dataesr/scientific-paragraphs-categorization huggingface dataset:
), prompted us to investigate the structure-and/or cell type-specific expression of NAPE-PLD in both rodents (rat and mouse) and human brains. First, by taking advantage of single-nucleus RNA transcriptomics (snRNA-seq) in the rat NAc (47) (GSE137763) and VTA (48) (GSE168156), we performed a clustering metaanalyses of transcripts encoding for eCBs-and NAEs-producing enzymes (Napepld, Dagla, Daglb) as well as for eCBs-and NAEs-related transducing effectors (Cnr1, Trpv1, Gpr119, Ppara, Pparg). Compared to Dagla and Daglb, we observed that accumbal Drd1-and Drd2medium spiny neurons (MSNs) expressed low levels of Napepld (6% of 2819 Drd1-MSNs.
Notably, certain behaviors exhibited a gradual return to typical patterns over time. This dynamic FXS phenotype exhibited heightened activity, featuring increased exploration, amplified social interest, and an unconventional approach to social interactions characterized by a higher frequency of shorter engagements. These findings contribute to the growing understanding of social behavior in individuals with FXS and underscore the significance of comprehending their adaptive responses in various environmental contexts. Eclipse. Several types of behaviors were extracted from the dataset (list and descriptions is provided in supplementary information in De Chaumont et al. ,  2019 30  ).
Acknowledgements We thank  Richard Newton  for support with images and  UCL Genomics  who did the array processing. For UK samples we would like to thank patients, their families and carers, UK neurologists and other referring physicians, co-workers at the  National Prion Clinic , our colleagues at the  National Creutzfeldt-Jakob Disease Research and Surveillance Unit, Edinburgh. Several authors at  UCL/UCLH  receive funding from the  Department of Health's NIHR Biomedical Research Centres  funding scheme. Some of this work was supported by the  Department of Health funded National Prion Monitoring Cohort study. Funding for the collection of Polish samples for study was partially provided by the  EU joint programme JPND and Medical University of Lodz. We thank  Dr. Maria Styczynska  from  Mossakowski Medical Research Centre; Polish Academy of Sciences; Warsaw , for kindly providing control DNA samples for the Polish cohort.
The Italian national surveillance of Creutzfeldt-Jakob disease and related disorders is partially supported by the  Ministero della Salute, Italy. The  German National Reference Centre for TSE  is funded by grants from the  Robert-Koch-Institute. The  Dutch National Prion Disease Registry  is funded by the  National Institute for Public Health and the Environment (RIVM) , which is part from the  Ministry for Health, Welfare and Sports, The Netherlands. PS-J was supported by  Instituto de Salud Carlos III [Fondo de Investigación Sanitaria ,  PI16/01652 ]  Accion Estrategica en Salud  integrated in the.
Acknowledgments S. B. H. was funded by the  French National Research Agency  ( ANR )  ANR-16-CE37-0009-01  grant and the  LABEX CORTEX  funding ( ANR-11-LABX-0042 ) from the  Université de Lyon , within the program  Investissements d'Avenir  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). This work received funding from  KU Leuven   C14/21/111 ,  IDN/20/016 ,  C3/21/027 ;  Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO-Flanders )  G0E0520N ,  G0C1920N. We thank also thank  Thomas Perret ,  Johan Pacquit  and  Marco Bimbi  for help with the hardware computational resources.
MEBRAINS template construction with the multi-brain toolbox The main processing tool for building the MEBRAINS template was the MB toolbox of SPM12  (Brudfors et al. , 2020)  (https://github. com/WTCN-computational-anatomygroup/mb), and as input we used information from both T1 and T2 images. We chose MB because it generates a probabilistic tissue classification model while performing the nonlinear registration, rather than just using voxel intensities directly. This approach has been shown to be a more robust method of registering medical images  (Klein et al. , 2009; Sotiras et al. , 2013). Furthermore, the algorithm  (Brudfors et al. , 2020)  used by MB can integrate many imaging modalities (e. g. , T1, T2, DW, CT), and can be applied with or without prior pre-processing (e. g. , skull stripping).
