dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations and Perspectives Although much effort has been devoted in providing a software compatible with multi-modal data, it is not equally featured across recording techniques. For example, fMRI cross-section is still a beta feature and electrocorticographic data-specific visualization tools are not implemented so far. Secondly, efforts must now be made to make Visbrain fully compatible with Jupyter in order to have visuals embedded inside notebooks and iPython for interactive shell. We are also considering adding the compatibility with the Brain Imaging Data Structure (BIDS;  Gorgolewski et al. , 2016; Niso et al. , 2018) , a set of guidelines for organizing behavioral, MRI and M/EEG data that facilitates data sharing and reproducibility. Finally, Visbrain also contains experimental functions for the compatibility with MNE-Python  (Gramfort et al.
CONCLUSIONS In summary, Visbrain is a Python open-source and crossplatform software for brain data visualization which provides, among others, the following features: (1) GPUpowered graphical rendering providing efficient data plotting, even for large datasets and real-time interactions. (2) Modularity and flexibility with respect to users' specific needs through neuro-oriented visual primitives that can be juxtaposed or superimposed into subplots, following a Matplotlib-like behavior. (3) Complete control over the aesthetic through highly customizable configuration of color properties, allowing better use of this particularly informative dimension. Visbrain is in its early stages of development, but the present core should hopefully motivate users and programmers to contribute to the project and build a community-driven, powerful, sustainable, and full-featured open-source solution for brain data visualization.
Neuro 2 incorporates additional ergonomic and mechanical design features, including more resistant microphones and antenna cables and a facility to check that the microphones are working, that are especially relevant for children. Whilst professionals adjust the Neuro One with the DigiMap 4. 0 fitting software, professionals adjust the Neuro 2 with a new fitting software, Genie Medical CI. The user interface of the new software was designed to facilitate adjustments of CI parameters.
The study was registered in the U. S. National Library of Medicine clinical trial database (NCT03288753). Participants kept their Neuro 2 sound processor after the end of the study and their travel expenses were reimbursed. Participants received no other financial incentive. Each participant attended three study visits. The testing and/or questionnaire time for each study visit lasted approximately 45 minutes for adults and 15 minutes for children. Participants, or their parents in the case of children, completed the questionnaires. Visit 1. For adult participants, speech identification was tested with the Neuro One sound processor. The participants completed the listening ability questionnaire as well as the satisfaction and usability questionnaire for the Neuro One sound processor. Visit 2 (15 days after Visit 1).
Data analysis A two-way repeated measures analysis of variance (ANOVA) investigated the effect of condition and study visit on speech identification scores in quiet and in noise. Post hoc Tukey's tests were performed. Prior to running the ANOVA, assumptions were confirmed with Kolmogorov-Smirnov's test showing normal distribution of the dependant variables, probability plots (residuals and expected values, showing normal distribution of the residuals), and Levene's test showing homogeneity of variances (homoscedasticity). A multivariate analysis, that is, a linear regression model, was conducted to identify the significant predictors of speech identification scores in noise with the Neuro 2 after three months. Six potential predictors were identified: age, duration of hearing impairment, duration of CI experience, duration of Neuro experience, number of active electrodes, and CI configuration (CI unilateral with or without contralateral HA and bilateral CI).
Differences observed in speech identification results Speech identification results were significantly better with Neuro 2 than with Neuro One and the effect size was large. As the available signal processing and coding strategies are the same in both sound processors, this could appear surprising. However, at least three reasons could explain this difference in scores. First, as mentioned above, participants received standard clinical care during the study and were tested with their usual programmes. Although this was not measured systematically for all participants, there was a tendency for more signal processing to be applied in the Neuro 2 programmes than in the Neuro One programmes, that is, the CAP signal processing features were more likely to have been engaged in the Genie Medical CI fitting software.
