dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: DMB was supported by a  Royal Society Wolfson Research Fellowship  (# WM170007 ),  Royal Society   International Exchanges Award  (# IES\R2\192137 ),  Japan Society for the Promotion of Science  (# JSPS/OF317 ) and  Higher Education Funding Council for Wales. PNA was funded by a  Canada Research Chair (CRC)  and  Natural Sciences and Engineering Research Council of Canada (NSERC)   Discovery grant. ARB was funded through a  NSERC postdoctoral  fellowship. RLH was funded by a  NSERC  grant. OFB was funded by the  Autonomic Province of Vojvodina, Serbia  (# 142-451-2541 ). NM was supported by  ANR-Hepatobrain ,  ANR-Epicyte ,  ERaNet Neu-Vasc ,  ANSES Epidemicmac  and  MUSE-iSite University of Montpellier.
Specifically, participants completed the Interview Task and the Personality Pairs Task (PPT). The Interview Task allows two abilities relevant to Mind-space to be tested. The first, as previously described, is the accuracy of mental state inferences-these can be assessed against ground-truth data provided by Candidate and Interviewer reports of their mental states. The second is the ability of participants to locate Target minds in Mind-space. Each Candidate and Interviewer completed measures of personality and IQ. Participants completing the Interview Task are asked to estimate the traits of Candidates and Interviewers, and thus their ability to estimate a Target's traits (i. e. , the ability to locate a Target mind within Mind-space) can be assessed. The other task, the PPT, is designed to test the accuracy of Mind-space, specifically a participant's understanding of how personality traits co-vary.
Methods Participants. 61 participants volunteered to participate. As no equivalent data or analyses on which to base power calculations exist, we recruited as many participants as possible with available funds to maximise the power of the study. However, a simulation-based power calculation conducted on the basis of effects observed in an independent dataset (N = 25) indicates that the present study has a power of > 99% (for full details, see Supplementary Information, section S2). Participants were recruited through advertisements placed across University of Oxford locations and online, and through a departmental online research participation platform. All participants were compensated with either money or course credits. One participant was excluded for failing two attention checks, one was excluded due to technical difficulties and four were excluded for not completing the Personality Pairs Task, leaving 55 participants (38 female, M AGE = 19.
An explanation of these models and the use of the lme4 package for R  42  is given by Bates and Mächler  43. Prior to testing our hypotheses, we investigated the effect of our trial types ('matched' , 'half ' , and 'mismatched' trials) on trait updates by fitting and comparing models which used trial type and time to predict the absolute magnitude of trait updates from prior to the interview to half-way through the interview, and from half-way through the interview to the end of the interview. Note that the mismatching was performed in order to ensure that participants updated their estimates of Target personality but updating was also expected in the other trial types. Then, model selection was performed by comparing statistical models for each of our three hypotheses. First, we assessed whether the accuracy of participant estimates of Targets' HEXACO traits predicted the accuracy of mental state inference and examined the variance in this effect across combinations of traits and mental state inferences.
As we hypothesised that the effect of the accuracy of each trait estimate may differ according to participant beliefs about its relationship with specific mental states, the variance in the random effects is an important model output. Second, we examined updates in trait estimates and specific mental state inferences, again inspecting the variance in any relationship according to the trait in question and the specific mental state inference being made. Third, we investigated the extent to which the accuracy of Mind-space (measured by the PPT) predicted the accuracy with which Targets were located, i. e. , the accuracy of trait estimates. Full details of the models considered in the selection process can be found in the Supplementary Information (section S3). Model comparison was conducted using the anova function from the lmerTest package  44  which applies a log likelihood ratio test and provides AIC and BIC values, which are model fit criteria that allow assessment of explanatory power relative to model complexity and generally prefer the most parsimonious model  45.
e. , sampled without replacement) within each group, effectively dissociating the trait estimate from its trait label. An equivalent model to our final model was then fitted to the shuffled dataset. This process was repeated 1000 times for each analysis, allowing parameter distributions to be constructed. If, contrary to our hypothesis, there were no systematic relationship between specific trait estimates and specific mental state inferences across participants and stimuli, then comparison of the values obtained from our models with the correct trait labels to the derived parameter distributions would reveal comparable parameter values. The analyses described above seek to determine whether the accuracy of mental state inference is related to the accuracy of trait estimates and, similarly, whether updates in mental state inference are related to updates in trait estimates.
