dataesr/scientific-paragraphs-categorization huggingface dataset:
To select the optimal binning, we looked at the branching ratio, since a branching ratio ~ 1 typically indicates a process operating near a critical regime. The branching ratio is calculated as the geometrically averaged (over all the time bins) ratio of the number of events (activations) between the subsequent time bin (descendants) and that in the current time bin (ancestors) -eq. 1, as: 𝜎 ! = ∏ " !"# #1 $%1 & & $%$#&' ($)1) & $%$#&' ($) ' 1 ( !"# )1 (1) and then geometrically averaging it over all the avalanches -eq. 2  (9). 𝜎 = ∏ " *%*+ !%1 (𝜎 ! ) 1 ( *%*+ (2) where σi is the branching parameter of the i-th avalanche in the dataset, Nbin is the total amount of bins in the i-th avalanche, nevents (j) is the total number of events active in the j-th bin, and Naval is the total number of avalanches in each participant's recording.
A uniform distribution was chosen because we do not have any theoretical assumption about the distributional shape of variability in leakage. All free parameters were constrained to be ≥ 0. In addition, parameters sz, sT e, sT r, and sλ were constrained to not exceed 180% of m, T e, T r, and λ respectively. The models were fit to each individual dataset by minimizing the following loss function (likelihood-ratio chi-square statistic): G 2 = 2 6 i=1 6 j=1 6 k=1 6 l=1 n ijkl log   n ijkl pred_n ijkl N i simul_N i   (5) Summations over i and j extend over the 6 motion coherence levels and the six trial types (pureC, CC, IC, pureI, II, CI; see introduction section of this experiment) respectively. Summations over k and l extend over the six bins bounded by PMT quantiles (. 1,. 3,. 5,. 7, and.
Stimuli We selected a set of 1058 french words (number of letters ranging from five to eight; M = 6. 50; SD = 1. 12) from the Lexique database  (New et al. , 2004). Word frequency ranged from one to six occurrences per million (M = 2. 96, SD = 1. 14). Words were presented in black against a grey background (font: Consolas), at the center of the screen. The height of the letters was 0. 76°.
Stimuli Four lists of french words (five to 12 letters) of differing frequencies (high frequency: range 10-2736 occurences per million; medium frequency: range 2-5; low frequency: range 0. 5-1; very low frequency: range 0. 01-0. 1) were created using the Lexique database  (New et al. , 2004). To obtain the stimuli used in the present experiment, the lists underwent the following steps. First, all plural and feminine agreements were removed. Second, low and very-low frequency words were screened by two students, and any words they did not know were eliminated. Third, a pool of 408 words was pseudo-randomly selected from each list, with the constraint of obtaining an homogeneous number of letters across pools. For each pool, 204 words were randomly selected to become pseudowords (i. e. , pronounceable non-words), created with the multilingual pseudoword generator Wuggy  (Keuleers and Brysbaert, 2010).
This work was funded by the  European Union  ( ERC Starting Grant  ITDA  n°101039226  allocated to MS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the  European Research Council. Neither the  European Union  nor the granting authority can be held responsible for them. supported by an  Australian Research Council   Discovery Early Career Researcher Award  ( DE200101130 ).
Indeed, prior work has identified differences in cerebellar volume and function distributed across the cerebellum, including within the vermis  (43, 46) , crus  (44, 65) , and lobules VI and VII  (66) (67) (68). Yet, these diffuse subregional findings are often not replicated, contributing to a lack of consensus regarding the cerebellum's role in PTSD. Importantly, better understanding the relevance of cerebellar structure in the pathophysiology of PTSD may help elucidate potential mechanisms that perpetuate chronic symptoms of PTSD and aid in our ability to develop targeted, effective interventions. To this end, the present study employed a mega-analysis of total and subregional cerebellar volumes in a large, multi-cohort dataset from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA)-Psychiatric Genomics Consortium (PGC) PTSD workgroup.
This ultimately resulted in volumetric estimates for the total cerebellum and 28 subregions, including the hemispheric anterior (lobules I-III, IV, and V), posterior (lobules VI, VIIB, VIIIA, VIIIB, IX, and crus I-II), and flocculonodular (lobule X) lobes, vermal lobules VI, VII, VIII, IX, and X, and the corpus medullare (the white matter core of the cerebellum). ACAPULCO achieves results comparable to other established cerebellum parcellation protocols (e. g. , CERES2), but may perform better for multi-site datasets  (69). Following segmentation, a two-step quality control procedure was employed, consisting of (1) removal of statistical outliers ± 2. 689 SD from the site mean, and (2) visual inspection of cerebellar parcels. Each subject's segmentation was visually inspected and scored by a minimum of two trained raters (AH, SL, MB, LB) on a scale from 1 (good) to 3 (poor/failed).
