dataesr/scientific-paragraphs-categorization huggingface dataset:
In the case of the Cho 2017 and MunichMI datasets we noted the presence of noise at approximately 25 to 30 Hz that interferes with the burst detection step. We therefore included an extra pre-processing step involving a custom implementation of the meegkit python package (v0. 1. 3, dss_line function)  [96]  to remove these artifacts. Considering only this subset of sensorimotor channels and all recording periods, we rejected trials using the autoreject python package (0. 4. 0)  [97]  (Table  1 ).
0 -7. 0 Table  1. Attributes of the datasets used in the study. Using a previously published iterative, adaptive procedure, we identified bursts within the beta frequency range from the TF matrix, and then extracted their waveforms from the "raw" time series (after low pass filtering as pre-processing) within a fixed time window of 260 ms, centered on the burst peak  [100]. Due to inability to parameterize spectra from all datasets we subtracted twice the standard deviation of the TF before fitting each peak as a 2D Gaussian, instead of subtracting the aperiodic activity from the TF matrices  [81, 101, 102] , before detecting beta bursts.
Feature extraction based on patterns of burst rate modulation Beta burst waveform analysis was performed for each dataset by creating a dictionary of detected bursts across subjects and experimental conditions ("left hand" or "right hand") (figure  1 ). This allowed us to create a matrix of burst waveforms by combining all detected bursts per subject, after robust scaling (scikit-learn package  [103] , v1. 0. 2). This representation of burst waveforms is suitable for applying a dimensionality reduction technique in order to better understand the variability in the recorded beta burst shapes. For the remaining of the analysis, we only considered channels C3 and C4, or channels 43 and 44 for the MunichMI dataset. Previous work from our group has demonstrated that principal component analysis (PCA) [104] (scikit-learn package, v1.
Classification In order to obtain classification results with our beta burst waveform-based features, we used a stratified, repeated cross-validation approach. For each dataset, we first randomized the trials' order and stratified the total number of trials of each subject in M=5 strata. Then, we used half of the trials of one stratum for creating an across-subjects burst dictionary, ran PCA on the resulting waveform matrix and kept track of the rest of the stratum's trials for cross validating the decoding results. For each subject separately, we then projected the bursts of the remaining four strata (the trials not used during the burst dictionary creation step or for cross validation) along each component and, after averaging the burst rate of each group during the task period, we employed a repeated cross validation with K=5 folds.
0. 2). All numeric computations were based on the numpy python package (v1. 21. 6; [109]), an environment running python (v3. 10). We compared trial-level classification results of the waveform-resolved burst features to the beta band power features using a generalized linear mixed model with a binomial distribution and logit link function with correct classification of each trial as the dependent variable, the type of classification feature as a fixed effect, and the subject nested within the dataset as random intercepts. We also compared classification results of the waveform-resolved burst features to the rest of the burst features using a similar model. Statistical analyses were conducted using R (v4. 1. 2) and lme4 (v1. 1-31;  [110] ). Fixed effects were assessed using type II Wald X 2 tests using car (v3.
Results We used six open MI EEG datasets for the purpose of examining the explanatory value of beta burst activity as a feature for BCI classification. For each dataset, we detected beta bursts in a subset of channels over the sensorimotor cortex under two conditions, "left hand" and "right hand" MI. Based on the bursts detected in channels C3 and C4 of each subject, we built dataset-specific burst dictionaries which capture the variability of the burst waveforms (figure  1 ) (see Materials and Methods).
g. over contralateral motor cortex there was a pronounced decrease in score along component 4 during the task, but this could be due to a reduction in the rate of bursts with high scores, an increase in the rate of bursts with negative scores, or a combination of the two. To better understand the rate modulation of bursts with distinct waveforms along each component over all experimental periods, we visualized the trial-averaged, baseline-corrected burst rate as a function of time and component score, for the first five components of a representative subject (figure  4 ; Zhou 2016 dataset, S1). In this particular case there were differences in burst rate modulation between channels C3 and C4, as well as between the two experimental conditions. During the task period there was a decrease in the rate of bursts with large positive or negative scores along component 4 on the contralateral channel for either condition.
