dataesr/scientific-paragraphs-categorization huggingface dataset:
Second study It is clear that the lead matrix L is skew-symmetric, i. e. L ⊺ = -L. We can turn this into a symmetric positive semi-definite matrix by taking the negative square, as A = -L 2 = L ⊺ L. In fact, let v be an arbitrary vector, we have v ⊺ Av = v ⊺ L ⊺ Lv = �Lv� 2 ≥ 0. The matrix A can be further turned into an SPD matrix by adding a small value on its diagonal  28. Now that we have the SPD matrix A + εI , we can use them as features and leverage the advantages of Riemannian classifiers. This idea is tested here on both publicly available BCI datasets, both for intra-and inter-subject classification to further evaluate the usefulness of signature features, including a benchmark with usual covariance features. The results are summarized in Table  2  ( ε was set to 0. 001). Although the signature-based method falls behind traditional Riemannian method for intra-subject classification, the difference is not substantial for inter-subject classification, and it shows better robustness, especially on Physionet dataset with a much larger number of subjects and a relatively low mean classification accuracy with the state-of-the-art methods.
Cyclicity analysis on EEG data To better understand the stronger performance of the second level signature, a cyclicity analysis as described in the Methods section was conducted on the same EEG data used in the first study (BCI Competition IV 2a dataset). The eigenvalues and eigenvectors of the lead matrices were computed. Figure  7  shows the absolute value   of the eigenvalues averaged across trials and participants. The first 2 conjugate pairs of eigenvalues dominate, which guarantees a good power of explicability of the corresponding eigenvectors. Figure  8  shows the number of times that one channel appeared in the top 10 largest elements of the first 2 eigenvectors corresponding to the 2 largest pairs of eigenvalues. The color of the bars indicates left/right MI. For the first eigenvector, the channel POz (labeled as 22 in the dataset) from the parieto-occipital region had the largest counts.
This might be due to the fact that during a task, brain signals are dominated by visual related signal processed in the occipital lobe and attentional networks related signals from the parietal lobe. Note that there was little difference between left/right conditions. For the second eigenvector, there was a large contribution from channels located above the sensorimotor cortex (e. g. C5 and C6 or 7 and 13 according to the channel labels in the dataset) and a clear difference between left/right conditions.
Conclusion This paper explored applications of the path signature for EEG-based BCIs. The first study used the path signature directly as a feature vector. Promising results were achieved with the signature truncated at the second level. The second study used the negative square of the lead matrices constructed from the second level signature and added a regularization term to obtain SPD matrices as features. A Riemannian classifier was applied on these signature-based SPD matrices. The classification results on several publicly available MI BCI datasets were compared to those with a Riemannian classifier applied on covariance matrices. The signature-based method showed better performance and robustness on users where the traditional use of Riemannian classifier fails although remaining close to the chance level.
This is not the case with EEG signal which might have opposite trends in different channels. Hence, this article advocated for the use of a mathematical method that was until now unexplored for both EEG analysis and BCI applications. Promising results are found which open new perspectives on how to design BCIs that would be more robust to inter-subject variability and that might help tackle -at least in part-the socalled BCI illiteracy. The path signature provides a novel way to generate features from multi-channel EEG data. The features are invariant under time reparametrization and translation and they capture the lead-lag relationship between channels. In the future, a thorough comparison with more methods and on more open datasets via MOABB  34  need to be done to further validate the utility of path signature for BCI applications.
ACKNOWLEDGMENTS The research works discussed in this article were supported by the  Disciplinary Group of Psychology and Neuroscience Xinxiang Medical University  ( 2016PN-KFKT-06 ),  Department of Education of Henan Province  ( 16IRTSTHN020 ),  Biological Science and Biotechnology Research Council  ( BB/C517317/1 ),  Wellcome Trust  ( 099758/Z/12/Z ),  National High-End Foreign Expert Recruitment Plan of China  ( G2022026006L ), Start-up fund from  Xinxiang Medical University  and visiting professorship from  University François-Rabelais of Tours.
