dataesr/scientific-paragraphs-categorization huggingface dataset:
We thus applied HippUnfold to 7T-TSE data and also illustrate one representative subfield segmentation result in Figure  6A. To demonstrate generalizability to pathological cases where hippocampal abnormalities can be confirmed, we also applied HippUnfold to a surgical epilepsy patient case. A 37 year-old female right-handed patient was investigated for surgical treatment of temporal lobe epilepsy, and clinical MR imaging at 1. 5T revealed a FLAIR hyper-intensity in the right hippocampus. The patient was imaged pre-surgically for a 7 Tesla MRI research study, and the 0. 7mm MP2RAGE T1w (UNI-DEN) image was segmented using HippUnfold, which is shown in Figure  6B. The patient underwent a right anterior temporal lobectomy and has been seizure-free (Engel class 1) for 4 years. Bilateral asymmetry is a strong indicator of epileptogenesis, and so results are examined for both the left and right hippocampi.
Automated error flagging Gold standard manual segmentations under the protocol used for subsequent unfolding were not available in novel datasets. Manually inspecting results from hundreds of subjects is time consuming. We thus streamlined this process by flagging potential segmentation errors by examining Dice overlap with a more conventional segmentation approach: deformable registration. For all datasets described above, we applied deformable fast B-spline registration  [36]  to the corresponding T1w or T2w template. Tissue segmentation results (generated at the nnUNet stage) were then propagated to template space and overlap with standard template hippocampal masks were examined, which is shown in Figure  7. Any subject with a Dice overlap score of less than 0. 7 was flagged and manually inspected for quality assurance.
This made up 34/2126 (1. 6%) samples in the HCP-YA T2w set (including training and testing subsets), 188/1312 (14. 3%) samples from the HCP-A T2w set, 37/1312 (2. 8%) samples from the HCP-A T1w set, and 3/92 (3. 3%) samples from the 7T-TSE set. Closer inspection revealed that the vast majority of flagged cases were due to missed tissue in the nnUNet segmentation, an example of which is shown in Figure  7B. It is interesting to note that the most flagged cases were seen in the HCP-A T2w dataset even though T2w is a popular acquisition protocol for hippocampal subfield segmentation  [18] , and showed the best performance within the HCP-YA test set (Figure  5 ). This was likely not due to the age of subjects since few of the HCP-A T1w were flagged as possible errors, but instead may have been due to T2w scan quality, which was observed to be poor in some subjects, causing poor definition of the outer hippocampal boundaries.
FAIR principles in development We designed this pipeline to employ FAIR principles (findability, accessibility, interoperability, reusability). As such, we have made use of several tools, conventions, and data standards to make HippUnfold extensible and easy to use. The default file input-output structure of the HippUnfold command line interface was built in compliance with the Brain Imaging Data Standards (BIDS)  [38]  Applications (BIDS-Apps) guidelines  [39] , and easily findable amongst the list of available BIDS Apps 1. This is achieved via Snakebids, a tool designed to interface between BIDS datasets and Snakemake  [40]. All aspects of HippUnfold use Snakemake  [41] , a workflow management system based on Python which is reproducible, scalable, and seamlessly combines shell commands, Python code, and external dependencies in a human-readable workflow.
There is no need to install these dependencies, which are containerized within the Singularity or Docker versions of HippUnfold. 1 https://bids-apps. neuroimaging. io/apps/ Altogether, this means that in a single line this pipeline can be applied intelligently to any BIDS-complaint dataset containing a whole-brain T1w image or a T2w image (whole-brain or limited field of view) without having to specify further details. Typical runtimes on a standard desktop are 30-60 minutes per subject, but this is further parallelized for faster processing when multiple subjects and added compute resources (or cloud computing) are available. Additional flags can be used to extend functionality to many other use-cases, including T1w only, T2w only, diffusion-weighted imaging, cases where a manual tissue segmentation is already available, or ex-vivo tissue samples.