Burst rate modulation for different burst groups varies per condition, channel and component. The differential modulation of burst rate is particularly pronounced contralaterally, in channel C4 during "left hand" trials and channel C3 during "right hand" trials along the fourth component. A clear distinction between conditions is evident when comparing the difference of rate modulation of the two channels for each waveform group. For each dataset we present the across-subject average results estimated with each method, as well as the results for each participant (figures 6, 7). For the Cho 2017 dataset, which contains a large number of participants, we only show the best ten subjects according to the results based on burst waveform features. The results of all subjects are provided separately (sup.
Discussion In this study, we showed for the first time that waveform-specific beta burst rate is a representation comparable to beta power within a framework of binary classification MI tasks. In an attempt to understand why, we compared multiple representations of beta activity modulation during the MI task. We showed that bursts of different shapes are selectively modulated following task onset, with distinct waveforms occurring with different probability during different points in time [100] (figures 4 and 5). This modulation can be encoded either by TF-derived features, or alternatively, burst waveforms. All of the TF-derived features were as informative as the overall burst rate when used as classification features, but less reliable than waveformbased features, across all datasets. The results presented in this article are based on features of beta bursts detected from only two channels, and are therefore not directly comparable to results of previous studies that have implemented standard designs within the BCI literature  [95, 113]  and incorporate all available recording channels, do not perform trial rejection, and utilize spatial filtering.
However, waveform-based burst rate features are more informative about imagined movements than beta power in channels C3 and C4. In this regard, our analysis is a first step in the direction of establishing a neurophysiologically informed alternative to currently existing methodologies of feature extraction. Our results rely on burst dictionaries that combine data from all subjects across a dataset. We have introduced this "transfer learning-like" approach because we have observed that it makes the dimensionality reduction step less susceptible to noise and it results in the same components for all subjects within a dataset, thus rendering the classification features and decoding results easier to interpret. Additionally, it is worth mentioning that due to the enforced orthogonality between the PCA dimensions, the resulting principal components are similar to a Fourier decomposition of the time series, which may be suboptimal by failing to capture components that optimally separate bursts that are differently modulated by the task.
Conversely, this property of PCA imposes restrictions on the resulting components that make them similar across datasets (sup. figure  4 ). This property could be taken advantage of and used in future work for cross-dataset transfer learning. An important question is whether this procedure would be suitable for online, real-time decoding. The superlets algorithm, and to a lesser extent the burst detection algorithm, are computationally expensive and increasing the number of recording channels, task duration, and frequency resolution would make it difficult to employ this analysis online. However, our results show that beta bursts with particular waveforms are more informative of MI than others. These waveforms could be used as kernels and convolved with online recordings to efficiently detect bursts directly in the time domain.
Acknowledgments This work was performed within the framework of the  LABEX CORTEX  ( ANR-11LABX-0042 ) of  Université de Lyon , within the program " Investissements d'Avenir"  (decision n°  2019-ANR-LABX-02 ) operated by the  French National Research Agency  (ANR). SP, MC, JB, and JM are supported by the  French National Research Agency (ANR)  project  HiFi  ( 2020-2024 ). MS and JB are supported by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 Research and Innovation Programme  ( ERC  consolidator grant  864550  to JB).
This is an ancillary study to a larger clinical trial, which aimed to assess the value of treating CRPS with a long-term (5 months) protocol of repetitive transcranial magnetic stimulation (rTMS). The use of high frequency rTMS (HF-rTMS) applied to the primary motor cortex (precentral gyrus) previously showed a good level of evidence of efficacy in the treatment of different chronic pain syndromes  (9) , including CRPS  (10, 11). The first objective of this ancillary study was to determine whether sudomotor dysfunction, assessed by ESC measures, could be associated with pain and activity changes in brain networks, as assessed by fMRI, in 11 patients with CRPS affecting an upper or a lower limb. In addition, there are very few data on changes in cerebral activity assessed by fMRI associated with the analgesic effects of motor cortex rTMS (review in 12), and in particular none in the context of CRPS to our knowledge.