Search strategy and data collection Data were collected from the Science Citation Index Extended in the Web of Science (WOS) Core Collection database. WOS is considered one of the most suitable databases for scientometric analysis  (Mongeon and Paul-Hus, 2016) , as it contains the full list of references and citations of each article from 1900 -citations that are not available in PubMed or Embaseand that were extracted in tag-delimited plain text files on September 23, 2022. The search terms used were a combination of keywords and MeSH terms related to brain stimulation. To focus on clinical aspects of brain stimulation, we excluded animal studies by excluding specific keywords. The full list of search terms is reported in our protocol (osf. io). Included publications types were 'article', 'reviews', 'editorial material 'and 'proceedings papers' with no restrictions on publication date or language.
Duplicates were removed with CiteSpace. To assess the quality of the retrieved articles, we conducted a filtering process using statistical values with a confidence level of 95% and a confidence interval of 10%. For a dataset of 20,000 citations, we inspected 2,000 randomly selected articles with a maximum of 100 irrelevant references. Furthermore, we inspected all highly cited WOS articles, which are papers that perform in the top 1% of cited papers in the same field. Reasons for the exclusion of articles are reported in Supplementary Figure  1  and Supplementary Table  1.
Data analysis Two different tools were used for analysis. The Bibliometrix R package (4. 0. 0)  (Aria and Cuccurullo, 2017)  was used to obtain information on authors and journals. CiteSpace is a Java application used for scientometrics. We used CiteSpace (6. 1. R2)  (Chen, 2006)  to retrieve co-cited networks using reference, authors, institutions and countries, and co-occurrence networks with keywords as units of measures. The citation count is the number of direct citations of a publication. Co-citation analysis extracts pairs of papers that are cited together in a source article  (Small, 1973) , and co-occurrence networks are the counting of paired data within a collection unit. Different metrics are used in CiteSpace to generate network analyses, with structural (betweenness centrality, modularity, silhouette score) and temporal metrics (citation burstness) as a combination of both (sigma metrics).
The generated clusters are labeled by CiteSpace based on noun phrases of the keyword lists of articles using the likelihood ratio test (p < 0. 001). Burstness is the intermittent increase and decrease in activity or frequency of an event over a specific time period. The retrieved dataset can thereby be reduced if CiteSpace detects specific time slicing by excluding empty intervals. Finally, Sigma is a metric of CiteSpace that combines burstness and betweenness centrality: (centrality +1) burstness  (Chen, 2006). The sigma metric estimates the influence of a node, with higher scores indicating higher influence. We report CiteSpace parameters in Supplementary Information 1.
Most highly cited papers The most cited articles according to our datasets are reported in Table  1. The three most cited papers were the  Nitsche and Paulus (2000)  clinical trial on excitability changes with tDCS  (Nitsche and Paulus, 2000)  and two clinical guidelines on the use of NIBS techniques, namely,  Rossi et al. (2009)  and  Rossini et al. (2015)  guidelines for rTMS  (Rossi et al. , 2009; Rossini et al. , 2015). The burstness analysis proposed that for the 2020-2022 time period, the three papers with the latest and most important strength of burst were  Lefaucheur, ' et al. (2014)  evidence-based guidelines, Ashkan and colleagues' 2017 review on the mechanisms of DBS  (Ashkan et al. , 2017) , and Lozano and colleagues' review on the future direction of DBS  (Lozano et al. , 2019)  (Table  1 ).
Analysis of co-authorship networks A total of 116,310 authors with a mean of 5. 72 co-authors per article were found in our dataset. The co-authorship network connects authors that share the authorship of a paper, informing the individual's body of research and the global collaborative network. We extracted the coauthor collaborative network of the last five years (2016-2022) (Supplementary Figure  6 ). The modularity score was significant (Q=0. 747), and the weighted mean silhouette score indicated highly credible clusters (S=0. 928). Nineteen different clusters were regrouped in a single network, gathering co-authors around the same topic of research, such as cluster # 'functional connectivity', which gathers mostly Chinese authors, and #2 'Parkinson disease' with European and American researchers.