This is because patients reported during the study visits 2 and 3 needs that more signal processing, for example, automatic adaptive directionality and noise reduction, could address and because the new fitting software, Genie Medical CI, eases professionals' adjustments of parameters. The professionals involved in the study reported adjusting the maps of most participants during the study. It is possible that refining of the maps led to significantly improved outcomes. Second, participants benefited from 3. 5 months of extra CI experience from Visit 1 to Visit 3. Most participants had less than one year of Neuro One experience at enrolment, yet a previous study showed that CI users improved their speech identification ability over the year following implantation  (Lazard et al. 2010). The Neuro One outcomes at Visit 1 might have been measured too early and therefore not a real representation of a plateau in performance or habituation.
Animals Swiss adult female mice (Janvier labs) were randomly divided in 2 groups in individual cages, in a temperature-controlled environment with 12-h light 12-h dark cycles and unrestricted access to water and standard chow diet (SAFE, Augy, France). Housing females were mated with Swiss CD1 males during two days. All experimental procedures were performed according to the institutional guidelines of laboratory animal care and approved by the governmental commission of animal research: Ethic Committee for animal experimentation of Paris Center and South # 59 (France), with authorization # 2016-3. The number of animals used in each group has been determined after power test analysis using R Software Two Sample t-test calculation (RcmdrPlugin. IPSUR) as previously described  [26]. Pregnant females were maintained in individual cages and during pregnancy; dams were daily injected intraperitoneally with physiologic saline (CC for Chow Diet Control group) or with human Resistin (obtained from Shenandoah Biotechnology, Warwick, PA, USA) at a dose of 20μg/animal (CR for Chow diet Resistin group) (Fig  1A ).
Data analysis and statistics Dams weight and plasma glucose levels during GTT are expressed as mean ±SEM. GTT Area under curve quantification has been done with imageJ and Excel Software. Metabolic/endocrine data and qPCR mRNA results are represented as boxplots made with R software, showing median and interquartile range (Q1-Q3). The number of animals used in each experiment was determined by power test analysis using R Software Two sample t-test calculation (RcmdrPlugin. IPSUR) based on previous experiments  (14, 18)  considering an α risk of 0,05% and a power of 90% (p<0. 05). Mann & Whitney non-parametric test has been used for all statistical analysis using Excel or R software (n = 4-9 per group). For dams' body weight analysis and glucose tolerance test (GTT) experiments, non parametric ANOVA including longitudinal data has been performed withy R Software and nparLD library.
Brainstorm is a free, open-source Matlab and Java application for multimodal electrophysiology data analytics and source imaging [primarily MEG, EEG and depth recordings, and integration with MRI and functional near infrared spectroscopy (fNIRS)]. We also provide a free, platform-independent executable version to users without a commercial Matlab license. Brainstorm has a rich and intuitive graphical user interface, which facilitates learning and augments productivity for a wider range of neuroscience users with little or no knowledge of scientific coding and scripting. Yet, it can also be used as a powerful scripting tool for reproducible and shareable batch processing of (large) data volumes. This article describes these Brainstorm interactive and scripted features via illustration through the complete analysis of group data from 16 participants in a MEG vision study.
Therefore, Brainstorm produces a variety of graphical and quantitative reports, for both point-and-click user interactions and automated data processing pipelines. All together, we believe these elements make Brainstorm a comprehensive yet accessible application for sophisticated and reproducible neuroscience research. Here we emphasize these unique software features in the context of a typical group data analysis workflow. We used simultaneous MEG/EEG recordings from 16 participants performing a simple visual recognition task from presentations of familiar (from celebrities), unfamiliar and scrambled faces. The original data was published by  Wakeman and Henson (2015)  and is also featured in the SPM tutorial "Multimodal, multisubject data fusion" 1. We used the version of this dataset organized according to the new MEG-Brain Imaging Data Structure (BIDS 2  )  (Gorgolewski et al.
