dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis of Behavioral Measures One-way ANOVA (performed in SPSS 23) between seniors, adults, and adolescents was conducted to examine group difference on cognitive functions (including working memory, depressive symptoms, and level of perceived stress) as well as task performance (including identification accuracy and counting errors). To further explore the relation between cognitive functions and EEG, we conducted a partial correlation between working memory and the size of local and global effects, where participants' scores on CES-D and PSS were controlled for to remove the potential influences of psychological factors (i. e. , depressive symptoms and level of perceived stress) on cognitive functions. We also conducted a stepwise multiple linear regression to evaluate whether all scores on working memory subtests were necessary to predict the size of local and global effects.
Although, we have only illustrated the use of our toolbox with neocortical surface data, the gradient-based functionality of BrainSpace is not restricted to these regions and can also be used with other datasets (e. g. , hippocampal measures, subcortical measures, and volumetric data). However, as of version 0. 1, visualization is only available for surface meshes. Extending the visualization functionality of BrainSpace to support other cerebral structures and volumetric data is a promising line of future work.
, 2016). The analgesic effect was found significantly better in the real rTMS group than both sham stimulation and physical therapy groups. The beneficial effect of real rTMS lasted up to 9 months using maintenance sessions, first every two weeks, then every two months. The main two rTMS targets in the pain domain, i. e. M1 and DLPFC, were also investigated in migraine and headache disorders. Since a first open-label study performed in 51 migraineurs  (Misra et al. , 2012) , one group repeatedly report sham-controlled data in favor of the beneficial effect in migraine of series of 3 HF-rTMS sessions delivered to the left M1 with a protocol similar to that is classically used in neuropathic pain  (Misra et al. , 2013; Kalita et al. , 2016). In addition, the clinical improvement was associated with an increase in beta-endorphin plasma level  (Misra et al.
Acknowledgments Dr. Filipovic  ´is supported by a grant ( OI175012 ) from the  Ministry of Education, Science and Technological Development of Republic of Serbia. Dr. Oliveira-Maia  is supported by grants  FCT-PTDC/ME C-PSQ/30302/2017-IC&DT-LISBOA-01-0145-FEDER  and  FCT-PTDC/ MED-NEU/31331/2017 , funded by national funds from  Fundação para a Ciência e Tecnologia (FCT/MCTES -) , with the former cofunded by  FEDER , under the  Partnership Agreement Lisboa 2020 -Programa Operacional Regional de Lisboa. Dr. Rektorová  is sup-ported by a grant ( AZV 16-31868A ) from the  Czech Ministry of Health.
| Outcomes The primary end point was the safety of M101, which we calculated by collecting all adverse events (AEs) (serious or not) within the first 3 months and all serious adverse events (SAEs) occurring between 3 and 12 months. Graft biopsies (preimplantation and at 3 months) were routinely performed in the 6 participating centers and classified locally according to the Banff classification. 13 condary efficacy end points included patient survival and graft survival, renal function (creatinine level and estimated glomerular filtration rate based on Modification of Diet in Renal Disease formula  14  ), and acute rejection. Different markers of delayed graft function (DGF) were also considered: at least one dialysis in the first week, more than one dialysis in the first week, the number of dialysis procedures per patient in the first month, and the number of days required to achieve a creatinine level lower than 250 μmol/L (a marker routinely used in the French organ transplant data system).
According to the French authorities (Agence Nationale pour la Sécurité des Médicaments), all the patients on the waiting list in the participating centers received an information letter on the study design and procedures. Additionally, before transplantation, the recipients signed a consent form. The study was conducted in full compliance with the amended Declaration of Helsinki and with the Harmonized Tripartite Guideline for Good Clinical Practice in the European Community (CPMP/ICH/135/95) and was approved by an independent ethics committee and other relevant authorities (EUDRACT 2015-A00818-41). The data were monitored by an independent data and safety monitoring board (IDSMB) of three independent experts. After the 10th transplantation, a stop-and-go procedure was applied, and the inclusions were temporarily suspended until the IDSMB authorized the continuation of recruitment.