As the only aspect of the underlying data which changes in these simulations is the trait labelling, differences in model parameters between the observed and simulated models would indicate the systematic relationships we predict. We would expect the greatest parameter differences to emerge in the variance of the random slope on the trial grouping term and the residual, as shuffling trait labels should decrease the amount of variance explained by allowing the relationships between trait update/error and mental state update/error to vary by trial. To examine this, we computed z-scores for each of our observed parameter values relative to the distribution of the same parameter across the simulated datasets and descriptively compared the resulting z-scores for different parameters.
This indicates that participants tended to change their trait estimates after seeing the first portion of the video regardless of whether the written profile was mismatched, and therefore that the mismatching of profiles was unnecessary. It is clear from the descriptive statistics and the effect of time that updates did occur but were independent of our attempts to induce them. Because of this, the effect of trial type will not be examined further. In the models that follow, data from all three timepoints (i. e. , before, during and after the interview) were used to examine updates. In contrast, accuracy models were fitted only to data collected at the mid-point of the interview and at the end, as it is unreasonable to assess the accuracy of trait and mental state inferences against a mismatched Target prior to observing that Target.
Acknowledgements This publication was made possible through the support of a grant from the  John Templeton Foundation. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the  John Templeton Foundation. E. L. L. was supported by the  Economic and Social Research Council  ( 2420999 ). J. R. C. acknowledges support from  ANR-17-EURE-0010  and  SFI-IRC 21/PATH-A/9615.
Deficits in neurocognitive functioning are trait-like vulnerabilities that have been widely studied in persons with substance use disorders (SUD), but their role in the craving-use association and relapse vulnerability remains poorly understood. The main objectives of this study were to examine whether executive capacities moderate the magnitude of the craving-substance use relationship, and if this influence is correlated with the functional connectivity of cerebral networks, combining rsfMRI examinations and ecological momentary assessment (EMA). Eighty-six patients beginning outpatient treatment for alcohol, tobacco or cannabis addiction and 40 healthy controls completed neuropsychological tests followed by EMA to collect real-time data on craving. Fifty-four patients and 30 healthy controls also completed a resting-state fMRI before the EMA.
The combination of neuropsychological and EMA data with rsFC would therefore provide novel insights into the pathophysiology underlying the relationships among executive functioning, craving and relapse in SUD, eventually identifying the specific functional circuits associated with a higher relapse risk following craving episodes. The present investigation examined this issue in outpatients with alcohol, tobacco, and cannabis use disorders. The main objectives were to (1) assess the independent associations of executive function with craving and substance use in daily life, (2) examine whether executive capacities moderate the magnitude of the craving-substance use relationship, and (3) explore whether this potential influence measured by EMA can be associated with changes in the brain's functional connectivity.
Addiction and Psychiatric Data DSM-5 diagnostic criteria for current substance use disorders were assessed using the Mini International Neuropsychiatric Interview, French Version 5. 0. 0 (MINI)  [20]. Substance-related data were assessed using a validated French version of the Addiction Severity Index (ASI), modified to take tobacco addiction into account  [21]. The    [20]. Substancerelated data were assessed using a validated French version of the Addiction Severity Index (ASI), modified to take tobacco addiction into account  [21]. The Interviewer Severity Ratings (ISR) from the drug, alcohol and tobacco sections of the ASI were used to assess the severity of each participant's addiction.
