dataesr/scientific-paragraphs-categorization huggingface dataset:
Each session was separated by a maximum of 2 days. Spectroscopic scan was acquired 1 h and 10 min after the drug administration. Given the short half-life of plasma oxytocin (3 to 6 min,  Fabian et al. 1969) , there was no predicted effect of oxytocin administration on spectroscopic data (MRS glutamate/glutamine concentration). Consistent with previous observations  (Aoki et al. 2015) , no evidence for an effect of oxytocin on metabolites was shown when tested for explicitly (see Supplementary Information for more details Table  S1 ). Moreover, prior to each session, participants were breathalyzed and a urinary sample was collected to test for drug use. The urinary drug test was undertaken to confirm the absence of drug use and to exclude drug use disorder. The alcohol test was undertaken to ensure that participants abstained before the sessions.
Magnetic resonance imaging and spectroscopy data acquisition Magnetic resonance imaging (MRI) and 1 H-MRS were performed using a 1. 5 Tesla Siemens Magnetom Avanto MRI scanner with an enhanced 32-channel phased-array head coil, tuned to 63. 6 MHz. A whole-brain, high-resolution T 1weighted 3D structural image was obtained using a magnetization-prepared gradient-echo sequence, consisting of 192 contiguous axial slices (TR = 2730 ms, Echo Time (TE) = 3. 57 ms, flip angle = 7°, matrix = 256 × 256, field of view (FoV) = 256x256mm, 1. 0 mm isotropic voxel size, GRAPPA acceleration factor = 2; acquisition time = 5 min 58 s). The T1-weighted image was used as an anatomical reference for each participant. Using these images, a single 1 H-MRS voxel was positioned in the right mid-insula of each participant (Fig.
MRI and MRS data analyses The T 1 -weighted structural MR images were segmented into gray matter (GM), white matter (WM), and cerebrospinal liquid (CSF) using Statistical Parametric Mapping 12 (SPM12; Wellcome Department of Imaging Neuroscience, University College London, UK). To calculate the tissue content of the MRS spectroscopic voxel, a binary mask of the region in each participant was created and registered to the segmented T 1weighted structural images. The proportion of each tissue type (tissue fractions) was calculated for the spectroscopic voxel (i. e. , volume of interest) by summing the Bstructural^voxels of each tissue type and dividing by the total number of Bstructural^voxels within the volume of interest (see Supplementary Information Table  S2  for details on voxel tissue content).
Metabolite concentrations were then calculated and corrected (partial volume, T 1 , and T 2 relaxation) using Gasparovic's methods  (Gasparovic et al. 2006). Raw time-domain 1 H-MRS data in the spectral dimension were analyzed using TARQUIN Version 4. 3. 5  (Wilson et al. 2011)   participants for presence of obvious spectral artifacts; and one for large baseline distortion, presence of obvious spectral artifacts, and CRLB > 20%). Metabolite concentrations in molality units of mmol/kg of tissue water were computed for total glutamate plus glutamine (Glx) and total N-acetylaspartate plus N-acetylaspartylglutamate (TNAA). Despite technical and methodological advances, the separation of glutamate from glutamine spectral peaks is constrained by homogeneity of the magnetic field at 1. 5 Tesla. Moreover, we computed the correlation coefficient C between derived N-acetylaspartate and Nacetyl-aspartylglutamate (|C| = 0.
2006). Local cortical gyrification index was computed within CAT12 using a high-resolution parametric mesh-based approach. This approach allows estimation of the mean curvature of the brain at different spatial scales. Large positive values (expressed in degrees) for local maxima correspond to gyri. Large negative values for local minima correspond to sulci. Values are then converted to positive values by step incorporating the absolute mean curvature. Finally, the data are smoothed using a surface-based heat kernel filter of 25 mm resulting in revealing higher values for areas with pronounced gyrification. Mean estimates of gyrification index were extracted (prior to normalization) for our ROI, the right insula. For each participant, ROI was labeled using the Desikan-Killiany Atlas which is included in CAT 12  (Desikan et al.