Participants We here re-analyze neuropsychological and fMRI data previously reported by  Wirsich et al. (2018). Within the framework of a work-package of the EU PharmaCog Innovative Medicine Initiative (http s://www. imi. europa. eu/projects-results/project-factsheets/pharma -cog), fifteen healthy subjects were recruited (all male, 25-40 years old, Body Mass Index, BMI 27, right-handed) to participate in an experiment distributed over ten different days in blocks of two days each. Before inclusion, volunteers were checked for their medical history, physical examination, vital signs, results of blood chemistry and haematology, urine drug screen, electrocardiogram (ECG) and their visual and auditory abilities. Volunteers who received medical treatment, smoked more than five cigarettes per day, took more than five caffeine or energy drinks per day or suffered from claustrophobia were not included.
In the original experiment, some of the subjects were administered doses of various neuroenhancer drugs prior to the fMRI scan and cognitive testing. For more details, see  Wirsich et al. (2018)  and Chan  Kwong et al. (2020). Here, however, we prefer to focus uniquely on the placebo sessions, being more interested in statistical comparisons between dFC speed in different modules, than comparisons between dFC speed in different medication conditions. The small number of subjects does not provide sufficient statistical power to study all possible comparisons (modules and drugs). Therefore, for homogeneity, we consider data acquired during the placebo sessions within the first block of two days. Subjects were assessed twice, once before and once after SD with one session of rs fMRI and cognitive tests.
fMRI data recording and processing For a detailed description on fMRI data acquisition, see  Wirsich et al. (2018). All images were acquired on a Siemens Magnetom Verio 3T MRI-Scanner (Siemens, Erlangen, Germany) with structural T 1 -weighted images acquired using a MPRAGE-sequence (TR ¼ 2300 ms, TE ¼ 2. 98 ms, 1. 0 Â 1. 0 Â 1. 0 mm, 176 slices), and 200 BOLD-sensitized EPI T2*-weighted MRI images acquired using a TR of 2. 7 s (3. 0 Â 3. 0 Â 3. 0 mm, TE ¼ 30 ms, 40 slices, acquisition time of 9 min 5 s). To process fMRI data we used the SPM8 toolbox (revision 4667, http://www. fil. ion. ucl. ac. uk. gate2. inist. fr/spm/software/spm8/) to slice time and spatially realign the volumes. The AAL atlas template (N ¼ 86 regions, see Fig. 4B  for the list of enclosed regions (left and right hemisphere;  Tzourio-Mazoyer et al.
Simultaneous EEG 64 electrode EEG-cap (BrainCap-MR 3-0, Easycap, Hersching, Germany, using 63 channels in the 10-20 montage plus one extra ECG channel, reference placed on the mid-frontal position FCz) was acquired during fMRI sessions as described by  Wirsich et al. (2018). EEG data was manually sleep staged for consecutive 30 s blocks by an expert (Awake, N1, N2, N3, according to the AASM sleep scoring scheme, http://www. aas mnet. org/). The acquired data was then manually sleep staged for consecutive 30 s blocks by an expert (Awake, N1, N2, N3, according to the AASM sleep scoring scheme, http://www. aasmnet. org/).
Control resting state fMRI dataset To verify that the key results of our analyses did not apply just to our specific dataset, we also repeated some of the analyses on an independent control dataset, selected from rs-fMRI data released as part of the Human Connectome Project (HCP), WU-Minn Consortium. We used the same selected subjects used for statistical benchmarking of functional network analyses in  Termenon et al. (2016). This sample includes 100 young healthy adults aged 20-35 years (54 females). Each subject underwent two rs-fMRI acquisitions on different days and here, not being interested in test-retesting issue, we used only data from the first day sessions. For this sessions, TR ¼ 720 ms and resting state scan duration was of 14 min and 24 s. The same parcellation as for the SD dataset was used.
2  and S8 we introduced a notion of similarity between FC(t) matrices following  (Hansen et al. , 2015) , based on the Pearson correlation between the entries of their upper-triangular parts: dFC(t 1 , t 2 ) ¼ Corr[UpperTri(FC(t 1 )), UpperTri(FC(t 2 ))] The dFC matrices thus depend on the window-size W adopted when evaluating the stream of time-dependent FC(t)'s. To perform the twodimensional projections of the sequence of FC(t) matrices in Fig. 2B  we fed the vectors UpperTri(FC(t)) as input features into a t-Stochastic Neighborhood Embedding algorithm (exact method) as described by  Hinton and Van der Maaten (2008) , using default perplexity ¼ 30 and exaggeration ¼ 4 parameters in the used MATLAB® (MathWorks R2017b) implementation.