Discussion Leveraging an international, multisite dataset from ENIGMA-PGC PTSD, we conducted a mega-analysis of total and subregional cerebellar volume in PTSD. Consistent with hypotheses based on published work  (43) (44) (45) (46) , PTSD was associated with smaller total cerebellar volume. We found subregional specificity linking PTSD to smaller volumes in the posterior cerebellum, vermis, and flocculonodular cerebellum. Effects of PTSD on cerebellum volume were consistent (and generally more robust) when examining symptom severity rather than diagnostic status. Overall, these findings contribute to an emerging literature that underscores the relevance of cerebellar structure in the pathophysiology of PTSD. Although the appreciation of the cerebellum for its contributions to cognitive and affective function is relatively recent, the current results bolster a growing literature confirming the cerebellum is not exclusively devoted to motor function and may, in fact, have unique relevance to psychiatric conditions including PTSD  (34, 37, 79).
Limitations: This is the largest study of cerebellar volumetry in PTSD to date, however, there are several notable limitations. PTSD is a heterogeneous disorder and is highly comorbid with other psychiatric conditions (e. g. , depression, substance use disorders) and environmental exposures (e. g. , childhood trauma) that are also linked to alterations in cerebellar structure  (72, 77, 79). Employing a mega-analysis in a large multi-cohort consortium dataset enabled us to observe small effect sizes of PTSD on cerebellar volume in our primary analyses, but many sites did not provide diagnostic or item-level data for relevant covariates. Consequently, we were unable to investigate effects of relevant covariates at the same scale. Future studies would benefit from investigating unique and shared phenotypes of PTSD and other psychopathology on the cerebellum to disentangle potential dissociable effects and complex interactions more elegantly.
Patients. We investigated the seizure onset features of individuals with DS analyzing the EEG recordings obtained from the database of Necker-Enfants Malades hospital, a tertiary center for rare epilepsy. Our study is based on retrospective EEGs performed during the classical follow-up of individuals with DS. We included DS patients with a SCN1A pathogenic variant and seizures recorded after the stormy phase. EEG recordings used in this study lasted from 40 minutes to 120 minutes. The 11-channel EEG data were recorded with a digital acquisition system (Natus Medical Inc), according to the 10/20 international system with Fpz as reference and with a sampling frequency of 256Hz. In compliance with French law, the consent regarding nonopposition for this study and the use of their data was obtain for each individual and their parents.
Funding. This work was funded by  UCAJEDI  (https://univ-cotedazur. fr/ucajedi-lidex-duniversite-cotedazur,  ANR-15-IDEX-01  to MM) and  Laboratory of Excellence "Ion Channel Science and Therapeutics " - LabEx ICST  (https://www. labex-icst. fr/en,  ANR-11-LABX-0015-01  to MM). FC received postdoc fellowships from the  Ville de Nice (Aides Individuelles Jeunes Chercheurs)  and the  Interdisciplinary Institute  for Modeling in Neuroscience and Cognition of the Université Cote d'Azur (Neuromod https://neuromod. univ-cotedazur. eu/). JL was a Ph. D. student of the  ED85 "Life and Health Sciences" (Université Cote d'Azur ) and received a fellowship from the  Laboratory of Excellence "Ion Channel Science and Therapeutics" -LabEx ICST.
MRI acquisition We acquired a high-resolution 3D structural dataset (T1-weighted, MP-RAGE, repetition time = 2. 300 ms, echo time = 2. 98 ms, 240 slices, eld-of-view = 256 mm, voxel size = 1 × 1 × 1 mm) for everyone at each time point in a 3T Magnetom Trio Tim scanner (Siemens Medical Systems, Germany) upgraded to a 3T Prisma scanner (Siemens Medical Systems, Germany) during the study.
e. , the nal classi cation was the one with probability > 0. 5). We conventionally set two levels of diagnosis con dence: an individual probability ≥ 0. 8 (or ≤ 0. 2) was considered to provide high diagnosis con dence, while probabilities between 0. 2 and 0. 8 were considered a "gray zone", with lower or insu cient diagnosis con dence for the clinical decision. Thus, we estimated the accuracy and the number of individuals with a high probability of being from the group for each classi cation. Finally, we aimed to explore if NfL and 14-3-3 levels could help diagnose the individuals of the gray zone of the MRI diagnosis for the following comparisons due to the available data: AD vs. CTR, FTD vs. CTR, and AD vs. FTD. Thus, we created a reduced dataset with subjects having MRI data, NfL, and 14-3-3 levels.
Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. Among the motor complaints, freezing of gait (FOG) is a common and disabling phenomenon that episodically hinders patients' ability to produce efficient steps. Concurrently, sleep disorders are prevalent in PD and significantly impact the quality of life of affected individuals. Numerous studies have suggested a bidirectional relationship between FOG and sleep disorders. Therefore, our objective was to systematically review the literature and compare sleep outcomes in PD patients with FOG (PD + FOG) and those without FOG (PD-FOG). By conducting a comprehensive search of the PubMed and Web of Science databases, we identified 20 eligible studies for inclusion in our analysis. Our review revealed that compared to PD-FOG, PD + FOG patients exhibited more severe symptoms of rapid eye movement sleep behavior disorder in nine studies, increased daytime sleepiness in eight studies, decreased sleep quality in four studies, and more frequent and severe sleep disturbances in four studies.
Data sources and search strategy The following two electronic databases, PubMed and Web of Science, were systematically searched. No limitation was placed on the date of the publication with databases screened up to  February 21, 2023. Keywords related to (1) the population, (2) freezing of gait, and (3) sleep were used. The search strategy included a combination of keywords, using the Boolean operator "AND" and "OR" and, if it was applicable, the Medical Subject Headings (MeSH) terms. The first category of keywords focused the search on patients with PD and included terms such as "Parkinson Disease", "Parkinson's disease", "Parkinson", or "PD". The second category specified FOG. It comprised all following terms: "freezing of gait", "fog", or "frozen gait". The third category was designed to focus on sleep disorders: "sleep", "insomnia", or "dyssomnia".
Data extraction After completion of the screening process, two reviewers (TM and MBC) independently extracted the data from each included study and competed the data for consistency. Any discrepancies between these two reviewers were resolved at a consensus meeting. If disagreement persisted, a third reviewer (NV) was consulted to reach a final decision. Data extraction was done following a prebuilt table including information about study metrics, population, main measures of sleep and FOG in patients with PD. Study metrics referred to the name of authors, title, year of publication, journal's name, country of study, study design, number of centers taking part in the study, funding, and conflicts of interest. Based on a recent published systematic review and metaanalysis on spatiotemporal data and lower limb angles during walking in PD and control populations, only data concerning PD + FOG compared with PD-FOG groups were extracted  [44].
Study selection The study selection process is depicted in Fig. 1. Initially, 147 records were identified through database searching, with 70 studies found through PubMed and 77 through Web of Science. An additional five studies were identified through manual hand searching. After removing duplicates (n = 55), a total of 97 unique records remained. Subsequently, these records were screened based on their titles and abstracts, leading to 22 full-text studies being reviewed for eligibility. Ultimately, 20 studies met the eligibility criteria and were included in this systematic review  [13, 15, 20, 21, 26, [37] [38] [39] [40] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56].
2 ± 0. 5 H&Y: 1. 8 ± 0. 5 phenotypes. ONOFF-FOG n = 26, 16 M Age: 70. 5 ± 8. 1 y PD duration: 10. 4 ± 5. 1 y H&Y: 3. 2 ± 0. 9 Li et al. To develop a nomogram for UPDRS-II Did not RBD: RBDSQ NM Training dataset Training dataset NA FOG risk based on data FOG-Q differentiate Sleep quality: n = 197, 111 M n = 182, 112 M collected from Chinese Clinical between ON PSQI Age: 68. 26 ± Age: 64. 20 ± patients with PD. (MDS- and OFF 10. 17 y 10. 40 y UPDRS-III) PD duration: PD duration: 5. 23 ± 4. 68 y 3. 47 ± 3. 64 y H&Y: 0: 1; 1: 52; H&Y: 0: 0; 1: 94; 2: 37; 3: 60, 4: 2: 55; 3: 27, 4: 4; 33; 5: 14 5: 2 Validation Validation dataset n = 166, dataset n = 173, 72 M 89 M Age: 62. 6 ± Age: 61. 23 ± 10. 30 y 10. 80 y PD duration: PD duration: 5. 98 ± 5. 03 y H&Y 1 : 0: 0; 1: 3. 35 ± 3. 41 y H&Y 1 : 0: 1; 1: 30; 2: 83; 3: 34, 78; 2: 77; 3: 16, 4: 14; 5: 5 4: 1; 5: 0 Lv et al.
