dataesr/scientific-paragraphs-categorization huggingface dataset:
Thresholdmann. A web tool for interactively creating adaptive thresholds to segment NifTI images. A simple intensity threshold can often provide a good initial guess for tissue segmentation, but in the presence of strong intensity gradients, a threshold that works well for one brain region can easily fail elsewhere in the same brain image. Thresholdmann ( https://neuroanatomy. github. io/thresholdmann ) is an open source web tool for the interactive application of space-varying thresholds to NifTI volumes. It does not require any software downloads or installation and all processing is done on the user's computer. NifTI volumes are dragged onto a web interface where they become available for visual exploration in a stereotaxic viewer ( Fig. 4 A ). A space-varying threshold is then created by setting control points, each with their own local threshold value.
The viewer is initialized with one control point at the center of the brain. The addition of further control points produces a spacevarying threshold obtained through radial basis function interpolation. Each local threshold can be adjusted in real time using sliders. Finally, the thresholded mask, the space varying threshold and the list of control points can be saved for later use in scripted workflows. Thresholdmann complements a variety of existing brain segmentation tools with an easy interface to manually control the segmentation on a local scale. The resulting masks can serve as starting points for more detailed manual editing using tools such as BrainBox ( https://brainbox. pasteur. fr ) or ITK Snap ( http://www. itksnap. org ). The interactive approach is especially valuable for non-human brain imaging data, where it has been successfully used to create initial brain masks for a variety of vertebrate brains -including many NHP datasets  ( Heuer et al.
BrainBox. A web application for real time, collaborative visualization, annotation & segmentation Automatic brain segmentations often need to be manually adjusted and in some cases a complete manual segmentation is required. Manual segmentation can however be difficult, time consuming, and it is often performed by researchers working in isolation, even for shared public data. BrainBox ( https://brainbox. pasteur. fr ) is an open source tool for the visualization, collaborative curation and analysis of neuroimaging data. BrainBox makes it possible for several researchers to simultaneously work on the same dataset in a shared virtual working space. BrainBox can be used to visualize and provide a layer of human annotation to any dataset available on the Web, for example, to data stored in Zenodo, FigShare, Google Drive, Dropbox, Amazon S3, GitHub, or the Open Science Framework.
After providing the URL of a dataset, Brain-Box presents an interactive viewer, and a series of editing tools, e. g. for segmenting brain regions, erasing, or building 3D models ( Fig. 4 B ). Several datasets can be combined into "projects " and project creators can invite collaborators. Access to the data can range from completely private to completely open projects. Finally, BrainBox implements a REST-ful API (Application Program Interface) which allows programmatic access to all its data. For example, annotations and segmentations can be queried by a local python script, or even a shared notebook running on Google Colab. BrainBox adds a layer of collaborative curation and analysis to shared neuroimaging datasets using only a web browser, allowing users to incrementally improve each other's work.
UNet NHP brain extraction tool. A convolutional neural network model for skull-stripping of NHP images. The UNet brain extraction tool provides a flexible brain labeling solution to facilitate the preprocessing of NHP neuroimaging data, particularly for multi-site datasets with different scan acquisition, quality, and surgical situations (e. g. , with head-holder implants) ( Fig. 4 C ). The UNet model was initially trained on a large sample of human neuroimaging data and transferred to the NHP with additional macaque training samples from the PRIME-DE data repository  ( Milham et al. , 2018 ). In order to run the tool with the default model, a user only needs to specify the input T1-weighted image. The full process requires ~20 s on a GPU (NVIDIA GTX 1070TI, 700 MB graphics card memory) and ~2-15 min on a CPU, which means it can easily be run on a personal computer.
The current default model has been successfully applied to 136 macaque monkey scans from 19 sites on PRIME-DE with good performance ( https://github. com/HumanBrainED/NHP-BrainExtraction ). The tool also provides a module for the user to upgrade the UNet model for a specific dataset (e. g. , raw T1w from a highfield scan, MP2RAGE, etc. ). Unlike the traditional neuroimaging tools, no strong imaging background is required for the user to adjust processing parameters for a specific dataset. Instead, the tool only requires a small macaque image-set training sample ( n = 1-2) to upgrade the model and improve performance accuracy for that specific dataset.
