dataesr/scientific-paragraphs-categorization huggingface dataset:
(a) Patient's 3D brain mesh (left hemisphere) with implanted SEEG electrodes. Reconstructed electrode images (blue) correspond to real trajectories and entry points (EpiTools software suite; Medina  Villalon et al. , 2018). The maximal Epileptogenicity Index (EI;  Bartolomei et al. , 2008)  values are represented as spheres on the respective contacts according to a color map from yellow to red. Sphere size is proportional to the normalized EI value of the contact (from 0 to 1). The EI shows maximal epileptogenicity within the left amygdala, which belongs to the epileptogenic zone (EZ; EI ≥ 0. 4), the left hippocampus, the parahippocampal cortex, the anterior insula, and the left pulvinar belong to the PZ (0. 1 ≤ EI <0. 4), whereas the remaining regions with available sampling belong to the NIZ (EI <0.
1). Bipolar contacts situated entirely within the white matter or within the CSF (as defined by automated segmentation) or showing an artifact SEEG signal (visually checked) were excluded from the analysis. The ROIs obtained from the SEEG-analysis data sets were then used for multimodal imaging measures. (b) Axial slices of T 1 w image, f, TSC, and NAA maps of the same patient. The ROI corresponding to the bipolar electrode contacts OF'4-5 (the left anterior insula, yellow dot circled in red on the T 1 w slice) is represented as the red dot on the f, TSC, and NAA maps.
| MRI data processing Three dimension 1 H-EPSI images were post-processed with the Metabolite Imaging and Data Analyses System (MIDAS, Trac, MRIR, Miami;  Maudsley et al. , 2006). This software ensures B 0 map correc- For each ROI (for details on ROI definition, see Section 2. 6), we fitted the mean signal intensity across each of the 24 TEs with a biexponential model using the equation: ROI signal ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A 2 f Á e -TE T2 Ã short þ 1 À f ð ÞÁe -TE T2 Ã long 2 þ Ric 2 " # v u u t ð1Þ where A is an amplitude scaling term, f is the sodium signal fraction of the short T 2 * decay component, and Ric refers to a Rician noiserelated scaling parameter  (Ridley, Marchi, et al.
| SEEG-signal analysis All signal analyzes were performed in a bipolar montage and computed using the open-source AnyWave software  (Colombet et al. , 2015)  available at https://meg. univ-amu. fr/wiki/AnyWave. For each patient, a bipolar montage containing pairs of contiguous contacts, of which at least one is within the gray matter, was generated The epileptogenicity of different brain structures was assessed by quantitative SEEG-signal analysis using the Epileptogenicity Index (EI;  Bartolomei et al. , 2008). The EI combines analysis of both spectral and temporal features of SEEG signals, respectively, related to the propensity of a brain area to generate fast discharges (12. 4-127 Hz), and to how early this area becomes involved in seizure. A normalized EI value is used, ranging from 0 to 1.
ACKNOWLEDGMENTS The authors would like to thank  L. Pini ,  C. Costes , and  V. Gimenez  for data acquisition and study logistics. The authors would also like to thank  A. Ivanov  and  C. Bernard  for helpful discussions. This study has received support from the  French government  under the " Programme Investissements d'Avenir," Excellence Initiative of Aix-Marseille University -A*MIDEX  ( AMX-19-IET-004 ), 7TEAMS Chair,  EPINOV  (Grant  ANR-17-RHUS-0004 ) and  ANR  ( ANR-17-EURE-0029 ); and from the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under the Specific Grant agreement nos. 785907  ( Human Brain  Project  SGA2 ) and 945539 ( Human Brain  Project  SGA3 ).
Subsequently, three-day plethysmography acclimation was conducted to familiarize the animals with the plethysmography chambers (Emka, France). This phase limited stress and resulted in calm animals, thereby allowing successful recordings with plethysmography software (Emka IOX software, Paris, France) on the 4th and 5th days of the week. The success rate (Sr) was obtained from the ratio of validated cycles (i. e. , signals matching respiratory cycles) and the number of detected cycles for each 20 s period. Sr = 100% indicates that all detected respiratory cycles were validated by the software. We considered only measurements of respiratory function with Sr > 60. Mean Sr was similar between groups (76 ± 4, 75 ± 6 and 78 ± 10 in C3HC, C6HC and sham groups, respectively). Baseline recordings were performed for two consecutive days to check for data reproducibility.
