dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants Nine healthy adult participants (all right-handed, age ranging from 24 to 27, 4 males) with normal or corrected-to-normal vision took part in the study and were paid for their participation. Data from two participants were rejected due to too many artifact-contaminated trials (e. g. , eye blinks, head movements and α wave). Valid data from seven participants were reported here. All participants were native Chinese who had more than 10 years of education in speaking and reading Chinese characters. Written informed consent was obtained from each participant prior to the experiment.
Procedure After electrode-cap placement, participants were seated in a dim light and soundattenuated shielded room, at a viewing distance of 60 cm from the computer monitor. Stimuli were presented with MATLAB (MathWorks, Natick, Mass) using the Psychophysics Toolbox (Version 3. 0. 12; http://psychtoolbox. org)  (Brainard, 1997; Kleiner et al. , 2007)  on a light gray background. The sequence of events for each trial, summarised in Figure  2 , is as follows: In each trial, a "+" fixation was presented at first in the center of the screen for 500 ms, which was followed by a blank for 800 ms. Then, a forward mask, a character, and a backward mask were presented orderly, each for 250 ms. Each trial was ended by a blank of 800 ms. The masks in each trial were randomly chosen from all the masks.
EEG recording and preprocessing Brain electrical activity was recorded from 32 scalp sites using tin electrodes mounted in an elastic cap (BrainAmp, Brain Product). Vertical and horizontal eye movements were monitored using two electrodes placed on the outer canthus of right eye and in the inferior areas of the left orbit. The ground electrode was placed along the midline, ahead of Fz, and impedances were systematically kept below 7 kΩ. Signals were digitised at a sampling rate of 500 Hz and band-pass filtered at 0. 016~70 Hz. Potentials were referenced on-line to the FCz electrode and averaged-referenced off-line. Participants were asked to minimise eye movements, head movement, and swallowing during the recording. EEG data was low-pass filtered at 30 Hz and high-pass filtered at 0. 1 Hz.
Decay function relating N1 latency and jitter, to gap We used the following exponential function to relate both N1 latency, and N1 jitter, to gap, separately for each subject: y = m * 𝑒 !" # , where y can be either N1 latency (ms), or N1 jitter (standard deviation, ms), g is gap (ms), m specifies the starting point (y at gap 0), and k is the inverse of the decay rate (small k means fast decay). Best fitting parameters were obtained by minimising the sum-of-squares error using Matlab's fminsearch. m (Matlab R2014a).
Statistical analyses of correlations For statistical analyses of correlations between N1 latency and RT, and between N1 latency and VPP latency, we used the lmer program of the lme4 package  (Bates, Mächler, Bolker, & Walker, 2014)  for estimating fixed and random coefficients (subjects). Our focus is on the coefficient between N1 latency and RT, or between N1 and VPP latencies, modelled as fixed effects in separate models. Variation in intercept across subjects is modelled as a random effect; hence the use of a linear mixed model. Our main results concern the coefficient associated with our fixed effects, which is evaluated in two ways. First, we examine an anovabased model comparison between a subjects-only model and the subjects-plus-coefficient model (the full model). Second, and more importantly, we use a parametric bootstrap of the full model to generate a confidence interval for the coefficient.
Gap-metric functions Our main data are mean ERP for each condition and each subject. A nice visual summary for each subject can be constructed by what we call a 'stack plot': ERP stacked vertically across conditions (in order of gap), and colour-coded for amplitude. The stack plot for subjects 1 is shown in Figure  3  (code for producing this stack plot is avalaible on Github: https://github. com/SourCherries/firecracker. Stack plots for the remainder of subjects can be found in supplementary Figure  1 ). A large time-course, from -800 to 600 ms is shown, so that one can see responses to both masking and target stimuli. The main observation is a systematic change in shape and overall timing from the N170 condition to the widest gap, and then to smaller and smaller gaps: as a mask is introduced and its temporal proximity to the target increases, the neural response to the target appears to delay and widen proportionally.