Analysis of co-cited journals We found 2792 journals in our dataset. The three journals with the most important and most recent growth of publications were Clinical Neurophysiology (n = 927), Brain Stimulation (n = 875), and Journal of ECT (n = 829) (Supplementary Figure  7 ). Furthermore, the journals with the most citations were Neurology (n = 18,740), Journal of Neuroscience (n = 18,108), and Brain (n = 18,071). The author's journal co-citation network can inform on the most suitable journals for publication. This network (1990-2022) presented significant modularity (Q=0. 6649) and a highly credible silhouette score (S=0. 8341) and gathered 11 clusters (Supplementary Figure  8 ). The most central and largest cluster was cluster #0 'brain stimulation journals', which gathered impactful neurology journals.
Limitations Different limitations impact this scientometric analysis. The most important limitation is linked to the nature of gathered data, from only one database, WOSCC. Indeed, for most databases, full text and citation analyses are not available. Furthermore, the combination of different databases is currently not possible considering digital object identifier errors between databases, which affect the accuracy of determined coverage differences (Pranckut ė, 2021). Moreover, the use of citation-related indicators in scientometric analysis can be a potential source of different biases, such as citation bias, where the probability of being cited depends on the outcome of a study  (Jannot et al. , 2013). Another frequent bias is citation distortions, where unfounded authority can emerge due to distorted social citations with important co-citation, establishing unfounded scientific claims as fact  (Greenberg, 2009).
Following the first TDT session, participants underwent neuronal saturation (see below), which was then followed by a second TDT and localizer session. At the beginning and end of the test day, outside the scanner, participants were asked to fill out the Multidimensional Fatigue Inventory  74  and the Karolinska Sleepiness Scale  75  to assess the evolution in their perceived levels of fatigue and sleepiness during the experiment. Materials. All experimental tasks were coded in Matlab 2019a (The MathWorks, Inc. , Natick, Massachusetts, United States), using Psychtoolbox  76, 77. On training day, participants sat in front of a 1280 by 1024 computer screen at a distance of 60 cm and accommodated their heads on a chin-rest, using a keyboard to respond across the various tasks. On test day, participants lied in the MRI scanner and employed bimanual fiber optic response pads (Current Designs Inc.
The acquisition was aligned to the calcarine scissure of the participants. Quality control. A quality control was conducted using mriqc software  78. MRI images were visually inspected in order to remove those which presented major spatial artifacts (deformations and movements). In addition, fMRI series exhibiting a Framewise Displacement value greater than 2 mm or a DVARS value greater than 0. 4% BOLD change were excluded for excessive motion according to Power's recommendations  79. As mentioned, images from a single participant presented major deformations and were therefore rejected. fMRI preprocessing. Susceptibility-induced distortions were estimated and corrected for all functional images using the top-up method  80  with FSL6. 0. 3 (FMRIB, Oxford, UK). In a second step, they were motion-corrected using linear transformation with the automated tool MCFLIRT in FSL and intensity-scaled to 1000.
Then, the PhysIO Toolbox with MATLAB v9. 7 was used to apply physiological noise correction  81. Both cardiac and respiratory signal were recorded using a finger pulse oximeter and a pneumatic belt during MRI acquisition. Respective physiological regressors were created using the RETROICOR algorithm  82  as well as heart rate variability  83  and respiratory volume per time  84  and regressed out of from the functional MRI time series. Due to the short repetition time used, no slice timing correction was applied. Finally, each series was realigned to the first localizer acquisition using rigid transformation with FSL flirt using mean as reference image and normalized to MNI space using SPM 12 by warping the T1 anatomical scans and applying the linear transformations to the functional scans.
MVPA analysis was carried out via The Decoding Toolbox 88 , in the baseline and conclusion sessions, separately for the RQ and LQ ROIs. The classifier was asked, with a leave-one-out cross-validation procedure, to label in which quadrant were the stimuli presented, given the observed brain activity. Therefore, for each participant, we derived a data point of classifier accuracy for each quadrant, at baseline and conclusion, for both the localizer and the TDT brain scans. In addition, to estimate a voxel-wise measure of classifying accuracy, the above analysis was repeated with a searchlight procedure using a 4-voxel radius within the subject-specific ROIs. Searchlight analyses generate accuracy maps by measuring the variation in activity in multiple, overlapping, group of voxels in relation to the experimental conditions  89  , thus outputting a measure of classifying accuracy for every voxel in the functional ROIs.