Signals were digitized with Powerlab data acquisition hardware/software (Acquisition rate: 4 k/s; AD Instruments, Dunedin, New Zealand) connected to a computer and analyzed using LabChart 7 Pro software (AD Instruments, Dunedin, New Zealand). EMG was integrated (50 ms decay). After the experiment, the animals were euthanized with exsanguination, followed by intracardiac perfusion of saline and 4% paraformaldehyde (4 • C) for tissue fixation and subsequent harvesting.
Data Processing and Statistical Analyses EMG analyses. The amplitude of at least 10 double-integrated diaphragmatic EMG inspiratory bursts during normoxia was calculated for each animal from the injured and the intact sides using LabChart 7 Pro software (AD Instruments). For inter-inspiratory burst basal signal analysis during normoxia, the amplitude of a minimum of 5 basal diaphragmatic EMG signals was calculated using LabChart 7. Statistical analyses. Data are presented as mean ± standard deviation (SD) or median (interquartiles), and differences were considered significant at p < 0. 05. GraphPad Prism software 9. 3. 1 was used for all statistical analyses. Normality of the data was checked with the Shapiro-Wilk test. Within-group comparisons (weight loss, EMG activity and respiratory variables) were performed using paired t-test or Wilcoxon's test (for non-parametric data and for groups with n < 7 animals), and between-group comparisons were performed using 2-way analysis of variance (diaphragmatic EMG and interburst) with the type of lesion and side (injured vs.
Total subscale scores range from 0 to100, with higher scores indicating better HRQoL. Sociodemographic data (age, gender, way of life, level of education, educational protective measures) and medical data (age at diagnosis and at onset of symptoms, location of tics, medical monitoring, treatment, comorbidities) were also collected. All data are available at Mendeley  [29]. Statistical analysis. Statistical analyses were performed with SAS v9. 4 software (SAS Institute) and conducted at a two-sided alpha = 0. 05 significance levelto test the null hypothesis of zero correlation between two variables. Data completeness. Respondent acceptability was determined by the frequency of missing values in the questionnaire. Factor analysis. Factor analysis using the principal axis extraction method and oblique oblimin rotation, which allows correlation of the factors, were performed to study the multidimensionality and distribution of the subscale items  [30].
Acknowledgments Members of the  The Syndrome de Gilles de La Tourette Study Group are Diane Cyrille ,  Clement Debosque ,  Philippe Derost ,  Loïc Duron ,  Candy Guiguet-Auclair ,  Isabelle Jalenques  (leader of the group, ijalenques@chu-clermontferrand. fr), Sophie Lauron ,  Guillaume Legrand  and  Fabien Rondepierre  ( CHU Clermont-Ferrand, Clermont-Ferrand, France );  Clara Jameux ,  Urbain Tauveron-Jalenques  and  Jeffrey Watts  ( UniversiteClermont Auvergne, Clermont-Ferrand, France );  Andreas Hartmann  ( Assistance Publique-Hopitaux de Paris AP-HP, Paris, France ), The authors thank the participants, and  J Watts  for advice on the English version of the manuscript, and the " Association Francaise Syndrome Gilles de la Tourette " for its help in the realization of the study.
The multivariate approach is considered to be statistically and conceptually more adequate than the univariate counterparts for lesion analyses. Indeed, while the univariate voxel lesion symptom mapping techniques assume independency among voxels, the multivariate voxel lesion symptom mapping approach detects which group of voxels together contribute to the emergence of behavioural deficits, assuming a statistical dependency  (Pustina et al. 2018). This approach allows us to consider that lesions usually extend to more than a voxel and, consequently, the probability that a voxel is lesioned is dependent on the probability of the surrounding voxels' conditions. For the white matter, the Tractotron software  (Foulon et al. 2018 ) was used to identify the probability of disconnections for each known tract and each patient, taking into account the contribution of clinical and neuropsychological variables.
We also recorded the lesion size and took into account the number of voxels of each lesion as a nuisance variable. The white matter tracts associated with PN were extracted for each known tract and for each patient via Tractotron software  (Foulon et al. 2018) , giving the probability of a disconnection. Then, grey matter structures and white matter tracts were analysed by means of linear models to identify the structures which may account for PN while taking into account any nuisance clinical variables. Finally, the presence of an integration of these structures in a network was analysed by means of a comparison of the models. All statistical analyses, unless otherwise specified, were computed on R version 3. 6. 3 (R Core Team 2020).
