dataesr/scientific-paragraphs-categorization huggingface dataset:
Inferring whether regions in a module are functionally related We combined Neurosynth meta-analyses decoding  ( Yarkoni et al. , 2011 )  with comparison to surrogate modules, to assign putative functional roles to each module. We used Neurosynth decoding to find terms related to perception, cognition and behavior selectively associated to the centroid co-ordinates of each brain region, based on a large database of fMRI studies. Then, we aggregated the terms associated with regions in each module and compared the occurrence frequencies of these terms to those of equally sized surrogate modules which were constrained to comprise anatomically proximal and bilaterally symmetric brain regions. Hence, we determined terms that were common to regions in a module, even after accounting for the anatomical proximity of its regions.
Finally, we investigated if identifying modules is confounded by amplitude of oscillations from individual nodes in a network. To do this, we compared modules of the 67 subject-level networks of phase-synchronization before and after removing amplitude-related differences in functional connection strengths, at each of the 18 frequencies, at six spatial scales (  = 1, 1. 8, 2. 6, 3. 4, 4. 2 and 5). We removed amplitude-related differences by relating the strengths of each functional connection to average amplitude of corresponding node-pairs via linear regression, and recovering the residuals. We compared modules identified before and after removing amplituderelated differences, with the partition similarity measure ( Section 2. 5. 4 ). We have made available MATLAB code to perform each stage of the analyses, via our GitHub repository ( https://github.
9 for left hemisphere and 0. 96 for right hemisphere). Similarly, the z -scores of modularity of the connectomes for 100 and 1000 randomized connectomes were highly correlated (0. 99 We performed the transformation from the inflated to flattened cortical surface using the tksurfer FreeSurfer command  (Fischl et al. , 1999). E. Color-coded modules for right hemisphere at 14 Hz on flattened cortical surface, at six spatial scales (  = 1 to 5). We converted from HSV to RGB before plotting the modules. Regions with unstable module assignments are gray. Small black rectangles in panels A-C indicate  values at which modules are visualised in panel E. for both left and right hemispheres). These results demonstrate that the results of our permutation-based tests on statistical significance of the identified modules are robust to the number of samples used.
, 2005 ; Damoiseaux et al. , 2006 ; Yeo et al. , 2011 ; Power et al. , 2011 ). The eight consensus modules comprised anatomically contiguous regions and respectively included regions in the superior-frontal (bright green), inferior-frontal (pale green), insula (olive), superior-temporal (brown), inferior-temporal (dark pink), parietal (light blue), lateraloccipital (dark purple), and medial-occipital (light purple) cortical areas ( Fig. 6 A ). Module colours reflect anatomical location of their constituent regions (see Section 3. 2 ). The consensus modules predominantly resembled modules at the lower frequencies (14-40 Hz) and intermediate spatial scales (  = 1. 5-2. 5) (Supplementary material, Fig. S6 ). We first used the Neurosynth meta-analyses-based decoding tool  ( Yarkoni et al. , 2011 )  to find terms related to perception, cognition and behavior, selectively associated with each brain region in the Destrieux brain atlas, where we identified each region by its centroid coordinates.
These terms were both sensitively and specifically associated to fMRI activation in the corresponding brain regions, according to a large database of fMRI studies. We then identified terms selectively associated with each module by finding terms that occurred more frequently ( p < 0. 05, FDR-corrected, permutation test, N = 74) across the regions in a module, compared to equally sized surrogate modules of anatomically contiguous regions. This effectively tested the hypotheses that regions comprising a module serve shared functional roles, even after accounting for their anatomical proximity. The terms for the superior-frontal module were related to attention and executive function while the inferior-frontal module was associated with affective processing and social cognition ( Fig. 6 A ). The parietal module related to sensorimotor, sensory and motor processing, while the medial-occipital and lateral-occipital modules were associated with basic and advanced visual processing respectively.
