dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional annotation of cortical regions showing altered asymmetry in left-handers We made use of the Neurosynth database  (38) , a platform for large-scale automated synthesis of taskbased fMRI data. This database defines brain-wide activation maps corresponding to specific cognitive or behavioural task terms, using meta-analyzed functional activation maps from studies referring to those terms  (38). At the time of accessing the database it included 1307 maps, representing activation patterns corresponding to 1307 cognitive or behavioural terms, from 14,371 human neuroimaging studies. We created one bilateral mask in MNI152 standard space by labelling all clusters showing altered average surface area asymmetry in left-handers, and another bilateral mask in MNI standard space that comprised all clusters showing altered average cortical thickness asymmetry in left-handers.
This was achieved through applying the 'mri_surf2vol' function in Freesurfer to the down-sampled, handedness-associated asymmetry cluster maps (defined in left hemisphere 'fsaverage5' surface space), then identifying the mirrored voxels (based on the midline of left and right hemisphere) corresponding to these clusters in the right of the symmetrical standard space, to define masks with bilaterally labelled voxels. These masks were then used separately as input to identify relevant cognitive and behavioral terms through the 'decoder' function of the Neurosynth database. This tool first creates a co-activation map for each input mask, based on all activation data in the database, and then tests each term-specific activation map for spatial correlation with the co-activation map. Cognitive terms with correlations >0.
This method uses a high-dimensional Bayesian regression framework to estimate posterior effect sizes of included SNPs, and has shown superior prediction statistics compared to approaches based on clumping by linkage-disequilibrium and P-value thresholding  (80). In total, effect sizes of 1,103,636 SNPs spanning the autosomes were used, based also on their presence in the 1000 Genomes project European-descent dataset, from where PRS-CS obtains its linkage disequilibrium information  (81). We used default parameters and the recommended global effect size shrinkage parameter phi=0. 01. The handedness polygenic scores in the imaging-genetic dataset were z-standardized for subsequent analyses (Supplementary Figure  5 ). We first tested whether polygenic disposition to left-handedness was associated with hand preference in the 28,802 right-handed and 3,062 left-handers, using logistic regression with hand preference as the outcome variable, and covariates of age, (age -mean_age) 2 , genotyping array, the first ten genetic principle components (see above), and sex.
Imaging genetic analysis of individual loci associated with left-handedness A previous genome-wide association scan for left-versus-right handedness, carried out in 1,766,671 individuals, reported 41 individually significant genomic loci after correction for multiple testing across the whole genome  (37). We found genotype data for 38 of these SNPs in the 28,802 right-handed and 3,062 left-handers of the imaging-genetic sample in the present study, and were able to identify a proxy SNP for one more of these lead SNPs (rs11265393 as a proxy for rs66513715, with linkage disequilibrium r-squared of 1 in European-descent data according to LDproxy (82)). We tested each of these 39 SNPs for association with each of the significantly heritable, cortical cluster-wise asymmetries (averaged over all vertices separately within each cluster) that showed alterations in left-handers, using an additive model in BGENIE (v1.
2) (72) with the same covariates as the heritability analysis (above). All 39 SNPs had imputation quality (INFO) scores > 0. 7. We applied FDR correction at 0. 05 to control multiple testing over all of these SNP-cluster combinations. Note that the previous genome-wide association study of left-versus-right handedness based on 1,766,671 individuals (37) included the UK Biobank participants, and therefore also included the participants with brain image data, although the brain image data were not used in that study. The sample overlap meant that we could not use those genome-wide, SNP-wise effect size estimates to calculate polygenic disposition to left-handedness in the current study, as the genome-wide screening and target datasets must not overlap for polygenic scoring analysis. In addition, the SNP-wise effect sizes have not been made publicly available from the GWAS of 1,766,671 individuals (37).
Acknowledgements This research was funded by the  Max Planck Society (Germany)  and grants from the  Netherlands Organization for Scientific Research (NWO)  ( 054-15-101 ) and  French National Research Agency (ANR , grant No. 15-HBPR-0001-03 ) as part of the  FLAG-ERA consortium  project ' MULTI-LATERAL ', a  Partner Project  to the  European Union's Flagship Human Brain Project. This research was conducted using the  UK Biobank  resource under application no. 16066 , with C. F. as the principal applicant. Our study made use of data generated by an image-processing pipeline developed and run on behalf of  UK Biobank. The funders had no role in study design, data collection and analysis, the decision to publish or preparation of the manuscript. Many thanks to  Nathalie Tzourio-Mazoyer  and  Bernard Mazoyer  for their founding roles in the collaborative work that led to this study.