Patients Data from 104 stroke patients with unilateral right hemisphere damage were collected as part of a joint project involving two centres based in Italy and the UK over a period of 10 years. The inclusion criteria were: (1) unilateral right hemisphere damage, secondary to a first-ever stroke, as confirmed by clinical neuroimaging and (2) right hand dominance. The exclusion criteria were: (1) a previous history of neurological or psychiatric illness; (2) medication causing severe cognitive or mood side-effects; (3) severe language impairment, general cognitive impairment, or mood disturbances that would potentially preclude the completion of the assessments made during the study and (  4 ) left hand dominance. The clinical MRI or CT neuroimaging data were available for all of the patients.
Their clinical and anatomical data had been described in two previous studies which focused on the neural correlates and diagnosis of anosognosia for hemiplegia  (Pacella et al. 2019; Moro et al. 2021 ) and disorders in the sense of body ownership  (Moro et al. 2022 ) and the data are shown in Table  1. As the data were collected from two different stroke recovery units, we took into account the neurological and neuropsychological tests that were most commonly administered to all of the patients in the centres and investigated the symptoms which are more frequently associated with PN (i. e. extrapersonal neglect, anosognosia for hemiplegia, sense of ownership and motor deficits). All of the patients gave written informed consent and the research was conducted in accordance with the guidelines of the Declaration of Helsinki (  2013 ) and approved by the Local Ethical Committees of each centre.
The patients' responses to each question were scored by two expert clinicians, with 0 = the patient recognises the arm as belonging to him/ her; 1 = uncertain answers indicating doubts about ownership and 2 = responses indicating disownership and/or attribution of the arm to somebody else (min score = 0; max score = 6). Missing data (education: 10. 58% of the whole group; lesion-assessment interval: 3. 85%; MRC 4. 81%; One Item test: 17. 31%; line crossing: 0. 96%; line bisection: 3. 85%; Bisiach test for AHP: 3. 85%, and disorders in the sense of body ownership: 13. 46%) were imputed by means of multivariate imputation by chained equations (van Buuren and Groothuis-Oudshoorn 2011), after having checked if they were missing at random  (Rubin 1987 (Rubin , 1996) )  by testing for associations between missing and observed data.
Lesion delineation The patients' neuroimaging data were acquired via computerised tomography (CT, n. 97) and magnetic resonance (MRI, n. 7) and lesions were segmented and co-registered using the manual procedure already described by Moro and colleagues  (Moro et al. 2016 ). The lesion drawing was performed blindly and independently by two of the authors (VM, SB). In cases of disagreement on a lesion drawing, a third anatomist's opinion was consulted (< 10%) and the differing opinions were discussed until an agreement was reached. Scans were registered on the ICBM152 template of the Montreal Neurological Institute, furnished with the MRIcron software (ch2, http:// www. mccau sland center. sc. edu/ mricro/ mricr on/). First, the standard template was rotated on the three planes (size: 181 × 217 × 181 mm, voxel resolution: 1 mm 2 ) to match the orientation of patient's MRI or CT scan.
Acknowledgements This project was supported by  Ministry of Education, Universities and Research-(MIUR) ,  Italy  ( PRIN 2017-2017N7WCLP  to V. M. and M. S. ),  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , Grant  METABODY: [ERC-2018-COG-818070 ], to A. F. and Grant agreement no. 818521  to V. P.
Funding Open access funding provided by  Università degli Studi di Verona  within the  CRUI-CARE Agreement. This project was supported by  Ministry of Education, Universities and Research-(MIUR) ,  Italy  ( PRIN 2017-2017N7WCLP  to V. M. and M. S. ),  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , Grant  METABODY: [ERC-2018-COG-818070 ], to A. F. and Grant agreement no. 818521  to V. P.
Microendoscopic data acquisition and processing Images were acquired with a head-mounted miniaturized microscope (nVista, Inscopix) at 4 frames per second over 1 minute (240 ms exposure time, 10% to 20% LED illumination, 1. 5 to 2. 5x gain). The 1-minute movies were spatially filtered (high-pass filter, cutoff at 40 μm), corrected for motion artifacts (Inscopix Data Processing Software), and saved as stacks of frames in TIFF format. Regions of interest (ROI) were defined as nonoverlapping bright regions that displayed fast activity in any stack. They were applied to every stack (ImageJ), and numerical data were saved as text files for further processing in Matlab (MathWorks).