Acknowledgments National Natural Science Foundation of China (NSFC)  to  Carl M. Gaspar  (grant No. :  31371132 ). This study was funded by the  National Natural Science Foundation of China (NSFC)  (grant No. :  31400974 ),  Zhejiang Provincial Natural Science Foundation of China (ZJNSF)  (grant No. :  LY18C090010 ), and Zhejiang Provincial Dominant and  Characteristic Disciplines Cultivation project of College of Education of Hangzhou Normal University  (grant No. : 9JYXK023 ) to  Ye Zhang.
First step signal processing: filtering, segmenting, labelling The data illustrating the methods presented in the next sections correspond to recordings from brainstem preparations. The neurograms are recorded at the level of V and VII motor roots, in two amphibian species, Lithobates catesbaianus and Pelophylax ridibundus , at different developmental stages, pre-and post-metamorphic, and in different conditions, as control condition, hypoxia or hypercapnia. The methods presented here focus on the signal processing tools only, since all the details concerning the experimental protocols, the resulting data and the physiological interpretation of these data are available in  [9]  for Pelophylax ridibundus , and in  [8]  for Lithobates catesbaianus. All these tools are implemented in Matlab, using standard functions that are mentioned at each corresponding step of the signal processing.
1 C ). The cut-off frequency of this low pass filter is 1. 6 Hz which allows low frequency analysis around the fundamental frequency of 1 Hz. In the case of P. ridibundus neurograms, whose typical fundamental frequency is around 2 Hz, i. e. about twice the fundamental frequency in L. catesbaianus , the value of W 0 is set at 100 ms leading to a S 100 signal (the matlab function used for this filter process is "filtfilt"). This resulting smoothed filtered signal, S 200 or S 100 , where all the high frequency noises haves been eliminated, allows the segmentation of the signal into individual successive cycles (see Fig. 1  C2 and C3) by automatic detection of the local minima of this signal (the matlab function used for this detection process is "findpeaks"). This segmentation process defines all the cycles of the neurogram.
Amplitude maps In a given S 10 signal, a reference cycle is chosen, for instance the cycle that is the most correlated with all the other cycles (see Fig. 5  A a1, the red burst; the matlab function used for the computation of the correlation is "xcorr"). Then we position all the other cycles according to the maximal value of the crosscorrelation function (sliding dot product) between each cycle and the reference cycle (  [4]  ). In a first step, the amplitude vectors representing the S 10 cycles are padded with zeros in order to get an identical length for all these vectors. In a second step, a symmetric matrix of crosscorrelation coefficients is computed, whose generic term of indices i and j is the maximal value of the crosscorrelation function between the amplitude vectors of cycle i and cycle j.
Amplitude and oscillation profiles The last step consists in representing all the buccal bursts (or lung bursts) of a S 10 signal, by a typical oscillatory pattern, i. e. an amplitude profile to which the low frequency component is eliminated. An amplitude profile is computed as the mean value of an amplitude map ( Fig. 5 C ). Once a reference cycle is chosen, this amplitude profile is the same whatever the vertical sorting rule that defines the amplitude map ( Fig. 5  C c2 and c3). A filtered version of an amplitude profile (black dashed line in Fig. 5  C c3) is computed using a zero-phase, amplitude maintaining Savitzky-Golay filter with a polynomial function of degree 3 and a moving window of 201 points (i. e. 0. 1 sec; the "sgolayfilt" function of matlab is used for this filtering process).
Comparisons between oscillation profiles A spectral analysis by Fast Fourier Transform (FFT) of the oscillation profiles illustrated in Fig. 7  A exhibits a same frequency range (18-28 Hz) for the dominant frequencies of each of them (using "fft" function of matlab), explaining the apparent similarity between these profiles computed on buccal S 10 signals of six different L. catesbaianus pre-metamorphic preparations. A careful comparison of these profiles using a crosscorrelation study shows that each oscillation profile has its own specific features. In order to evaluate how much a single oscillation profile is representative of the whole buccal burst population of a given neurogram, two oscillation profiles are computed from two distinct sets of the same number (half the total number) of buccal bursts randomly selected in the corresponding S 10 signal.