Outputs of HippUnfold follow the standards for BIDS derivatives, and include preprocessed input images, volumetric subfield segmentations, inner, midthickness, and outer hippocampal surfaces, vertex-wise morphometric measures of thickness, curvature, and gyrification, and a brief quality control (QC) report. All surface-based outputs are combined into a Connectome Workbench  [42]  specification file for straightforward visualization analogous to HCP neocortical reconstructions. Outputs can be specified to include images in the original T1w space or in the resampled, cropped space that processing is performed in. All code, code history, documentation, and support are offered online 2.
Together, these methods achieved similar or better Dice overlap scores than what is typically seen between two manual raters on all subfields. We tested performance on new datasets ('generalization' datasets with different characteristics than the HCP training set) and saw good performance in nearly all cases. Specifically, we tested other common imaging protocols including different sample age groups (HCP-A) and thick-slice 7T TSE acquisitions often used in targeted hippocampal subfield imaging  [18]. Though error rates were low, we do show how and why such errors sometimes occur, highlighting the importance that future users examine the brief quality control reports included for each subject. Thus, while HippUnfold is shown to work well with all datasets examined here, we expect the widespread adoption of higher-resolution acquisition techniques will further improve feasibility at other research institutes.
Data HippUnfold was designed and trained with the Human Connectome Project (HCP) 1200 young adult subject data release (HCP-YA)  [46] , and additionally tested on the HCP Aging dataset (HCP-A)  [47] , and anisotropic (or thick-slice) 7T data (7T-TSE) from  [29]  which is considered optimal by many hippocampal subfield researchers  [18]. Informed consent and consent to publish were obtained by the original authors of the open source data examined here. Each of the three datasets included research ethics board approvals, as well as informed consent and, in the HCP-Aging dataset, assessment of the subjects' ability to provide consent. For the single epilepsy patient case examined here, research ethics board approval and consent were collected at the Western University (HSREB # 108952, Lawson: R-17-156).
These data are summarized briefly in Table  1. Table  1. MRI datasets used in training, evaluation, and comparison to extant methods. Methods employed include those proposed here (HippUnfold), the same processing but with manual segmentation (similar to previous work  [21] ) (manual unfold), Freesurfer v7. 2. 0 (FS7)  [22] , and an atlas of manual segmentations  [29]  used in ASHS  [13]. U-Nets perform classification of each input image voxel, and it is not constrained by smooth displacements used in deformable atlas registration. This is important because smooth deformable registration can be ill-posed for an atlas with a different hippocampal folding configuration than the target. For example, when trying to register a hippocampus with 2 anterior digitations to one with 4 anterior digitations, topological breaks may be seen which leads to loss of detail and disproportionate stretching or compression of some subfields, an issue that is discussed in  [48].
Once the gold-standard training data was obtained, we applied a U-Net implementation called nnUNet  [11]. nnUNet was built to include many state-of-the art deep learning techniques including sensible hyperparameter selection, built-in 5-fold cross-validation, and other features that have been shown to perform well and minimize possible sources of bias in medical imaging. We thus applied all default parameters in our use of this tool. Training was repeated using the same labelmaps but different underlying images for T1w, T2w, and DWI images. For each of these modalities, training took place on an NVIDIA T4 Turing GPU over 72 hours. Additional new models (or fine-tuned models) can also be trained and supplied within our code framework. Training data is available online at  [50].
HippUnfold detailed pipeline The command-line interface and options for HippUnfold are fully described online and in Supplemental file 1. A brief description of this pipeline is outlined here: 1. Preprocessing and resampling. Data is gathered via snakebids  [55] , which automatically and flexibly queries the specified BIDS directory for T1w and T2w images. Data is loaded and saved using NiBabel  [56]. Processing of each image is as follows: a. T1w: N4 bias correction is performed using the Advanced Normalization Toolkit (ANTs)  [57]  followed by affine registration (NiftyReg  [36] ) to CITI168 atlas  [58]. This transformation is composed (Convert 3D or c3d  [59] ) with a precomputed transform from CITI168 to oblique to the long-axis of the hippocampus. Images are resampled to 0. 3mm 3 and cropped to 128x256x128 voxels centered on the CITI168 left and right hippocampi.