Ethics This study was part of a larger trial (Hodaj H. , Payen J. F. , Hodaj E. , Sorel M. , Dumolard A. , Vercueil L. , Delon-Martin C. , Lefaucheur J. P. Long-term analgesic effect of trans-spinal direct current stimulation compared to non-invasive motor cortex stimulation in complex regional pain syndrome. Brain Communication, 2023, in press) investigating the analgesic effect of various techniques of non-invasive brain or spinal stimulation in patients with CRPS, registered in clinicaltrials. gov (NCT02817880) and ethic acceptance was provided by Institutional Review Board of Sud-Est V, Grenoble, France (N ° 6705). Only patients of the rTMS treatment group that underwent fMRI were enrolled in this ancillary study and gave written informed consent to participate.
rTMS procedure Stimulation was performed using a MagPro stimulator (MagVenture (distr. Mag2Health), Farum, Denmark) and either a flat B65 coil (MagVenture) in patients with upper limb pain or an angled B70 figure-of-eight coil (MagVenture) in patients with lower limb. The motor cortical representation of the painful region was targeted using a TMS Navigator system, integrating individual brain MRI data (Localite, Sankt Augustin, Germany). Stimulation was performed at 10Hz (HF-rTMS) with an intensity set at 80% of the resting motor threshold (measured only once on motor evoked potential recording at the beginning of the first rTMS session) and the coil held in postero-anterior orientation. Each rTMS session consisted of 40 trains of 5-sec duration with intertrain interval of 25 sec for a total of 2000 pulses in 20 minutes.
The fMRI analysis was performed using SPM12 software (The Welcome Department of Cognitive Neurology, London 2 ) in two steps. In the first step, individual analysis is done in the individual's referential and in the second step group analysis is done in a common referential. In the first step, preprocessing is performed. This includes rigidbody motion parameters evaluation and inverse motion application to the fMRI images, mean fMRI image calculation, correction for time delay between slices in each fMRI volume, registration of the mean fMRI image onto the anatomical image acquired at baseline, application of the registration to all fMRI images and a spatial smoothing with a Gaussian isotropic filter of 3-mm (full-width at half maximum). Eventually, since all the fMRI images are in the individual's referential, individual statistical analysis was performed using a linear generalized model assessing the contrast images of cortical activation resulting from ipsilateral and contralateral motor tasks at baseline and one month after treatment.
The second step was conducted for group analysis purpose. Each anatomical image of the pre-treatment session was segmented to extract the grey matter image and to compute the deformation field to be applied to each individual, using DARTEL software  (14) , to match a symmetrical template, provided by the CAT12 software 3. The deformation fields computed for each patient were further applied to all individual contrast images. In order to get rid of the pain laterality of the patients, those presenting a left-sided pain had their MR images left-right flipped across the midline. This flipping operation keeps the accuracy of the anatomical location thanks to the symmetrical template that was used  (15). Doing so, the left side on the brain images corresponds to the hemisphere contralateral to the painful side and the right side corresponds to the hemisphere ipsilateral to the painful side.
Statistical analysis Statistical comparisons of VNS and ESC data between baseline and one-month after the rTMS treatment were performed using Wilcoxon signed rank test for paired values. Correlation between VNS and ESC in affected limb were performed using the Spearman's correlation test both at baseline and after treatment. In all cases, the level of statistical p value significance was set at 0. 05 and calculations were done with R software. 2. http://www. fil. ion. ucl. ac. uk/spm/ 3. neuro. uni-jena. de/cat12-html/cat_versions. html The inference of neuroimaging data were conducted at the group level in the following way. First, main sensorimotor network was investigated for the influence of chronic pain and rTMS therapy in the following manner. The contrasts corresponding to the ipsilateral task and flipped contralateral task at baseline were tested against null hypothesis and their common pattern elicited by these two tasks was computed using conjunction analysis.
