dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011). A third-order spatial gradient noise cancellation is applied to the data. The recorded signal is rejected when head movements exceed the threshold of +/-1cm around the median position. Sensor jumps (> 10 picoTesla) are detected and corrected with custom software. Blinks, saccades, and heartbeats are removed from the signal thanks to an Independent Component Analysis (ICA), using eeglab MATLAB toolbox  (Delorme & Makeig, 2004). This ICA is computed on band-pass filtered signal (0. 5-45Hz). Eye-movements and heartbeatrelated components are determined by visual inspection of component topographies and time courses and removed through an ICA inverse transformation applied to the non-filtered signal. The ICA-corrected signals are then band-stop-filtered between 47 and 53 Hz, 97 and 103 Hz, and 147 and 153 Hz (zero-phase shift Butterworth filter, order 3) to remove power-line artifacts.
ERF source reconstruction Segmentation of the individual T1-weighted MRIs is conducted using the FreeSurfer software package  (Fischl, 2012; http://surfer. nmr. mgh. harvard. edu). Segmentation is visually inspected and then imported (15,002 vertices) in the Brainstorm toolbox  (version 3. 191209 (09-Dec-2019)  on Matlab R2015a;  Tadel et al. , 2011 ; http://neuroimage. usc. edu/brainstorm) with which further source analyses are conducted. The white matter-grey matter boundary segmented by FreeSurfer is used as a source space for subsequent weighted minimum norm estimation. A noise covariance matrix is computed on resting state data, which has been preprocessed with the same pipeline as data collected during task performance. After coregistration between the individual anatomy and MEG sensors, the forward model is computed with the OpenMEEG software  (Gramfort et al.
, 2010 ; https://openmeeg. github. io/). Cortical currents are estimated using a distributed model consisting of 15,002 currents dipoles from the time series of the 275 gradiometer signals, using a linear inverse estimator (weighted minimum-norm current estimate, signal-to-noise ratio of 3, whitening PCA, depth weighting of 0. 5). Sources orientations are constrained to be orthogonal to the grey-white matter boundary of the individual MRIs. The results are then projected on a standard brain model anatomy (ICBM152; 15,002 vertices) for group analysis purposes.
TF at sensor-level To investigate the dynamics of theta and alpha power during the task, a time-frequency analysis is performed using the Fieldtrip toolbox (v20190626;  Oostenveld et al. , 2011)  on Matlab R2017A. Time-frequency analysis is computed from -2500 to +8000ms relative to S1 onset, using Morlet wavelet decomposition with a width of four cycles per wavelet (m=7; Tallon-Baudry & Bertrand, 1999), for frequencies from 4 to 20Hz, with steps of 1Hz and 50ms. For each single trial, the corresponding ERF is subtracted before wavelet decomposition to focus on induced (and not evoked) activity. The relative change in power is then obtained by subtracting then dividing each power value with the power in a pre-cue baseline period (-1800 to -1000 ms relative to S1 onset) in the corresponding frequency range.
TF: head model For source reconstruction, the processing of the T1 MRI is performed using CTF software (CTF Systems Inc. , VSM Medtech Inc. , Vancouver, Canada). For each participant, an anatomically realistic single shell headmodel based on the cortical surface is generated from individual head shapes  (Nolte, 2003)  and imported in the Fieldtrip toolbox (v20190626;  Oostenveld et al. , 2011). Then, a grid with 0. 5-cm resolution is normalized on a MNI template and morphed into the brain volume of each participant.
We would like to thank  Lesly Fornoni  and  Anne Cheylus  for their technical support. Funding: This work was supported by a grant from  " Agence Nationale de la Recherche" (ANR)  of the  French Ministry of Research   ANR-14-CE30-0001-01 (attributed to ABC). 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). CC-BY-NC-ND 4. ) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made.
