dataesr/scientific-paragraphs-categorization huggingface dataset:
Resting state fMRI analyses. Resting-state data pre-processing and statistical analyses were carried out using the SPM software package (Version 12. 0), based on the MATLAB platform (Version 15a). For pre-processing, functional volumes were slice-time and motion-corrected, co-registered to the high-resolution structural image, spatially normalised to the Montreal Neurological Institute (MNI) space using the unified-segmentation algorithm  [31] , and smoothed with an 8 mm FWHM Gaussian kernel. With the goal of ensuring that motion and other artefacts did not confound our data, we first employed an extensive motion-correction procedure and denoising steps, comparable to those reported in the literature  [32]. In addition to the removal of six realignment parameters and their first-order derivatives using the general linear model (GLM)  [33] , a linear de-trending term was applied as well as the CompCor method that removed five principle components of the signal from white matter and cerebrospinal fluid  [34].
Moreover, the volumes affected by motion were identified and scrubbed based on the conservative settings of motion greater than 0. 5 mm and global signal changes larger than z = 3. Out of the 207 participants, a total of fifteen participants, who had more than 15% of their data affected by motion was excluded from the analysis  [35]. Therefore, all resting-state analyses reported are based on a sample size of 192 participants. Finally, a band-pass filter between 0. 009 Hz and 0. 08 Hz was employed in order to focus on low frequency fluctuations. Following this procedure, two seed regions were generated from the univariate maps of Self > Other and Other > Self regressed against our detail PCA (see  Fig 2). This yielded two clusters centred on caudal posterior cingulate cortex for Self > Other [MNI: -2, -58, 26] and intracalcarine cortex for Other > Self [  MNI 16, 4].
31, p <. 05). This region of the DMN was therefore less active during self judgements for individuals who tended to think about themselves frequently in the lab. Thus a more liberal ROI analysis found no support for the view that hyper responsiveness in regions of the DMN to personally or self-relevant content is linked to the expression of off-task thought in semantically impoverished conditions. In order to quantify the strength of the evidence in favour of the null hypothesis we conducted a Bayesian analysis of the associations produced via our ROI analysis (for review of method see  [39] ). The Bayesian framework allows us to quantify how much more likely the data are under the null hypothesis compared to the alternative hypothesis. The Bayes inference about Pearson correlation coefficient measures was conducted in SPSS (version 25) using default settings.
Brains were removed, sectioned (40 m), and stained for Nissl substance to locate injection or recording sites (labeled by passing a DC current through each electrode before perfusion). Experimental design and statistical analysis. The criteria for inclusion in final analysis were correct electrodes or cannulae placements in NAcC. For behavioral analyses, the primary dependent variables were the response probability and latency of incentive stimulus (INC), GO, and NOGO. All analyses were conducted in MATLAB (MathWorks). For electrophysiological analyses, the primary dependent variable was the mean z-score normalized firing, the onset latency and response durations. These dependent variables were analyzed with paired, unpaired t tests or ANOVAs. A Bonferroni correction was applied to account for multiple comparisons.
Onset was determined by the time of the first of four consecutive bins falling outside the confidence interval. The offset was determined in analogy, by searching the first of five consecutive bins within the confidence interval. Color-coded maps and average PSTHs across neurons were constructed with smoothed 20 ms bins. Before averaging, the firing rate of each neuron during each bin was transformed to a z-score using 0. 5 s preceding the event as a baseline. Code/data accessibility. The data and analysis routines used in this study are available upon request.
This work was supported by the  ATIP-Avenir program , the  Wheeler Center for Neurobiology of Addiction  and the  European Union 's  Horizon 2020 research and innovation program  under Grant  767092  to F. A. , and from the  French Ministry of Education and Research  to M. S. We thank  Saleem M. Nicola  for critical commenting on the paper;  Bruno Poucet ,  Thierry Hasbroucq , and  Boris Burle  for helpful discussions;  Dany Paleressompoulle  for technical support; and.