Alignment and segmentation pipelines. In addition to tools that specialize in a single step of structural analysis, there are also several pipelines listed on PRIME-RE that can take care of multiple structural analysis steps. 3. 2. 2. 4. 1. Macapype. An open multi-software framework for nonhuman primate anatomical MRI processing Macapype ( https://github. com/Macatools/macapype) provides an open-source framework to create customized NHP-specific MRI data processing pipelines based on Nipype  ( Gorgolewski et al. , 2011 ). Nipype is a widely-adopted Python framework for human MRI data processing, which provides wrappers of various commands and functions from well-known neuroimaging software (e. g. , AFNI, FSL, SPM12, ANTs). In Macapype, custom scripts specific to NHP data processing are also wrapped.
These include a brain extraction tool (AtlasBREX;  Lohmeier et al. , 2019 )  and a script for computing registrations between a subject brain and the NMT macaque template (NMT_subject_align. csh; Seidlitz et al. , 2018 ). Users can thus flexibly construct customized pipelines by putting together various processing modules (and parameters) that are optimal for their dataset. Macapype consists of several configurable modules for: 1) Data preparation steps, such as image reorientation, deoblique-ing, cropping, and the registration and averaging of multiple images; 2) Anatomical preprocessing steps like bias-correction with ANTs N4BiasCorrection;  ( Tustison et al. , 2010 )  and/or T1w x T2w bias field correction  ( Rilling et al. , 2012 ) , denoising using the adaptive non local means filter  ( Manjón et al.
, 2010 ) , brain extraction (e. g. with AtlasBREX), and brain segmentation (e. g. using AntsAtroposN4 or SPM Old Segment). Two examples of modular anatomical pipelines are already implemented in the distributed version of Macapype. They are customized for the preprocessing, brain extraction and segmentation of macaque anatomical data, and have performed robust segmentations on different datasets. The first pipeline creates a high quality segmentation in native space for surface reconstruction. Briefly, the pipeline takes the T1w and T2w images as inputs, and first applies cropping, biascorrection (via T1w x T2w and N4 intensity bias correction), and denoising (adaptive non-local means filtering) to improve image quality. Next, brain extraction is performed using AtlasBREX. For segmentation, transformations between the subject skull-stripped image and the NMT macaque template are first computed using the NMT_subject_align.
csh script  ( Seidlitz et al. , 2018 ) , and then applied to register the NMT tissue segmentations to the subject image. Finally, segmentation is performed using AntsAtroposN4. sh to segment the subject image in native space, with the template segmentation as priors. The second pipeline reaches the same goal with different packages. This pipeline provides an iterative sequence for normalization (source to template space) of T1w and T2w, and provides segmentation with both SPM12 (OldSegment) and FSL FAST. A docker file is included in the package to allow users to get a fully embedded version of the pipelines working on any computer without previous installation of MRI processing software. Both pipelines are compatible with BIDS  ( Gorgolewski et al. , 2016 )  formatted datasets. The two pipelines have been demonstrated to achieve robust skullstripping and segmentation on anatomical data from different primate species: Pipeline 1 was tested on both macaque  ( Milham et al.
@animal_warper. AFNI program that aligns structural data to a template. @animal_warper is a bidirectional alignment program made for animal neuroimaging. While human neuroimaging has typically required a standard template, many animal researchers may prefer to keep their data in the native acquisition space and move atlas regions and tissue segmentations into the native space of the subject. Alternatively, data can be transformed to a standard template space to allow voxelwise group analysis and have the advantage of a standardized coordinate reporting system. The @animal_warper program proceeds in a series of alignment steps. First, the center of the input dataset is moved to match the center of the template. Then, an affine alignment uses a 12-parameter transformation to match translation, rotation, shear and scale.