In all cases, data are resampled to cropped coronal oblique space to match the nnUNet training setup. It is possible to skip this step only if a manually segmented hippocampal tissue class image is also provided (in which case nnUNet is not applied). 2. Tissue class segmentation. If a manually segmented hippocampal tissue image is not supplied, then the input image will be run through nnUNet  [11] , a state-of-the-art implementation of a deep convolutional neural network (U-Net) designed for image segmentation  [61, 62]. The output of nnUNet is a segmentation of tissue classes: hippocampal grey matter (GM) and the surrounding tissues which are used in defining unfolded coordinate boundaries: SRLM, medial temporal lobe cortex (MTLc), pial surface, hippocampal-amygdalar transition area (HATA), indusium griseum (IndGris), cysts, and the dentate gyrus granule cell layer (DG) (which also makes up part of hippocampal grey matter but which marks an endpoint of the unfolding coordinate framework and so it was given a distinct label).
It should be noted that in principle this step is not necessary for our pipeline, but in practice it helps avoid possible errors due to nnUNet segmentation faults (see main text Figure  5 ). The reference template that we applied was created using manual segmentations from an open source ex-vivo dataset  [64]  that was manually segmented according to our previous manual hippocampal unfolding protocol  [12]. Labelmaps from 22 samples were combined using a standard template building ANTs script 'buildtemplateparallel. sh'  [65]. This template generation entails averaging all images and then registering each sample to the average, iteratively refining and sharpening the average image. This ex-vivo dataset was selected for template building because we had high confidence in the quality of these segmentation since they contained higher resolution and contrast than other datasets while still including multiple samples.
6, iterations=100). gyrification metric is defined as the ratio of native-space surface area over unfolded-space surface area, where the surface area is calculated at each vertex as the average of areas of connected triangles. Additional data (for example, fMRI, DWI, or others) can be sampled at each vertex with the code provided in (the volume to surface mapping command in Connectome Workbench). With the implicit registration provided by unfolded space and the tessellation of these surfaces, such data can readily be compared across hippocampal samples without the need for further registration. These data can be subgrouped according to subfield labels, as in ROI analysis styles, or each vertex can be examined separately as in searchlight or data-driven analysis styles. Alternatively, gradient-based analyses can be applied based on Laplace coordinates and their corresponding surface mesh tessellations (see  [70]  for example).
Supplemental file 1. HippUnfold Documentation. This document fully describes the HippUnfold installation, command-line interface, options, outputs, and provides several useful pieces of information including worked examples and useful tips on viewing data in other common platforms. Supplemental file 2. Side-by-side snapshot comparison of HCP-A segmentations results from HippUnfold, FS7, and ASHS from the left hemisphere. Snapshots were taken at the conronal centroid, centroid +15 slices, centroid+30 slices, and the sagittal centroid. Supplemental file 3. Side-by-side snapshot comparison of HCP-A segmentations results from HippUnfold, FS7, and ASHS from the right hemisphere. Snapshots were taken at the conronal centroid, centroid +15 slices, centroid+30 slices, and the sagittal centroid.
Acknowledgements This work was supported by a  Canadian Institutes for Health Research   Project Grant  ( CIHR  Grant #  366062 ) to A. K. and S. K. AK was supported by the  Canada Research Chairs program  # 950-231964 ,  NSERC   Discovery Grant  # 6639 , and  Canada Foundation for Innovation (CFI) John R. Evans Leaders Fund project # 37427 , the  Canada First Research Excellence Fund , and  Brain Canada. J. D. was funded through a  Natural Sciences and Engineering.
