dataesr/scientific-paragraphs-categorization huggingface dataset:
The last frame stayed intact until the end of the trial, and the stimulus was displayed over it for 200. The trial was terminated upon participant response or timed out after 2 seconds. A trial lasted for about 5 seconds, resulting in blocks of about 5 minutes each. We used face sketches as used in  Yang et al. (2020) , which were generated from the Radboud Face Dataset  (Langner et al. , 2010). Number stimuli were generated at the beginning of the session for each participant, under the constraint that they were 3-digit integers. In total, 10 different face stimuli and 10 different number stimuli were used for each participant. Pre-existing dataset from  Wagenmakers et al. (2008)  To discard the possibility of better performances emerging from the fitting algorithm or data acquisition, we also lead our analyses on a pre-existing dataset taken from  Wagenmakers et al.
BF co-activates with cortical areas enriched with cholinergic synapses during attention Extending on our prior work examining the BF cholinergic connectome with multimodal imaging  (Chakraborty et al. 2023) , we first examined which brain areas are consistently co-activated with the BF during attentional engagement. To do so, we employed meta-analytic connectivity mapping (MACM) of the BF (see Methods, Figure  2A ). MACM provides a robust and replicable query of brain imaging studies in the BrainMap database (>4,000 studies representing >100,000 participants), returning a list of brain activation coordinates which have been observed to co-occur with activation in a priori seed region. Our MACM query returned 88 task imaging experiments reporting BF task-related co-activations in 1357 cognitively normal adults (Figure  2B ).
(https://brainmap. org/sleuth/) separately on two seed regions of interest (ROIs): (1) the a priori anatomically defined nuclei of the BF; (2) the suprathreshold clusters identified in the ALE analysis of ACh agonist imaging studies (increases and decreases combined). The binarized MNI space masks for each ROI were entered into the BrainMap database, along with two other search criteria specifying (1) normal mapping and (2) activations. Using the search criteria, MACM queries the database for imaging studies which report coordinates for brain areas that are coactivated with the seed ROI during a particular task or under specific conditions. The coordinates are then entered into an ALE to identify brain regions that are consistently activated across studies.
Sutherland et al. 2015)  and reference lists were also examined to retrieve additional relevant studies. Following the initial screening of titles and abstracts, the selected articles underwent further screening based on predefined eligibility criteria. Studies were included in the meta-analysis if they (1) used fMRI or PET (2) reported activation data obtained from healthy, neurologically intact, non-elderly (i. e. mean age < 50 years) participants; (3) involved cholinergic manipulation of participants by either pharmacological administration or cigarette smoking in a placebo-(or abstinence) controlled within-subjects or between- subjects design; (4) employed tasks with at least two levels of cognitive load/demand; (5) reported activation coordinates for brain regions that demonstrated interaction between drug and task; and (6) reported peak activation coordinates in standardized stereotaxic Materials and Methods Literature Search and Selection Criteria for Pharmacological neuroimaging studies Ag(H>L)>Pla(H>L)] and (2) Activation decreases under ACh modulation and attention [ACh- We conducted a literature search on the PubMed database Ag(H>L)<Pla(H>L)].
In the current investigation, we examined for the first time how cerebellar regional anatomy may support cognitive function, capitalizing on a large dataset of transdiagnostic population employing a dimensional approach in agreement with the Research Domain Criteria framework  (Cuthbert 2014). Our aim was to first outline gray matter volume changes in the cerebellum across distinct facets of cognitive function (e. g. , executive function, working memory, cognitive flexibility, processing speed). Considering we were interested in cohorts with detailed cognitive phenotyping, we used the Healthy Brain Network (HBN)  (Alexander et al. 2017) , a landmark transdiagnostic mental health neuroimaging and behavioral dataset in a few thousand children and adolescents. The HBN includes predominantly unmedicated children and teenagers allowing us to examine cognitive cerebellar correlates unconfounded by chronic psychotropic consumption.
Subjects In the current investigation, we used data coming from an openly shared dataset, the Healthy Brain Network (HBN) project  (Alexander et al. 2017 ). The HBN is a transdiagnostic dataset of neuroimaging and psychopathological assessments from a cohort of psychiatric or at-risk population of children and adolescents (5-21 years)  (Alexander et al. 2017). Participants with severe neurological disorder or acute psychotic episodes are excluded in this cohort. In our study, considering our focus was on neurocognitive functioning, we excluded subjects with an intellectual deficiency (age-corrected IQ below 70, as measured with the Wechsler Adult Intelligence Scale (WASI-II) or the Wechsler Intelligence Scale for Children (WISC-V)  (Na and Burns 2016). The full clinical assessment of the HBN cohort is described elsewhere in depth  (Alexander et al.