Finally, a nonlinear warp is computed to align structural details to the template. The inverse warps and inverse affine transformations are computed and applied for the reverse direction of template to native space. "Follower" datasets, like ROIs drawn in the native space, can also be transformed into a target space. Datasets and ROIs typically follow from native to template space; templates, atlases and segmentations follow from template to native space. The widely varying kinds of data used in animal imaging require different cost functions for alignment to flexibly deal with the voxel resolution and with the kinds of tissue contrast in each imaging modality. Several features of @animal_warper address some of the idiosyncrasies of animal alignment. First, the user can specify a "feature size " that controls blurring and cost functions.
For many macaque MRI datasets, a value of 0. 5 mm, which roughly matches the apparent voxel resolution, is a useful feature size. Other species, such as mouse, marmoset, and rat, may require a feature size that accommodates the finer voxel resolution typically used with these animals. Animal brain sizes can vary dramatically from each other and from any particular template, so a "supersize" option allows for up to a 50% difference in size. The program applies a kind of spatial regularization for ROI and atlas regions that goes beyond the typical nearest neighbor interpolation; a modal smoothing is applied to all transformed ROIs. This kind of smoothing assigns to each voxel the most common voxel value in a user-specified radius around it. @animal_warper is developed within the larger AFNI software ecosystem.
The computed transformations serve as input to a general fMRI processing pipeline implemented with afni_proc. py (see Section 3. 2. 3. 1 ). Output datasets in native space are viewable in the AFNI GUI and marked with the appropriate space. Data that have been transformed to a standard space have all the functionality of the "whereami" atlas command line and GUI. While NifTI datasets are marked as being in a scanner, Talairach, MNI or other space, @animal_warper adds an identifying space to the data. Atlas regions are generated in the native space both as volumes ( Fig. 5 A ) and as individual surfaces in GifTI format. Every region can be shown by itself or with any or all other atlas regions along with a simple surface rendition of the template in the native space of the subject. Quality control reports are generated as simple png images.
PREEMACS. Brain surfaces and cortical thickness from raw structural data PREEMACS (pipeline for PREprocessing and Extraction of the MACaque brain Surface) is a set of common tools, customized for Rhesus monkey brain surface extraction and cortical thickness analysis. Some of the advantages of using PREEMACS are: 1) it avoids manual correction, 2) the pipeline was developed in the standard MNI monkey space, 3) it provides visual reports in each module as quality control, and 4) its highly accurate surface extraction with vertex correspondence between subjects. PREEMACS has a modular design, with three modules running independently. These modules perform the canonical workflow for MRI preprocessing  ( Alfaro-Almagro et al. , 2018 ; Esteban et al. , 2019 ; Glasser et al. , 2013 )  using different previously-available functions from FSL  ( Jenkinson et al.
, 2002 ) , ANTs  ( Avants et al. , 2008 ) , MRtrix  ( Tournier et al. , 2012 ) , MRIqc  ( Esteban et al. , 2017 ) , DeepBrain ( https://github. com/iitzco/deepbrain ), and Freesurfer  ( Fischl, 2012 ). Inputs to the pipeline should be one (or preferably more) T1-and T2weighted volumes per animal from the same session. Module 1 prepares the raw volumes for initial preprocessing in six steps: volume orientation, conformation, image cropping, intensity non-uniformity correction, image averaging, resampling and skull-stripping. Module 2 is the quality control module. It was adapted from the MRI Quality Control tool (MRIqc)  ( Esteban et al. , 2017 )  for humans to obtain image quality metrics and provide a visual report from the results of Module 1. Module 2 uses these quality control metrics to provide a classification of image quality that allows PREEMACS to estimate whether the input will yield an appropriate surface reconstruction.
Finally, Module 3 obtains cortical thickness measures based on the brain surfaces using an NHP-customized version of Freesurfer v6. To evaluate the generalizability of this procedure, PREEMACS was tested on two different NHP datasets: PRIME-DE  ( Milham et al. , 2018 )  (57 subjects) and INB-UNAM (5 subjects). Results ( Fig. 5 C ) showed accurate and robust automatic brain surface extraction for both datasets. PREEMACS thus offers a robust and efficient pipeline for the automatic NHP-MRI surface analysis.
afni_proc. py. AFNI program that generates a complete fMRI processing script from a list of data files and the desired processing steps and options. AFNI's afni_proc. py program  ( Cox, 1996 )  allows a researcher to create a complete fMRI processing pipeline for individual subjects, from raw inputs, through alignment to standard space, and regression modeling. To use it, one specifies input datasets (e. g. , EPIs, anatomicals, tissue masks), major processing blocks (motion correction, warping to standard space, blurring, regression, etc. ), and detailed choices for each block (e. g. , what kind of alignment, the blur radius, order of the polynomial for baseline modeling and types of motion regressors). Thus, one can plan the processing hierarchically, for conceptual clarity and organization.
