging challenges. Our approach allows performing association and prediction analyses on tens of thousands of participants with more than 650,000 vertexwise morphological measurements of grey-matter structure per individual.
Finally, we show how the same LMMs can be used to construct grey-matter scores that achieve brain MRI-based prediction in independent samples. As such, our approach unifies association and prediction analyses, in order to unravel the brain-phenome relationships  (Rosenberg, Casey, & Holmes, 2018)  in big-data neuroimaging. To demonstrate the applicability and usefulness of our methods, we analysed two of the largest MRI datasets available (UKB [split into discovery N=9,888 and replication  N=4,561]  and HCP  [N=1,110] ) and considered a wide range of phenotypes spanning demographics, blood cell composition, diet, psychiatric and traumatic history, physical capacities, and substance use. We discuss our results in the context of the recent commentary article of  Smith & Nichols (Smith & Nichols, 2018).
5. 1. UK Biobank (UKB) sample(s) The UKB participants were unselected volunteers from the United Kingdom  (Sudlow et al. , 2015)  living near the imaging centres (Manchester for 96. 5% of our sample, Newcastle for the remaining 3. 5%). Exclusion criteria included: presence of metal implant, recent surgery and health conditions problematic for MRI imaging (e. g. hearing, breathing problems or extreme claustrophobia)  (Miller et al. , 2016). MRI acquisition parameters have been reported previously  (Miller et al. , 2016)  and are summarised in Appendix S1. We split the available UKB data into a discovery and replication sample based on their imaging date. The discovery sample consisted of 9,497 adults aged 62. 5 on average (SD=7. observations were available for a participant, we used the one collected as part of the imaging assessment or the closest in time to that.
In addition, we extracted subcortical radial thickness and log Jacobian determinant (surface deformation, somewhat analogous to a relative surface area  (Roshchupkin et al. , 2016) ) for 13,560 vertices across the 7 subcortical volumes (Boris A. Gutman et al. , 2013). Overall, the imaging data used in the analyses comprised 652,283 vertex measurements per individual: 299,009 for cortical thickness, 299,034 for cortical surface area, 27,120 for subcortical thickness and 27,120 for subcortical curvature. For comparison with ROI based processing, we extracted cortical thickness and surface area of 34 cortical regions  (Desikan et al. , 2006; Fischl et al. , 2004)  and volumes of the subcortical structures (ENIGMA processing). To further the comparison of processing 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: personality, emotion, mental health assessment (Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) and Adult Self Report (ASR) (Thomas M Achenbach, 2009; T. M. Achenbach, Dumenci, & Rescorla, 2003) ), detailed cognition, Pittsburgh sleep index (PSQI)  (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989) , or results from the urine drug tests (see Dataset S2).
, 2014) ), which showed that in the UKB discovery sample we had good power to detect a small morphometricity (R 2 >2. 2%) but only a moderate grey-matter correlation (r GM >0. 35). Statistical power was a lot reduced in the HCP due to the smaller sample size. We demonstrated two further utilities of LMMs for neuroimaging data analyses. First, we conducted post-hoc analyses to test the associations with each modality and each cortical  (Desikan et al. , 2006)  or subcortical structure. We used BRMs specific to each region and brain measurement (Figure  1 ), which bridges the gap between ROI and vertex-wise analyses. Second, we define as "best" processing the MRI cortical processing that maximises the association with a trait of interest, from the minimal number of features (vertices). Thus, we evaluated which of our FreeSurfer processing (fsaverage -no smoothing; fsaverage -smoothing fwhm5, 10, 15, 20, 25; fsaverage6, 5, 4, 3 -no smoothing; ENIGMA ROI processing) maximised the brain-morphometricity for all the UKB traits (See Appendix S2 for details about QC).
6. 1. Associations between phenotypes and all grey-matter structure vertices For the phenotypes of interest, we summarised in circular barplots (Figure  2 ) the proportion of phenotypic variance associated with all 652,283 vertex-wise grey-matter measures (brain-morphometricity, R 2 ) as well as with baseline covariates (see Methods). Figure  2  shows only the results that were significant after Bonferroni correction (p UKB_discovery <2. 8e-4 and p HCP <2. 9e-4). The full results are available in Dataset S3, S4 (see.