¼ 0. 001 ↑ (0. 83; 33 %) Training dataset: PD þ FOG: 3. 38 ± 2. 35, PD-FOG: 2. 55 ± 2. 23 p ¼ 0. 002 ↑ (1. 12; 34 %) Validation dataset: PD þ FOG: 4. 44 ± 3. 65, PD-FOG: 3. 32 ± 3. 01 [38] NS: p = 0. 794 PD + FOG: 3. 00 (2. 00; 5. 00), PD-FOG: 3. 00 (2. 00; 5. 50) RBDSQ4 [48] NS: p = 0. 445 PD + FOG: 2. 33 ± 2. 40, PD-FOG: 1. 94 ± 2. 32 [47] NS Baseline: PD + FOG (transitional): 2. 23 ± 2. 35, PD-FOG: 1. 64 ± 2. 02 p ¼ 0. 006 ↑ (1. 05; 64 %) Baseline: PD þ FOG (continuing): 2. 69 ± 2. 30, PD-FOG: 1. 64 ± 2. 02 NS Follow up: PD + FOG (transitional): 2. 73 ± 2. 29, PD-FOG: 1. 84 ± 2. 02 p ¼ 0. 006 ↑ (0. 59; 36 %) Follow-up: PD þ FOG (continuing): 2. 63 ± 2. 12, PD-FOG:1. 84 ± 2. 02 RBDQ-HK: RBD, n (%) [26] NS: p = 0. 641 PD + FOG: 28/64 (44 %), PD-FOG: 47/99 (48 %) [56] p ¼ 0. 001 ↑ PD þ FOG: 108/255 (42.
Acknowledgments: This work received support from the following sources: the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM- CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313),  Human Brain Project  ( HBP SGA 2 ,  785907 , and  HBP SGA 3, 945539 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  National Institute of Health (NIH)  ( R01DA049238 , A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministerium für Bildung und Forschung  (BMBF grants  01GS08152 ;  01EV0711 ;  Forschungsnetz AERIAL 01EE1406A ,  01EE1406B ;  Forschungsnetz IMAC-Mind   01GL1745B ), the  Deutsche Forschungsgemeinschaft  ( DFG  project numbers  186318919  ( FOR 1617 ),  178833530  ( SFB 940 ),  402170461  ( TRR 265 ),  290210763  ( VE 892/2-1 ),  NE 1383/14-1 );  Faculty of Medicine at the Technische Universität Dresden , MeDDrive Grant; the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1  and  MR/S020306/1 ), the  National Institutes of Health (NIH)  funded ENIGMA (grants  5U54EB020403-05  and  1R56AG058854-01 ).
Further support was provided by grants from: -the  ANR  ( ANR-12-SAMA-0004 ,  AAPG2019 -GeBra ), the  Eranet Neuron  ( AF12-NEUR0008-01 -WM2NA ; and  ANR-18-NEUR00002-01 -ADORe ), the  Fondation de France  ( 00081242 ,  2012-00033703 ), the  Fondation pour la Recherche Médicale  (FRM;  DPA20140629802 ;  DPP20151033945 ), the  Mission Interministérielle de Lutte -contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University IDEX 2012 , the  Fondation de l'Avenir  (grant  AP-RM-17-013  ), the  Fédération pour la Recherche sur le Cerveau  ( FRC Neurodon 2015 ); the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  U. S. A. (Axon, Testosterone and Mental Health during Adolescence ;  RO1 MH085772-01A1 ), and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to  Knowledge Centres of Excellence.
Local haplotype analysis A neighbour-joining network of a region extending 1Mb on either side of kcnl-1 gene were established based on the VCF file dataset of CeNDR release 20220216 including 15 focal strains of a total of 48 isotypes. The region flanking the kcnl-1 gene was extracted using the vcftools command line and the 48 isotypes from were then extracted using vcfselectsamples command line from galaxy (https://usegalaxy. org). The distance matrix and unrooted neighbour-joining trees were generated using the vk phylo tree nj command-line from the VCF-kit tools  (Cook et al. , 2017; Widmayer et al. , 2022). Tree figures were made using the software iTOL (v5. 6. 3)  (Letunic and Bork, 2019).