Accordingly, we took advantage of the high-resolution CT scans of the same subjects, applied the same transformations as those used to register the corresponding T1 and T2 images to the reference template, and averaged the resulting CTs to build the CT template. Thus, multi-brain allowed us to build the following three templates using T1, T2 and CT brain images of 10 monkeys: MEBRAINS_T1, MEBRAINS_T2 and MEBRAINS_CT, respectively. We generated the volumetric templates as follows: i) Learn the MB tissue probability model. We adapted Example 1 from the MB repository (https://github. com/WTCN-computational-anatomy-group/mb). As input we used the set of 10 pairs of T1 and T2 images and additionally the same set of images mirrored across the midsagittal plane to create a symmetric template. This group-wise image registration generated the following datasets: an optimal K class tissue template; optimal intensity parameters; deformations that are used to warp between different volumes; tissue segmentations; and bias-field corrected versions of the input scans.
In general, we kept the default settings to run the MB modelling (as in Example 1 mentioned above). The following parameters were modified in our script: regularization of the nonlinear registration (changed from 1 to 2), number of tissue types K (set to 14), and voxel size (set to 1). ii) Register the T1 and T2 individual volumes to the MB tissue model using the MB deformations generated during the learning step, as in example 2 of the MB repository (https://github. com/WTCN-computational-anatomy-group/mb). We used a 3 rd degree B-spline interpolation algorithm, and co-registered the CT volumes with the T1 volumes. iii) Create T1, T2 and CT templates by averaging the corresponding individual images registered to the MB tissue model. Intermediate T1, T2 and CT templates are created by gradually averaging more and more individual images that are registered to the implicit MB template.
Generation of a MEBRAINS surface template Surface representations of the brain enable a more precise spatial localization and reduce the occurrence of errors arising from the spatial proximity of brain structures that are actually located at quite a distance from each other along the cortical ribbon  (Logothetis et al. , 2001; Zhu and Vanduffel, 2019). Additionally, they are a prerequisite for generating cortical flat maps, which are useful tools for the analysis and visualization of functional and structural neuroimaging datasets  (Sultan et al. , 2010; Van Essen et al. , 1998; Vanduffel et al. , 2001; Vanduffel et al. , 2014) , particularly for topographic representations such as retinotopy  (Arcaro and Livingstone, 2017; Janssens et al. , 2014) , somatotopy  (Arcaro et al. , 2019)  and tonotopy  (Bodin et al.
Registration of 3D datasets to MEBRAINS Since it is essential to link MEBRAINS to commonly used template spaces, we developed a multi-method workflow to register 3D data to MEBRAINS. Independent of the method/algorithm used, registration of 3D volumes can be achieved as follows: • Step 1. Preparatory pre-processing of the data to roughly adjust the image geometry (i. e. , resolution, dimensions, position) performed with FreeSurfer, FSL  (Woolrich et al. , 2009)  and MATLAB. This step does not necessarily require MB. • Step 2. Register the brain anatomy (e. g. , other template volume or individual anatomy) to MEBRAINS. This process is achieved by calculating and applying the transformation functions (matrices and deformation volumes). Noteworthy, the transformations generated for a specific volume (e.
g. , a template) can be applied to different entities (e. g. , atlas, connectivity maps) represented in that space. The specifics of the registration performed with MB are found under "https://github. com/WTCN-computational-anatomy-group/mb -Example 3: Fitting a learned MB model", and were applied to individual brain anatomy/template volumes. • Step 3. Evaluate the quality of the registration and improve it by adjusting different parameters of the registration algorithm. If the object to be registered is a template brain or an individual anatomical dataset, the process is finished. We used "https://github. com/WTCN-computational-anatomy-group/mb -2. Warping with MB deformations -image-to-template -pull" to apply the deformation generated in the previous step to the brain anatomy/template. • Step 4.
It is The copyright holder for this preprint this version posted June 24, 2023. ; https://doi. org/10. 1101/2023. 06. 21. 545953 doi: bioRxiv preprint listed in "https://github. com/WTCN-computational-anatomy-group/mb; "4. Register and warp atlas to MB space". Since no single tool functions seamlessly, the best strategy is to combine functions from different software packages. This is illustrated by the existence of an open-source, community-developed initiative like Nypype  (Gorgolewski et al. , 2011)  (https://nipype. readthedocs. io/en/latest/), facilitating interactions between different software packages (e. g. , ANTS, SPM, FSL, FreeSurfer, Camino, MRtrix, MNE, AFNI, Slicer, DIPY). Like all methods, MB also harbors some problems. For example, recall that the MEBRAINS template is built using both T1 and T2 weighted images.