Acquisition of Brain Imaging Data Anatomical and functional brain imaging data were collected using a 3. 0 Tesla GE MRI system with a 32-channel MRI head coil. Anatomical MRI volumes were acquired using a sagittal three-dimensional T1-weighted scan (repetition time, 8. 5 ms; echo time, 3. 2 ms; flip angle, 11 • ; FOV, 256 mm × 256 mm; voxel size, 1 mm × 1 mm × 1 mm; 176 slices). The resting-state functional MRI volumes were acquired using a single-shot echo-planar sequence (RT, 2200 ms; ET, 27 ms; flip angle, 80 • ; FOV, 192 mm × 192 mm; voxel size, 3 mm × 3 mm × 3. 5 mm; 42 axial slices; number of volumes, 300). The total duration of the resting-state fMRI scan was 11 min, during which, the participants were instructed to keep their eyes closed, to not fall asleep and to not think about anything in particular.
Data Preprocessing Resting-state functional MRI and T1-weighted MRI images were preprocessed using fMRIPrep 20. 2. 1  [27, 28]  which is based on Nipype 1. 5. 1  [29, 30]. Briefly, the T1-weighted structural scans underwent correction for intensity nonuniformity (INU), followed by skull stripping; brain tissue segmentation of the cerebrospinal fluid (CSF), white matter (WM) and gray matter (GM); and normalization to the Montreal Neurological Institute (MNI152NLin6Asym) space using nonlinear registration with ANTs. Preprocessing of the resting-state fMRI scans included slice-timing correction, susceptibility distortion correction, motion correction, co-registration to each subject's preprocessed T1-weighted scan, normalization to the MNI152NLin6Asym standard space, resampling to a 2 × 2 × 2 mm grid and spatial smoothing (FWHM = 6 mm).
Definition of Functional Connectivity at Two Levels of Topological Organization Brain functional connectivity was analyzed at two topological levels, namely regionto-region connectivity and resting-state networks, to explore the feasibility of the combined analysis of the EMA and rsFC datasets. For the low-level connectivity analysis, we used the parcellation of the 512 finely resolved regions based on a recently introduced multi-scale functional parcellation (dictionaries of functional modes, DiFuMo). This scale of connectivity (the 512-dimensional DiFuMo) provides close results to analyses performed on signals at a higher resolution (the 1024-dimensional DiFuMo and voxel level)  [31]. For each subject, we extracted blood-level dependent (BOLD) time-series from each of the 512 functional brain regions and estimated the functional connectivity between regions by computing the Pearson correlations between their respective BOLD time-series.
Statistical Analysis ANOVA and chi-square tests were performed to compare the patients and controls on quantitative and qualitative variables, and to compare patients across substance groups. EMA and clinical data were analyzed using hierarchical linear and nonlinear modeling  [34]. Data were time-lagged so that craving levels at any given assessment (T0) were used to predict substance use at the subsequent assessment on the same day (T1). All analyses were adjusted for the status of the T1 outcome variable as measured at the T0 assessment. The magnitude of these within-person coefficients were then examined as a function of the neuropsychological test scores, adjusting for age, sex, education, comorbidity and SUD subtype. For neuroimaging data, two permutation tests (50,000 permutations; networkbased statistics NBS1.
Discussion The present study examined whether executive functions moderate the dynamic, prospective association between craving and substance use among individuals with SUD, and whether such moderation may be linked to differences in functional brain connectivity. These questions were investigated through the joint analysis of data collected within the contexts of daily life using EMA along with data gathered through functional neuroimaging. Our results indicate an unexpected association between certain neuropsychological performance scores and the magnitude of the prospective link between craving and substance use in daily life. Specifically, better verbal fluency and resistance to interference capacities were both associated with a greater propensity to use substances when individuals were experiencing craving.
It should be noted that executive functions were assessed as stable individual traits, but we cannot exclude the fact that quick fluctuations in the capacity for inhibition in daily life may have an impact on the predictive value of craving episodes, contributing to further substance use. Furthermore, sex differences may modulate the interactions among neuropsychological characteristics, craving and substance use, and further studies should examine this issue. Concerning the neuroimaging data, the proof-of-concept approach combining EMA and rsFC datasets led us to adopt a conservative analytical strategy by removing all anti-correlations, thus focusing only on positively correlated networks, as these are the most consistently cited in the literature with regard to addiction  [18]. This approach, in combination with the two permutation tests, one for each statistical contrast, provides a high level of confidence about the significant differences reported and a clear directionality for the resulting effects, but it does not deliver a complete account of more subtle differences and anti-correlated networks in particular.