Food Reinforcement Apparatus and training procedure Experimental chambers (MedAssociates, www. medassociates. com) were enclosed individually in sound-attenuation chests. Each experimental chamber had a recessed food tray, and two levers in the right wall. The floor was constituted by bars that were connected to shockers that could deliver foot shock, with electric current set to 0. 45 mA. Each chamber was equipped with a food-pellet dispenser, which could deliver 45 mg pellets to the food tray. Experimental events were controlled by computers using MedAssociates interface and Med-PC IV software; Med-PC code used to conduct the procedures is available upon request. A diode light was present on each lever. One lever was assigned to be the active lever and the corresponding light was used as a conditioned stimulus for food reinforcement.
Anxiety-related behaviors: Elevated plus maze procedure The elevated plus maze (Viewpoint, Lyon, France) was composed of 4 arms (50 cm long * 10 cm wide at 42,5cm from the floor). Two arms had no walls (considered as open arms) and two had black walls on its edges (considered as closed arms). Each arm was placed on the opposite side of its copy separated by a central square (10*10 cm) in the middle giving access to all the arms. Rats position was recorded automatically and in real time by a camera and video tracking software (Viewpoint, Lyon, France). The software defined 4 virtual areas delimiting with a center zone. Anxiety was measured as the time spent in open arms so that more time spent in open arms indicated a lower level of anxiety.
Statistical Analysis Data were analyzed in GraphPad Prism. Data were checked for normality distribution using the Shapiro-Wilk test. When the assumption of sphericity had been violated, data were analyzed using analysis of variance (ANOVA) corrected by the Greenhouse-Geisser method. Data Weight gain and food consumption were assessed by two-way ANOVA for repeated measures followed by Sidak's multiple comparisons post hoc test. PSS data were normalized using natural logarithm transformation as previously done  (Desmercieres et al. , 2022). For baseline measures of PSS and PR break points, we used two-way ANOVA for repeated measures with time and diet as factors followed by Sidak's multiple comparisons post hoc test. Anxiety level and pain sensibility were assessed with a Mann-Whitney test because Shapiro-Wilk test revealed that distribution of data was not normal.
Single-Particle Tracking and Analysis Cells were imaged using an Olympus IX71 inverted microscope equipped with a 60X objective (NA 1. 42; Olympus, Rungis, France) and a 120 W Mercury lamp (X-Cite 120Q, Lumen Dynamics, Zaandam, The Netherlands). Individual images of gephyrin-YFP and homer1c-DsRed, and QD real-time recordings (integration time of 30 ms over 1200 consecutive frames) were acquired with an ImagEM EMCCD (Hamamatsu, Massy, France) camera and MetaView software (Meta Imaging 7. 7). The cells were imaged within 45 min following appropriate drug pre-incubation. QD tracking and trajectory reconstruction were performed with homemade software (Matlab R2017a; The Mathworks, Natick, MA, USA) as described in  [27]. One to two sub-regions of dendrites were quantified per cell. In cases of QD crossing, the trajectories were discarded from the analysis.
Fluorescence Image Acquisition and Analysis The sets of neurons compared for quantification were imaged simultaneously. Image acquisition was performed using a ×100 objective (NA 1. 40) on a Leica (Nussloch, Germany) DM6000 upright epifluorescence microscope with a 12-bit cooled CCD camera (Micromax, Roper Scientific, Evry, France) run using MetaMorph software Series 7. 8 (Roper Scientific). Quantification was performed using MetaMorph software (Roper Scientific) (Figure  S1 ). To assess NKCC1 and KCC2 clusters, the exposure time was fixed at a non-saturating level and kept unchanged between cells and conditions. For each neuron, a well-focused dendrite was chosen, and an ROI surrounding the dendrite was manually selected. Only primary dendrites were considered for the cluster analysis. The region of interest was started after the cell body and the dendrite of ~20-130 µm length were considered.
Then, NKCC1a and NKCC1b trajectories were reconstructed with homemade software, and the diffusion coefficient and the size of the explored area were extracted for each trajectory from the mean square displacement (MSD) plot versus time function. The observation of individual trajectories showed that NKCC1a and NKCC1b explored a large surface area of the somato-dendritic membrane (Figure  6A ). A quantitative analysis performed on the whole (synaptic + extrasynaptic) population of trajectories revealed that the median value of the diffusion coefficient (Figure  6B ) and the median value of the explored area (Figure  6C ) were not significantly different between NKCC1a and NKCC1b, indicating similar diffusion properties. In comparison with KCC2 trajectories, we found that NKCC1a and NKCC1b trajectories were longer and extended over a larger area of the membrane (Figure  6A ), suggesting reduced diffusion constraints for NKCC1a and NKCC1b in the plasma membrane.