Functional specificity of identified modules suggests their behavioural relevance We used information from an independent database of fMRI studies to infer the functional role of each module. Regions in different modules had shared involvement in cognitive functions of attention and executive function, affective processing and social cognition, somatosensory processing, language and auditory processing, memory function, visual processing, advanced visual processing and sensorimotor processing respectively. The demonstrated functional specificity of these modules suggests that they operate as distinct brain systems. In line with proposed frameworks on brain function  ( Tononi et al. , 1994 ; Tononi et al. , 1998 ; Balduzzi and Tononi, 2008 ; Lord et al. , 2017 ; Shine et al. , 2018 ) , strong connections within modules might support segregated information processing  ( Chan et al.
Acknowledgments The authors gratefully acknowledge the support of  Human Brain Project  (Grant No. 604102  ),  Sigrid Juselius Foundation  and  Academy of Finland  (J. M. P. project Nos. 253130, 256472, 281414, 296304, 266745. S. P. project numbers:  266402 ,  266745 ,  303933 ,  325404 ) to complete this project. Further, the authors are grateful to  Jonni Hirvonen  and  Santeri Rouhinen , for help with data processing, and to  Dr. Franceso Cardinale  and  Annalisa Rubino  for facilitating the SEEG recordings.
Data and code availability statement We have made available (1). module assignments at different spatial scales for each of the studied frequencies, (2). module assignments of the consensus modules, and (3). Neurosynth terms relating to perceptual, cognitive and motor processing, at: https://data. mendeley. com/datasets/ypx74nmfs8/1 We have made available MATLAB code to reproduce our results, via our GitHub repository: https://github. com/nitinwilliams/eeg _ meg _ analysis/tree/master/FC _ modules.
70, and alpha = 0. 05, a sample of N = 24 was necessary to achieve a power of 0. 95 (test family: t-test; means: difference from constant, one sample case; one-tailed). Twenty-five participants took part in experiment 1 (mean age: 23 years, range: 19-32; 16 female; right handed: 21). Participant #14 was excluded from group analysis due to a poor psychometric fit (Figure  S1 ; see below for exclusion criteria). Thirty-two new participants took part in experiment 2 (mean age: 23 years, range: 18-46; 25 female; right handed: 27). The data from participant #1 were discarded because the participant did not follow the instructions, but named the number before the performing the ba/bo task. The data from participant #32 were not included in the analysis because the desired N had already been reached.
Note that the reported results do not change substantially when including participant #32. Participants #5, #11, #15, #19, #25, and #26 were excluded from group analysis due to poor psychometric fits (Figure  S2 ). Twenty-seven new participants took part in experiment 3 (mean age: 25 years, range: 18-50; 13 female; right handed: 25). The data from participant #19 were discarded due to technical problems during data acquisition. Participants #11 and #16 were excluded from group analysis due to poor psychometric fits (Figure  S3 ). Participants were recruited via student mailing lists and personal contact (exp. 3). All participants had normal or corrected-to-normal vision and no history of neurological or psychiatric disorders. All participants were naïve to the purpose of the experiments, provided written informed consent and received monetary compensation (€8/h) or course credit for their participation.
Setup and stimuli The experiment was conducted in a darkened, sound-attenuated room, with indirect light coming from the experimenter's PC screen. Participants viewed a 19″ CRT monitor (SAMTRON 98PDF; 1280 x 1024 pixels; refresh rate 85 Hz) using a chinrest to stabilize head position. The viewing distance was 57 cm. All stimuli were created using Matlab 2019a (MathWorks, Natick, MA), and the Psychophysics toolbox 3. 0. 12 (Brainard, 1997) , on a PC with Windows 10 and an AMD Radeon 6450 graphics card. Participants provided verbal responses, and used the PC keyboard to navigate through the experiments (e. g. , to terminate a pause). Verbal responses were recorded using a Behringer XM8500 microphone and a Focusrite Scarlett Solo audio-interface, and stored as wav files. Figure  1  illustrates the display sequence used in all experiments.
Data analysis Data pre-processing, descriptive and inferential statistics were performed using R 4. 1. 1 (R Core Team, 2021) and RStudio 2021. 09. 0, Build 351  (RStudio Team, 2021). Data visualizations were created with the R package ggplot2  (Wickham, 2016). Trial-by-trial data in csv-format and R scripts for data analysis are available at an online repository (https://doi. org/10. 17605/OSF. IO/T4J7W). The recorded verbal responses were decoded manually (experiments 1-3: "ba", "bo"; experiment 2: "1", "9"; experiment 3: "left", right"). In the rare case that the audio file contained more than one response (e. g. , when a participant corrected the first response), only the first response was decoded. Training trials were discarded. Trials without audible verbal responses, and trials with incomprehensible verbal responses were marked and removed from the analysis (experiment 1: 0%; experiment 2: 0.