Statistical analyses We conducted all of our analyses with the statistical software JASP, Version 0. 8. 6. Data were checked for potential bivariate outliers, using the function bagplot from the package Baplpack^  (Rousseeuw et al. 1999 ) in R Version 3. 5. 2. Two outliers were identified and removed from the analyses. Normality of distributions was tested, given that the obsession OCDS subscale score was not normally distributed, nonparametric correlations were run. In this present work, we tested all our hypotheses by calculating Bayesian and traditional correlation and linear regression tests. Along with frequentist statistics, we calculated the corresponding Bayes factor (BF) which was used as the basis of decision-making in respect of the compared hypotheses. A BF above 3 shows compelling evidence toward the alternative hypothesis (i.
a Negative correlation between insular Glx concentration and compulsion subscale of the OCDS, and b negative correlation between insular Glx concentration and alcohol use severity indexed by AUDIT scores. Data were checked for potential outliers alcohol-related glutamatergic excitotoxicity), indexed here as subtle changes in insular Unfortunately, our anecdotal evidence was insufficient to support this theory; future studies combining MRS and structural measures are needed to explore this point. Nevertheless, our findings seem to indicate a combined disruption of right insular neurochemistry and structure, which seem to be associated with alcohol craving and alcohol use. The insular cortex processes afferent interoceptive signals, which are a likely basis to craving states  (Critchley et al.
Conclusion We quantified the neurochemical and morphological integrity of the insular cortex in alcohol users. Together, our data provide evidence for disruption of insular glutamate-glutamine concentration and a modulation of brain surface parameters by alcohol use. These changes may underpin a loss of control over alcohol and a shift toward compulsive drinking. Further (longitudinal) studies should explore the evolution of interoceptive processes in relation to the integrity of insular cortex through different developmental stages of alcohol and drug disorders.
Acknowledgements We thank  Dr. Jessica Eccles ,  Dr. Marta Łukowska ,  Joely Smith , and  Dr. Nick Dowell  for their assistance. Funding The present study was supported in part by  Rotary Foundation  ( Rotary Global Grant   GG1526999 ), the  Society for the Study of Addiction , and the  European Research Council  (Advanced Grant  CCFIB AG 234150  awarded to HDC) and the  European Union 's  Horizon 2020 program  (668863-SyBil-AA; awarded to TD and HDC). Sophie Betka  is grateful to the  Society for the Study of Addiction (SSA)  for funding support under their PhD Studentship scheme and states that the opinions expressed are those of the author(s) and do not necessarily reflect the views of the SSA itself.
The Childhood Trauma Questionnaire score was used as confounding variable in the analyses to account for the remaining variance between groups. Results: Compared to FH-controls, FH+ participants had smaller grey matter volumes in the frontal and cingulate regions as well as in the bilateral nucleus accumbens and right insula. The FH+ participants' fMRI datasets denoted a blunted activation in the middle cingulum with respect to FH-controls' during the processing of reward magnitude, and a greater activation in the anterior cingulum in response to anticipation of a small win. Conclusions: Family history of alcohol use disorder is linked to structural and functional variations including brain regions involved in reward processes.
Magnetic resonance imaging (MRI) Acquisitions of high-resolution images were conducted with a 3. 0 T Siemens Trio scanner and a 12-channel coil at CENIR-ICM platform, Pitié-Salpêtrière Hospital (Paris, France). Pre-processing steps and statistical analyses were run with the Statistical Parametric Mapping software (SPM12, Welcome Trust Centre for Neuroimaging, University College London, UK).
Structural brain imaging. The anatomical 3D-T1-weighted sequence was carried out with the following parameters: sagittal slice plane, Repetition Time (TR) = 2. 3 s, Echo Time (TE) = 2. 93 ms, 256 Â 256 view matrix, 160 slices, voxel size = 1. 1 Â1. 1 x 1. 1 mm and duration = 554 s. A quality control has been conducted on T1 dataset from which images with a benign abnormality (n = 1), artifacts from excessive movements of the eyes's lens (n = 4) and head (n = 1) were excluded. The images were spatially normalized and segmented onto the MNI (Montreal Neurological Institute) tissue probability maps (TPM) into grey matter, white matter, cerebrospinal fluid, bone, soft tissue, background imagesby fitting iteratively to TPM voxel's intensities  [51]. Using the software R, the package "mvoutlier" detected extreme values from a normal distribution (https://cran.