Study Design and Participants The present study has a cross-sectional design, based on baseline data derived from a multicenter RCT  (Hypher et al. , 2019)  with 75 pABI participants and 59 healthy controls (HCs). Inclusion criteria included: (i) age 10-17 years; (ii) a diagnosis of pABI; (iii) a period of at least 12 months since injury/illness diagnosis, or 12 months since completion of cancer treatment; and (iv) executive dysfunction in daily life as determined by a semi-structured interview. Exclusion criteria were as follows: (i) injury acquired before 2 years of age; (ii) cognitive, sensory, physical, or language impairment affecting the capacity to attend regular school (i. e. , unable to follow educational goals of peers and regular classroom teaching, even with adaptation or extra help); (iii) pre-injury neurological or developmental disease, severe (neuro)psychiatric disorder and/or use of stimulant medication; (iv) recently detected brain tumor relapse; (v) not fluent in Norwegian; and (vi) food allergy interfering with the ability to complete the CCT.
Recruitment and Assessment Procedures Data were collected between 2017 and 2019 at St. Olav's Hospital, Trondheim University Hospital, and Oslo University Hospital. Participants were recruited from the trauma referral centers for the Central, South-Eastern, and Northern regions of Norway. Seventy-seven participants with pABI who met inclusion and exclusion criteria and completed baseline assessment in the RCT study were included in this study. For a full description of recruitment procedures, see  Hypher et al. (2019 Hypher et al. ( , 2021) )  and  Brandt et al. (2021). In addition, further two participants with severe motor and language dysfunction but who were able to attend regular school, were unable to complete the CCT without adult interference and were thus excluded. So, data from 75 participants with pABI were available for analysis in the present study.
Ethics Study procedures and monitoring were performed according to ICH Guideline for Good Clinical Practice and Norwegian procedures and regulations for Clinical Trials, described by the Norwegian Clinical Studies Infrastructure Network. Written informed consent was signed by participants (>16 years) or primary caregivers (<16 years). The study was approved by the Regional Committees for Medical and Health Research Ethics, Norway (2017/772/REK), and conducted in accordance with principles of the Helsinki Declaration and the standards for Ethical Research Involving Children (ChildWatch International and UNICEF). Clinical Trial Registration No. : NCT03215342.
Statistical Analysis Dependent variables were checked for normality by use of the Shapiro-Wilks test and inspection of Q-Q plots. Demographic and injury severity characteristics and cognitive sub-tests are presented as mean (± standard deviation, SD) for normally distributed data, and otherwise as median with interquartile range (IQR). All data analyses were conducted using IBM-SPSS version 27 (IBM Corp, 2020). In the presence of missing data, we used available case analysis, utilizing all cases for which a variable is present. Thus, the number of cases differs for each variable. Reported p-values are two-sided. Due to multiple assessments, a pragmatic αlevel of ≤0. 01 was considered significant, chosen to partially account for multiple testing. A more formal approach to control for multiple comparisons (e.
ACKNOWLEDGMENTS We would like to thank the participants and their families,  Head of Clinics , patient advisers, and the staff at  St. Olav's Hospital, Trondheim University Hospital and Oslo University Hospital  for their participation and support. We would especially thank our research nurses, occupational therapist  Lene Bobakk , test-technicians, neuropsychologists, and pediatricians involved in this study. We also would like to thank  Cathy Catroppa  and  Agata Krasny-Pacini  for discussions concerning the study, pediatrician  Bård Forsdal  for help in recruiting patients from at the  University Hospital of North Norway , the  Unit for Applied Clinical Research, Norwegian University of Science and Technology (NTNU)  responsible for the monitoring and developing the webCRF, with special thanks to  Anne Caroline Wiik.
