dataesr/scientific-paragraphs-categorization huggingface dataset:
First, the basal forebrain is a small subcortical structure with poorly defined anatomical boundaries. We therefore used a probabilistic atlas to localize its constituent nuclei. However, when using probabilistic BF atlases in combination with data collected at spatial resolutions typical of 3T structural (1. 5 mm 3 ) and functional MRI (3 mm 3 ), aliasing of adjacent structures has been shown to systematically overestimate the BF gray matter  (72). To mitigate this issue, we used high spatial resolution dMRI (1. 05 mm 3 ) and rsfMRI (1. 6 mm 3 ) data acquired at 7T. Second, our measures of structural connectivity were estimated using streamline tractography on diffusion-weighted imaging, which can be susceptible to false positives and negatives in certain brain areas  (72). It is therefore possible that the regional variation in BF structure-function correspondence is partly explained by regional variation in tractography performance.
Materials and Methods We used high-resolution minimally pre-processed 7T MRI HCP data (n=173)  (31)  and the existing stereotactic atlas of the BF  (32)  to build structural and functional connectomes. Any further pre-processing and connectivity matrix construction was done on the compute cluster. Workflows were built using Snakemake  (73)  with the full workflow available on GitHub (see data and code availability for specifics). Individual connectomes were averaged and reduced to a 2-dimensional m-by-n matrix describing the pairwise connectivity strength between m BF ROI voxels and n cortical regions  (35). The BrainSpace toolbox  (20)  was used to capture the gradients which, as well as any further analysis, was done using Jupyter Notebook  (74).
Data Acquisition High-resolution 7T dMRI and rsfMRI data were downloaded from the HCP data repository  (19). We used the minimally pre-processed data described in ref  (31)  consisting of 173 healthy subjects (69 male, 104 female) aged 22 to 35 years. The dMRI images were collected with 1. 05 mm 3 isotropic voxels, TR=7000 ms, TE=71. 2 ms, b-values=1000, 2000 s/mm 2 , FOV=210 x 210 mm 2. Resting-state fMRI images were collected with 1. 6 mm 3 isotropic voxel size, TR=1000 ms, TE=22. 2 ms, FOV=208 mm 2 , spanning 4 runs of 16-minute duration each, per subject. For anatomical imaging, two T 1 -weighted (T 1 w) scans were obtained using a three-dimension (3D) magnetization-prepared rapid gradient-echo (MPRAGE)  (75)  sequence and two T 2 -weighted (T 2 w) images using a 3D T 2 -SPACE sequence, all with identical geometries and a 0.
Structural Connectivity Reconstruction Diffusion tractography was performed to get a connectivity matrix for diffusion data. As part of the minimal preprocessing pipeline data release, all subjects underwent FreeSurfer processing (v5. 3. 0-HCP)  (78). The BF ROI mask was then first resampled and transformed to the individual subjects' minimally preprocessed volume space (0. 7mm 3 ). Volumetric cortical labels were built by mapping the HCP-MMP 1. 0 surface parcellation  (35)  using Connectome Workbench's ribbon-constrained label-to-volume-mapping function and FreeSurfer-derived surfaces. The BF ROI voxels were used as seeds, and the 180 cortical regions in each hemisphere were combined and used as targets to perform probabilistic tractography using FSL's probtrackx  (79)  with 5000 streamlines per BF ROI voxel (see Supplemental Fig.
Functional Connectivity Reconstruction First, the BF ROI mask in the minimally preprocessed volume space was resampled to 1. 6mm 3 isotropic voxel size to match resolution of the rsfMRI data and added to the subject's subcortical parcellation. A functional connectivity matrix was then created for each subject by calculating the temporal correlation between BF voxels and cortical ROIs. All four runs (i. e. , two sets of 16 min. runs with posterior-to-anterior and anterior-to-posterior phase-encoding) of the minimally preprocessed and ICA-FIX denoised rsfMRI data (81) were used. Since the BF ROI is not included in the dense timeseries provided by HCP, these were regenerated using the updated subcortical parcellation to include the BF ROI voxels for further processing. Subsequent processing included ROI-constrained subcortical smoothing to match the cortical sampling density using the scripts provided by HCP  (78) , as well as additional signal filtering (i) based on the average WM and CSF timeseries using ciftify (82) and (ii) by applying a Wishart filter as proposed previously  (83, 84)  to selectively smooth unstructured noise more than the structured blood oxygen level-dependent signal.