rproject. org/web/packages/mvoutlier/index. html) leading to the exclusion of outlier volumes (n = 4). Then, covariances were calculated with the tool "check sample homogeneity" (http://www. neuro. uni-jena. de/vbm/check-sample-homogeneity/) and volumes with covariances greater than two standard deviations from the mean were detected with the R package "extreme values" (https:// cran. r-project. org/web/packages/extremevalues/index. html) thus outlier covariance volumes (n = 8) were excluded to control for errors of segmentation. Then, images were modulated and 10-mm full-width at half-maximum (FWHM) Gaussian kernel smoothed. Overall, following visual quality control and outliers' detection, 18 (19. 8%) out of 91 acquisitions were excluded. The remaining sample for analysis was composed of N = 73 participants.
Significance was set at a height threshold at p < 0. 05 uncorrected and an extent threshold at p < 0. 05 FWE corrected (cluster size > 8000 voxels). 2. 2. 4. 2. Functional brain imaging analyses. Between-group comparisons of BOLD signal maps were also performed as voxel-to-voxel t-tests across the whole brain for the contrast of anticipation of a "big versus small win"  [53, 59]  to assess neural response to the processing of reward magnitude; as well as for the two conditions independently, i. e. anticipation of a big win and anticipation of a small win. Statistical maps had a height threshold at p < 0. 001 uncorrected and an extent threshold at p < 0. 05 FWE corrected (cluster size > 80 voxels)  [60]. The BrainVISA/Anatomist software (http://brainvisa. info) was used to build the figure which displays both structural and functional results.
Acknowledgements This research was supported by the  French Ministery of Health  ( Programme Hospitalier de Recherche Clinique   P110132 , 2011), the  Fondation pour la Recherche Médicale  ( DPA20140629802 ,  2014 ) and the  Fédération pour la Recherche sur le Cerveau  ( C16/0932A01 ,  2016 ). The study was sponsored by  Assistance Publique Hopitaux de Paris , represented by the  HEGP Clinical Research Unit (Pr Gilles Chatellier). We would like to thank the team of  CENIR -Centre for NeuroImaging Research, ICM -Institut du Cerveau et de la Moelle épinière, Hôpital Pitié-Salpêtrière, Paris, France. We would also like to thank the IMAGEN consortium that provided the revised version of the Monetary Incentive Delay task and  Joelle Francis  for her advices. Appendix A. Supplementary data.
The third contribution is to explore the relation between procedural and semantic coordination in the matching task, that is, whether procedural and semantic coordination are independent from each other, or linked to each other. For example, if semantic coordination becomes more difficult, will this affect procedural coordination as well? Because participants in the classic version of the matching task quickly converge on conceptual pacts, the development of procedural coordination is inescapably confounded with increasing semantic coordination. Investigating this third issue thus requires a dataset where semantic coordination can be varied systematically. We used a matching task corpus (transcripts from Experiment 1,  Bangerter et al. , in preparation)  where participants completed the task in either of two conditions: The classic (or control) condition, where cards did not change over trials, and new cards condition, where they placed a new set of cards on each trial.
Procedure and materials Participants read and signed informed consent forms upon arriving in the lab. They then arranged a set of eight cards depicting humanoid tangram shapes as used in other matching task experiments. These were displayed to participants on a computer screen in two rows of four columns using a program we developed in Flash (Action Script). While the directors' view included cards already in placement slots, matchers saw their placement slots displayed above the cards. They moved cards to the slots by clicking and dragging them. Directors' cards could not be moved. When they were done with a trial, they each pressed a blue button on their screen to move on to the next trial. Participants first completed a practice trial where they placed eight cards depicting everyday objects (e.
Experimental design and dependent variables There were three main independent variables (IVs) in this study. The first one was condition, which had two levels (between-subjects): classic and new cards. The second was trial number (five levels; within-subjects). Both linear and quadratic trends of trial number were tested (both were centered to simplify the interpretation of the results). We included quadratic trends to be able to detect nonlinear phenomena (e. g. , a particular coordination variable decreases over trials but then increases again at the end of the experiment), which are quite common in matching task data (e. g. ,  Clark & Wilkes-Gibbs, 1986 ). The third independent variable was participant role (withinsubjects), which had two levels: Director and Matcher. Four dependent variables (DVs) were examined, each measuring a specific aspect of procedural coordination.