Extraction of dFC modules Packaged in the redundant format described, the MC matrix can be considered as the adjacency matrix of a graph whose nodes are FC links. Thus, communities of temporally co-varying FC links can simply be evaluated by applying any algorithm for node-community detection to the MC matrix. We thus extracted modules by applying a standard Louvain algorithm on the group-averaged MC matrix (before SD), adapted to weighed undirected graphs with mixed positive and negative entries ("symmetric treatment" in the Louvain method's implementation in the Brain Connectivity Toolbox by  Rubinov and Sporns (2010) ). The Louvain method has one adjustable parameter whose default value is Γ ¼ 1. By taking larger (or smaller) values, the algorithm will tend to return modular decompositions with a larger (smaller) number of modules.
e. restricting the MC matrix to include only rows and columns corresponding to links belonging to the considered modulefor both modular decompositions M 1 and M 2. Finally, we correlated the sequence of meta-strengths for equally-labeled modules in M 1 and M 2 , to assess how similar modules were in terms of their meta-hubs and node ranking in terms of metastrengths. The large inter-decomposition meta-strength correlations reported in S9A for all modules confirm that our choice of decomposing MC into 5 modules leads to robustly preserved modules (and their associated meta-hubs). To construct Figure  S5E , we extracted 500 alternative modular decompositions of the MC matrix mediated from the control HCP dataset and maximally aligned each of them to the reference modular decomposition of Fig.
EEG data was manually sleep-staged for consecutive 30 s blocks by an expert (Awake, N1, N2, N3, according to the AASM sleep scoring scheme, http ://www. aasmnet. org/). Although some of the subjects displayed transient sleep episodes, especially after the stress of SD (see Materials and Methods for subject inclusion and rejection criteria), we observed that dFC network reconfigurations did not stop during sleep but were continually ongoing at a non-vanishing speed. In particularmost likely because of the limited amount of datawe could not find a systematic   S3. impact of transiently entering into sleep stages and the concurrently measured dFC speeds. In Figure  S2A , we show that median dFC speeds tended to slow down at the single-subject level during sleep stage N1 with respect to awake state, however, this effect was only tendentially significant for a few window-sizes belonging to the long window sizes range.
A third dFC module (dFC #3) was organized around highly dominant meta-hubs in precentral gyrus, fusiform gyrus and precuneus. A fourth module (dFC #4) was mainly dominated by meta-hubs situated in inferior frontal gyri (pars opercularis, pars triangularis and pars orbitalis), rolandic operculum, rectus gyrus and insulae as well as, shared with the second dFC module, superior, medial and middle frontal gyrus. Finally, a fifth module (dFC #5) was centered on meta-hubs, again, within superior frontal gyrus, superior, middle and inferior frontal gyri (orbital parts) as well as medial orbitofrontal cortices. Importantly, we could confirm the existence of an analogous modular structure in the resting state MC matrix extracted from a completely independent control dataset (see Materials and Methods).
In  Faskowitz et al. (2019)  and even more in  Betzel et al. (2019) , edge-centric functional connectivities (eFCs) were already used as a way to seize the spatiotemporal complexity of regional co-activation dynamics. Here, dFC as a stochastic exploration of possible functional network configurations provides a transparent geometric interpretation of a sophisticate statistical analysis. Distinct dFC modules in the MC matrix can indeed be seen as being associated to distinct walkers in FC space, performing simultaneously independent random walks. Remarkably, these modules were very robust and largely overlapping between our dataset and MC extracted from an independent control dataset (Figure  S5C -E;  Termenon et al. , 2016). Furthermore, these modules were also evident in the eFC matrix of figure S5B, but MC analysis revealed them even more clearly.