Assessments We were motivated to examine distinct contributions of the specific cognitive function aspects to the cerebellar anatomy. In the HBN database, cognition is quantified via the NIH Toolbox Cognition domain  (Weintraub et al. 2013). The four subscales of the NIH Toolbox Cognition domain used in the HBN are detailed as follows. 1) The NIH Flanker assesses inhibitory executive control and attention and requires participants to focus on a target stimulus and ignore flanking stimuli. 2) NIH Card Sort assesses cognitive flexibility and requires participants to apply one rule to two target pictures (e. g. , matching by color) and then another (e. g. , matching by shape). 3) NIH List assesses working memory function and requires participants to sequence visually and orally presented stimuli e.
Canonical correlation analysis We performed our multivariate analyses with scikit-learn library  (Pedregosa et al. 2011). We employed a regularized kernel Canonical Correlation Analysis (CCA), using an open-source python pyrcca package  (Bilenko and Gallant 2016) , as a multivariate approach to evaluate the association between cerebellar anatomy (component with anatomical features "X") and cognitive phenotype (component with clinical scores of interest "Y"). In brief, CCA solves the canonical spaces in which the maximal correlation of projected datasets occurs, not preassuming the directionality of the relationship between datasets  (Hardoon et al. 2007 ). One documented disadvantage of CCA is overfitting to noise correlation of the datasets. To overcome this limitation, we implemented the algorithm used by the pyrcca toolbox.
Acknowledgements This project has been funded in whole or in part with Federal funds from the  National Cancer Institute, National Institutes of Health , under Contract No. HHSN261201500003I. The content of this publication does not necessarily reflect the views or policies of the  Department of Health and Human Services , nor does mention of trade names, commercial products, or organizations imply endorsement by the  U. S. Government. The human tissue samples from France were provided by AP-HM tumor bank  AC-2013-1786 ,  BB-0033-00097.
LC-MS/MS instrumentation and analytical conditions Analyses were performed with a Dionex Ultimate 3000 HPLC system (Thermo Electron, Villebon-Sur-Yvette, France) coupled with a triple quadrupole Endura mass spectrometer (Thermo Electron). Xcalibur v4. 0 software (RRID: SCR_014593; Thermo Electron) was used to control the system. Samples were loaded onto a ZORBAX SB-C18 column (150 x 1 mm, 3. 5 μm, flow of 90 µl/min; Agilent, Les Ulis, France) heated at 40°C. LC and MS/MS conditions used are detailed in Table  S1. Identification of the compounds was based on precursor ions, selective fragment ions and retention times obtained for the heavy counterpart present in the IS. Selection of the monitored transitions and optimisation of collision energy and RF Lens parameters were determined manually (for details, see Table  S1 ).
Data and Statistical Analysis Statistical analysis was performed using GraphPad Prism 6 Software (RRID: SCR_002798). To compare the groups, two-way ANOVA was used and followed by Tukey's multiple comparisons test only if F was significant and there was no variance homogeneity. Normality and variance homogeneity were checked with the D'Agostino & Pearson omnibus normality test and Levene's test, respectively. If assumptions were violated, a non-parametric approach, the Aligned-Rank Transform (ART) ANOVA, was used  [35]. For the behavioural experiment, non-linear regression with a 4-parameters logistic equation was used to define the average day at which half of the MPE remains and the Hill slope coefficient in both males and females. The means of each parameter were compared using an unpaired t-test after that normality and variance homogeneity were checked.
ACKNOWLEDGMENTS AND GRANT SPONSOR - This work was funded by  CNRS, University of Strasbourg (Unistra)  and  French Ministère Délégué à la Recherche et à l'Enseignement Supérieur  (PhD fellowship to F. G. , A. -K. B. and V. H. ). We thank the following research programs of excellence for their support: FHU Neurogenycs,  French National Research Agency (ANR)  through the  Programme d'Investissement d'Avenir  (contract  ANR-17-EURE-0022 ,  EURIDOL graduate  school of pain).