INTRODUCTION Brain modeling and understanding is a very active field of research involving different disciplines, such as neuroscience, image and signal processing, statistics, physics, and biology. These last years, neuroimaging modalities have been developed to explore the brain for both structural and functional features. It is now recognized that these images are providing very promising noninvasive observations of the brain  (Bullmore & Sporns, 2009b; Mwangi, Tian, & Soares, 2014; Richiardi, Achard, Bunke, & Van De Ville, 2013). One consequence of the availability of such massive datasets is the need to develop more and more sophisticated models to unravel the possible alteration of brains due to the impact of different pathologies. In this context, representing the brain as a global system is capital.
For the study of brain disorders, these metrics have been used in order to extract biomarkers for pathologies such as for example Alzheimer's disease  (Supekar, Menon, Rubin, Musen, & Greicius, 2008) , schizophrenia  (Lynall et al. , 2010) , and multiple sclerosis  (Filippi et al. , 2014). Extracting quantitative parameters of brain networks is compulsory to conduct any statistical analysis. In this framework, statistical and machine learning approaches on graph metrics on all nodes allow the quantification of differences between groups  (Richiardi et al. , 2013). For any dataset, any graph metric can be computed either at the global level with one value for an entire network or at the nodal level with one value for each node and a vector of values for the entire network. It has already been shown that global values may not discriminate two groups of subjects  (Achard et al.
In addition, a new methodology is proposed to incorporate interesting networks features already identified in specific datasets directly in the manifold learning approach. Contrary to statistical tests at nodal levels where each feature is treated independently of others, our approach based on machine learning is able to analyze joint variations between local descriptors. This paper is focusing on two already published datasets. The first one consists in fMRI datasets on 20 healthy controls and 17 coma patients from Achard et al. (Achard et al. , 2012). The second one is based on  (Achard & Bullmore, 2007)  where 15 young healthy subjects and 11 elderly healthy subjects were scanned using resting state fMRI. Our first experiment compares data driven approaches such as Linear Discriminant Analysis (LDA) and Random Forests (RF) to an ad hoc description such as the hub disruption index κ.
Comatose study The data were acquired in a previous study aimed at characterizing resting state connectivity brain networks for patients with consciousness disorders. The description of the data and results is reported in  (Achard et al. , 2012). The patients were scanned a few days after major acute brain injury, when sedative drug withdrawal allowed for spontaneous ventilation. Therefore, all patients were spontaneously ventilating and could be safely scanned at the time of fMRI. authorities. Written informed consent was obtained directly from the healthy volunteers and from the next of kin for each of the patients. Resting-state data were acquired for each subject using gradient echo planar imaging technique with a 1. 5-T MR scanner (Avanto; Siemens, Erlangen, Germany) with the following parameters: relaxation time = 3 s, echo time = 50 ms, isotropic voxel size = 4 x 4 x 4 mm 3 , 405 images, and 32 axial slices covering the entire cortex.
Young and elderly study The data used in this study have already been analyzed in two papers  (Achard & Bullmore, 2007)  and  (Meunier, Achard, Morcom, & Bullmore, 2009). The goal of these papers was to identify the changes in brain connectomes for elderly subjects in terms of topological organization of brain graphs. The data consist of 15 young subjects aged 18-33 years, mean age=24 and 11 elderly subjects aged 62-76 years. Each subject was scanned using resting-state fMRI as described in  (.
Preprocessing and wavelet graph estimation Brain network graphs were determined following  (Achard et al. , 2012)  for comatose study and  (Achard & Bullmore, 2007)  for young and elderly study. For each subject, data were corrected for head motion and then coregistered with each subject's T1-weighted structural MRI. Each subject's structural MRI was nonlinearly registered with the Colin27 template image. The obtained deformation field image was used to map the fMRI datasets to the automated anatomical labeling (AAL) or to a customized parcellation image with 417 anatomically homogeneous size regions based on the AAL template image  (Tzourio-Mazoyer et al. , 2002). Regional mean time series were estimated by averaging the fMRI time series over all voxels in each parcel, weighted by the proportion of gray matter in each voxel of the segmented structural MRIs.
We estimated the correlations between wavelet coefficients of all possible pairs of the N = 90 or 417 cortical and subcortical fMRI time series extracted from each individual dataset. For the coma, only scale 3, 0. 02-0. 04 Hz, wavelet correlation matrices were considered. For the young and elderly, the wavelet scale considered corresponds to 0. 06-0. 11 Hz. The choice of these wavelet scales or frequency bands is explained precisely in the corresponding papers  (Achard & Bullmore, 2007; Achard et al. , 2012). To generate binary undirected graphs, a minimum spanning tree algorithm was applied to connect all parcels. The absolute wavelet correlation matrices were thresholded to retain 2. 5 % of all possible connections. Each subject was then represented by a graph with nodes corresponding to the same brain regions, and with the same number of edges.