This was confirmed by the quantitative analysis showing a greater diffusion coefficient (Figure  6D ) and explored area (Figure  6E ) for NKCC1a than for KCC2 on the dendritic surface. Our data indicate that NKCC1a and NKCC1b are less restricted in their movement in the We first compared the membrane dynamics of NKCC1a and NKCC1b using the quantum-dot-based single-particle tracking (QD-SPT) technique in mature hippocampal neurons. To this end, neurons were transfected at DIV 14 with recombinant HA-tagged NKCC1a or NKCC1b and stained live at DIV 21-24 with a QD-coupled anti-HA antibody, and real-time imaging was performed (see Section 2). Then, NKCC1a and NKCC1b trajectories were reconstructed with homemade software, and the diffusion coefficient and the size of the explored area were extracted for each trajectory from the mean square displacement (MSD) plot versus time function.
Human neuromagnetic activity is characterised by a complex combination of transient bursts with varying spatial and temporal characteristics. The characteristics of these transient bursts change during task performance and normal ageing in ways that can inform about underlying cortical sources. Many methods have been proposed to detect transient bursts, with the most successful ones being those that employ multi-channel, data-driven approaches to minimize bias in the detection procedure. There has been little research, however, into the application of these data-driven methods to large datasets for group-level analyses. In the current work, we apply a data-driven convolutional dictionary learning (CDL) approach to detect neuromagnetic transient bursts in a large group of healthy participants from the Cam-CAN dataset.
CDL was used to extract repeating spatiotemporal motifs in 538 participants between the ages of 18-88 during a sensorimotor task. Motifs were then clustered across participants based on similarity, and relevant task-related clusters were analysed for age-related trends in their spatiotemporal characteristics. Seven task-related motifs resembling known transient burst types were identified through this analysis, including beta, mu, and alpha type bursts. All burst types showed positive trends in their activation levels with age that could be explained by increasing burst rate with age. This work validated the data-driven CDL approach for transient burst detection on a large dataset and identified robust information about the complex characteristics of human brain signals and how they change with age.
With the increasing interest in transient burst-based analyses has come a surge in development of analysis methods for detecting and characterising bursts. At present, there is no gold-standard method for detecting and characterising transient bursts in electrophysiological data, and each proposed method comes with associated advantages and limitations. In addition, there is no obvious framework to complete a grouplevel analysis of the combined spatial and temporal characteristics of identified bursts and most techniques have not been optimized for use with large datasets. The simplest, and most commonly used burst detection method uses amplitude thresholding to detect bursts of high power activity within a pre-defined frequency range of interest. This method, popularized by  Shin et al. (2017)  defines bursts as local maxima in the time-frequency representation that exceed a pre-set power threshold (multiple of the median power) and fall within a pre-defined frequency range  ( Brady et al.
CDL has been previously validated on single subject datasets to recover biological artifacts, non-sinusoidal mu patterns with sensorimotor topography, occipital alpha bursts, and evoked-type responses  ( Allain et al. , 2021; Dupré la Tour et al. , 2018 ). Despite the success of CDL and other data-driven methods on single subject studies, the validity of multi-channel, data-driven methods for use in between-subject comparisons and group-level analyses has been largely unexplored. Some work has employed HMM extended to a multisubject setting by concatenating data across participants to identify common repeating states  ( Baker et al. , 2014; Becker et al. , 2020; Quinn et al. , 2018; Seedat et al. , 2020; Vidaurre et al. , 2016 ) , and few studies have demonstrated that ICA and EEG Microstates yield consistent patterns across participants  ( Himberg et al.