Geodesic Distance Geodesic distance along the cortical surface was calculated using the geodesic library (https://github. com/the-virtual-brain/tvb-gdist) based on the algorithm that approximates the exact distance along the shortest path between two nodes (or vertices) on a triangulated surface mesh  (87). An average BF seed node was created for the left and right hemispheres separately by (i) projecting the BF mask onto the 59k_fs_LR white matter surface of the individual subjects using Connectome Workbench's volume-to-surface-mapping function, (ii) averaging across all subjects to get a probability map, (iii) resampling to the 10k_fsavg surface-space as suggested by the HCP study (https://wiki. humanconnectome. org) and (iv) then by thresholding at 0. 5 to obtain a final binary BF seed on the cortical surface.
Acknowledgments RH was supported by a  BrainsCAN postdoctoral fellowship. This research was enabled in part by the support provided by the  Digital Research Alliance  (https://alliancecan. ca), and the  Natural Sciences and Engineering Research Council  (TWS). Data was provided in part by the  Human Connectome  Project,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the  16 NIH Institutes and Centers  that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University. The PET data used in the analysis was supported by the  National Institutes of Health  [ P01 NS015655 ,  RO1 NS070856 ,  P50 NS091856 ,  P50 NS123067 ].
Data and code Availability The Human Connectome (HCP) project dataset is available at http://www. humanconnectomeproject. org/. The workflow for reconstructing structural connectivity matrix from the HCP data and a subcortical region of interest is available at https://github. com/sudesnac/diffparc-smk  (92) ; and the functional connectivity workflow at https://github. com/khanlab/subcorticalparc-smk  (93). All other code used to conduct the reported analyses and create the figures are available at https://github. com/sudesnac/HumanBF-Connectivity  (94).
| Cytogenetics Among the 44 fetuses, 38 genetic analyses were performed after amniocentesis, 4 were done after choriocentesis, 1 on fetal cordonal blood sampling, and 1 was done postnatally on blood sampling. In 34 cases, deletions detected by aCGH encompassed the WBSCR, with sizes ranging from 1. 37 to 1. 7 Mb, whereas 2 deletions were identified by BoB's, 2 by FISH using specific WBS probes, and 2 by SNPa (size 1. 4 and 1. 87 Mb) (Table  1 ). All duplications were detected by aCGH and also encompassed the WBSCR with sizes ranging from 1. 41 to 1. 8 Mb (Table  2 ). All fetuses had isolated 7q11. 23 CNVs. Parental analysis of 7q11. 23 deletion showed that deletions occurred de novo in 24 cases. In 16 cases, the segregation could not be provided due to missing data or unavailable parents' samples.
These data are based on sequence similarity and phenotype (Homologene, https:// www. ncbi. nlm. nih. gov/homol ogene/). Precise amino acid sequences of homologue proteins may differ and could affect function. Mouse and human carbohydrate molecules seem to be similar,  7  whereas the human lipidome is more diverse than that of the mouse. 8 ell types of the brain are broadly similar in mice and humans, although human-specific types of astrocyte  9  and cortical inhibitory interneuron  10  have been suggested. Transcriptomic analysis suggests that cortical neuronal types are broadly conserved between mouse and human, with more subtypes of inhibitory than excitatory cells. 11 ranscriptomic analysis of single, cortical microglia revealed comparable, if subtly different, signatures for human and mouse cells.
These cultures maintain all cell types in a correct spatial context. 103 The anatomical and physiological properties of cultured, adult human tissue are conserved over several weeks. This preparation has enabled optogenetic control of firing in identified neurons  100  and imaging of Ca transients in specific cell types. 94, 101 I G U R E 2 Epileptic activities and intracellular Cl -in human tissue. Data from tissue slices were obtained after operations on patients with (A) mesial temporal lobe epilepsy associated with hippocampal sclerosis, (B) cortical glioma, and (C) focal cortical dysplasia. Typical magnetic resonance images for each syndrome are shown. Green boxes show regions from which slices were prepared. Symbols indicate estimated chloride levels in different cell types for tissue derived from the three epileptic conditions.