DOWNLOAD AND INSTALLATION OF DATA AND SOFTWARE All the files featured in this communication are available from openneuro. org, https://openneuro. org/datasets/ds000117. The full analysis requires a total of 400Gb of available disk space. To reproduce the analyses presented here readers should download and install Brainstorm 6. The interactive environment of Brainstorm can be run without a Matlab license. However, users without a Matlab license cannot execute Matlab scripts and therefore cannot run the automated scripts that reproduce the analyses presented here. Brainstorm scripting requires Matlab 2008a or later.
SINGLE-SUBJECT, SINGLE-RUN DATA ANALYSIS We first present analyses performed on one data run from one of the participants. We then show how the resulting pipeline can be extended to other runs and other participants, prior to group statistical inference. At the core of Brainstorm's architecture and user experience is a data manager tool, which facilitates data organization and sorting by experimental conditions and groups of participants. Brainstorm exploits the principled data organization of MEG-BIDS: all data volumes (raw and FreeSurfer processed MRI, MEG/EEG) can be readily registered at once to the Brainstorm database following the menu "File > Load protocol > Import BIDS dataset" 7. However, here we describe the steps necessary to import multimodal data into Brainstorm so that the analysis described below can be extended to new data sets.
Structural MRI: T1 Volume and Derivatives We created a new study or protocol in the software database named "Frontiers2018Single, " to which we added a new subject with "sub-01" as coded ID. By right-clicking on the subject folder and selecting "Import anatomy folder" in the contextual menu, we loaded all the MRI data from the FreeSurfer folder of the downloaded dataset "derivatives/freesurfer/sub-01/sesmri/anat. " These included the individual structural MRI, the tessellated cortical surfaces and anatomical atlases registered to the individual anatomy 8. The scalp surface was reconstructed automatically by Brainstorm from the T1 volume data. Figure  1  shows a screen capture of typical Brainstorm structural (surface and volume data) MRI displays. Brainstorm computes a 4 × 4 affine transformation that registers the subject's T1 MRI to the MNI coordinate system using the spm_maff8 function (included in Brainstorm's distribution) from SPM12  (Ashburner and Friston, 2005).
Brainstorm also sets default coordinates for anatomical fiducials (NAS = nasion, LPA = left ear, RPA = right ear) for registration with MEG coordinates. These approximate points are based on standard MNI coordinates. The actual individual locations of these reference points are also available from the MEG data file and are used to initialize MEG/MRI coregistration. In principle, as few as three points are sufficient for registration but more robust alignment can be readily achieved using the individual digitized head shape (see next section), as with the present dataset 9. When the individual head shape is not available or has poor quality, the positions of the NAS/LPA/RPA fiducials must be defined manually using Brainstorm's MRI Viewer. Alternatives to FreeSurfer can be used to import MR derivative data into Brainstorm: BrainVISA  (Rivière et al.
Raw MEG/EEG Files The continuous FIF file from the first MEG acquisition run of sub-01 10 was added to the Brainstorm database using the "Review raw file" contextual menu over the subject entry. This operation creates a link to the original raw data file, whose contents can be reviewed and manipulated without requiring Brainstorm to duplicate the raw data file 11. The product of most of the following pre-processing steps is efficiently saved in this link file without changing the contents of the original raw data file. We ignored two options offered interactively while linking the recordings to the database: automatic registration with MRI and importation of stimulus triggers. We describe below how to proceed with these issues manually. A channel file is created next to the link to the raw recordings: this database entry describes the data channels available (names, types, 3D positions).
Definition of Experimental Events A stimulus trigger signal marked the presentation times of stimulus images on the screen, with different codes for the three experimental categories. These signals were recorded on channel STI101 in the present data. There was a known, constant delay of 34. 5 ms between each trigger pulse in STI101 and the actual presentation of the stimulus image. The following binary event codes were used at the time of data acquisition (bit number 3 coded for face, bit 4 for unfamiliarity, and bit 5 for the scrambled stimulus): Familiar faces: 5 (00101), 6 (00110), 7 (00111); Unfamiliar faces: 13 (01101), 14 (01110), 15 (01111); Scrambled images: 17 (10001), 18 (10010), 19 (10011). There are other event codes available in the recordings, but they are not described in the README file distributed with this dataset.