• Testing data: 66 T1 images (34 training data; 32 new data from KU Leuven). For all T1 images, ground-truth was derived from human-curated masks either created by us or taken form the repository from the U-net brain extraction package (https://fcon_1000. projects. nitrc. org/indi/indiPRIME. html, https://github. com/HumanBrainED/NHP-BrainExtraction. git).
Relative quality of the MEBRAINS template To quantitatively evaluate the quality of our template relative to that of other templates, we used a method inspired by  (Seidlitz et al. , 2018). We chose for comparisons the following T1 templates: our MEBRAINS and ANTS10 templates, the NMT v2. 0  (Seidlitz et al. , 2018)  and Yerkes19  (Donahue et al. , 2018; Van Essen et al. , 2012)  templates, and the combination of the T1/T2 images of MEBRAINS and ANTS10. The two latter datasets were introduced to emphasize the usefulness of our multimodal approach. The processing of these 6 datasets included the following steps: a. For each template, we segmented the amygdala (Am), caudate (Cd), claustrum (Cl), nucleus accumbens (NAc), putamen (Pu), white matter (WM), cortical gray matter (GM) and lateral ventricle (LV).
b. Normalization of the variations in T1 image intensity across each volume by N4 bias field correction  (Avants et al. , 2011)  (using ANTS's N4BiasFieldCorrection). T1/T2 images were generated from the original T1 and T2 images (without N4 bias field correction). c. Using volume contraction (AFNI), we selected the kernel of each segment by excluding the external 3 voxels thick shell of each sub-cortical region. d. We calculated the average gray matter (meanGM) of N randomly selected voxels (N = 50) for each segmented region (Am, Cd, Cl, NAc, Pu, and GM). For the white matter, we calculated the average white matter intensity (meanWM) of all voxels from the WM kernel. For LV, we calculated the standard deviation of the intensity of the e3. Steps e1-e2 were repeated for all 6 templates (the four T1 and the two T1/T2 datasets).
Registration of 3D datasets to MEBRAINS The purpose of a template is to offer a standardized stereotaxic space for the analysis and/or visualization of neuroscience data, often requiring the co-registration of different volumes (e. g. , individual brain anatomies, templates). Given the aforementioned advantages and limitations of MB, we propose a multi-method workflow with 4 major steps to integrate data into MEBRAINS space: Steps 1-3 encompass standardized preprocessing procedures, the actual computation of transformation functions (such as matrices and deformation volumes) necessary to register an anatomical image to MEBRAINS, as well as a quality assessments and improvements of the registration. Step 4 is only required if a data set instead of a structural anatomical volume needs to be registered, such as retinotopic maps, connectivity maps or parcellation schemes.
In this case, steps 1-3 are performed with the reference anatomy, and the transformations/deformations functions are then applied to the associated datasets. To demonstrate the validity and flexibility of our workflow, we first describe the result of our registration procedures when applied to some frequently used macaque brain templates, although they can be applied to any individual or averaged anatomical 3D volume. In a second step, we provide an example of how Step 4 can be implemented to transform a parcellation scheme of the macaque brain from the Yerkes19 surface to the MEBRAINS surface and volumetric templates.
g. , NMT v2. 0), yet produced distorted registrations for many of the skull-stripped templates. The most optimal registration method for all registered templates was ANTS. Figure  8  shows ANTS10_T1, the 8 selected templates, and a meta-template (the average of the ANTS10_T1, and all template datasets, excluding 112RM-SL), all registered to MEBRAINS using ANTS. Furthermore, figure 8 also provides a unique opportunity to compare other templates with MEBRAINS. At qualitative level, MEBRAINS reveals comparable anatomical details as NMT V2. 0, unlike the other templates. Figure  9  shows a quantitative evaluation of the quality of the registrations of the different templates with MEBRAINS (in Figure  8 ) using Pearson correlation and focal entropy differences -which was scaled to improve comparisons with the correlation method (0total dissimilarity; 1total similarity).