7%; experiment 3: 1. 4%; mean percentage of trials). For each participant and condition, the proportion of "left stimulus first" responses were calculated based on the verbal "ba/bo" responses. In experiment 2, the proportion of correct responses in the number identification task was calculated for each participant. In experiment 3, the proportion of correct responses in the arrow identification task was calculated for each participant. A logistic model was fitted to the proportion data of each participant as a function of the SOA (in frames), separately for digit 1 and digit 9 (experiment 3: arrow left and arrow right). The data were collapsed across both instructions (i. e. , name the stimulus that appeared first/last). The logistic model allowed us to determine the point of subjective simultaneity (PSS) for each cue condition.
Using the psignifit4 toolbox  (Schutt, Harmeling, Macke, & Wichmann, 2016)  in Matlab 2019a we verified the PSS estimates (see supplementary tables S1 and S2). The PSS represents the SOA for which the observer perceives the stimuli as simultaneous. A shift in the PSS in either direction thus indicates that the cue induced a temporal bias  (Casarotti et al. , 2007). When piloting the first experiment, we noticed that poor psychometric fits were associated with extreme PSS values. Therefore, we decided to exclude the data from participants showing poor psychometric fits. For each participant's proportion data (collapsed across both cue conditions), we calculated McFadden's pseudo-R 2 as a goodness-of-fit measure; participants with pseudo-R 2 < 0. 18 were excluded from group analysis  (Louviere, Hensher, & Swait, 2000).
g. , a null model H0 and one alternative model H1). The Bayes factor (BF) refers to the ratio of marginal likelihoods of different models under consideration, and quantify the change from prior to posterior model odds. The prior odds describe the beliefs about the models before observing the data. The BF thus describes how the evidence from the data should change beliefs. The subscripts on BFs refer to the models being compared, with the first and second subscript referring to the model in the numerator and denominator, respectively. For example, a BF10 of 5 indicates that the data are five times more likely under H1 than under H0. We used the ttestBF and extractBF functions from the BayesFactor package. TtestBF uses a Cauchy prior (scale parameter: sqrt(2)/2 =. 707). Models, priors, and methods of computation are provided in Rouder et al.
Participants and task In this research we enrolled 14 adults, seven MBS participants (MBS group: MBS 4 females and 3 males, mean age = 40, 43 years; s. d. = 11,03) and seven healthy control participants. Controls were matched for age, gender and level of education. Participants in the MBS group had a diagnosis of unilateral or bilateral facial paralysis  (Terzis and Noah, 2003). See Table  1  for demographic data and clinical information for MBS participants. All the participants did not report any psychiatric or physical illness. Participants performed a simple change detection task in which they had to judge if a test image was different or not compared to a target image. This task has been successfully used to investigate changes in neural activity, as well as connectivity, during face processing  (Wood et al.
EEG acquisition and pre-processing Electroencephalography activity was recorded from 128 channels using an HydroCel Geodesic Sensor Net (HCGSN-128) connected to a Geodesic EEG System (EGI GES 300). Data were collected continuously with a sampling rate of 500 Hz using the vertex as online reference. Channel impedance was kept under 60 k. For the purpose of the present research we analyzed the preprocessed data used in  Sessa et al. (2022). Briefly, pre-processing consisted in downsampling the data to 250 Hz and band pass filtering (0. 1-45 Hz), epoching between -500 to 1500 ms relative to target onset, rejection of artifactual components after ICA using the ICLabel algorithm  (Pion-Tonachini et al. , 2019) , bad channel interpolation and referencing to the average of all channels. Further details regarding the pre-processing can be found in  Sessa et al.