Covariates may be regressed on the reduced space. In the present work, this was achieved using a classic radial basis function interpolation. The choice of the ISOMAP is two fold: firstly, the estimated reduced space is a smooth manifold, and preserves the global structure of the dataset. Notably, the reduced space exhibits a continuum function of subjects. Secondly, the cost function of the ISOMAP allows the integration of additional constrained scores. ISOMAP was performed by computing a nearest neighbor graph connecting the four nearest neighbors according to the euclidean distance. This distance reflects correctly the local topology of the graph metrics space. The choice of four neighbors is driven by the relatively small number of subjects in the study. The classification score of the ISOMAP was computed using a non linear Support Vector Machine (SVM) approach with radial basis function kernel in the reduced space Hearst, Dumais, Osuna, Platt, and Scholkopf (1998).
The algorithm is described in Algorithm 1 and available here https://github. com/renardfe/CCML. while All points are not included do 1) Select the most distant sample y k to the already selected samples 2) Compute x k by minimizing E wrt x k (α and other x i 's fixed) as min x k E. 3) Update the scale α by minimizing E wrt α (x i 's fixed) as min α E. 4) Update x j 's of samples already included by minimizing E as min {x j } j=1. k-1 E.
end while From the obtained embedding, a generative model y = f (x) (7) can be devised, where y is a vector in the original space (the connectome space), x is a vector from the manifold embedding, and f is a regression function. Multivariate adaptive regression splines (MARS)  (Friedman, 1991)  is chosen for the regression function f for its nice properties (one regression for each coordinate of f , i. e. n regressions): locally linear and globally nonlinear. The parameters of f can be determined using the dataset {y i } i=1. N and the corresponding reduced vectors {x i } i=1. N using equation: y i = f (x i ) + i = y i + i , ( 8 ) where i is the residual between a sample and its prediction y i. The residuals allow to evaluate the accuracy of the regression function. This kind of model is not original, PCA being the most well known case where the model is defined as y = A x + , see e.
No free lunch for graph metric descriptors We hypothesize that there is no best descriptor adapted to all datasets. The optimality depends on 1) the kind of the data and 2) the kind of question/task addressed. This idea is known as the "no free lunch == D R A F T November 21, 2020 (Received. ) == / Title: A covariate-constraint method to map brain feature space into lower dimensional manifolds Authors: Author Names theorem"  (Wolpert, 1996) : if an algorithm performs well on a certain class of problems then it has necessarily poorer performances on the set of all remaining problems. In the present study, we showed that the κ index yields good classification performances in separating a comatose population from a healthy population. However the MEAN index better describes the groups of elderly and young people (see Fig.
3 ): for this dataset, the κ index cannot separate the two groups, but the MEAN score can. It is interesting to notice that several descriptors can map correctly a population, while providing different information. The "no free lunch theorem" also applies to manifold learning algorithms. The underlying question is the one of choosing an interpoint distance in the data space. A given interpoint distance will yield a specific structure of samples in the reduced space. Therefore, the retained interpoint distance chosen will depend on the final goal: mimicking the structure of the initial data points, enhancing class separation with a view to achieving better classification performances, focusing on a specific property of the data, etc. The proposed algorithm CCML aims at mimicking the structure of the initial data points, and this to be done using explicitly a particular characteristic, chosen and imposed by the investigator.
N vectors (samples) {y i } i=1. N of a graph metric over n graph nodes Result: reduced space representation {x i } i=1. N of the dataset, where the first coordinate of each xi corresponds to the covariate. Update the scale α by minimizing E wrt α, x i fixed, as min == D R A F T November 21, 2020 (Received. ) == / Title: A covariate-constraint method to map brain feature space into lower dimensional manifolds Authors: Author Names Algorithm 1 -covariate constrained manifold learning (CCML) Input -dataset: Initialization: select the two most distant samples Determine their reduced coordinates by minimizing E with α = 1 Application: a generative model for the prediction of the variation in a subject with regard to the changes of a covariate -13-α E.
Method The variables examined here relate to the specificities of 248 engineering students. Among these, 39 high-performing students, called CMI 4 students, were selected to participate in an intensive engineering course taught in English. All students attended a regular English class and an electronics class, which was taught either in English (60 students) or in French (188 students), while approximately 40% of students had access to CYS. Data was collected during a 30-minute summative evaluation (pre-test) before classes began and again at the end of the semester (post-test). The short answer evaluation questions, formulated by English and electronics teachers, included technical illustrations and instructions such as responding in a short answer to the questions 'find the band-rejection filter' or 'describe the following graph'.
However, we present very long-term follow-up data and over 30 years of observations for some, providing rich insight into disease progression, disease management pre-and post-HSCT, and residual disease burden. Psychosocial function was assessed using a protocol developed at the RCIMD and therefore has limited applicability. There is a need for standardized MPSI-H disease-specific tools to assess the impact of disease on quality of life and social adaptation at every stage of life, as there remain many unmet needs. Finally, longitudinal testing and long-term follow-up of neurocognitive development has inherent limitations. As there is no one test that provides measurement of all neuro developmental domains and across all ages (from early infancy to adolescence and adulthood), it was necessary to use different tests with limited comparability and interpretability across different age groups.