Data analysis The data were analyzed using logistic mixed models in SAS 9. 4 (GLIMMIX procedure). Mixed models allow including random intercepts (accounting for the potential variability across dyads, across participants and across items) and random slopes (accounting for the fact that dyads, participants and items may differ in their sensitivity to any within-units IVs in the design)  (Baayen, Davidson, & Bates, 2008; Barr, Levy, Scheepers, & Tily, 2013; Jaeger, 2008). We used logistic models because the four DVs were binary  (Agresti, 2002; Jaeger, 2008). Four sets of analyses were conducted -one per DV. Each set followed a rationale similar to that used by  Bangerter et al. (in preparation). Specifically, in each set, we started by running an analysis which included participant role as the only IV.
The purpose of this was to determine whether one of the roles was more likely to resort to the kind of coordination considered in the analysis. We then analyzed the data separately for Directors and Matchers (we decided not to run a single analysis including participant role and all other IVs in order to make the results easier to interpret). Significant interactions were interpreted based on the corresponding b coefficient. In line with  Barr et al. 's (2013)  suggestion, all models included the maximal random effects structure justified by the design. Here, the maximal random effects structure would include by-dyad, by-participant and by-item (i. e. , by-figure) random intercepts, as well as random slopes corresponding to all within-dyad, within-participant or within-item IVs (although note that by-participant random effects were not included in the models when the participants' data [Directors and Matchers] were analyzed separately).
Instruments The patients performed the DNMS task and the DMS task using the PEBL software  30. In order to counteract an eventual order effect, the sequence of the two tests was systematically alternatively changed. Consequently, half of the subjects performed the tests in the following order: DNMS/DMS and the other half in the reverse order DMS/DNMS. As far as the DNMS and DMS tests are concerned, we used the methodology of Elliott et al. 18  that studied OFC activity in healthy subjects performing the DNMS and the DMS tasks. In both tasks, subjects were initially shown during 1 s a matrix filled with 16 red and yellow squares characterized as a complex and abstract visual stimulus. Then after a delay of 5 s, they were presented with two different matrices, one of which was the sample stimulus, identical to the first matrix presented.
In the DNMS task, the subject was asked to choose the novel stimulus, whereas in the DMS the subject was asked to choose the familiar stimulus (Fig. 2 ). The subject had no time limit for choosing the correct stimulus related to the task. This task was repeated 30 times for every subject. For each subject, we have measured two main parameters; the number of correct answers and the response time during each trial. The subject had no time limit to answer questions. Before starting the recorded session, a test of five trials was done for every subject to check the understanding to the task analysis First, we measured parametric correlation (Pearson test) on sociodemographic data to compare continuous variables between OCD patients and HC. Then, Student's t tests were performed to compare the results to the DNMS and the DMS tasks between OCD patients and HC using two parameters: the number of correct answers and the response time.
Subsequently, in order to find out the most specific task for OCD patients, we determined that task among DNMS and DMS appeared to be the most difficult for OCD patients. Finally, a bootstrapping method was used to confirm the previous results from the Student's t test. The bootstrapping method relies on random sampling with replacement. It is an appropriate way to control and check the robustness of the results on a sample size with <40 subjects  31. We have performed the statistical analysis tests using RStudio software in its 3. 5. 0 version. The threshold for statistical significance was set at p < 0. 05.
EMG recording EMG activity was recorded using 8-mm Ag/AgCl surface electrodes placed over the vastus lateralis (VL). EMG was recorded continuously during the whole experimental session using a BIOPAC MP100 system and AcqKnowledge software. The signal was filtered (3. 5 Hz to 350 Hz), pre-amplified (×1000), and sampled at 2 kHz. For the MVC, the root mean square (RMS) of EMG activity was calculated over a 500-ms period around the highest MVC value (i. e. , 250 ms before and 250 ms after the peak MVC). During the endurance task, RMS was calculated over five-time periods (corresponding to 0-20, 20-40, 40-60, 60-80, and 80-100% of the total duration).