Data analysis and statistics A Butterworth low-pass filter was applied (cutoff frequency at 1. 1 Hz). After detrending, the 5th and 95th values were computed for each 1-minute recording. The 5th percentile value was considered as the baseline level, and the difference between the 5th and 95th values defined the signal amplitude. Fast spikes were detected as fast and large events with a two-threshold procedure. Respectively, threshold 1 selected the fastest events from all recordings (90th percentile of all derivative values from all recordings, from all ROIs), and threshold 2 eliminated the smallest of these events for each recorded neuron (set at 2 SDs of all the raw signals over the course of a 24-hour period for each ROI). For each recording, the spike frequency was calculated as the number of spikes detected during 1 minute.
The activity density was computed as the spike frequency divided by the number of recorded cells in the field of view. The onset of spikes (derivative crossing above 0) was computed as their time of occurrence. Ca 2+ i spikes occurring in a highactivity frame in a statistically significant number of neurons delineated the clusters of coactivity. The threshold corresponding to a significance level of P < 0. 05 was defined as the number of activated cells in a single frame that exceeded only 5% of 500 surrogate datasets obtained by randomly transposing intervals of activity within each cell  (44). The extent of clusters of coactivity was computed as the sum of all the cells participating in any of the coactive events during a 1-minute recording, normalized by the total number of cells recorded in the field (expressed in Cell.
Clusters/Cell/min). Nonparametric JTK-Cycle analysis  (22)  was used to estimate the daily phase of the GCamp6f signal parameters from datasets recorded over one 24-hour period. Circular analysis and statistics were conducted using Oriana (Kovach Computing Services). Other numerical and statistical analyses were conducted with IgorPro (WaveMetrics, Inc), Prism 7 (GraphPad Software, Inc).
Funding This work was supported by the  Centre National de la Recherche Scientifique  (P. M. , P. F. , and X. B. ), the  Agence Nationale pour la Recherche (ANR)  grant nos. ANR-15-CE14-0012  and  ANR-18-CE14-0017-01 , and the  France BioImaging   ANR-10-INSB-04  " Investments for the Future " to P. M. , the  Fédération pour la Recherche sur le Cerveau  (X. B. ), and a fellowship from the  Secours Populaire Libanais  to L. E. C. H.
The search was performed in two different databases, PubMed and Web of Science, and the search included preferentially the MeSH (Medical Subject Headings) term of the compound with the construct "AND SH-SY5Y", although other entry names or synonyms were used. The list of all the search terms used can be found in Supplementary Table  1. For some groups, rather than searching individually for each compound, the group MeSH term was used since all the congeners of the group contained the key words, this is the case for PCBs, PBBs, PBDEs, and parabens. The articles found in the two databases were merged eliminating duplicates. Only articles written in English and original studies (no reviews) were selected. A first selection was done based on the titles, and a second one based on the abstracts of the articles.
To investigate the CA1 source of this Gamma activity, we used a second dataset combining wide-field voltage sensitive imaging (VSI) with a high-density silicon probe in CA1 in mice expressing genetically encoded voltage indicators (GEVI)  (Song et al. , 2018)  during natural NREM sleep (Figure  6 A-B ), and repeated the same analysis of Figure  3. In this second dataset, we also showed that the Slow Gamma observed in the hippocampus is linked to the RS component and is mainly observed in the stratum radiatum layer (n=5 mice, Figure  6 C-E ).
VSD imaging recordings After the surgical procedure, RH1961 was dissolved in 0. 5 mg/ml HEPES-buffered saline and administered to the exposed cortex for 40-60 minutes. After that, 1. 5% agarose in HEPESbuffered saline was applied over the cortex and sealed with a coverslip to minimize respiration artifacts. Using a CCD camera (1M60 Pantera, Dalsa, Waterloo, ON) and an EPIX E8 frame grabber controlled by XCAP 3. 7 imaging software (EPIX, Inc, Buffalo Grove, IL) and XCAP 3. 8 imaging software (EPIX, Inc, Buffalo Grove, IL), VSD image were acquired in 12-bit format at a frame rate of 200 Hz (EPIX, Inc. ). Images were captured using a macroscope with front-to-front video lenses (8. 6 x8. 6 mm field of view, 67 µm per pixel). Two LEDs (627-nm center, Luxeon K2) were used to excite the VSD, and the fluorescence emitted was filtered through a 673-nm to 703-29 nm bandpass emission filter.