Identification and selection relevant studies MT-R then conducted systematic searches of the PubMed, Embase, and Cochrane Library databases, covering the period from the inception of each database to 11th Dec 2020. The search strategy was informed by terminology for SVD identified in other SVD research  1  and for each intervention and outcome. Strategies were tested and modified to optimise sensitivity and specificity before the final search was run. The search results were loaded into the web-based platform Covidence for assessment and consequent systematic review by the MWG. Different combinations of two MWG authors independently screened the titles and abstracts of publications registered in Covidence and assessed the full text of potentially relevant studies. We focused on randomized controlled trials and systematic reviews of RCTs, but also considered other types of study such as health registry data analyses, large observational studies (minimum size 100 subjects) and systematic reviews or individual patient data meta-analyses of observational studies since we anticipated a lack of high quality RCTs.
CXX European Stroke Journal 6(2) intensive antihypertensive treatment compared with the standard treatment group (0. 29% vs. 0. 48%; P¼0. 03), for a starting volume of 21. 1 and 20. 2 ml (more WMH than most other studies, Table  3 ). Additional studies focussed on ccSVD patients, on larger samples, with a range of ccSVD burden and characteristics, and longer follow-up periods, are warranted to assess more precisely the impact of antihypertensive medication both on ccSVD progression and clinical outcomes. Of note, ongoing trials include: the LEOPOLD trial (NCT02472028), comparing intensive BP lowering (SBP<135 mm Hg) to routine BP management in 820 older ccSVD patients with hypertension and memory complaints (MMSE>20), with WMH progression over 3 years as a primary outcome and secondary clinical endpoints including stroke, cognitive decline, dementia, gait, MACE, and mortality; the CEREBRAL trial  58  compares the impact of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers on fatal or non-fatal cerebrovascular events and progression of cerebrovascular lesions in 346 hypertensive patients aged !65 years, with any cerebral ischaemic lesion (overt or covert); trials testing the impact of BP lowering drugs on microvascular function and on cerebral autoregulation capacity (NCT03082014; http:// www.
80 n ASPREE substudy (ENVIS-ion) described in 2012, will examine cognitive function and brain MRI at baseline and after 3 years of treatment in 600 subjects aged 70 years or older randomized to aspirin or placebo, NCT01038583. 81 Another ASPREE substudy, the Study of Neuro-cognitive Outcomes, Radiological and Retinal Effects of Aspirin in Sleep Apnoea (SNORE-ASA), assesses cognition, brain MRI and retinal changes in 296 subjects at study entry and after 3 years. 82 he LACI-2 study is a prospective randomised open label blinded endpoint (PROBE) trial of 400 patients with lacunar ischaemic stroke testing the effect of cilostazol (and in partial factorial design, isosorbide mononitrate), on clinical (vascular events, cognition, dependency, death) and imaging markers of SVD at one year. 83 The COMCID trial in Japan tests effect of cilostazol in patients with MCI  84.
Does lipid lowering treatment (I), compared to less intense or avoiding this intervention (C) reduce risk of ischaemic or haemorrhagic strokes? One study  88  performed a post-hoc analysis of data from the ROCAS (Regression of Cerebral Artery Stenosis) study,  89  which was a randomized, doubleblind, placebo-controlled trial evaluating the effects of statins upon asymptomatic middle cerebral artery (MCA) stenosis progression among stroke-free individuals. The primary focus of this post-hoc analysis of 207 subjects was to assess WMH progression as an outcome (see below), but the study also reported stroke as an outcome with no difference between groups (3 events in each arm). Guo et al  90  reported a combination of a prospective population-based cohort study (N¼827) and a subset of patients from a randomized, double-blind,.
Physical exercise Two RCTs and three relevant observational studies of physical exercise were identified in the literature search. Due to varying outcomes, the data were not suitable for pooling in meta-analysis, so the results are summarized narratively below. Physical exercise and ischaemic or haemorrhagic strokes. The literature search did not find any RCTs investigating the impact of physical exercise on the incidence of ischaemic or haemorrhagic strokes in individuals with ccSVD. An observational analysis from the Radboud University Nijmegen Diffusion tensor and Magnetic resonance imaging Cohort (RUNDMC) study with a total of 503 participants with SVD on neuroimaging without dementia (some participants may have had TIA or minor stroke) suggested that higher baseline physical activity as assessed with a questionnaire is related to lower incidence of cerebrovascular events (composite endpoint consisting of TIAs, ischaemic and haemorrhagic strokes and vascular dementia) (adjusted HR: 0.