(2022). The preprocessed data as well as the pre-processing script can be accessed at https://osf. io/krpfb/. In order to estimate brain activity from the preprocessed scalp recordings, we first created a forward model using the three-layer boundary element method (BEM) from OpenMEEG, implemented in Brainstorm, and then estimated an inverse solution with the weighted Minimum Norm Estimation (wMNE) with default parameter. Finally, the estimated distributed source activity was downsampled to the 148 cortical parcels of the  Destrieux et al. (2010) , averaging the activity of all the vertices included in each parcel.
| Participants The patient sample consisted of 58 French patients (22 females; 48 right-handed and 2 ambidextrous; mean age 42. 8  ± 10. 7 [24; 66] ; mean educational attainment 14. 1 ± 3. 5 [9; 20] ), all of them having undergone an awake surgery for a left histopathologically confirmed LGG in Montpellier University Medical Center' Department of Neurosurgery. They were assessed more than 3 months after the surgery in a period, wherein neuropsychological impairments are relatively stable. The following exclusion criteria were applied: (i) higher-grade glioma, (ii) other neurological, neurodevelopmental or psychiatric diseases and (iii) uncorrected vision or audition impairment. To compare patients' behavioural data to those of a control group, we additionally recruited 72 neurologically healthy participants (35 females; 62 right-handed and 3 ambidextrous; mean age 40.
| Main task of interest: Famous face naming task To test the ability to access PNs in visual modality, a famous face naming (FFN) task was designed. This task has been scripted in a Matlab environment (Release 2020b, the MathWorks, Inc. , Natick, MA) using  Cogent 2000 toolbox (www. vistab. ucl. ac. uk). It lasted approximatively 30 min and consisted in 60 coloured pictures of famous personalities that participants had to name within a 10 s interval. To be considered as correct, a response included necessarily the last name eventually preceded by the first name or included the nickname commonly used (e. g. , « Bourvil » instead of « André Robert Raimbourg »). Accuracy (FFN score) was calculated as the total number of correct responses. A misnaming or an unnamed face was considered as incorrect responses.
fil. ion. ucl. ac. uk/spm/software/spm12/) Clinical Toolbox (https://github. com/neurolabusc/Clinical), which runs in the Matlab environment. As a first step, the resection cavities were delineated using the MRIcron software (https://github. com/neurolabusc/ MRIcron). They were further inflated using a smoothing procedure (5-mm full-width-at half-maximum [FWHM] Gaussian kernel with a threshold of 0. 3). The obtained lesion masks were then binarized and inserted during the registration process; the enantiomorphic technique involves that the area covered by the lesion mask is substituted with the corresponding undamaged homologous area within the healthy contralesional hemisphere. The output resolution was 1-mm isometric. To ensure satisfactory accuracy, all normalized MRIs were carefully checked, and no issues were detected at this stage.
| Voxel-wise lesion-symptom analyses To assess the relationships between the location of resections and the patient's behavioural performances without anatomical priors, we used a mass-univariate voxel-based lesion-symptom mapping (VLSM) approach  (Bates et al. , 2003) -an option that still remains valid under stringent conditions  (Ivanova et al. , 2021). These analyses were performed with the Matlab toolbox by DeMarco & Turkeltaub (DeMarco & Turkeltaub, 2018) (https://github. com/atdemarco/ svrlsmgui). General linear models were used to compare the behavioural performances of patients with versus without a given voxel lesioned and that for each voxel included in the analyses (i. e. , all voxels lesioned in at least three patients), taking into consideration a number of nuisance variables.
| Disconnectome analyses To further explore at the brain-wide level the extent to which white matter disruptions affected levels of performance, disconnectome maps (DM) were generated with the BCB toolkit  (Foulon et al. , 2018). On the basis of the DTI dataset of 10 neurologically healthy participants  (Rojkova et al. , 2016)  (the average age of whom being comparable to that of patients included in this study), the software estimates the probability for each voxel (from 0 to 1) of the MNI-152 template to be 'disconnected' by the lesion, here the resection cavity. The obtained DMs were thresholded at p =. 5 meaning that only voxels with a probability of at least 50% to be disconnected were represented. The DMs were then separately correlated with the different regressed measures of FFN score (see the above section) using simple linear models estimated with SPM12' second-level of analyses (https://www.
| Double case study We included in this study two additional patients with a right-sided temporal surgical resection massively damaging the right ILF. More specifically, we wanted to assess whether crossed PN retrieval deficits could be observed following interruption of the right ILF in the case of left-handedness. The patient of interest (C. G. ) was a left-hander, whereas the control patient (A. S. ) was a right-hander. The same experimental protocol than described above (taking into account the same exclusion criteria) was conducted with both patients. Their behavioural performances were contrasted to those of 10 control participants (one group by patient) matched in terms of age and educational attainment (sociodemographic data are reported in Table  S3 ).