, 2021). This study used indirect lesion network mapping to estimate lesion-induced connectivity deficits aggregated at the level of individual brain regions and quantified the causal contribution of preserved connectivity to excellent functional outcome with the main aim of better understanding the structural mechanisms underlying the clinical efficacy of thrombolysis. Previous work has not addressed the connectivity-preserving effects of thrombolysis on individual connections, their spatio-topological determinants, or their consequences for global measures of brain network integration and segregation. In the present study, we used data from the clinical WAKE-UP trial to investigate the effect of thrombolysis on the topological structure of the cerebral structural connectome in a lesion network mapping paradigm.
| Study design The WAKE-UP trial (NCT01525290) was an international, doubleblind, placebo-controlled randomized clinical trial of the efficacy and safety of systemic thrombolysis with alteplase in acute ischemic stroke patients with unknown time of symptom onset  (Thomalla et al. , 2018). Key inclusion criterion was demonstration, on MR imaging, of an acute ischemic lesion in diffusion-weighted imaging (DWI) without established hyperintensity in fluid-attenuated inversion recovery (FLAIR). The WAKE-UP trial provided evidence that thrombolysis is effective and safe in MR imaging-selected patients with unknown symptom onset based on a DWI-FLAIR mismatch. For this post-hoc secondary analysis, imaging and clinical data of all randomized patients were reviewed. We included patients with isolated anterior-circulation stroke who received DWI and FLAIR imaging both prior to and 22 to 36 h after randomization.
| Imaging data processing Stroke lesions were segmented based on DWI data using a software developed for the WAKE-UP trial (Stroke Quantification Tool, SONIA) as described previously  (Cheng, Boutitie, et al. , 2020; Forkert et al. , 2014). In summary, individual DWI and FLAIR datasets were rigidly co-registered. Apparent diffusion coefficient (ADC) maps were calculated based on two DWI datasets with b-values of 0 s/mm 2 and between 500 and 1500 s/mm 2 according to the imaging protocol of the study site. Stroke lesions were segmented on ADC maps using a semi-automated procedure with initial manual delineation and secondary automated refinement based on an ADC threshold of 620 Â 10 À6 mm 2 /s. Images were visually checked for quality and plausibility of segmentation results by cross-modal inspection of FLAIR and ADC maps and manually corrected, if necessary.
| Quantification of structural network disruption We quantified disruption of structural brain networks by mapping stroke lesion masks in MNI space onto a set of pre-defined, weighted structural reference tractograms obtained from diffusion tensor imaging (DTI) and DTI tractography in 73 healthy participants using the Network Modification (NeMo) toolbox for Matlab  (Kuceyeski et al. , 2013; MATLAB, 2021). The NeMo tool has previously been applied to investigate lesion-dysfunction relationships  (Kuceyeski et al. , 2015) , white-and grey matter pathologies in normal aging  (Glodzik et al. , 2014) , as well as prediction of clinical outcome 6 months after stroke  (Kuceyeski et al. , 2016). Specifically, streamlines running through the ischemic lesion were removed from the reference tractograms, resulting, for each patient, in a set of 73 lesioned tractograms.
Streamline counts of individual connections as well as the global graph measures of each lesioned network were divided by corresponding streamline counts and null graph measures of the networks obtained from intact tractograms and averaged across the reference set. Reported connectivity strengths and network measures for stroke patients are thus relative to reference values in the healthy population (n = 73) included in the NeMo software. In addition to global metrics of structural network topology, we aimed to localise potential effects of thrombolysis by studying the change of structural connectivity of individual edges from before to 22 to 36 h after randomization in WAKE-UP. In order to address the multiple testing problem inherent in mass univariate approaches of testing all edges in the structural connectome, the network-based statistic (NBS) approach was used to identify subnetworks of alteplaseresponsive edges, in which progressive loss of connectivity was reduced in the alteplase group compared with placebo.