This was done both specifically within the mask of the sensorimotor network and without mask to explore regions located out of the sensorimotor network. Since the covariates are orthogonalized previous to the multiple regression in the SPM software, the estimated neural correlates of each covariate are further independent. The patterns corresponding to each of those covariates as well as the common pattern were further derived. For the regression analysis in the sensorimotor network, the statistical significance was considered when p<0. 001 at voxel level and extent >25 voxels. Outside this network, for sake of exploration, we reported regions where p<0. 001 at voxel level and extent >10 voxels. Within the areas correlated to the covariates, the mean activation was extracted for each subject and time-point.
fmrib. ox. ac. uk/fsl/; accessed February 4, 2018)  [67]. The data were denoised xix using the non-human primate adapted ICD-FIX command (https://github. com/Washington-University/NHPPipelines) for spatial Independent Component Analysis (ICA, i. e. melodic) followed by automatic classification of components into 'signal' and 'noise'. We then applied spatial smoothing using an isotropic Gaussian kernel of 3 mm full width at half maximum.
Acknowledgments We thank the  NeuroSpin  support teams for help in data acquisition and analysis, and  Dr. Anjali Tarun  and  Dr. Daniela Zöller  for providing us with human iCAPs for figures and  Dr. Younes Farouj  for the code to run iCAPs. Part of this work was supported by  Institut National de la Santé et de la Recherche Médicale , the  Avenir program  (B. J. ),  Commissariat à l'Energie Atomique, Collège de France ,  ERC Grant NeuroConsc  (to S. D. ),  Foundation Bettencourt-Schueller , the  Roger de Spoelberch Foundation , and internal funding from the  Department of Physical Medicine and Rehabilitation at the University of Pittsburgh  to E. P.
Statistical Analysis Med-PC data were analyzed using custom-made, freely available software written in Python, Med_to_csv (https://github. com/hedjour/med_to_csv) that uses raw data files to create complete tables for further analysis in GraphPad Prism. Data were checked for normality of distribution using the Shapiro-Wilk test. In our previous study, in the absence of a delay, PSS data did not show a normal distribution as a function of the electrical charge variable, but they did after natural logarithmic transformation of the variable. With the introduction of a delay, PSS data did not show normal distribution even after logarithmic transformation. Therefore, for statistical analysis, we used a nonparametric repeated measure Friedman rank test to analyze the effects of different delays in all rats.
Acknowledgements This work was supported by the  Centre National pour la Recherche Scientifique , the  Institut National de la Santé et de la Recherche Médicale , the  University of Poitiers , the  Nouvelle Aquitaine   CPER 2015-2020 / FEDER 2014-2020 program "Habisan"  and the  Nouvelle Aquitaine  grant  AAPR2020A-2019-8357510  (PI :  M. Solinas ) and the  IRESP/Inca  grants  « IRESP-19-ADDICTIONS-20 »  (PI :  M. Solinas ) and  « IRESP-AAPSPA2021-V1-07 »  (PI :  N. Thiriet ). The contribution of LVP was supported by the  Intramural Research Program  of the  NIH ,  National Institute on Drug Abuse. We thank  Pauline Belujon  for helpful comments on a previous version of the manuscript. This study has benefited from the facilities and expertise of  PREBIOS platform (Université de Poitiers).
Acknowledgements The authors thank  Gregory Artaxet  and  Eva Bruchet  ( NutriNeuro lab ) for taking daily care of the animals,  Léa Décarie-Spain  for help during surgery,  Lola Fauré  for some illustrations and the  Bordeaux Imaging Center (a service unit of University of Bordeaux  and a national infrastructure,  France BioImaging ) where part of microscopy was completed. This work was supported by  INRAE  (to G. F. ),  CNRS  (to E. C. ), and  French National Research Agency  ( ANR-14-CE13-0014   GOAL  and  ANR-15-CE17-0013 OBETEEN  to E. C. and G. F. and  ANR-16-CE37-0010  ORUPS to G. F. ). I. B. was the recipient of a PhD fellowship from the  French Ministry of Research and Higher Education  ( 2018-2021 ).