The command generates a full, commented processing script, which is then executed to carry out single subject processing. In practice, a typical afni_proc. py command contains 20-25 options, which is a very compact way to specify a full pipeline. For convenience and assured mathematical correctness, afni_proc. py automatically takes care of several aspects of the processing. For example, all warps (motion correction, B0 distortion, alignment to anatomical and to standard space) are concatenated before being applied to the EPI dataset to minimize smoothing due to regridding. This allows the user to focus on the parameter choices in processing, rather than on the technical programming aspects, which greatly reduces the number of bugs in an analysis stream, particularly if one updates or tweaks existing code.
Because the processing script itself is explicitly created and saved, researchers can check exactly what steps are occurring in their analysis, and the whole process is documented, which increases reproducibility. While afni_proc. py was mainly developed in conjunction with human brain researchers, it is fully compatible with animal data and has been applied in animal studies. For example, it integrates directly with AFNI's @animal_warper command (see 3. 2. 2. 4. 2 ) and its list of possible hemodynamic response functions includes a stimulus response shape for MION (monocrystalline iron oxide nanoparticle), a contrast-agent that is commonly used in animal neuroimaging studies. When performing EPI-anatomical alignment, one can furthermore set the minimum "feature size " of structures to match to a value that is relevant for smaller animal brains.
There are currently two demos available in AFNI for macaque fMRI processing with afni_proc. py: one for task-based data (visual stimuli, MION contrast), and the other with resting state data from PRIME-DE  ( Milham et al. , 2018 ). Each dataset uses the stereotaxic NMT v2 as its standard space and demonstrates nonlinear warp estimation with @an-imal_warper (see Sections  3. 2. 1. 1 , 3. 2. 2. 4. 2 , and Jung et al. , 2020 ). AFNI is freely available, and most programs are written in C (as well as Python, R and shell), for generality and computational efficiency.
C-PAC. A flexible pipeline for performing preprocessing and connectivity analyses with various tools. The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is an open-source platform that allows users to configure their own analysis pipeline for structural and functional MRI data. It is designed and tested for use with human, non-human primate, and rodent data. One of the key strengths of C-PAC is its ability to perform different processing strategies on the same dataset. Multiple tools can be specified for the same type of operation (for example, brain extraction or registration to a template), with the different results getting saved in separately-labeled output directories. This allows users to compare a set of methods and evaluate what type of preprocessing decisions may be best for their data.
A web-based pipeline editor is available ( http://fcp-indi. github. io ), but pipeline configuration files can also be edited with any text editor for quick and easy modifications. C-PAC also features a visual quality control interface which allows users to inspect the quality of brain extraction, segmentation, and registration outputs. C-PAC is available as a Docker or Singularity container, allowing users to quickly get started. It is cloud-compatible through Amazon Web Services (AWS), and a machine image is available with a ready-to-use C-PAC Docker container for users who wish to run large-scale analyses. Finally, users can also point directly to a data directory that is hosted on the AWS cloud storage service (S3), and C-PAC will download and organize the data automatically.
Pypreclin. A workflow pipeline dedicated to macaque functional and anatomical MRI preprocessing Pypreclin  ( Tasserie et al. , 2020 )  was originally developed to face the challenges of artifacts induced by intracranial implants and body movements during awake fMRI acquisition. The pipeline development was further extended to a greater panel of experimental parameters such as BOLD-fMRI, CBV-fMRI (MION contrast agent), anesthesia with different pharmacological agents, or different RF coil configurations. Pypreclin is a Python module that is available on an open source repository ( https://github. com/neurospin/pypreclin ) with HTML documentation. Pypreclin development aimed at including state-of-the-art algorithms to allow the automatic preprocessing of raw fMRI data. The diversity of acquisition conditions and hardware in the PRIME-DE database  ( Milham et al.