ACKNOWLEDGMENTS We apologize to authors of excellent articles not cited for reasons of space. We thank  J. -C. Poncer ,  D. Fricker ,  L. Menendez de la Prida ,  S. Baulac ,  K. Toth , and  M. -C. Potier  for helpful comments on the manuscript. We appreciate permission to use an adapted Figure from  Williams  et al. ,  J Neurosci  2009;29: 2103-12  (copyright 2009 Society for Neuroscience) and permission to use an adapted figure from Nadler and Cuthbertson, Brain Res 1980;195: 47-56  (copyright 1980 Elsevier).
Introduction Tinnitus is defined as an auditory sensation without external acoustic stimulation or meaning which may be experienced as disagreeable and impacting quality of life  [1]. Prevalence is high, at 10% of the population  [2] , and impact can be severe  [3]. Tinnitus remains a scientific and clinical challenge that is only partially understood, largely due to its intrinsic heterogeneity  [4]. To elucidate this heterogeneity, the European School for Interdisciplinary Tinnitus Research (ESIT) was set up, one aim being to collect epidemiological data in a centralized database at European level  [5]. This extended the Tinnitus Database project (https://tinnitus-database. eu), launched in 2010 by Landgrebe's and colleagues  [6]. The first step in patient follow-up through the Tinnitus Database was a screening step, for which ESIT developed a standardized screening questionnaire, the ESIT-SQ  [7].
Method The translation was made between April and December 2020, and the exploratory survey data were collected between September and December 2021. Translation adhered rigorously to the good conduct guidelines laid out by Hall et al. [14]. Thus, 3 translators were recruited: 1 professional translator, 1 neuroscience PhD student specializing in tinnitus, and 1 ENT specialist (respectively, F. Decobecq, R. Guillard and A. Londero). They were all graduates, and native French speakers, with a good level of English. After a first meeting to explain the procedure, F. Decobecq and R. Guillard produced 2 independent translations, which A. Londero then combined in a version that was discussed and validated in a dedicated meeting including all 3 translators. The France-Acouphènes tinnitus patients' association (https://www.
The Siopi platform is a community mobile phone app enabling mutual aid between patients with comparable symptom profiles and introducing those who wish to the ecosystem of clinical research. Before medical data, such as responses on our survey, are collected by the application, users are informed of the procedure and accept the general conditions of use, including anonymized export of the database for public-sector research purposes. Responses were collected in a database hosted by an approved health-data host, and separated from personal data so as to ensure strict anonymity. Siopi developed a specific module for the survey, and users were informed by email of the possibility of participating: information about tinnitus communities had been posted on social media was shared by the France-Acouphènes patient association with the members of its network.
In total, 105 users responded to the survey between September and December 2021. The dataset had no missing data. Respondents reported no particular difficulties in filling out the questionnaire. Respondents were informed that the data would be used for research purposes via Siopi's data management policy and directly on the survey homepage. Moreover, on filling out the questionnaire, respondents received a notice about the nature of the study, enabling them to apply their data privacy rights and decline use. Siopi, in charge of data processing, exported the anonymized survey responses for analysis from the secure database in comma separated value (CSV) using the Jupyter Notebook module under Anaconda Python 3. 7 software to obtain the visualizations reported below in the Results section.
We adhered to a rigorous translation procedure (see  [14] ) intended to reduce the risk of error in three ways: firstly, two translators made two independent translations which a third translator combined, thus minimizing the risk that the final translation might be colored by the translator's personal style. Secondly, the translators' native language was French, ensuring that the translation would be linguistically and culturally adapted for the target population. And thirdly, the questionnaire was validated in 9 experienced tinnitus patients, to ensure that it was clear and non-ambiguous. The exploratory survey had certain limitations. Data were collected via a digital platform open to all-comers with no supervision of response entry by a health professional. Responses were selfreported by platform users, who may have had problems with certain medical terms and in self-assessing medical criteria such as degree of hearing loss.
Also, some items referred to past events, memory of which could be defective. Notably, a large number of respondents chose the response "Other" in the question about the sound of their tinnitus, suggesting that the range of possible responses failed to match the experience of many patients. Moreover, the sample was too small to represent the exact characteristics of the French tinnitus population as a whole. Finally, although the questionnaire was validated to some extent in reports of previous translations in other languages  [10, 11] , it would still be useful to complete the validation by test-retest analysis and extensive psychometric tests. We hope that this will soon be done in studies of the international data-sets collected by the Tinnitus Database project. The Tinnitus Database aims to collate epidemiological data such as those collected using ESIT-SQ at European level.