Research Council doctoral Canadian Graduate Scholarship (NSERC CGS-D). R. A. M. H was supported by a BrainsCAN postdoctoral fellowship for this work. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. Data and/or research tools used in the preparation of this manuscript were obtained from the National Institute of Mental Health (NIMH) Data Archive (NDA). NDA is a collaborative informatics system created by the National Institutes of Health to provide a national to support and accelerate research in mental health.
Dataset identifier: dx. doi. org/10. 15154/1520707. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDA. Data was also provided in part by Berron et al. , 2017, in their published work "A protocol for manual segmentation of medial temporal lobe subregions in 7 Tesla MRI" [29] which includes MRI images and subfield segmentations.
This coupling between metabolism, blood ow and neural function in the eye was rst reported in 1989 in macaque monkeys  25, 26  , describing a higher blood ow and glucose metabolism during ickering than constant light. The modulation of retinal blood ow by local neuronal activity in the retina has been getting more attention in the recent years, including in the context of AD  27, 28  , nevertheless, it remains a largely unexplored and underexploited data given its high potential as a biomarker of the brain's health. We propose in this work to investigate how functional ultrasound imaging can be applied to the retina in vivo to obtain a robust measurement of its hemodynamic response function. Ultimately, our objective is to assess the presence of neurovascular alterations non-invasively with ultrasound.
2mm over 4. 4mm from an initial position (Xi) over the nasal side of the eye to a nal position (Xf) over the temporal side of the eye ( g. 1. d,e). We accumulated images for 180s for each plane (900 compounded images), the entire 3D scan lasted 63 minutes. Density maps (D) were computed by counting all the positions detected in one pixel during the acquisitions. Velocity maps were computed as the mean velocity of every microbubble which passed through the pixel during the acquisition. For more details on the data processing, please refer to the data processing section described in Demene et al  35. Experimental paradigm. All measurements were performed in the right eye according to the following time schedule. After a short resting period to obtain stable hemodynamic conditions, power doppler measurements were performed.
An acquisition consists of a baseline recording of 45 seconds followed by 50 consecutive trials and terminated by a recovery period of 75 seconds, during which the RBV is continuously recorded. Each trial comprises 15 seconds without stimulation, 0. 8 seconds with white light and another 15 seconds without stimulation. One complete acquisition lasted 1660s. Light Stimulation. The animal was maintained in a dim light room for 45 minutes before starting the imaging session. For light stimulation, a custom-built device was used, consisting of a white LED delivering light ashes through a microcontroller (Arduino Uno, Ivrea, Italy). The microcontroller was programmed with the Arduino Software (IDE). In order to synchronize the stimulation pattern with the scanner, the LED is triggered by the scanner through the microcontroller to ash for 0.
8s every 30s to complete 50 trials ( g. 1. f). Functional ultrasound sequences. Functional ultrasound imaging acquisitions were performed noninvasively through the eye with the same probe used for ULM imaging. The ultrasonic probe was placed upon the eye with acoustic coupling gel in order to image the retinal plane of interest where the long posterior ciliary arteries (LPCAs) were visible as they represent a reproducible and central landmark of the vasculature of the posterior part of the eye. The transducer was connected to a prototype functional ultrasound scanner (Iconeus One, Iconeus, Paris, France). Data was acquired by emitting continuously a group of 11 plane waves tilted from -10° to +10°, red at 5. 5kHz pulse repetition frequency. The backscattered echoes of each group were summed to get compound images at 500Hz framerate, that allows correct sampling of the blood signal  21.
Doppler images were computed from blocks of 200 compound images averaged after ltering with the SVD spatio-temporal clutter lter 36 and removing the 60 rst singular values to discard noise and tissue motion. Each pixel of the nal Power Doppler images is 100x100μm 2 in plane. The slice thickness is approximately 300μm. Activation map. Datapreprocessing consisted in a linear detrending, neither spatial nor temporal smoothing was used. For each animal, activation maps were computed with a single-subject global linear model (GLM) approach as routinely used method for the processing of fMRI data  37. The design matrix used to detect retinal activation speci c to the light stimulation only included the stimulus pattern as a regressor. The real stimulus duration was 0. 8s but a theoretical 5s stimulus was used in the design matrix to account for the hemodynamic response delay.