Diffusion-weighted MRI tools Diffusion-weighted MRI is a technique to visualize the organization of the brain's white matter structure ( Fig. 1 D ). Although tracer data are generally considered to be the gold standard for connectivity analysis, their invasive nature and high costs restrict their applicability. Diffusion MRI, however, can be performed both ex vivo and in vivo to obtain high quality, repeatable images that can be compared across species. Several MRI software packages contain sets of diffusion processing tools that can be applied to either human or non-human datasets. The diffusion data processing and probabilistic tractography functions in FSL  ( Behrens et al. , 2007 ; Jenkinson et al. , 2002 )  were recently expanded with the Xtract tool to facilitate explicit comparison of fiber tracts across species.
Mr Cat. Pipelines for processing of structural, functional, and diffusion non-human MRI data. The MR Comparative Anatomy Toolbox (Mr Cat; www. neuroecologylab. org ) specializes predominantly in processing post-mortem data of a wide variety of brains, which are preprocessed using adaptations of FSL tools implemented in the 'phoenix' module. A number of post-processing modules are furthermore aimed at providing quantitative comparisons of brain organization across species. These are often based on connectivity measures, including matching of areal connectivity fingerprints across species  ( Mars et al. , 2016 )  or comparisons of 'connectivity blueprints' of the cortex with the whole brain that allow the description of different brains in terms of a common connectivity space  ( Mars et al. , 2018b ).
Diffusion-MRI. Pre-and postprocessing steps for diffusionweighted imaging The Diffusion-MRI repository hosts Jupyter notebooks in Python and bash that guide users through multiple pre-and post-processing steps in the analysis of diffusion-weighted imaging (DWI) data. A step-by-step example analysis is provided of macaque DWI data from PRIME-DE  ( Milham et al. , 2018 ). While the workflows were developed and tested with macaque data, they can easily be adapted for other primate brains. The workflow, which is based on tools from the Nipype  ( Gorgolewski et al. , 2011 ) , Dipy  ( Garyfallidis et al. , 2014 ) , FSL  ( Jenkinson et al. , 2012 )  and MRtrix3  ( Tournier et al. , 2012 )  software libraries, requires single-shell or multi-shell DWI data in NIfTI file format as input, with reverse phase-encoding acquisition containing at least one non-diffusion-weighted image.
Preprocessing includes denoising  ( Veraart et al. , 2016 ) , correction for susceptibility distortions, eddy current distortions, and subject movement artifacts using TOPUP  ( Andersson et al. , 2003 )  and EDDY  ( Andersson and Sotiropoulos, 2016 ). Post-processing includes standard and advanced DWI models to fit the diffusion signal, including Diffusion Tensor Imaging (DTI)  ( Basser et al. , 1994 ) , Diffusion Kurtosis Imaging (DKI)  ( Jensen et al. , 2005 ) , Neurite Orientation Dispersion and Density Imaging (NODDI)  ( Zhang et al. , 2012 ) , Single-Shell 3-tissue Constrained Spherical Deconvolution (SS3T-CSD) ( https://3tissue. github. io ), and Multi-Shell Multi-Tissue Constrained Spherical Deconvolution (MSMT-CSD)  ( Jeurissen et al. , 2014 ). The outputs are parametric maps extracted from the various model fits.
B ) Examples of DTI-based tractographic output in a highresolution macaque dataset in AFNI, following distortion correction with TORTOISE. Both images show a (frontal) coronal view of whole brain tracking using AFNI's 3dTrackID: the left panel shows the results of deterministic tracking (as tract fibers), and the right panel shows the results of probabilistic tracking (as WM volume surfaces). Coloration reflects the orientation of the local structure relative to the coordinate frame, where red, blue and green are parallel to the x-, y-and z-axes, respectively. In the probabilistic panel, the FA value modulates the brightness (higher FA is brighter). The images are displayed using SUMA. quality control. FATCAT contains validated deterministic and probabilistic tracking tools  ( Taylor et al.