Phylogenetic data from mouse to modern humans To understand what phenotypes in animal models can be related to human AUTS2 syndrome 6,7,17   , we took advantages of recent public databases of large-scale In Situ Hybridation (ISH) performed in two mammalian neurobiology models, mouse and marmoset. GenePaint is a digital atlas of gene expression patterns in various tissues and species with strong focus on mouse embryos (https://gp3. mpg. de/). Expression patterns are determined by non-radioactive in situ hybridization on serial tissue sections. Allen Brain Atlas is an anatomically comprehensive digital atlas containing the expression patterns of ∼20,000 genes in the adult mouse brain (www. mouse. brain-map. org). Recently, the open Marmoset Gene Atlas (https://gene-atlas. brainminds. jp/) established a genome-wide, high-resolution atlas of the gene expression throughout the common marmoset (Callithrix jacchus) brain.
Auts2 expression in developing and adult mouse brain We took advantage of public databases to analyze developing mouse brain (Genepaint (https://gp3. mpg. de/)) and adult mouse brain (Allen Brain Atlas at www. mouse. brainmap. org). ). Auts2 expression is observed in multiple areas of E14. 5 mouse brain, including neocortex, hippocampus, dorsal thalamus, Septum, Striatum, Olfactory epithelium, Hypothalamus, Tegmentum, cerebellum and medulla oblongata (Fig. 2 ). Interestingly, concerning the expression of Auts2 in the tegmentum, it was demonstrated that virtually all of the dopaminergic TH positive neurons, that are located in tegmentum, in particular in Subtantia Nigra (SN) and Ventral Tegmental area (VTA) express Auts2 21. Interestingly, SN and VTA are located at the posterior region of the tegmentum, at the junction between midbrain and pons (Fig.
Transcription factor sites analysis Neanderthal sequences were downloaded from "UCSC Neanderthal portal". We used Neanderthal contigs (mixed of 6 specimens sequenced). We selected fragments aligned on hg18 reference and analyzed only human sequences which had Neanderthal homologues. The analysis was conducted on sequences having exactly the same number of human and Neanderthal nucleotides. We obtained 12,437 pairs containing 1,212,584 nt for each species. We excluded from the analysis pairs which were identical between human and Neanderthal. This procedure reduced the dataset to 7,193 sequence pairs containing 797,226 nt (r1 254,125 nt, r2 241,463 nt, r3 301,638 nt) AUTS2 locus was analyzed with the genomatix matinspector program for transcription factor sites discovery with stringent conditions (core sim = 0.
ACKNOWLEDGEMENTS We thank  Tanya Pekar ,  Ying Wang ,  Yan Li ,  Maria Lim  and  Yangning Lu  for technical support and discussions; We thank open exchange of unpublished observations with the Ma and Gao groups. We Thank  Zhaowen Wang  for the unc-7 zwIs141 allele. Jun Meng  personally thank her cat, Dahuang. This work was supported by the  Canadian Institutes of Health Research Foundation Scheme 154274  (MZ) and  European Research Council  ( ERC Starting Grant , Kelegans) (TB).
Funding: This work was conducted within the framework of the  LabEx CeLyA  ('' Centre Lyonnais d'Acoustique ",  ANR-10-LABX-0060 ) of  Université de Lyon , within the program '' Investissements d'avenir"  ( ANR-16-IDEX-0005 ) operated by the  French National Research Agency (ANR). This work was funded by a " Pack Ambition Recherche"  ( COGAUDYS  project) from the  Auvergne-Rhône-Alpes Region , awarded to  A. Caclin ,  A. Moulin ,  L. Ferreri , and  B. Tillmann.
Acknowledgements The authors were supported by  NIH  grant  R01 NS120954 ,  R56 MH113634 , and  R01 DC013803. Data were 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.
Acknowledgments We thank the TRiP consortium at  Harvard Medical School  ( NIH/NIGMS   R01-GM084947 ) and the BDSC and VDRC fly stock centers for providing transgenic RNAi fly stocks, and  Eloïse de Trédern  for help in subcloning the mito-δCKAR plasmid. We are also grateful to  Dr Jaime de Juan-Sanz  for insightful comments on the manuscript. The authors acknowledge funding from the  European Research Council  ( ERC Advanced Grant   EnergyMemo n°741550 , to T. P. ), from the  Agence Nationale de la Recherche  (ANR, to P. -Y. P) and from  DIM ELICIT 's grant from  Région Ile-de-France  (equipment grant, to P. -Y. P). T. C. was funded by a doctoral fellowship from the  French Ministry of Research.