Event-Related Epoching We performed the temporal segmentation of continuous data about each event of interest (i. e. , visual stimulus presentations) by selecting "Import in database" from the contextual menu over the filtered version of the continuous file 19 (epoching is also available from the process "Import recordings > Import MEG/EEG: Events"). We selected the Familiar, Unfamiliar, 18 See online tutorial Artifact cleaning with SSP. 19 See online tutorial Import epochs. and Scrambled event categories from the list displayed, and defined the epoch as  [-500, 1200 ] ms about each event. This epoch duration is long enough to capture all eventrelated brain responses of interest and to define a prestimulus baseline for subsequent standardization procedures. It also provides additional temporal padding to absorb further filtering edge effects when applied to single trial data segments (as with low-pass filtering, time-frequency decomposition and connectivity analysis).
The single trials for all 3 experimental conditions were imported in the database, each trial data being stored in a separate file. If a trial co-occurred with a "bad" event or segment, Brainstorm marked it as bad, which de facto excluded it from further analysis. We found in sub-01/run-01 that most bad trials were toward the end of the run, with more occurrences of eye blinks and body movements, likely due to subject fatigue. Note that trials can be toggled as good or bad via the database explorer, selecting the menu item "Reject trial" or "Accept trial" interactively (Figure  6 ). Single trials can be rapidly inspected visually, browsing through files using convenient keyboard shortcuts. Additionally, Brainstorm offers several visualization features for groups of trials, such as sensor-specific raster and cluster plots (Figure  6 ).
Estimation of Noise Statistics From Empty-Room Recording (MEG) Explicit inclusion of noise characteristics can benefit source estimation. For instance, minimum-norm estimators can include second-order sample statistics of sensor noise in the linear amplitude estimation of distributed sources. These statistics are summarized in an estimate of noise covariance between sensors, which takes the shape of a matrix. Brainstorm's online documentation describes how this matrix can be estimated from EEG and MEG data. Here we estimated the MEG noise covariance from the empty-room measurements provided in the dataset. For EEG, we used the pre-stimulus baseline segments from all epochs. For consistency, we preprocessed the empty-room recordings in an identical manner to the task data. We therefore applied the same notch filter before estimating the noise covariance matrix.
We created a new subject called "subemptyroom" and linked all the empty-room data from folder derivatives/meg_derivatives/sub-emptyroom with menu "Review raw file" (preprocessed with MaxFilter/SSS). We then applied the same notch filters at 50, 100, 150, and 200 Hz. The estimation of the noise covariance for each session was obtained via a simple right-click over the filtered version of the empty-room data by selecting "Noise covariance > Compute from recordings. " The noise environment outside the MEG and the state of the MEG sensors may change over time, it is therefore recommended to use empty-room data recorded just before or after the experimental recording itself, or at least from the same day  (Gross et al. , 2013). In this dataset, emptyroom measurements were available for 8 different dates between April and December 2009.
AUTOMATION: SCRIPTING AND REPORTING OF ANALYSIS PIPELINES Using the GUI is a convenient solution to explore options for data analysis on a subset of the data. We describe here how scripting can be used to reproduce efficiently an identical analysis pipeline on all runs and participants. The Brainstorm pipeline editor automatically generates Matlab code from the "Generate. m script" menu item. The resulting script reproduces exactly the processing steps of the interactive GUI version of Brainstorm. "For" loops can be added to the script to batch the analysis across additional data runs, other participants or studies. We provide multiple online examples of such scripts, which can further help users assemble their own custom pipelines. Online recommendations and examples for editing Brainstorm scripts are available 27.