Apparatus The experiment was conducted in a professional audiobooth (IAC) equipped, for this occasion, with the following audio chain components: a Mac Pro (3,7 GHz Quad-Core Intel Xeon E5 / OS X v. 10. 11. 5), a RME Fireface 800 audio interface for the D/A conversion and for the amplification (headphones output), and professional headphones (Sennheiser HD650). The experimental user interface was developed and run on the TCL-LabX environment (v. 3. 13)  [27]. This software was chosen for this open-access nature and, more deeply, for being adequate to the experimental protocol (see Sec. 4. 4) we chose to implement this listening test. In fact, its graphical interface assumed the representation of the sound corpus items (Figure  1 ), the management of all participant's actions (playback / move / select items, enter text-see Sec.
The random selection of the 4 designers for each block was counterbalanced across the participants so as, at the end of the experiment, each designer appeared 32 times. A short break of 10 minutes was allowed between the two blocks to avoid auditory fatigue. During that break the participant had to fill out a short questionnaire to collect basic personal data (age, gender, native country, musical practice). The entire experiment took 60 to 90 minutes per participant.
Data formatting Thanks to the usability and versatility of the TCL-LabX software, the raw data output from each participant's experiment took the form of 4 files: i/ a list-file remaining the sound files used in the test corpus and their correspondences with the graphical boxes used in the user interface (see Figure  1  above); ii/ a log-file listing some system information (version, paths, etc. ), plus all the actions the participant made on each of these graphical boxes during the test (so that one can rebuild the organizational scheme that led to the final categorical representation); iii/ a class-file describing the constitution of the sound categories (experimental step 1); iv/ a text-file containing, for each class, the participant's comments and prototype selection (experimental step 2).
, 2021) , alcohol consumption  (Cummings et al. , 2021) , and has also been associated with changes in eating habits with a sharp increase in binge and compulsory eating and overall weight gain  (Cherikh et al. , 2020). Indeed, eating is a simple way to control stress and anxiety and it has been predicted that the prevalence of obesity would increase sharply due to the pandemic and the related lockdown periods (see  Alifano et al. , 2020, and follow-up papers). This paper presents the analysis of the lockdown impact on developing potentially addictive behaviors concerning gender. It relies on the data generated by an online survey administered in France by two of the paper authors, belonging to the Addiction Unit of the Department of Psychiatry of University Hospital of Nice, 1-3 weeks after the beginning of the spring 2020 lockdown.
Our survey was conducted during the 2nd week of the spring lockdown of 2020 in France, namely between the 24th of March and the 31st of March 2020. A stable panel (N = 1,087) of adult subjects was surveyed during 7 consecutive days, i. e. , after 1-2 weeks from the beginning of the lockdown. The dataset contains information about people's emotional states and reactions to these states in the context of potentially developing addictions to food consumption, smartphone usage, and videogame play. The main reason for our interest in these three types of addictive behavior during the 2020 lockdown period relies on previous psychological literature suggesting gender differences in non-creative activities leading to these addictions through time. As for food consumption, studies in psychology have detected an association between the recurrent occurrence of binge eating and behavioral manifestations of loss of control over eating behavior on the one side and marked distress due to psychological seizures on the other side  (Davis, 2013).
With this, we formulate our last hypothesis. Hypothesis 4 [Gender differences in emotional mediation]: The negative emotions driving gender-related potentially addictive behaviors are themselves gender-related. This last hypothesis aims at detecting whether women's (resp. , men's) specific potentially addictive behavior is correlated with specific female-related (resp. , male-related) negative mental states due to the COVID-19 lockdown, or whether gender-related specific potentially addictive behavior is independent of genderrelated emotions. The next section describes how the survey was designed and implemented, to obtain the data used to test our four research hypotheses.
Gender-Related Potentially Addictive Behaviors and Gender-Related Emotions We conclude the data analysis with the test of Hypothesis 4, i. e. , of whether the negative emotions driving the increase of genderrelated non-creative activities are themselves gender-related. Recall that as far as emotional states variables are concerned, our questionnaire includes two types of items: emotions elicited without relating them to specific behavior (unconditional: sadness, discouragement, and nervousness) and emotions elicited conditionally to the (non-creative) activity aimed at managing them (conditional: boredom, emptiness, and stress). Relying on Table  2 , at the beginning of section "Results" we have highlighted that gender differences are systematically detected for each of the six emotional states variables, with women showing significantly higher sensitivity than men to unconditional emotions sadness, discouragement, and nervousness, and men showing significantly higher sensitivity to boredom, emptiness, and stress on average over the three non-creative activities they are aimed at managing.