Acknowledgments We especially thank  Ms Hajer Sayeh  for helping make available the French version of  ESIT-SQ , which we produced on the Tinnitus Database platform. We thank  Mr Louis Korczowski  for help in developing the Siopi application. We thank the  Jean-Jacques Foundation and Félicia Lopez-Loreta and BPI France  for funding the Siopi application and this present publication. We also thank the AFREPA and the  France-Acouphènes association  for the work of their volunteers and their support during the study. The French version of ESIT-SQ is in open access. The exploratory survey data are available on request.
Clinical and audiovestibular data Clinical data regarding age, gender, duration of MD, vestibular drop attacks, cochlear signs (hearing fluctuations, tinnitus, fullness), vestibular signs (vertigo attacks, average frequency during the last 6 months, duration) were systematically recorded. Audiometry comprised pure-tone average (PTA, 0. 5-4 kHz), and dissyllabic (French Fournier lists) word discrimination score (WDS). Audiometries (AC40 R , Interacoustics, Middelfart, Danmark) were conducted in a standard audiometry booth with a headphone. Electrocochleography (ECoG) was measured by an extratympanic electrode (Elios R , Echodia R , Clermont-Ferrand, France) and averaging the response to 1,000 clicks at 85 dB HL. Action (AP) and summation potential (SP) amplitudes were recorded. SP/AP > 0. 33 was considered in favor of a hydrops  (9).
Data management and analysis Data was collected and managed by Excel R (Microsoft 2010, Microsoft Inc. Redmond, VI, USA), and analyzed by Prism R (v. 6, Graphpad Inc. , San Diego, CA, USA). Most continuous variables did not pass D'Agostino and Pearson's test for normal distribution. Consequently, they were compared by a Mann-Whitney nonparametric test between two unpaired groups, Kruskal-Wallis test for more than 2 unpaired groups or a Wilcoxon signed-rank test in case of paired comparisons. Nominal variables were compared by a Chi-2 test. Correlations between continuous variables were evaluated by the Spearman coefficient and a F-test. R2 > 0. 5 with p < 0. 05 were considered as significant. Inter criterion reliability was tested by Cohen's kappa on XLSTAT R (Addinsoft, New York, NY, USA).
Logistic regression analysis was conducted on Statview software R (v. 6, SAS Inc. , Cary, NC, USA). Values were presented as means ± standard deviation (SD), n, median and range. Supporting data is available at an online repository: Bozorg Grayeli, Alexis (2022), Confrontation of Endolymphatic Hydrops Diagnosis on 3-Tesla MRI to Clinical and Audiovestibular Findings in Meniere's Disease, Dryad, Dataset, https://doi. org/10. 5061/dryad. hqbzkh1j7.
Literature Search and Study Selection We used the Google Scholar and Web of Science databases for the literature search, which was closed on 1 June 2022. We used the following search syntax: ('sequence learning' OR 'statistical learning' OR 'procedural learning' OR 'procedural knowledge' OR 'implicit learning' OR 'implicit memory' OR 'motor learning' OR 'motor skill' OR 'serial reaction time task') AND ('TMS' OR 'rTMS' OR 'TBS' OR 'cTBS' OR 'iTBS'). This search syntax returned 943 hits on the two databases. Following the PRISMA guidelines  (Page et al. 2021) , duplicate records were removed before screening the records (39 records). These remaining records were reviewed based on their titles and abstracts and we selected 55 reports that seemed relevant, irrespective of publication year or status (see Figure  2 ).
Author Contributions: Conceptualization, L. S. -B. , T. V. , O. P. , N. S. , T. Z. K. , Z. T. and D. N. ; methodology, L. S. -B. , T. V. , O. P. , N. S. , T. Z. K. , Z. T. and D. N. ; investigation, L. S. -B. ; resources, D. N. ; writing-original draft preparation, L. S. -B. , T. V. , Z. T. and D. N. ; writing -review and editing, L. S. -B. , T. V. , O. P. , N. S. , T. Z. K. , Z. T. and D. N. ; visualization, Z. T. ; supervision, T. V. , N. S. and D. N. ; funding acquisition, L. S. -B. and D. N. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Brain Research Program (project NAP2022-I-2/22) and the ANR grant awarded within the framework of the Inserm CPJ (to D. N. ). Prepared with the support of the Richter Gedeon Talentum Foundation established by Richter Gedeon Plc.