One practical recommendation for Brainstorm scripting is to split data analysis pipelines in blocks. For instance, importing anatomical data and raw MEG/EEG files, frequency filtering, power spectrum estimation, artifact detection and cleaning do not require user interaction, and can all be processed at once from one single script. Interventions are required from users for reviewing raw files, marking/confirming bad channels and data segments, selecting SSP or ICA components, etc. Another batch script can then be generated for epoching and averaging data, producing source models and extracting measures of interest (e. g. , time-frequency decompositions and connectivity metrics). The outputs of executed scripts are consigned to a log, for verification of proper execution and debugging. Screen captures are added to this execution log via the "File > Save snapshot" menu item.
We 27 See online tutorial Scripting. note, however, that big-data repositories are emerging in the field (e. g. , OMEGA, MEG-HCP, and Cam-Can), which require more automated procedures, but put less emphasis on qualifying as much of the initial data volume as possible to subsequent analyses  (Van Essen et al. , 2012; Niso et al. , 2016; Taylor et al. , 2017). Analysis pipelines that involve manual steps can be replicated, with additional care and documentation. For instance, the user-selected bad channels, bad segments and rejected spatial components need to be clearly documented. Following this idea, we provide a script that reports the rejected data segments for all 16 participants to the present study dataset 28. The bad channels were identified manually; the bad segments were detected automatically, confirmed manually, then exported as text and copied at the end of this script.
Condition-Specific Average Responses For each experimental condition (Familiar, Unfamiliar, and Scrambled) and each subject, we derived event-related MEG signal averages. The subject-level average across all 6 runs was obtained by weighting each run-specific average by the number of good trials in each run. This approach reduces the influence of noisy runs, with a smaller number of good trials, on the subject-level average. For each data type (sensor data, source maps, and time-frequency decompositions), we used the Process1 panel after selecting all subject data from all runs. We then used process: "Average > Average files, " with the options: "By trial group (subject average), Weighted. " This produced three event-related averages (one per condition), for each subject, saved in their respective "intra-subject" folders in the Brainstorm database.
We selected all subject-level source map averages in Process1 and ran the process "Standardize > Baseline normalization" with options Baseline = [-200,-5] ms, Z-score, Overwrite. We also standardized the magnitude of time-frequency decompositions for each subject and condition with eventrelated synchronization/desynchronization (ERS/ERD) scaling. This procedure centers and normalizes the modulus of wavelet coefficients for each frequency bin, with respect to their sample mean over baseline, as shown in Figure  10. We distribute a Brainstorm database that contains the outputs of all analyses described so far (Frontiers2018Group. zip, 14 Gb, available from Brainstorm's download page 34  ). These outputs can be reproduced by running the Matlab scripts tutorial_frontiers2018_single. m 35  and tutorial_frontiers2018_copy.
Grand Averages Across Participants We produced grand arithmetic averages of sensor data across participants for the Faces, Scrambled, Familiar, and Unfamiliar conditions. We selected in Process1 all the MEG/EEG subjectlevel average files from the participant-specific "Intra-subject" folders in the Brainstorm database. We then ran the process "Average > Average files" with options "By trial group (grand average), Not weighted. " As anticipated, we observed in EEG a greater negative component around 170 ms for Faces vs. Scrambled, and sustained signal differences after 250 ms between the Familiar and Unfamiliar conditions (Figure  11 ). We computed the differences of grand averages between conditions of interest using the Process2 tab for selecting the grand averages for Faces (FilesA) and Scrambled (FilesB).
CONCLUSION Brainstorm provides interactive and user-friendly tools to design automated and standardized processing pipelines, with an emphasis on quality control and verification of data integrity. The application features convenient tools to perform visual inspection of the outcome of all steps of a data analysis pipeline via execution reports. The analyses presented here are entirely reproducible via the following procedure: (1) Download the data in tutorial_dir (170 Gb): https:// openneuro. org/datasets/ds000117. (2) Note that getting the data from a web browser as a single zip file did not work well at time of submission, another more reliable solution using the Amazon AWS CLI software is described on the online tutorials 41. (3) In this analysis, we used only the "derivatives" folder (85 Gb), all the other folders can be safely deleted if disk space is an issue.