4 mm diameter) centered on the peak z-score location of the activation map. Data processing and analysis were performed using Matlab (MATLAB Release 2018a, The MathWorks, Natick, Massachusetts, United States). Hemodynamic response measurement and parameters estimation. The retinal hemodynamic response was extracted and averaged from the same region of interest. The times-series were averaged between trials for each animal. For a visual comparison, the hemodynamic response was averaged within groups for comparison between the transgenic retinal hemodynamic response and the control response. In order to extract quantitative parameters, we tted the hemodynamic response using a simple model. While several models of hemodynamic responses have been proposed in the fMRI literature such as the two-gammas function or inverse logit function  38  , we choose to use a simpler parametrized model which allows direct interpretation of key physiological parameters  39  such as the initial response delay, the response rise time, the response amplitude, the response decay time, the undershoot amplitude and return to baseline time.
For this, we have opted to use the 4 cosines model which is the concatenation and scaling of four half-period cosines and is determined by six independent parameters (see supplementary gure 1). Non-linear curve tting of the RBV response by the 4 cosines model was then performed using the interior point method with constraints (Matlab 2018a, The MathWorks, Inc. , Natick, Massachusetts, United States. ). In order to evaluate the number of trials necessary to obtain robust hemodynamic parameters, we performed a bootstrap analysis by randomly sampling with replacement a subset of trials in the experiment and rerunning the t and parameters estimations. Curves representing the different parameters robustness were constructed by evaluating their standard deviation and mean. Statistical analysis. To compare the different hemodynamic response parameters of the hemodynamic retinal response of the transgenic (TG) and wild type (WT) groups, a two-tailed Wilcoxon test was performed (because of TG group non-normal distribution) using GraphPad Prism (GraphPad Software, La Jolla California USA).
Acknowledgements This work was supported by the  European Research Council (ERC)   Synergy Grant Scheme  (holistic evaluation of light and multiwave applications to high-resolution imaging in ophthalmic translational research revisiting the Helmholtzian synergies,  ERC  Grant Agreement # 610110 ), by the  Agence Nationale de la Recherche (ANR)  within  Programme Investissements d'Avenir, Laboratoire d'Excellence (LABEX) LIFESENSES  ( ANR-10-LABX-0065 ) and  Institut Hospitalo-Universitaire FOReSIGHT  ( ANR-18-IAHU-0001 ). Disclosure : MT and TD are cofounders and shareholders of the Iconeus company which designs, manufactures and commercializes preclinical functional ultrasound imaging systems for research.
INSTRUMENTS AND DATA COLLECTION The data collection in the current study was carried out using two types of instruments. First, we collected data to measure indicators of the environmentrelated domain of smart cities including public space, building, housing, pollution control, renewable energy, smart grid, water management, and waste management  (Yin et al. , 2015)  through interviews with government officials who were in charge of city development. Second, the data collection activity also involved distributing a five-point Likert scale survey for each city administration and the public to obtain additional information for measuring the indicators of the environment-related domain. To see if the questionnaire's design was appropriate for achieving the study's objective, it was pilot tested  (McQuirk & O'Neill, 2016)  to ensure that the instructions and questions of the questionnaire were clearly expressed in a comprehensible manner and were of a fair length  (Schleef, 2014).
DATA ANALYSIS The data collected from interviews were descriptively analysed through coding categorization and pattern classification  (Hsieh & Shannon, 2005)  based on the indicators or subdomains of the environment-related domain of smart cities  (Yin et al. , 2015) , with the goal of providing comprehensive explanatory a social phenomenon interpretations (Tesch 1991 as cited in  (Dey, 2005). The data obtained from the questionnaire were quantitatively analysed using SPSS 23 for Windows through a descriptive statistics procedure. This analysis was undertaken to look at the participants' views of smart cities, especially the environmentrelated domain.