Behavioral predictions Behavior in decision-making tasks is characterized by the mean response time of correct and incorrect responses and the response accuracy. The previous analyses yielded four different anticipation determination method: on cued data, we either used the cue as the class of anticipation (the "ground" method) or the label predicted by the classifier (the "classifier" method). On uncued data, we either trained the classifiers on pre-stimulus uncued data using labels predicted on post-stimulus activity (the "post-stimulus" method) or used the classifiers created during the "classifier" method trained on cued data to generate the anticipation labels of the uncued trials (the "cued-pre-stimulus" method). For each anticipation determination method, we therefore computed these quantities for each participant separately.
Stimuli Target stimuli consisted of a sound pulse at 1000 Hz for the sound stimulus, and a sketch of a face for the visual stimulus. The sketches were generated by  Yang et al. (2020)  from the Radboud Faces Database  (Langner et al. , 2010). Cue stimuli consisted of an open-source drawing of an eye and one of an ear, displayed at the center of a screen over a random dot square cloud. All stimuli lasted for 200 ms.
EEG data acquisition EEG signals were recorded at 1000 Hz using 32 active AgCl electrodes, and the actiCHamp Plus amplifier (Brain Products GmbH, Gilching, Germany). The electrodes were placed according to the 10/20 international system. Electrode Fz served as the reference electrode upon acquisition. To avoid contamination by stimulus-related activity, we applied offline a non-causal finite impulse response band-pass filter with cut-off frequencies 0. 1-35Hz to remove high-frequency artifacts (muscle activity in particular) and lowfrequency artifacts, such as sweat. Data was subsequently epoched on the relevant periods of interest (see below).
The anticipation labels were then obtained by combining the stimulus labels and the labels obtained from this pipeline. These labels were then used as the cue labels in the pipelines described earlier. The different pipelines used to generate the classes of anticipation on both cued and uncued trials are summarized Figure  9. In the following, we will refer to the anticipation on cued trials as "ground" anticipation when the cue label is used as the anticipation class, or as "classifier" when the anticipation class is the one predicted by the pre-stimulus classifier. On uncued trials, we will refer to "cued-pre-stimulus" anticipation when the anticipation is the one predicted by the aforementionned classifier trained on cued trials, or to "post-stimulus" when this anticipation is the one predicted by the pre-stimulus classifier trained using the labels predicted by the post-stimulus classifier trained on cued data and subsequently applied to uncued trials.
Each pedal independently measures all three components of the force and torque applied to their surface, as well as the inclination angle of the pedal in reference to the crank arm (ICS-RM, Sensix, Poitiers, France). Force values up to 2. 8 kN can be measured while the inclination angle is measured with a resolution of 0. 072°. A magnetic ring encoder (LM13, RLS, Ljubljana, Slovenia) placed around the spindle of the crankset acquires the crank angle with a resolution of 0. 06°. Data are acquired at a 1 kHz sampling rate and displayed in real time, as well as recorded in a text file for later analysis. Leg orthoses (Hase Bikes, Waltrop, Germany) were modified to attach the legs to the force-measuring pedals. To ensure that there are zero degrees of movement between the leg orthosis and the pedal, custom connecting metal plates were manufactured (Figure  2 ) and attached to the pedals, providing a grooved surface to which the orthosis can be attached.
Force values up to 2. 8 kN can be measured while the inclination angle is measured with a resolution of 0. 072 •. A magnetic ring encoder (LM13, RLS, Ljubljana, Slovenia) placed around the spindle of the crankset acquires the crank angle with a resolution of 0. 06 •. Data are acquired at a 1 kHz sampling rate and displayed in real time, as well as recorded in a text file for later analysis. Leg orthoses (Hase Bikes, Waltrop, Germany) were modified to attach the legs to the force-measuring pedals. To ensure that there are zero degrees of movement between the leg orthosis and the pedal, custom connecting metal plates were manufactured (Figure  2 ) and attached to the pedals, providing a grooved surface to which the orthosis can be attached. The grooves in the metal plates allow for the adjustment of the position of the orthosis, and therefore the foot, in reference to the pedal.