Relative quality of the MEBRAINS template To quantitatively compare the quality of different templates, we segmented a number of anatomical structures from four T1 templates (MEBRAINS_T1, ANTS10_T1, NMT v2. 0, Yerkes19) and two T1/T2 datasets (MEBRAINS_T1/T2, ANTS10_T1/T2) (Figures 12A). Depending on the quality of the template, the exact border of a structure may be difficult to estimate. Therefore, to be conservative in our comparison, we excluded the 3 most external voxels at each boundary of each of these compartments: for example, 3 voxels at the pial and 3 at the grey-white matter boundary for the cortical ribbon. As an example, Figure  12B  shows the result of this process for MEBRAINS_T1. We used two different indices, inspired by  (Seidlitz et al. , 2018) , to compare the quality of the templates (C2N and KI, see methods).
Finally, we started to populate the MEBRAINS with previously published architectonic data  (Donahue et al. , 2016; Niu et al. , 2020; Niu et al. , 2021; Rapan et al. , 2021)  neurotransmitter receptors distribution patterns or that of their subunits and/or the corresponding encoding genes, tractography datasets, as well as architectonic parcellation schemes of the macaque monkey brain. In this framework, the "Julich Brain Macaque Maps"  (Donahue et al. , 2016; Niu et al. , 2020; Niu et al. , 2021; Rapan et al. , 2021) , which are based on the quantitative analysis of differences in the distribution patterns of cell bodies and of multiple types of classical neurotransmitters, and to date had solely been available via the Yerkes19 surface template  (Donahue et al. , 2018; Van Essen et al. , 2012) , have now been registered to the MEBRAINS template.
CODE AVAILABILITY The following code is available on GitHub or software package webpages: -           Seidlitz et al. , 2018) , Yerkes19  (Donahue et al. , 2018; Van Essen et al. , 2012) , D99  (Reveley et al. , 2017) , MNI  (Frey et al. , 2011 ), F99 (Van Essen, 2004) , INIA19  (Rohlfing et al. , 2012) , ONPRC18  (Weiss et al. , 2021) and 112RM-SL (McLaren et al. , 2009) ), as well as our ANTS10_T1 volume (i. e. , the template built with ANTS using the same 10 datasets as MEBRAINS_T1) were registered to MEBRAINS using ANTS. The meta-template represents the average of all these datasets with the exception of 112RM-SL. CC-BY-NC 4. 0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
ACKNOWLEDGEMENTS This work received funding from  Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO-Flanders )  G0D5817N ,  G0B8617N ,  G0C1920N ,  G0E0520N ; the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement No  945539  ( Human Brain Project SGA3 );  KU Leuven C14/21/111 ,  C3/21/027 ,  IDN/20/016 ; the  Federal Ministry of Education and Research (BMBF)  under project number  01GQ1902. We would also like to thank all members of the PRIMatE data sharing consortium (https://fcon_1000. projects. nitrc. org/indi/indiPRIME. html) for collecting, publishing and making their data available.
using either the RheMAP (Sirmpilatze and Klink, 2020)   Jupiter notebook (https://github. com/PRIME-RE/RheMAP. git), or antsRegistrationSyNQuick to generate the registration and antsApplyTransforms to apply it. c. AFNI (Cox, 1996) -generate the registration with 3dQwarp and apply it with 3dNwarpApply. d. MINC (Vincent et al. , 2016) -generate the registration with minctracc and apply it with mincresample. e. ART (Ardekani et al. , 2005) -generate the registration with 3dwarper and apply it with applywarp3d.
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is This is a fast and stable U-Net based pipeline for brain extraction that exhibited superior performance compared to traditional approaches using a heterogenous, multisite non-human primate (NHP) dataset. The pipeline includes code for brain mask bioRxiv preprint doi: ; https://doi. org/10. 1101/2023. 06. 21. 545953 this version posted June 24, 2023. The copyright holder for this preprint. CC-BY-NC 4. 0 International license made available under a prediction (https://github. com/HumanBrainED/NHP-BrainExtraction. git), model- building, and model-updating, as well as macaque brain masks of PRIME-DE data (https://fcon_1000. projects. nitrc. org/indi/indiPRIME.
html). A major advantage of the pipeline is that it uses a transfer-learning framework leveraging a large human imaging dataset to pre-train a convolutional neural network (U-Net Model), which is transferred to NHP data using a much smaller NHP training sample. Furthermore, the generalizability of the model can be improved by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data- Exchange (PRIME-DE) consortium (136 macaque monkeys with skull-stripped masks repository, publicly available) aided diagnosis, including lesion and tumor detection, and diagnostics of psychiatric and neurodegenerative disorders (e. g. , Schizophrenia, Alzheimer's disease, Parkinson's disease, brain age estimation). Traditional neuroimaging pipelines involve computationally intensive, time-consuming optimization steps, often requiring manual interventions (Henschel et al.