Participants were recruited informally and took part voluntarily in the experiment (M age = 21. 6 ± 1. 8, 10 m). All were naive to the experimental procedure, with no history of mental or visual conditions. Participants had normal or corrected-to-normal vision and provided written informed consent to participate. They received 50€ in compensation for their participation. The study was approved by the Ethical Review Board of the Comité de protection des personnes Sud-Est V. rANOVAs on MVPA accuracies and behavioral analyses, except correlations which were done in Matlab, were carried out in jamovi  91  including the package gamlj  92  for linear models. Contrasts and analyses with neuroimaging data were carried out via SPM 12  93  , unless otherwise specified. Behavioral effects were assessed by means of a generalized linear mixed model on response accuracy in the TDT.
For this analysis, an explicit mask was restricted to the functionally relevant parieto-occipital portions of the brain, obtained by aggregating each subjectspecific functional ROI and excluding any clusters anterior to the central gyrus and/or inferior to the lower bound of the occipital lobe. Most voxels of the single ROIs fell within this mask (91% on average), with the majority of voxels located in the hemisphere contralateral to stimulation. The yielded data-driven mask is available for scrutiny in the public data repository. As a result of this analysis, we expected to find an alteration in the BOLD activations between baseline and conclusion of the experiment in the portions of the brain contralateral to the saturation procedure. Classifier-behavior correlation (Pearson) was assessed by extracting the random coefficients of the Session-Quadrant interaction from the model on TDT accuracy data (representing individual deviations of each participant from the mean effect) and correlating these values to the delta (conclusion-baseline) in classifier accuracy in the saturated ROI of each participant, while brain-behavior correlation (Pearson) was assessed by correlating the same random coefficients to the delta (conclusion-baseline) in mean brain activity inside the saturated ROI of each participant.
Data availability All the data employed in this work, along with instructions to reproduce the analyses, can be found at the following public repository: https://zenodo. org/record/7395703  96. Moreover, the source data to recreate the plots present in the paper has been provided as supplementary data (Fig. 1 -Supplementary Data 1, Fig. 2b-Supplementary Data 2, Fig. 2c-Supplementary Data 3, Fig. 3b-Supplementary Data 4, Fig. 3c-Supplementary Data 5, Fig. 4-Supplementary Data 6).
Drugs Currently in Clinical Trials In recent years, multiple treatments to restore autophagy have entered clinical testing phases  [188]. Ambroxol may be a promising one; firstly, used as a cough syrup, it can enhance GCase activity, permit better lysosomal function, and decrease reactive oxygen species (ROS) apparition  [189]. A clinical phase II study is ongoing until 2023 in a singlecenter, double-blind, randomized, and placebo-controlled manner (NCT02914366)  [190]. Based on the promising results of the AIM-PD phase 2 clinical trial (NCT02941822), the University College London is starting a phase clinical trial in 2023, gathering 330 people with PD across approximately 12 clinical centers in the United Kingdom  [191]. Another GCase activator drug is in the clinical phase study under the LTI-291 name.
Acknowledgements This work was supported in part by the  European Commission   H2020 Framework Programme  under Grant  101017727  of the Project EXPERIENCE. V. van  Wassenhove / Timing & Time Perception  (2023) Downloaded from Brill. com  12/11/2023 04:16 :46PM via Open Access. This is an open access article distributed under the terms of the CC BY 4. 0 license. https://creativecommons. org/licenses/by/4. 0/.
Genomic DNA was isolated from peripheral blood using standard methods (see Supplementary Methods). DNA concentrations were measured with the NanoDrop Spectrophotometer. First step: fluorescent long-range PCR. The intronic FGF14 GAA repeat locus was amplified by fluorescent longrange PCR (fLR-PCR). fLR-PCR products were analyzed on an ABI 3130xl, 3500xl, or 3730xl DNA Analyzer (Applied Biosystems, Foster City, CA, USA) with a 50-cm POP-7 capillary using the GeneScan 1200 Liz Dye Size Standard (catalog no. 4379950, Applied Biosystems) following the manufacturer's instructions (Supplementary Table  S2 ). Results were analyzed using the GeneMapper software using the built-in microsatellite default settings (version 6. 0, Applied Biosystems). The PCR primers are predicted to amplify a 300 bp fragment based on the reference sequence, which includes 50 GAA triplets.