5 objective lens (MV PL-APO -Olympus) with a 5. 7 mm working distance. Whole brains were imaged in a chamber filled with dibenzyl ether. The ImSpector Pro control software was used. Images were acquired at 0. 63 × magnification with an exposure time of 100 ms in a z-stack with 3. 5 μm intervals (0. 035 NA-7 µm thickness light-sheet). Data were acquired in two channels: Autofluorescence and antibody-specific channel. Autofluorescence was acquired at excitation wavelength of 488 nm and emission wavelength of 525/50 nm. Antibodyspecific channel was acquired at excitation wavelength of 639 nm and emission of 680/30 nm. Fixed slices microscopic imaging. Immunofluorescence two dimensional images were generated using a Leica SP8-UV laser scanning confocal microscope (PL-APO 10X, 0. 4 NA, imaging medium Leica) for sections co-stained with NeuN and GAD67, or a Zeiss AxioImager Z1 microscope (PL-NEO 10X, 0.
Neural recordings Simultaneous recordings in the two FEF/hemispheres were carried out using two 24-contact Plexon U-probes. The contacts had an interspacing distance of 250 µm. Neural data was acquired with PlexonOmniplex® neuronal data acquisition system (Omniplex). The data was amplified 400 times and digitized at 40,000 Hz. The neuronal data was low-cut filtered at 300 Hz. All analyses were performed on the MUA recorded from each of the 48 recording contacts. A threshold defining the MUA was applied independently for each recording contact and before the actual task-related recordings started. All further analyses of the data were performed in Matlab (The Mathworks INC. , Natick, Massachussetts).
Data pre-processing In each task, MUA activity was epoched per trial. Spike trains were filtered with a Gaussian kernel (δ = 60 ms). First, MUA activity previous to the cue onset was epoched from -500 ms to 0 with respect to the onset of the Cue for the Pre-Cue analysis. Then, MUA activity was epoched between 500 ms to 0 ms prior to Target onset for the Delay analysis. To extract the averaged firing rates, these epoched MUA data were baselinecorrected (independently for each channel and each trial) relative to the time period from -500 ms to -300 ms before the cue.
, 2016). Here, we wanted to study and compare the population activity structure in the three different tasks and describe how much the variance in the neuronal population can be explained by task-related aspects such as task, spatial target location, and their interaction. To address this, we applied dPCA (demixed Principal Component Analysis  (Kobak et al. , 2016; Machens, 2010) ) on the data which captures a maximum amount of variance explained by predefined sources of variability in each extracted component and reconstruct the time course of the category-specific response modulation. Demixed PCA was applied in the two different time intervals (Pre-Cue and Delay). The purpose of the first dPCA analysis was to identify specific task-related components during the Pre-Cue period. Then, another dPCA was performed during the delay period, with the aim to extract task and spatial-related components, as well as components associated with their interaction (nonlinear mixed selectivity).
Both analyses were performed using appropriate Matlab based toolbox developed by Kobak and colleagues  (Kobak et al. , 2016). We considered the 20 first demixed components that accounted for > 90% of the total variance. We used the decoding axis of each dPC (assigned to task in the Pre-Cue analysis, or task, position and interaction in the Delay analysis) as a linear classifier to decode the different types of trials (Endo, Exo and Mem Sacc for task, and Up Left, Up Right, Down Left and Down Right for position), following the procedure described by Kobak and colleagues  (Kobak et al. , 2016). This method allows to quantify the capacity of each demixed component to classify a trial between the classes of a given category. To extract the statistical significance of this accuracy, we shuffled 100 times all available trials between classes and we thereby computed the distribution of classification accuracies expected by chance.
The three first dPCs (i. e. the two first task-related components (#1 and #2), and the first task-independent component (#3)) explained 80% of the variance, indicating that with only few components we recovered the majority of data variance. Therefore, population activity was accurately represented by the dPCA components. The angle between the two first components related to the task, did not significantly deviate from orthogonality (88°). This was confirmed by a second analysis performed on individual sessions (mean = 82°, std = 4°). This thus indicated that these two first components represented independent task-related information. Firing rates projected onto the first dPCA component associated to the task show a dissociation between the two attentional tasks and the working memory task (Figure  3C ).