The MEBRAINS template represents the cornerstone of the "MEBRAINS Multilevel Macaque Brain Atlas" (https://atlases. ebrains. eu/viewer/monkey) developed in the framework of the Human Brain Project, which is freely available to the neuroscientific community via the interactive siibra-explorer on the EBRAINS platform (https://atlases. ebrains. eu/viewer/monkey). Thus, MEBRAINS constitutes a spatial reference system to which a myriad of structural and functional in vivo and post mortem datasets with different degrees of spatial and temporal resolution will be anchored. Examples of in vivo datasets are electrophysiological, probabilistic retinotopy, category selective or resting state fMRI data as well as DTI datasets. Post mortem datasets include 3Dreconstructions of sections processed for visualization of cell bodies, myelinated fibres,.
= the template is available in the original format (OF) or only in a skull stripped (SSF) format. AFNI (https://afni. nimh. nih. gov/), MINC (https://www. mcgill. ca/bic/software/minc), ART (https://www. nitric. org/projects/art/), Jip (http://www. nitrc. org/projects/jip), MRIcron (https://www. nitrc. org/projects/mricron), and ITK-SNAP (http://www. itksnap. org/pmwiki/pmwiki. php) are open source publicly available. -U-Net Brain extraction tool for nonhuman primates (https://github. com/HumanBrainED/NHP-BrainExtraction) is publicly available and requires a python environment. Authors will provide by request the supplementary trained models. 2018b). The repository includes example MATLAB scripts for template generation, registration to the template, different images co-registration -FreeSurfer (https://surfer.
In line with the classically described ERD and ERS phenomena, the non-invasive BCI community still heavily relies on signal power as the target feature for classification, although, notably, state of the art Riemannian classifiers  [83] [84] [85]  and some deep learning approaches  [86, 87]  have independently moved on from explicitly using frequency-specific power features. In this article we propose a shift in perspective, by demonstrating how beta band activity during MI tasks is modulated in terms of patterns of distinctly shaped bursts that are better descriptors of transient activity changes. We have previously argued that analyzing beta burst activity should enable us to gain access to classification features that are at least as sensitive as beta band power  [88]. If this hypothesis is valid, then we should be able to test it and verify it using publicly available datasets.
Here, we show that this approach allows us to achieve better classification results than those obtained when assessing signal power in binary MI classification tasks, when comparing burst features to signal power from EEG channels C3 and C4. We validate our approach against six open EEG BCI datasets, and provide links between the decoding performance and the modulation of different features considered for classification across datasets and subjects. Although our results obtained by using beta burst features are in most cases inferior to state-of-the-art, namely because our analysis only included two channels and focused solely on the beta frequency band, they are, conversely, superior to those obtained using only beta band power in these channels. This analysis demonstrates the utility of beta burst analysis for BCI and paves the way to improve classification performance in the near future.
Datasets We used six open EEG MI datasets: BNCI 214-001  [89] , BNCI 2014-004  [90] , Cho 2017  [91] , MunichMI  [92] , Weibo 2014  [93]  and Zhou 2016  [94] , all available through the MOABB project  [95]. Briefly, all datasets contain recordings of subjects who were required to perform sustained motor imagery following the appearance of a visual cue on a screen. For our analysis we only considered trials corresponding to the "left hand" or "right hand" classes even if other classes were available in some of the datasets.
Data pre-processing For each dataset, recordings were loaded per subject using the MOABB python package (v0. 4. 6) MotorImagery class, and were filtered with a low pass cutoff of 120 Hz. The low pass cutoff was set to 95 Hz for the Weibo 2014 dataset, because the corresponding sampling frequency of the recordings is 200 Hz. For most of these datasets numerous channels are available, so we defined a subset of channels over the sensorimotor cortex that we deemed relevant for the task and applied pre-processing (Table  1 ). Then, in this work, we only analyzed data from channels C3 and C4. Each trial was aligned to the cue onset, and the task period was defined as the time between cue onset and the end of the MI task. We used the time window within one second prior to the cue onset as the baseline period (Table  1 ).