The size of the alleles (expressed in triplet repeat units) was calculated using the following formula: (size of PCR amplification product-150)/3. Second step: bidirectional RP-PCRs. Two RP-PCRs targeting the 5′ end (5′ RP-PCR) and the 3′ end (3′ RP-PCR) of the locus were used in parallel to ascertain the presence of a GAA expansion at the repeat locus. RP-PCR products were analyzed on an ABI 3130xl, 3500xl, or 3730xl DNA Analyzer with a 50-cm POP-7 capillary using the GeneScan 1200 Liz Dye Size Standard following the manufacturer's instructions (Supplementary Table  S2 ). Results were analyzed using the GeneMapper software using the built-in microsatellite default settings. The presence of characteristic saw-toothed products indicated the presence of a GAA repeat expansion at the FGF14 repeat locus.
Agarose gels were imaged with the Uvidoc HD6 Gel Documentation System (Uvitec) using the default UV-Gel settings in the Uvitec-1D software. The molecular weight of PCR amplification products was measured by the molecular weight detection tool in the Uvitec-1D software (Uvitec). 2. Samples with at least one allele of 250 or more repeat units detected on fLR-PCR and no sawtooth profile or interrupted profile on RP-PCRs. Cases with at least one allele of 250 or more repeat units and no sawtooth profile or an interrupted profile on RP-PCRs next underwent Sanger sequencing to determine the sequence and repeat motif of the FGF14 locus. Sanger sequencing of LR-PCR amplification products using the BigDye Terminator v1. 1 Sequencing kit (catalog no. 4337451, Applied Biosystems) was performed using the ABI 3130xl DNA Analyzer (Supplementary Table  S3 ).
The resulting sequences were analyzed using Sequence Scanner version 1. 0 software (Applied Biosystems) or SnapGene v. 5. 0. 8 software (Dotmatics). The performance of the PCR protocols was assessed using different formulations of Taq DNA polymerase, different input amounts of genomic DNA, and genomic DNA extracted from saliva, formalin-fixed paraffinembedded cerebral tissue, and fresh frozen cerebral tissue (see Supplementary Methods and Supplementary Results). We further assessed the effect of DNA shearing on the results of the proposed strategy (see Supplementary Methods and Supplementary Results).
Statistical analysis. We compared methods against each other using the Passing-Bablok regression model (R package: mcr using the function mcreg(dataset1, dataset2, method. reg = "PaBa")). Confidence interval of the slope that does not include 1 indicates statistically significant evidence of a proportional bias between two methods. Confidence interval of the intercept that does not include 0 indicates statistically significant evidence of a systematic bias between two methods. Linear relationship between two sets of measurements was tested by the CUSUM test of linearity. We used the Bland-Altman analysis to compare the measurements of the same variable by two methods (GraphPad Prism 9). Correlations were calculated using the Pearson correlation coefficient. We analyzed the data in R (version 4.
Results FGF14 repeat sizing by fLR-PCR. We first determined how repeat size estimates of larger alleles by fLR-PCR compare and correlate to (1) targeted long-read nanopore sequencing and (2) LR-PCR estimates by agarose gel electrophoresis. We used 28 French Canadian participants for whom long-read sequencing data was available and measured repeat length by fLR-PCR. Nanopore sizing was performed on size-selected alleles of more than 80 repeat units to increase coverage depth, as described previously  5. We observed that expansions (GAA) >400 repeat units could not be accurately sized by fLR-PCR, as shown in Fig. 2A , as they fall beyond the limit of detection of capillary electrophoresis. Therefore, such large expansions needed to be sized by agarose gel electrophoresis of LR-PCR amplification products.
Acknowledgements The authors thank the families for their participation in this project. This work was supported by the  Fondation Groupe Monaco  and the  Montreal General Hospital Foundation  (grant  PT79418 ). D. P. holds a Fellowship award from the  Canadian Institutes of Health Research (CIHR). H. H. is supported by the  Wellcome Trust , the  UK Medical Research Council (MRC)  and by the  UCLH/UCL Biomedical Research Centre. R. L. P. is supported by The  Canadian Radiological Society. The funders had no role in the conduct of this study.