0, Neuroelectrics, Barcelona, Spain). The set of electrodes was held in place using a thermoplastic curved plate connected to elastic bands that were attached around the head. Following recommendations to optimize comfort and safety, Ag/AgCl sintered ring electrodes were used  (Minhas et al. , 2010)  with a conductive gel. Impedance of each electrode was kept under 5 kOhm. If the impedance exceeded this limit during testing, the experimental session was cancelled and participants were asked to reschedule the session for another time. Current intensity was set at 2 mA in all HD-tDCS sessions. Depending on the experimental sessions, participants received either an anodal stimulation over the right dlPFC or a sham session (over the right dlPFC). The double-blind mode of the NIC software was used to ensure that neither the experimenter nor the participants knew if the session included real or sham stimulation.
The participants also completed a battery of neuropsychological tests. The second night of PSG recording was used for sleep and EEG analysis. Information on current pathologies and medical treatment was also collected. All participants gave written informed consent. The study was approved by the local ethical committee (consultative committee for the protection of persons participating in biomedical research, CPP Sud-Ouest et Outre Mer III). The study was registered with ClinicalTrials. gov, identifier: NCT01650454.
The first night was for adaptation and to identify the presence of any organic sleep disorders, while the second night was used for sleep and EEG analysis. Following the application of a notch filter (50 Hz) and a band pass filtered at 0. 53-35 Hz, sleep stages and associated events were visually scored according to standard procedures (48) by the same experienced technician. The analyses of sleep EEG recordings were performed after an automatic artifact rejection using the ASEEGA software (version 3. 30. 14, Physip, France)  (49) (50) (51) (52). Artifacts are detected automatically based on both temporal and frequential criteria, where non-physiological abrupt variations are discarded. EEG spectral power per 30 s artifact-free epoch (Cz-Oz) was calculated using the fast Fourier transform with the Hanning window after an automatic artifact rejection.
ACKNOWLEDGMENTS We thank the following clinical research assistants from  CHU de Bordeaux ,  F-33000 Bordeaux , France:  M. Bacarisse ,  C. Valtat ,  A. Boiseau , and  V. Bibène  for selecting participants, collecting, and monitoring data, and for providing administrative, technical and logistic assistance. We thank:  CIC 1401-EC ,  Bordeaux University Hospital, Inserm  and the  University of Bordeaux  for supporting this work.
Data Encoding. The position of the finger was sampled at 60 Hz and resampled to 100 Hz using cubic spline interpolation. The x and y coordinates were then separately smoothed with a Gaussian filter with = 20 ms. At each time point, we calculated the implied endpoint (iEP) -the topof-screen position that the finger would reach if it kept moving in its current direction. As a measure of the online decision, we used either the actual horizontal position of the finger, denoted by x, or the implied endpoint. The x coordinates and the iEP are measured on a scale ranging from -1 (left end of screen) to +1 (right end), with x=0 being at the middle of the screen. The y coordinates use the same scale (1 unit = 9. 84 cm).
Participants One hundred and twenty-two students from a French technical university took part in the study. A psychologist conducted a short interview with the participants and found that women, a minority in this technical school, were a non-homogenous population (great age distribution, reported psychological history, and intake of substances). Therefore, only data from male students were considered in the analysis (n = 98; age = 17-25, means ± SD = 19. 0 ± 1. 5). The interview did not lead to the detection of any neuropsychiatric or psychological history in any of the participants. All participants provided written informed consent prior to participation in the study and were debriefed after the study. The study was approved by the University of Geneva ethics committee and conducted in accordance with the approved guidelines.
Judges were told orally that the focus is on "internal states" of passengers that have lost their luggage and that the films come from a hidden camera at an airport. Judges were told that in one video clip a passenger can display several mental states and moments of neutrality and that they had to indicate them all. They could describe what they see in sentences, through expressions or labels either orally (transcribed by the experimenter) or in a written format on a piece of paper or directly in the provided ANVIL software, with which they were assisted (ANVIL, Video Annotation Research Tool. http://www. dfki. de/kipp/anvil/). Seven laypeople, administrative staff from the technical university, were invited to act as judges for the task. The two first judges to participate (an account officer and a junior secretary) found the task to be extremely difficult.