As the first stage, we are planning to elaborate the strategy for processing data to European Open Science Cloud (EOSC) services to enable the i-PROACH implementation process when integrating the i-PROACH platform with existing e-infrastructures and cloud-based environments across EU countries. We will establish dialog between actors involved in official agreements and resource allocation and proceed with legalization processes to enable the integration of i-PROACH with healthcare databases and systems; data hosting at local data storage services and further transfer of research data (anonymized or de-identified) to i-PROACH cloud. 6. Access and integration of health-related data from multiple sources create unprecedented opportunities for increasing the quality of healthcare by generating, integrating and managing health data from heterogeneous sources when healthcare providers are enabled to share part of their data with the different stakeholders involved in the provision of health services to patients.
Behavioural data analysis We investigated the effect of right hemisphere lesions on the perception of touch intensity and pleasantness, on the contralesional and ipsilesional forearm separately, by comparing stroke patients and healthy controls intensity and pleasantness ratings in turn. As the data were normally distributed, separate ANOVAs were run with touch type (CT-optimal vs. CT-suboptimal) and group (stroke patient vs. healthy controls) as factors, for each rating type and each forearm. An additional ANOVA comparing stroke patients and healthy controls was conducted for the imagined tactile pleasantness ratings (velvet vs. sandpaper). We were able to collect contralesional touch intensity ratings on only 39 out of the total sample of 59 patients due to an administrative error (the experimenter took binary, 'yes' or 'no' responses to the tactile stimuli instead of using the rating scale in the remaining patients).
For the same reason, we only had ipsilesional touch intensity ratings for CT-optimal touch on 36 and CT-suboptimal touch on 20 patients. This unfortunately meant that our sample was reduced to 39 patients for the analyses of intensity ratings on the contralesional forearm and of 20 patients for the ipsilesional forearm. We were able to record pleasantness ratings for contralesional forearm touch on 35 and 39 patients for CT-optimal and CT-suboptimal touch velocities respectively (data of 21 and 13 patients were missing due to the fact that patients did not feel the touch and gave an intensity rating of 0; the remaining 3 and 8 missing data were due to an administrative error). For the right ipsilesional forearm, pleasantness ratings of 56 and 41 patients were recorded at CT-optimal and CT-suboptimal touch velocities respectively.
Thus, the sample of the analysis of touch pleasantness was of 35 patients for the contralesional forearm and of 41 patients for the ipsilesional forearm. Moreover, as supplementary analyses, we also considered patients with intact tactile perception on the contralesional forearm. For these analyses, only patients that gave intensity ratings above two were included (N = 25). We used both frequentist and Bayesian statistics to assess the observed effects, depending on the aim and hypothesis in each case. The complementary use of these two statistical approaches is recommended by a number of authors to facilitate a fuller understanding of the data (see e. g. Dienes and Mclatchie, 2018; Dienes, 2014; Jarosz and Wiley, 2014; Howard et al. , 2000). Bayesian statistics were performed in order to allow further interpretation of the observed effects, in particular, the extent to which data provided support for the alternative versus null hypotheses.
Lesion mapping methods and analyses Routinely acquired clinical scans obtained on admission (<2 days post stroke) were collected for the 59 patients (49 via computerized tomography, CT; and 10 via magnetic resonance imaging, MRI). We note that testing patients in the acute post-stroke phase entails challenges but avoids any confounds relating to plasticity and functional reorganization  (Baier et al. , 2014; de Haan and Karnath, 2018). The patient's lesion was mapped by means of the MRIcron software  (Rorden and Brett, 2000)  on the standard T1-weighted MRI template (ICBM152) of the Montreal Neurological Institute (MNI) coordinate system. Lesions from these scans were segmented and co-registered using a manual procedure, as this method remains the best methods to date for lesion mapping of clinical scans and shown to be more accurate than automatized methods  (Maier et al.
Normal t-tests were used as the behavioural data entered in the VLSM models were normally distributed  (de Haan and Karnath, 2018). This method allows controlling for lesion size, which is included as a covariate of non-interest. Note that to avoid spurious results due to low numbers of lesioned voxels, only voxels lesioned in at least 10 participants were tested. This results in color-coded VLSM maps that represent voxels where patients with lesions show a significantly different behavioral score from those whose lesions spared that voxel at an a level of 0. 01 after correction for multiple comparisons using the false discovery rate  (Curran-Everett, 2000). Software to perform VLSM  (Bates et al. , 2003 ; https://aphasialab. org/ vlsm/) was run using MATLAB R2016b (Mathworks, Inc). It is to note, that in accordance with recent recommendation by de Haan and Karnath (2018), as no correlations were found between CT pleasantness sensitivity (or pleasantness ratings) and any of the neuropsychological scores that differed between healthy controls and right hemisphere stroke patients (HADS Depression scale, Digit Span backward, MOCA memory scale, and Premorbid IQ-WTAR), none of these variables could be considered as nuisance variables and were not considered in the VLSM lesion analyses.