, 2004; Michel and Koenig, 2018 ). However, none of these studies explored variability between subjects, and group-level differences and trends, highlighting the need for methods to explore group-level trends in transient bursts detected by a robust data-driven method. The favourable characteristics and promising preliminary results of the CDL method make it a logical candidate for group-level investigations of transient bursts. The objective of the current work is thus to use the CDL method to detect and characterise ageing trends in task-related transient bursts at the group level in a large, open-access dataset. Here, we detect (in single subjects) repeating spatio-temporal atoms in sensorimotor MEG data from the Cam-CAN dataset  ( Shafto et al. , 2014; Taylor et al. , 2017 ) , and cluster similar atoms across participants to allow for grouplevel analysis.
, 2019; Brady et al. , 2020 ). This work presents, for the first time, the detection of group-level trends in transient bursts using a flexible, multi-channel, data-driven CDL method. By combining this powerful detection algorithm with the big data available in the Cam-CAN dataset, we can increase our understanding of the role of neuromagnetic transients in normal healthy ageing and provide an improved framework for analysing transient bursts at the group level in future work.
Participants and experimental paradigm MEG data were collected from 650 participants in Phase 2 of the Cam-CAN examination of healthy cognitive ageing. Participant ages ranged from 18 to 88 years of age, with an equal distribution in age per decile and equal proportions of males and females. All participants provided written, informed consent prior to participating in each phase of the study. The study was conducted in compliance with the Declaration of Helsinki and data collection was approved by local ethics boards  ( Shafto et al. , 2014 ). In the current work, we report findings from 563 participants (86. 6% of the original 650 datasets) who had sufficient MEG and anatomical MRI data required for localization. Participants who did not have anatomical MRI data were excluded from analysis to ensure consistent localization procedures were applied across all included participants.
Data acquisition Data were obtained from the Cam-CAN dataset  ( Shafto et al. , 2014; Taylor et al. , 2017 )   1  MEG data were acquired at 1000Hz with in- line band-pass filtering between 0. 03 and 330 Hz using a 306-channel Vectorview system with continuous head position monitoring  (Elekta Neuromag, Helsinki, Finland). Digitization of anatomical landmarks ( i. e. fiducial points; nasion and left/right preauricular point) and additional points on the scalp was also performed for co-registration of MEG and MRI coordinate systems. Electrooculogram (EOG) and electrocardiogram (ECG) were recorded concurrently. T1-weighted magnetic resonance images (MRI) were acquired using a 3T Siemens Tim Trio system with a 32-channel head coil.
The bottom row on the left corresponds to stimuli events (visual or auditory). Each atom on the right can be associated with a response type (auditory, visual, heartbeat and eye blinks). cal landmarks ( i. e. fiducial points) and additional head points. MEG head digitization data was aligned to the MRI using a semi-automated process (MNE-Python coreg, v. 0. 14) described in  Bardouille et al. (2019). Coregistration data are available at https://github. com/tbardouille/ camcan _ coreg. Participants missing MRI data were excluded from further analyses.
MEG data processing All MEG processing was completed in the Python programming environment (v. 3. 7. 7), using the MNE-Python library (v. 0. 23. 0)  ( Gramfort et al. , 2013; 2014 ). Python code for this analysis is available at https: //github. com/tbardouille/camcan _ CSC _ beta. Data were pre-processed using temporal signal space separation (tSSS) to perform environmental noise reduction, and reconstruction of missing or corrupted MEG channels  ( Taulu and Simola, 2006 ). The task data was then parsed into trials synchronized to each button press, with a duration of 3. 4s, including a 1. 7s pre-movement interval. The 3. 4s window length was selected to ensure a sufficient post-movement interval to capture the entire beta rebound response. Trials were excluded if the button press occurred more than 1s after the cue (indicating poor task performance) or if the button press occurred within 3s of the previous button press (which provided insufficient baseline for subsequent analysis).