Acknowledgements DB acknowledges support from the  Mission for Interdisciplinarity of the CNRS  ( Infiniti program 2017-2018, "BrainTime" ) and from the  EU Innovative Training Network "i-CONN"  ( H2020 ITN 859937 ). DL has been funded by the  Agency of Research of innovation (ANII, Uruguay , grant  POS EXT 2015 1 123495 ). VJ acknowledges funding by the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under the  Specific Grant  Agreement No. 785907  ( Human Brain  Project  SGA2 ). Experimental results have been obtained within the  PHARMACOG consortium  funded by the  European Community's Seventh Framework Program for the Innovative Medicine Initiative  (Grant Agreement no. 115009 ).
| TMS parameters Biphasic TMS pulses induced an anteroposterior followed by posteroanterior current in the brain (AP-PA) using a MagPro Cool B65-RO butterfly coil (MagVenture A/S, Denmark) plugged in a MagPro ×100 TMS stimulator (MagVenture A/S). The coil was positioned and hold by a TMS robot (Axilum Robotics, France), navigated using Localite neuronavigation software (Localite GmbH). EMG electrodes were placed in a tendon-belly montage over the FDI. MEPs were recorded using a Dantec Keypoint portable EMG recording system (Natus Medical Inc. ) and a CED micro 1401 MKII recording system (Digitimer, Cambridge Electronic Design, Cambridge, UK) for the six last subjects. The rMT assessment was performed using the threshold hunting method  (Awiszus, 2003)  over the hotspot. The coil was positioned tangentially to the scalp surface in a posterior to anterior direction angled perpendicular to the central sulcus for M1.
| EEG preprocessing EEG signals were processed using Fieldtrip  (Oostenveld, Fries, Maris, & Schoffelen, 2011)  and Brainstorm3  (Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011)  software, and other custom scripts written in MATLAB (The MathWorks Inc. ). EEG signals were preprocessed semiautomatically based on the methodology described in  Rogasch et al. (2014) , for each condition (three targets, six conditions, and one sham) and each subject. First, the channels showing electrical noise (flat signal or peak-to-peak amplitude superior to 100 μV) spanning more than 15% of the trials were discarded from the analysis (on average, 1. 4 ± 3. 2 channels per condition). EEG signals were then epoched around the TMS pulse, using a -1 to +1 s time window of interest. TMS artifacts were discarded by cutting out the -5 to +17 ms period surrounding the TMS pulses.
Two rounds of independent component analysis (ICA) were then applied in order to remove noise remaining in the signal. The first ICA suppressed the muscle artifacts, while the second ICA aimed at removing the decay artifact, ocular activity, auditoryevoked potentials, and other noise-related artifacts  (Rogasch et al. , 2014). Before the second ICA, the signal was spline interpolated over the -5 to +17 ms period, band-pass filtered (1-80 Hz), re-referenced using the average reference, and cleaned from bad trials (leading to a mean of 73. 9 ± 9. 7 trials left per condition). The ocular components were automatically identified using a threshold of 0. 7 on the correlation product ρ between the spatial topographies of the components and a template of typical horizontal eye movements and blinks build from our own database by averaging over subjects.
Other artifact components (decay, auditory-evoked potentials, and other noises) were detected by thresholding the z-score (above 4) of their mean activity against the prestimulus period, and by visual inspection. On average, 9 (±4. 2) components were removed from the signal. Cleaned EEG time series were reconstructed using the remaining components and any isolated channel still showing remaining noise was discarded from further analysis. Time series of rejected channels were finally inferred using the activity averaged over their neighboring channels (see  Harquel et al. , 2016, figure   3 , for an illustration of the main preprocessing steps). Additionally, for the realistic sham data that are affected by a pronounced decay artifact, we applied a decay subtraction procedure between the two rounds of ICA (adapted from  Conde et al.
, 2019). Briefly, this procedure consists in subtracting the best fit of a twoexponential function from each trial of each channel. We used the nlinfit() function from MATLAB to estimate the five coefficients of the following regression function: A × exp(B × x) + C × exp(D × x) + E, with x being the time series of a specific trial and channel. Since the timing of the decay varies across conditions and channels, the fitting was optimized by processing it on increasing time window widths, from 200 to 800 ms by step of 100 ms. The width minimizing the mean squared error between the actual signal and the fitting function during the whole period of interest (0-1,000 ms) was taken.
Source reconstruction for each nonnormalized TEP was performed following the default procedure proposed in Brainstorm 3 software  (Tadel et al. , 2011). First, the cortex and head meshes (15,000 and 10,000 vertices, respectively) of each individual were generated using the automated MRI segmentation routine of FreeSurfer  (Reuter, Schmansky, Rosas, & Fischl, 2012). The locations of EEG electrodes were coregistered on each subject's anatomical MRI. The forward model was then computed using the symmetric boundary element method developed in the open MEEG freeware, using default values for conductivity and layer thickness  (Gramfort, Papadopoulo, Olivi, & Clerc, 2010). The full noise covariance matrix was then computed for each subject using the temporal concatenation of the baseline periods of all conditions.