Normal pH-aCSF (pH 7. 4) and metabolic acidosis-aCSF (pH 7. 23) differed in terms of NaHCO 3 concentration (21 and 15 mM, respectively)  (Suzue, 1984; Murakoshi et al. , 1985; Gestreau et al. , 2010; Loiseau et al. , 2014). Electrical activity of a fourth cervical ventral nerve root (C4) was recorded using a suction electrode, filtered (300-1000 Hz), amplified (×10000; Differential AC Amplifier Model 1700; A-M systems), integrated (time constant 100 ms), and digitized through a PowerLab 4SP, with a sampling frequency of 2500 Hz, and visualized and analyzed using LabChart 7. 2 data acquisition and analysis software (ADInstruments, Castle Hill, Australia). As previously reported, f R was defined as the burst frequency recorded from C4 for 1 min (burst•min -1 )  (Suzue, 1984; Loiseau et al. , 2014; Joubert et al.
Statistics Data were analyzed with GraphPad (GraphPad Prism8, San Diego, CA, United States). Normality of data distribution for f R , c-FOS and c-FOS/orexin values was assessed using d'Agostino and Pearson omnibus normality test. Baseline f R are expressed as mean ± standard error of the mean as data distribution is normal. As electrophysiological and histological series include group(s) with a non-normal distribution, all results are expressed as a median and interquartile range [Q1; Q3]. Parametric or non-parametric tests were performed, depending on normality. Within each group, the effect of an experimental condition on f R was assessed using a one-sample t test or a Wilcoxon signed rank test. Comparisons of the effect on f R between two groups were performed using twotailed unpaired t tests or Mann Whitney tests.
ACKNOWLEDGMENTS We thank pharmaceutical company  Actelion  for generously supplying us with almorexant ( ACT-078573 , ( 2R)-2-{(1S )-6, 7-dimethoxy-1-[2-(4-trifluoromethylphenyl)-ethyl]-3,4-dihydro -1Hisoquinolin-2-yl}-N-methyl-2-phenyl-acetamide),  Professors Thomas Similowski  and  Christian Straus  for discussing results and their significance,  Dr. Isabelle Rivals  for her help in carrying out statistical analyses and  William Hempel  ( Alex Edelman & Associates ) for editing the English text.
Each experimental session only involved one experimental condition. For MIINCONGRUENT, the following MI script was used: "Focus on the sensations associated with ventilatory movements. Feel the air coming into your nose, and the pressures within your lungs and chest. Feel the muscles contractions and stretches associated with natural inspiratory and expiratory movements". For MICONGRUENT, the following MI script was used: "Focus on the sensations associated with the effort of voluntary breath-holding. Feel the muscles contractions pressuring your lungs and the stretches associated with the control of your motionless chest. Focus on the absence of movements from the respiratory tract". A mental calculation task during CONTROL may have controlled for the state of attentional focus under MIINCONGRUENT and MICONGRUENT.
e. , R-wave). R-waves are then used to estimate both HR and HR variability. Specifically, the R-R interval refers to the time window separating two successive Rwaves in the ongoing cardiac signal. The root mean square of successive differences (RMSSD) and the standard deviation of R-R intervals (SDNN) can then be calculated. These variables are known to, respectively, reflect the parasympathetic nervous system activity and provide a global index of the activity of the autonomic nervous system  [25]. HR variability measures were processed using the Kubios HRV Standard 3. 0. 1 software (MATLAB ® , © 2017 The Mathworks, Inc). HR variability was finally normalized relative to the baseline recording. *** Please insert Figure  1  about here ***.