, a Matlab software that derives tract-level disconnection estimates using different population-based approaches (https://github. com/jdwor/LQT). More specifically, the re- section cavities were individually embedded into the HCP-1065 trac- tography atlas (http://brain. labsolver. org/diffusion-mri-templates/ tractography) and used as regions-of-interest. For each candidate tract, streamlines whose trajectories intersected with the re- section cavity map were considered as 'disconnected'. Next, a per- centage of interrupted streamlines was calculated. Among the 70 available tracts, we selected the only ones that were damaged in at least 25% of patients in a minimal proportion of 5%-allowing us to maintain statistical power. This included the following 22 white mat- ter tracts (the proportion of patients matching these criteria are indi- cated in parentheses): the UF (53.
82  ). We included the quadratic terms for the number of prey and the number of predators. We also included the second order interactions between: the number of predators and the residence time, the residence time and the female ratio, the residence time and the mean body mass. The addition of the identity of each independent plastic box as a random intercept was necessary to account for the experimental design, and increased the fit of the mixed models for each of the response variables (Akaike Information Criterion (AIC) decreased with more than two units, Ref. 83. The dredge function on the full models was used for each response variable (package MuMIn, Ref. 84  ) to select the best model (the one with the lowest AIC and with the highest Phi coefficient (model precision)). We did not detect zero inflation for the best models for each of the response variable (DHARMa residuals diagnostic, R package DHARMa, Ref.
85  ). For each best model, the pseudo-R-squared was calculated (R package MuMIn, Ref. 84  ). Following Dick et al. 52  , an adaptation of the "impact potential" metric was employed, and calculated as: abundance × maximum feeding rate. The rounded abundance was estimated in the field as a mean number of M. soledadinus adults at each of the population locations sampled. The maximum feeding rate was determined from the number of eaten prey at the last experiment time point for each population, from each single treatment condition: the lowest predator density (one predator) and the highest prey density (20 prey). This allowed the per capita effect and the population-level effect to be combined and compared, to gain a fuller understanding of ecological impact among beetle populations. All statistical analyses were performed in R, version 3.
Funding The salary of  Charly Géron  was funded by the  ASICS  project ( ANR-20-EBI5-0004 ,  BiodivERsA ,  BiodivClim  call  2019-2020 ). The research was funded by the  French Polar Institute  (Project  136-SUBANTECO ). Ross N. Cuthbert  is funded by the  Leverhulme Trust   Early Career Fellowship  ( ECF-2021-001 ).
Contributions In this paper, we focus on the relevance of a unique constant friction coefficient to predict accurately granular collapses on inclined planes. To this end, we perform numerical simulations at the continuum level, both in 2D and 3D, as well as experiments of granular column collapses over erodible beds with slopes varying from 0 • to 20 •. We model the granular material as a dilatable, fully plastic continuum with a Drucker-Prager yield surface and non-associated flow rule. We solve the resulting nonsmooth rheology without any regularisation by leveraging the Sand6 software (https://gitlab. inria. fr/elan-publiccode/sand6), a numerical scheme introduced by  Daviet & Bertails-Descoubes (2016a,b)  and inspired from nonsmooth optimisation algorithms originally developed in the context of rigid body contact dynamics  (Moreau 1994).
The maintained C++ Sand6 software implementing the semi-implicit scheme to solve the continuum conservation equations with the Drucker-Prager rheology, used to study the role of the unique friction coefficient is available at https://gitlab. inria. fr/elan-public-code/sand6. Simulations of the experimental collapses were performed using a fork of the Sand6 code available at https://gitlab. com/groussea/sand6py which provides a python binding of the C++ library along with analysis scripts.