d. ) in 349 patients with mild VCI, in a non-randomized open label setting. In the active treatment group, MMSE scores remained stable (22. 4 at baseline and 22. 9 at 9 months) while in the control group MMSE score declined 2 points (21. 5 to 19. 6 at 9 months). 149 ther drugs, such as Cerebrolysin, 152 huperzine 153 and actovegin  154  have been studied in vascular dementia or poststroke cognitive impairment, but not specifically in mild VCI nor in ccSVD. We are not aware of any ongoing trials with (conventional) anti-dementia drugs such as are used in Alzheimer's disease in ccSVD; few trials are ongoing in mild VCI aiming at reducing cognitive decline, such as a Chinese trial with i. m. -injections of mouse nerve growth factor in patients with VCI (https://clini caltrials. gov/ct2/show/NCT04041349), and the CONIVaD trial,  155  a RCT of nimodipine plus choline alphoscerate (a cholinergic precursor) or placebo during 12 months in patients with mild VCI due to SVD.
Acknowledgements We thank the reviewers of the PICO Qs and final text, editorial support of the  ESJ  and  ESO  staff  Luzia Balmer  and  Sabrina Mutter  for excellent administrative support. We thank  William White  for providing additional data from the INFINITY trial and  John O'Brien  for providing additional data from the SCOPE trial. We thank  Marian Kavanagh  for assistance with referencing.
In addition, we replicate our network stability findings and the influence of individual structural network clustering levels in an independent resting-state dataset from the Cambridge center for Ageing and Neuroscience dataset (Cam-CAN;  Taylor et al. , 2017 ; Shafto et al. , 2014 ). By combining different neuroimaging modalities (see supplementary material for fMRI and biological markers' results) with cognitive measures, our study reveals that the underlying structural white matter connections influence dynamic functional connectivity and cognitive performance, furthering our understanding of the neural basis of the variability in cognitive performance across older adults.
Participants Data is the same as in our previously published report  ( Hinault et al. 2020 ) , and includes 40 young adults (18-35 years) and 40 older adults (60-85 years; see participants' characteristics in Table  1  ). All participants are right-handed and reported normal or corrected-to-normal vision. Age groups were matched by years of education, gender, and arithmetic fluency. All older adults performed within normal range (i. e. , score > 26) on the Montreal Cognitive Assessment (MoCA;  Nasreddine et al. , 2005 ). No participant reported a history of neurological or cognitive disorders, traumatic brain injury, or major psychiatric disorders. Participants were not on any neurologically/psychiatrically active medication at the time of testing. See  Hinault et al. (2020)  , for additional information regarding exclusion criteria and participants' characteristics.
This method is part of the default preprocessing pipeline in Brainstorm  ( Tadel et al. , 2011 ) , and has been widely used to remove artifacts in previous EEG studies (e. g. ,  Hardmeier et al. , 2014 ; Hinault et al. , 2019 b). Power-spectrum density was estimated over the whole recording period to identify bad electrodes and then interpolate data from those electrodes with other neighboring electrodes. The CapTrack camera system was used to record the spatial positions of the electrodes on the cap. These positions were co-registered to that of the individual's anatomy using fiducial points and were used to improve the source reconstruction accuracy. Age-related brain atrophy can introduce changes in the amplitude of event-related activations  ( Moretti et al. , 2013 ). Therefore, FreeSurfer  ( Fischl 2012  ) was used to generate cortical surfaces and automatically segment cortical structures and intracranial volume from each participant's T1-weighted anatomical MRI, to account for individual brain atrophy levels during source reconstruction.
DTI analyses Preprocessing of diffusion data was done using ExploreDTI  ( Leemans et al. , 2009 )  and included the following steps: (a) Images were corrected for eddy current distortion and participant motion; (b) a non-linear least square method was applied for diffusion tensor estimation, and (c) whole brain DTI deterministic tractography was estimated using the following parameters, for each participant: uniform 2 mm resolution, fractional anisotropy (FA) threshold of 0. 2 (limit: 1), angle threshold of 45°, and fiber length range of 50-500 mm. The "network analysis " tools in ExploreDTI were used to quantify the FA value of tracts connecting regions of the Desikan atlas, using the individual cortical parcellation from Freesurfer. The individual connectivity matrix included all groups of traced fibers identifying a putative tract passing through or ending at two ROIs of the atlas, or null values when no fiber was successfully traced.