For psychometric scores, we entered the fixed effect of EXPERIMENTAL CONDITION. For the analysis of MI vividness scores, we also included the fixed effect of TRIAL (with interaction term). A backward stepwise procedure was used to fit the random-coefficient regression model formulae  [31, 32]. Effect sizes were calculated in terms of proportion of explained variation (i. e. , partial coefficients of determination, RP 2 ) using an ad hoc procedure for linear mixed effects models implemented in the r2glmm package  [33, 34]. RP 2 were interpreted based on the rule of thumb provided by Cohen  [35] : 0. 01 < RP 2 < 0. 13 was considered a small effect size, 0. 13 < RP 2 < 0. 26 was considered a medium effect size and RP 2 > 0. 26 was considered a strong effect size. Visual inspection of residual plots did not reveal any obvious deviations from homoscedasticity or normality.
Conclusion The purpose of the present study was to investigate the effect of congruent and incongruent MI practice on maximal breath-hold performance. Data confirmed the hypothesis that MI of breathing, but not MI of breath-holding, elicited increased breath-hold performance. The influence of MI of a breathing state on psychological and physiological factors determining the break-point of breath-holding might account for these beneficial effects. Figure 1. Raw HR (beats per minute) and R-R intervals (s) recordings during a breath-hold trial in a representative participant. The plateau is highlighted in blue (time window: 15-62 s). A: Bradycardia in response to the onset of breath-hold  [28]. B: Breaking point (i. e. , time of the first breath out). C: Signal noise artifact outside the plateau (nb.
FUNDING This work in OR and CS laboratory was supported by grants from the  Agence Nationale de Recherches sur le SIDA (ANRS)  to OR and CS, from  Sidaction  to OR and CS. Work in CV lab was funded by the  Belgian Fund for Scientific Research (FRS-FNRS, Belgium) , the  Fondation Roi Baudouin , the  NEAT program  ( Networking to Enhance the Use of Economics in Animal Health Education, Research and Policy Making ), the  Walloon Region (Fonds de Maturation) ,  Les Amis des Instituts Pasteur à Bruxelles , asbl. , and the  University of Brussels [Action de Recherche Concertée (ARC)  grant]. The laboratory of CV is part of the  ULB-Cancer Research Centre (U-CRC ). The work in VG laboratory was supported by  University College Dublin. SD, VG, PM, AM, OR, and CV have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  691119-EU4HIVCURE-H2020-MSCA-RISE-2015.
The dataset enables exploration of higher-order cognitive faculties, self-generated mental experience, and personality features in relation to the intrinsic functional architecture of the brain. We provide multimodal magnetic resonance imaging (MRI) data and a broad set of state and trait phenotypic assessments: mindwandering, personality traits, and cognitive abilities. Specifically, 194 healthy participants (between 20 and 75 years of age) filled out 31 questionnaires, performed 7 tasks, and reported 4 probes of in-scanner mindwandering. The scanning session included four 15. 5-min resting-state functional MRI runs using a multiband EPI sequence and a hig h-resolution structural scan using a 3D MP2RAGE sequence. This dataset constitutes one part of the MPI-Leipzig Mind-Brain-Body database.
Drug screening prior to MRI data acquisition Each of the participants was instructed not to use illicit drugs within two weeks of the scanning appointment. Participants were also requested to abstain from alcohol and caffeine consumption, as well as nicotine on the night prior to the scanning day and on the day of scanning. Before the beginning of the MRI session, participants' urine was biochemically screened with a MULTI 8/2 strip test (Diagnostik Nord, Schwerin, Germany) for the presence of buprenorphine (cutoff 10 ng/mL), amphetamine (cutoff 1000 ng/ mL), benzodiazepine (300 ng/mL), cocaine (cutoff 300 ng/mL), methamphetamine (1000 ng/mL), morphine/heroine (cutoff 300 ng/mL), methadone (cutoff 300 ng/mL), THC (cutoff 50 ng/mL). Cutoff levels are those recommended by the American National Institute on Drug Abuse (NIDA  65  ).
MRI data acquisition All magnetic resonance imaging (MRI) data was acquired using a whole-body 3 Tesla scanner (Magnetom Verio, Siemens Healthcare, Erlangen, Germany) equipped with a 32-channel Siemens head coil at the Day Clinic for Cognitive Neurology, University of Leipzig. For all the MRI data provided here, the scanner remained stable and did not undergo any major maintenance or updates that would systematically affect the quality of the acquired data. For each participant the following scans were obtained: 1) a high-resolution structural scan, 2) four rs-fMRI scans, 3) two gradient echo fieldmaps and, 4) two pairs of spin echo images with reversed phase encoding direction. A low-resolution structural image of each participant was acquired using a FLAIR sequence for clinical screening.