CA1 LFP and ECoG recordings We employed a transparent ECoG grid to obtain an electrophysiological profile of the neocortex. The recording sites on the grid are grouped in a 6x5 matrix (each site is spaced 1 mm apart) and cover a total area of 6 mm x 5 mm. To capture neocortical activity, we first grounded the grid to the animal's nose with a subcutaneously implanted electrode. The ECoG matrix was then placed across the mouse's right neocortex, encompassing the same widefield as the optical imaging. After that, we applied HEPES-buffered saline to the exposed cortical region to keep it hydrated. The electrophysiological data were captured using 32channel headstages (Intan Technologies' RHD2132) coupled to an Open Ephys recording system. The raw signals were recorded at 20 kHz sampling rate and filtered between 0.
1 and 7500 Hz with a pre-amplifcation of 20x. For the VSD recordings, we recorded the hippocampus LFP at 20 kHz sampling rate in a Digidata 1440 (Molecular Device Inc, CA) data recording system. The recorded data were filtered (0. 1-10000 Hz) using a Grass A. C. Model P511 (Artisan Technology Group, IL). While for the VSI recordings we used a 16 channels silicon probe (Atlas Neuroengineering, Belgium). The CA1 profile data were acquired using a headstages (RHD2132, Intan Technologies) with an Open Ephys system. The signals were recorded at 20 kHz sampling rate and filtered between 0. 1 and 7500 Hz.
Ripple detection We initially downsampled the data to 1 kHz and filtered in between 150-300 Hz. After, we detected the peaks of the envelope filtered data that crossed a threshold between 3 and 5 s. d. plus the mean. Based on the points that crossed 1. 5 s. d. before and after the detected points, the start and end of the ripple were determined. Intervals above 30 ms and shorter than 300 ms were considered ripple events. In addition, the time between the end of one ripple and the beginning of the next had to be at least 100 ms.
Resting state data During the resting state experiment, the monkey is seated in a primate chair without any task or stimulation. Registration of electrophysiological activity is synchronized with a video recording of the monkey's behavior. Based on this, periods of 'true resting state' (RS), defined as no movements and eyes open, are chosen for the analysis. Eye movements and minor head movements are included. Each monkey is recorded twice, with a session lasting approximately 15 and 20 min for monkeys E (sessions E1 and E2) and N (sessions N1 and N2), respectively, and the behavior is classified by visual inspection with single second precision, resulting in 643 and 652 s of RS data for monkey E and 493 and 502 s of RS data for monkey N.
Separation of putative excitatory and inhibitory neurons Offline spike-sorted single units (SUs) are separated into putative excitatory (broad-spiking) and putative inhibitory (narrow-spiking) based on their spike waveform width  (Barthó et al. , 2004; Kaufman et al. , 2010; Kaufman et al. , 2013; Peyrache, 2012; Peyrache and Destexhe, 2019). The width is defined as the time (number of data samples) between the trough and peak of the waveform. Widths of all average waveforms from all selected sessions (both resting state and reach-to-grasp) per monkey are collected. Thresholds for 'broadness' and 'narrowness' are chosen based on the monkey-specific distribution of widths, such that intermediate values stay unclassified. For monkey E the thresholds are 0. 33 ms and 0. 34 ms and for monkey N 0.
This results in 95 % confidence intervals m ± t(df) • s of 0. 192 ± 0. 093 for rates and 0. 32 ± 0. 14 for covariances in monkey E and 0. 19 ± 0. 14 for rates and 0. 26 ± 0. 17 for covariances in monkey N. For both monkeys the within-epoch rate-correlations distribution does not fulfill the normality assumption of the t-test. We therefore perform an additional non-parametric Kolmogorov-Smirnov test for the rate comparison. The differences are again significant; for monkey E D = 1. 00, p = 6. 66 • 10 -8 ; for monkey N D = 1. 00, p = 8. 87 • 10 -13. For all tests we use the implementations from the Python scipy. stats module (SciPy v. 1. 4. 1).
Acknowledgements This work was partially supported by  HGF  young investigator's group VH-NG-1028, European Union's  Horizon 2020 research and innovation program  under Grant agreements No. 785 , 907  ( Human Brain  Project  SGA2 ) and No. 945 , 539  ( Human Brain  Project  SGA3 ),  ANR  grant  GRASP  and partially funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) - 368482240/ GRK2416. We are grateful to our colleagues in the NEST and Elephant developer communities and for continuous collaboration. All network simulations were carried out with NEST 2. 20. 0 (http://www. nestsimulator. org). All data analyses performed with Elephant (https://neuralensemble. org/elephant/). We thank  Sebastian Lehmann  for help with the design of the figures.