To note, such difference cannot be interpreted as related to the response movement effector (hand response or saccade), since data was analysed during pre-cue activity (prior to motor preparation). The projection of the firing rates onto the second dPCA associated to the task show a dissociation between the exogenous (blue) and endogenous (green)s tasks suggesting a dissociation as a function of the attentional task-demands specifically within the attentional tasks. Supplementary Figure  3  shows, per each session, the projection of the dataset onto each of these components per task (trial-based). The third dPCA is associated with task-independent variance. In order to assess whether the tuning of each of these two demixed components was statistically significant, we used these components as linear decoders to measure their ability to encode task-related information.
Acknowledgements We thank engineer  Serge Pinède  for technical support,  Jean-Luc Charieau  and  Fabrice Hérant  for animal care, and  Johan Pacquit ,  Sylvain Maurin  and  Thomas Perret  for informatics assistance. All procedures were approved by the local animal care committee ( C2EA42-13-02- 0401-01) in compliance with the European Community Council, Directive 2012/63/UE on Animal Care. J. A. is now affiliated to  Instituto de Investigación Sanitaria La Fe ,  Plataforma de Big  Data, IA y Bioestadística, 46026, Valencia, Spain; C. G. is now affiliated to  Laboratory in Sensory Physiology ,  Otto Von Guericke University , 39120, Magdeburg, Germany; E. A. is now affiliated to  School of Innovation, Design, and Engineering, Mälardalen University , IDT,  721 23 Västerås, Sweden ; C. W. is now affiliated to  UMR 1253 ,  iBrain, Université de Tours, Inserm, Tours, France.
Funding This work was supported by the  Centre National de la Recherche Scientifique (CNRS) ,  Direction Général de l'Armement (DGA)  to E. A. ,  Fondation pour la Recherche Médicale (FRM)  to E. A. ,  Fondation de France  (FDF) Berthe Fouassier to A. M and S. B. H. , and french  National Research Agency  to S. B. H. ( ANR-11-BSV4-0011 ,  ANR-11-LABX-0042 ,  ANR-IDEX-0007 ).
Cells were blocked in FACS buffer containing 15% normal mouse serum for 15 min and subsequently stained for CD45, CD3e, CD8a, CD19, Ly6G, TCRβ, F4/80, NKp46, NK1. 1, CD49a, CD49b, CD127 (Ebioscience, 50-1271-80), ST2, KLRG1 diluted in FACS buffer for 30 min on ice. Cells were washed with FACS buffer and resuspended in FACS buffer containing Sytox Blue. Samples were acquired on the BD LSR Fortessa-X20 cytometer and data were analyzed using FlowJo (version 10, LLC) software.
Brains were washed with PBS and dehydrated by methanol series (20%, 40%, 60%, 80% 1 h each and 100% 2 h). Subsequently, the brains were placed in 66% dicholoromethane (Sigma, 270997), 33% methanol overnight, and afterwards in 100% dicholoromethane for 15 min. The brains were cleared in BABB (2:1 benzyl alcohol (Honeywell, 108006)/benzyl benzoate (Acros organics, 105862500)) overnight. Brains were acquired using the light sheet Ultramicroscope version II (LaVision Bio-Tec). 3D imaging analysis were performed using Imaris software (Version 9. 1. 0, Bitplane).
Acknowledgements Figures  1A ,  3B ,  5A  were created with BioRender. com. We thank  Toufik Guelmami  and  Michel Pontier. The flow cytometry core facility, notably  Marc Barad ,  Sylvain Bigot  and  Laurence Borge. We acknowledge  Mathieu Fallet  and  Sebastian Mailfert  of the ImagImm platform and  Lionel Chasson  of the histology platform. Gérard Eberl  ( Pasteur Institute, Paris, France ) for kindly supplying the RORc eGFP mice,  Ralf Adams  ( Max Planck Institute for Molecular Medicine, Münster, Germany ) for the Cdh5 CreERT2 mice and  Eric Vivier  ( CIML, Marseille, France ) for the Ncr1 iCre mice.
Statistical analysis. Data were analyzed using JASP (version 0. 17. 2. 1. 45  ) and the figures were made in R (version 4. 0. 3  46  ). Median reaction times (RTs) of correct trials were entered into a mixed-model analysis of variance (ANOVA). A 2 × 3 × 3 ANOVA was used with Montage (DLPFC, FM) as a between-subject factor and with Stimulation Type (sham, anodal, cathodal) and Trial Type (neutral, congruent, incongruent) as withinsubject factors. No-go trials could not be included in the RT analysis as these required the suppression of motor response. Interference effects were calculated for all stimulation types by extracting the median RTs of the congruent trial type from the median RTs of the incongruent trial type (Interference effect = RT incongruent -RT congruent ). Lower interference effect score indicates better recruitment of cognitive control.