The phase-encoding direction was A-P for runs 1 and 3, and P-A for runs 2 and 4. Further parameters were set as follows for all four runs: voxel size = 2. Scans for distortion correction. Two prominent methods exist to correct for geometric distortions in EPI images: fieldmaps, which represent the degree of distortion as calculated from two phase images with different echo times  70, 71  , and reverse phase encoding, in which pairs of "blip-up blip-down" images are acquired with opposite phase encoding directionthus opposite distortionsand used to model a middle distortion-free image  72, 73. This dataset contains scans required for both methods to accommodate different preprocessing approaches and facilitate method comparison. Before each pair of resting-state runs with the same y-axis orientation (see above), the following scans were acquired in the same orientation as the subsequent resting-state scans: a pair of spin echo images (voxel size = 2.
MRI data preprocessing To enhance data usability we provide preprocessed data from 189 subjects (five participants did not have all four resting-state scans available, and were excluded from preprocessing). Data from five participants were further excluded due to failure at the preprocessing stage. The raw MRI data of these subjects are not corrupted, and are therefore available in the main database. Preprocessing pipelines were implemented using Nipype  74  and are described in more detail below. All code is openly available (https:// github. com/NeuroanatomyAndConnectivity/pipelines/tree/master/src/lsd_lemon). Importantly, the preprocessing performed here is just one out of a multitude of possible pipelines that could be conceived for this dataset. The decisions taken at individual processing steps will not be suitable for every application.
Of the signal of all voxels included in this mask, the first six principal components were included as additional regressors in a second GLM, run on the residual time series from the first GLM. The denoised time series were temporally filtered to a frequency range between 0. 01 and 0. 1 Hz using FSL, mean centered and variance normalized using Nitime  85. The fully preprocessed time series of all for runs were temporally concatenated. To facilitate analysis in standard space, the previously derived transformation was used to project the full-length time series into MNI152 2 mm space. The preprocessed data are made available in the subjects' native structural space and MNI standard space, along with the subject's brain mask and all regressors used for denoising.
Data security and data anonymization procedures Data for all participants was stored on our instance of the eXtensible Neuroimaging Archive Toolkit (XNAT  86  ) v. 1. 6. 5. at the MPI-CBS. Access to the initial project was restricted (via XNAT's private project mode) to members of the Neuroanatomy & Connectivity Group at MPI-CBS for initial curation and quality assessment of data. All data comprised in the MPI-Leipzig Mind-Brain-Body database were derived from MPI-CBS so data import into XNAT was done from a local secured network. A specially customized XNAT uploader was used to upload all participants' data to XNAT. The native DICOM format was used for MRI data, whilst a standard ASCII ( *. csv, *. txt) format was employed to upload all other experimental data such as surveys, test batteries, and demographical data.
The anonymization measures applied to the MRI data consisted of removal of DICOM header tags containing information which could lead to the identification of test subjects as well as the defacing of all structural (NIFTI) scans. Specific surveys and test batteries containing sensitive information are only available via the restricted project in XNAT for which access needs to be applied for (see the Usage Notes section below). Neurobehavioral Systems, Inc. , Berkeley, CA, http://www. neurobs. com). We provide the respective source codes of the Adaptive Visual and Auditory Oddball Target Detection Task e. g. ,  55  ; cf. 56 (Oddball; https://github. com/NeuroanatomyAndConnectivity/opendata/tree/master/scripts), Conjunctive Continuous Performance Task 51 (CCPT; https://github. com/NeuroanatomyAndConnectivity/ConjunctiveCon-tinuousPerformanceTask), and Emotional Task Switching  50, 58  (ETS; https://github.
MRI data The dataset is organized in concordance with the Brain Imaging Data Structure (BIDS) format  90. This facilitates data analysis, for example with BIDS-Apps 91 (http://bids-apps. neuroimaging. io). BIDS-Apps encapsulate standard MRI analysis tools within an application that understands the BIDS format and allows to automatically access relevant data and metadata. MRI data are currently available from three locations:.