Graph theory analyses Graph analyses were performed with the Braph toolbox  ( Mijalkov et al. , 2017 ). For EEG data, graph measures were calculated based on the average PLV matrix of 200 ms sliding time windows (50% overlap) across the epochs of interest and in each frequency band. For each time window, matrices were defined based on the average PLV synchrony between each region of the Desikan atlas and all other regions (i. e. , 68 × 68 matrices, see supplementary material). For DTI data, matrices were defined based on the average FA value of tracts connecting each region of the Desikan atlas with all other regions. Therefore, the same matrix structure was defined for both EEG and DTI data (see Fig. 3  C, and supplementary material for videos of time-varying EEG network metrics). Weighted network analyses were used to investigate the strength of the connection of a given node with every other node instead of binary network analysis, to avoid the need to threshold the connectivity matrices, a procedure that is always arbitrary to some degree (e.
Cambridge center for ageing and neuroscience database The Cam-CAN dataset  ( Taylor et al. , 2017 ; Shafto et al. , 2014 ;  available at http://www. mrc-cbu. cam. ac. uk/datasets/camcan/) was used in a control analysis to determine whether the time-varying connectivity and structural network results of the EEG dataset could be replicated in an independent dataset. To match the age and gender characteristics of the EEG groups, data from 47 young adults (20-30 years) and 47 older adults (65-75 years), in participants that completed both structural and functional neuroimaging sessions, were selected for analyses. Details on the demographic and behavioral data can be found at: https://camcan-archive. mrc-cbu. cam. ac. uk/dataaccess/. The selected MRI data included T1-weighted, diffusion images and behavioral tasks (the visual short-term memory task in particular) are detailed in Table  S2 , see supplementary material.
Regarding MEG data, approximately 9 min of eyes-closed resting-state data were acquired. Digitization of anatomical landmarks (i. e. , fiducial points; nasion and left/right preauricular point, as well as additional points on the scalp) was performed for registration of MEG and MRI coordinate systems. Electro-occulogram and electrocardiogram were recorded to capture eye movements and heartbeats, respectively. Preprocessing involved temporal signal space separation (tSSS): 0. 98 correlation, 10 s window; bad channel correction: ON; motion correction: OFF; 50Hz + harmonics (mains) notch. The same preprocessing steps as EEG data were followed for MEG data. For MEG data, the selected IAF was the average of the alpha peak observed with gradiometers and magnetometers averaged at posterior sites.
Confirmation from an independent dataset In order to determine the generalizability of our findings, we analyzed the behavioral, DTI and resting state MEG data from the publicly available Cam-CAN dataset  ( Taylor et al. , 2017 ; Shafto et al. , 2014 )  as control data. Young and older participants were randomly selected from the Cam-CAN database to match our EEG groups in age and gender balance (see Table  S2 ). Analysis of the DTI data showed that young and older participants differed in efficiency ( p < 0. 001) and clustering coefficient ( p < 0. 003) metrics. When analyzing resting state MEG data, significant differences between age groups were observed in the variability across time of alpha clustering and high-gamma network efficiency (Table S2, Fig. 4 C ). No main effect of age, or interaction involving any other frequency or metric was observed.
Also consistent with findings observed in the EEG dataset, regression analyses revealed that the variability of alpha clustering across time explained a significant amount of the working memory performance variance in older adults (R-2 = 0. 139, p = 0. 01), but not in young adults. This result was not observed with high-gamma efficiency or with structural network metrics, or with other cognitive measures. Similarly to the EEG dataset, data-driven subgroup identification based on structural clustering revealed two subgroups in older adults (17 and 30 participants; see subgroups' characteristics in Table  S2 ). While subgroups showed similar age, grey matter volume, MMSE score and level of education, significant differences were observed in the variability of both alpha clustering (as in our EEG subgroup data) and highgamma efficiency.