M is of size [N i NK], where N i is the number of intensities (6), N is the number of time bins (451), and K is the number of subjects (  22 ). The matrix M was thus decomposed into N i (6) independent components (data dimension) using the logistic infomax ICA algorithm  (Bell & Sejnowski, 1995)  with the natural gradient feature from Amari, Cichocki, and Yang as implemented in EEGLab  (Makeig, Bell, Jung, & Sejnowski, 1996). Finally, the dynamical signature of each component was assessed in each individual by means of its time/frequency (TF) decomposition obtained using Morlet wavelet transform between 7 and 45 Hz (window width of 7 cycles, 0. 5 Hz bandwidth). Individual TF power maps were normalized (z-score against baseline) and averaged across subjects.
| Local neural activity scales with stimulation intensity LSA recorded in the three regions with the five intensities showed an overall dose-dependency relationship. This finding replicates previous TMS-EEG or TMS-fMRI studies, either stimulating the primary motor cortex  (Bohning et al. , 1999; Komssi et al. , 2004; Komssi et al. , 2007)  or the left middle frontal gyrus  (Kähkönen et al. , 2005a). The earlier TMS-EEG studies have intrinsic limitations in terms of data acquisition and analysis, precluding a full understanding of the input-output response function to TMS. Although ROI analyses in the source space have been criticized  (Farahibozorg et al. , 2018) , the LSA profiles computed from each of the three ROIs encompassing the stimulated area, allowed us to distinguish complex shapes of biological input-output relationships.
This might reflect saturation in synchronous activation of local neurons involved in the generation of these components. Comparing M1 and DLPFC,  Kähkönen et al. (2005b)  found different reactivities of motor and prefrontal cortices and different dynamics. A quadratic polynomial function described the data of motor cortex, whereas a linear model fitted better for the response-stimulus intensity function of prefrontal TMS. We also found that the dynamic of the input/output functions differed across the three regions, suggesting different dynamical properties of neuronal responses. This provides additional evidence that TMS-EEG can noninvasively probe regional differences in cortical microcircuits underlying functional cytoarchitecture  (Harquel et al. , 2016). Of interest, a recent study investigated the effect of stimulation intensities of intermittent theta burst stimulation on the cortical properties assessed with TMS-EEG.
ACKNOWLEDGMENTS This work was funded by the  Agence Nationale pour la Recherche  grant " ANR-15-CE37-0015-01 " and by  NeuroCoG IDEX UGA  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ). Data were acquired on a platform of  France Life Imaging Network partly funded by the grant  " ANR-11-INBS-0006. ".
The user's feedback was taken into consideration when working on the design of a low-cost customized bionic arm. The user was heavily involved in the mechanical design phase of the bionic arm. The prosthetic arm presented in this work is controlled by a multi-channel sEMG sensor that is used to acquire the muscles' activities. The muscles' activities represent different gestures, which are used to perform the required action by the hand attached to the arm. The hand has 9 degrees of freedom (DOF), which enable it to perform different accurate actions as per the user's demand. The schematic chart illustrating the steps required to control the bionic arm is shown in Figure  2. A database of sEMG gestures is created with all of the units associated with signal processing, feature recognition, and machine learning to catalog the signals that are required for movements of the finger actuators in the bionic hand to perform specific hand postures.
Multi-Channel Wearable Armband Different wearable sensor systems are available in the market for acquiring different bio-signals, such as electromyography (EMG), electroencephalography (EEG), electrodermal activity (EDA), and electrocardiogram (ECG)  [20]. The Myo armband is a wearable armband used primarily for Human Computer Interaction (HCI) applications, where humans can deal with computers and systems in an interactive way. The structure of Myo consists of eight EMG (electromyography) sensors and an inertial measurement unit (IMU), which consists of a gyroscope, accelerometer, and a magnetometer  [21]. Due to the engineered design of the multi-channel wearable armband, the user wears it in the small portion of the forearm below the elbow. Myo is used for detecting the different gestures and movements that are applied for sEMG database collection.