Supporting Information Figures S1 to S7 (PDF) Table  S1  to S7 Data S1: Raw data (separate Excel file) doctoral fellowship provided by the French government through the UCA JEDI Investissements d'Avenir managed by the Agence Nationale de la Recherche (ANR-15-IDEX-01). We acknowledge additional support by the Centre National de la Recherche Scientifique (CNRS), the Institut national de la santé et de la recherche médicale (Inserm), and the Université Côte d'Azur (UCA).
Acknowledgements C. elegans strains and substrate samples were kindly provided by the  Caenorhabditis elegans Natural Diversity Resource (CeNDR) ,  Marie-Anne Félix  and  Jean-Antoine  Lepesant. We thank all students and technicians, including  Céline Ferrari ,  Alex Lassagne ,  Ghada Bouzouida ,  Nicolas Schwartz ,  Matthieu Duval , for contributing to egg retention measurements. We thank the team of  Erik Andersen , in particular  Sam Widmayer , for help with the GWAS analyses. For helpful discussion and comments, we thank  Kevin C. Collins  and  Henrique Teotónio. We would also like to thank CeNDR (https://elegansvariation. org/) and WormBase (https://wormbase. org/) for providing resources and tools without which the analyses performed here would not have been possible.
Brain structure and function are intimately linked, however this association remains poorly understood of the complexity of this relationship has remained understudied. Healthy aging is characterized by heterogenous levels of structural integrity changes that influence functional network dynamics. Here, we used the multilayer brain network analysis on structural (diffusion tensor imaging) and functional (magnetoencephalography) data from the Cam-CAN database. We found that the level of similarity of connectivity patterns between brain structure and function in the parietal and temporal regions (alpha frequency band) was associated with cognitive performance in healthy older individuals. These results highlight the impact of structural connectivity changes on the reorganisation of functional connectivity associated with the preservation of cognitive function, and provide a mechanistic understanding of the concepts of brain maintenance and compensation with aging.
We investigated changes in brain structure and function over time in young and older healthy participants from the Cam-CAN database (Cambridge Center for Aging and Neuroscience  30, 31  ). This database includes multimodal neuroimaging data (MEG, MRI, DTI) as well as cognitive performance evaluation for each individual. Our objectives were two-fold: i) To investigate changes in the interaction between structural integrity levels and synchronized functional networks between young and old individuals, with the underlying hypothesis that a decrease in white matter integrity could negatively impact brain function. ii) To study the impact of such structure-function relationship on participants' cognitive performance, where we expected that these changes would be associated with cognitive performance and reveal unique individual differences therein.
Results Two groups of participants (20-30 years for the younger group and 60-70 years for the older group) were formed from the Cam-CAN  30, 31  database. Connectivity analyses were performed on MEG data, and in particular, two measures were studied: phase locking value (PLV), which measure synchrony between regions, and transfer entropy (TE), which measure the directionality of the coupling between brain regions. The data from these two measures were combined with DTI data to form two multiplex structure-function networks (see Figure  1 ). From these networks, the multiplex participation coefficient could be calculated. This coefficient was then studied to determine the level of similarity of connectivity between the two layers (structural and functional) of the network. The different phases of data processing, creation of multiplex networks and statistical analysis are described in the materials and methods section.
Participants All participants aged 20-30 years and 60-70 years were selected from the Cam-CAN database  30, 31  , in line with demographic characteristics of individuals recruited in previous work  41, 18. Thus, we analysed data from 46 young (29 women and 17 men; aged 22-29 years) and 46 older healthy adults (29 women and 17 men; aged 60-69 years) (Table  1 ). All participants were right-handed, showed normal cognitive functioning  42  (Montreal Cognitive Assessment (MoCA) score >26), and no neurological or psychiatric conditions.
, 2023; Grady et al. , 2016; Singh-Manoux et al. , 2012; Varangis et al. , 2019). Indeed, failing to deactivate the DMN subnetwork seems to worsen performance in synergistic cognitive functions, based on intra-linguistic and control-executive processes, such as verbal fluency  (Muller et al. , 2016; Whiteside et al. , 2016). Overall, the main objective of this study is to investigate how the reorganization of the language connectome relates to the variability of cognitive performances across the lifespan. By assuming that language is a complex function in synergy with long-term memory and executive functions, we aimed to model how changes in the neural mechanisms that support this intercognitive synergy underlie this variability. To address this objective, we leveraged a population-based resting state fMRI dataset from the CamCAN cohort  (Cam-CAN et al.