Thus, four possible categories were possible based on Trial n-1 and Trial n congruence: congruent trials preceded by congruent trials (cC), congruent trials preceded by incongruent trials (iC), incongruent trials preceded by congruent trials (cI) and incongruent trials preceded by incongruent trials (iI). Accuracy data were analyzed similarly to median RTs, except that accuracy scores of no-go trials were also included in the Trial Type factor (neutral, congruent, incongruent, no-go). The presence of adverse effects was analyzed in separate mixed analyses of variance with Stimulation type (anodal, cathodal, sham) as within-subject factors and Montage (DLPFC, FM) as a between-subject factor. To assess whether adverse effects were comparable, we evaluated the Stimulation type × Montage interaction for each symptom.
Patient data To study the accuracy of the model, it is tested against a retrospective patient cohort of 12 patients. The dataset used in this study is the same as the one used in a previous VEP study  [18, 31, 19]. The dataset includes invasive and non-invasive multimodal neuroimaging data such as T1-MRI, diffusion MRI and SEEG. For more details of the data acquisition process please refer to  [31]. Informed written consent was obtained for all patients in compliance with the ethical requirements of the Declaration of Helsinki, and the study protocol was approved by the ethics committee -Comité de Protection des Personnes sud Méditerranée 1. For each patient with a known surgical outcome, a connectome is generated by running the reconstruction pipeline at high spatial resolutions with brain surface modeled as a triangular mesh containing 327684 vertices.
Some of the patients are excluded due to issues with running the reconstruction pipeline at high spatial resolutions. Out of the 25 patients in the original dataset, the reconstruction pipeline ran successfully for 12 patients at high spatial resolution. These 12 patients are used for empirical validation in this study. Among the 12 patients, 7 are seizure free post surgery with their surgical outcome rated as Engel score I. In the remaining 5 patients surgery failed to achieve seizure freedom and their outcomes are classified as either Engel score II, III or IV. More details of the patients such as age, gender, Engel scores and inclusion/exclusion criteria are provided in Table  1. Patient IDs in Table  1  are not known to anyone outside the research group and does not reveal the identity of the patient to anyone outside the research group.
Synthetic data To study the accuracy of the EZ predictions by comparing them with ground truth two synthetic datasets are generated using the connectome and gain matrices of the patients id004 and id022. EZ are defined by choosing regions close to the implanted electrodes. Specifically, for the patient id004 EZ consisted of three regions: Right-Temporal-pole, Right-T2-posterior and Right-Thalamus. EZ for id022 consisted of four regions: Left-Superior-parietal-lobule-P1, Left-Intraparietal-sulcus, Left-Postcentral-gyrus, Left-Postcentral-sulcus. SEEG electrode implantation, centers of the brain regions in EZ, structural connectome and gain matrix of patient id004 and id022 are shown in supplementary Fig. 1  and supplementary Fig. 3  respectively. For all the vertices belonging to regions in EZ the tissue excitability parameter (x 0 ) is set to -1.
Feature extraction The raw SEEG data is preprocessed to extract SEEG log power. Preprocessing involves high pass filtering raw SEEG data from 10 Hz, computing the power over a sliding window, applying a log transformation and finally a low pass filter of 0. 05 Hz is applied for smoothing out any short spikes in the data. In order to reduce the computational cost of fitting, the SEEG log power time series is down sampled to 300 time points. Data augmentation is a technique in machine learning to improve optimization when the observations are sparse. A common technique for data augmentation is to increase the number of observations by adding noise to the observed data. In this study, each time series is augmented by generating 50 more time series by adding Gaussian noise with mean zero and standard deviation of 0.
Performing model inversion entails computing gradients over the generated solutions. Typically these gradients are computed using automatic differentiation  [39]  which builds a computational graph of all the operations or transformations involved in mapping a particular parameter configuration of the neural field model (Eq. 6) to the SEEG data features. Computationally intensive functions such as the homogeneous connectivity term in Eq. 7 can lead to prohibitively high memory usage in computing gradients. To avoid this, in this study, we use a pseudo-spectral method to estimate the homogeneous connectivity.