, 2010 ). This method was selected over other traditional clustering methods because it allowed for simultaneous consideration of multiple clustering metrics ( i. e. both spatial and temporal vectors), which is not possible with other "out of the box " clustering methods. To compare two atoms (  1 ,  1 ) and (  2 ,  2 ) , we used the Pearson correlation coefficient of the  vectors 𝑅 𝑢 = ⟨𝑢 1 ,𝑢 2 ⟩ ‖𝑢 1 ‖ 2 ‖𝑢 2 ‖ 2 and the maximum cross-correlation coefficients of the  vectors. Cross-correlation was used for the  vector to account for phase differences in otherwise similar waveforms. The algorithm then stepped through each atom of interest sequentially (in no particular order), and clustered atoms with one Fig. 3. Two-variable correlation matrices from a representative sample of participants who were excluded (left) or not excluded (right) from the dataset.
To select the optimal  for the global clustering, the threshold was once again varied from  = 0. 2 to  = 0. 9 and clustering was performed for each threshold. Because of the high computational time associated with clustering on a 538-person dataset, threshold selection was performed using 10 randomly selected 50-person datasets. The  value threshold  was selected based on the analysis of both qualitative and quantitative metrics. Firstly, the  and  vectors of a selection of atoms in each cluster were manually inspected to qualitatively assess the success of the clustering. Functional labels ( e. g. "occipital alpha ", "left central beta ", "eyeblink artifact ", etc. ) were assigned to atoms in each cluster to assess whether similar types of atoms were being appropriately clustered together for various  thresholds.
This qualitative analysis suggested that  = 0. 4 yielded the most appropriately grouped atom clusters. This selection was supported by quantitative metrics comparing the number of clusters detected at each  threshold to the number of "top clusters " ( i. e. common clusters, defined as clusters for which a minimum of 25% of participants had atoms present in the cluster; see Fig. 4  ). Fig. 4  shows that  = 0. 4 yielded the highest number of top clusters relative to the overall number of clusters, suggesting that the top clusters were most representative of the group. These findings were consistent across 10 random selections of data, suggesting that the choice of threshold was stable, and that the selection and ordering of subjects did not have a large effect on the overall results. = 0. 4 was thus selected as the optimal clustering threshold for the global analysis and was used in subsequent analysis of the entire dataset.
Global clustering on the entire dataset yielded 226 clusters of atoms, 11 of which were considered "top clusters " by the criteria that a minimum of 25% of participants had atoms present in the cluster. The 25% value was selected as a reasonable trade-off between maximizing the number of participants in the top clusters and ensuring that movementrelated atoms of interest were being captured. Fig. 5  shows representative atoms for each of the top clusters identified when the minimum percentage was varied to values of 50, 35, 25, 20, and 15%. Minimum percentages above 25% primarily captured eyeblink artifacts as these were the most stereotypical atoms in the population. The 25% cutoff was the greatest cutoff that provided insight into movement-related atoms of interest ( e. g. contralateral sensorimotor beta) and was therefore selected as an appropriate threshold to define top clusters.
, 2020 ). If, on average, atoms in the cluster had a difference between post-movement and pre-movement that exceeded 10%, the cluster was also considered to have a rebound component. The 10% threshold used to compare task intervals was selected based on the magnitude of task-related changes in beta activity reported in previous analyses of the Cam-CAN dataset  ( Bardouille et al. , 2019; Brady et al. , 2020 ). Using both average power  ( Bardouille et al. , 2019 ) , and burst-based  ( Brady et al. , 2020 )  analyses of beta activity, task-related changes on the order of 10-30% were observed. Therefore, to capture beta events along with other task-related event types for which the magnitude of this change is not defined, the threshold was set to the lower end of this range. Of the 11 clusters included in this analysis, seven met the criteria for both focality and activity changes and were therefore classified as task-related.
Demographic analysis The demographic characteristics of each task-related cluster were first investigated by creating histograms depicting the age and sex distribution of the participants whose atoms were included in the cluster. Participants were only counted once per cluster, regardless of how many atoms the participant had assigned to a given cluster. The demographic distributions for each cluster were then compared to the demographic distribution of the overall dataset (538 participants). The cluster and overall distributions were then quantitatively compared by conducting a Chi-squared test with a Bonferroni-corrected  = 0. 007 (as the result Black boxes indicate the clusters that were determined to be "task-related " (as defined in Section 2. 7 ). It can be observed that task-related clusters resemble common induced responses ( e.