dataesr/scientific-paragraphs-categorization huggingface dataset:
Results In this section, we implement qtQDA based on two different estimates of covariance matrix. For qtQDA classifier, we use R package "qtQDA" which is available at https://github. com/goknurginer/qtQDA. For the discriminant function in the qtQDA package, We use trended dispersion estimate. Then, we compare the classification error rates using two real RNA-Seq data sets. These are not only publicly available data sets but also commonly used data sets in order to test the performance of RNA-Seq classification methods. The first data is cervical cancer data (see  [14] ). The cervical cancer data is composed of 714 microRNAs and 58 samples where 29 samples are tumor and 29 samples are non-tumor. The second data is HapMap data (see  [15, 16] ). Similar to cervical cancer data, the HapMap data also includes two groups of samples; CEU and YRI where CEU represents Utah residents with Northern and Western European Ancestry and YRI represents Yoruba in Ibadan and Nigeria, respectively.
After conducting gene selection procedure, we randomly split the dataset into two sets: training set and test set. 70% of the dataset is randomly assigned to the training and the rest 30% of the dataset is assigned to the test set. Training set is used to train the classifiers and test set is used to measure the misclassification rate. The whole procedure is repeated 300 times for different number of genes and the average misclassification rate is computed. The comparison results are given Table  1. It is obvious to see that improving the covariance matrix estimate, i. e using local dependence function to estimate the covariance matrix, leads generally better results. Interestingly, for both data sets, qtQDA performs better then L-qtQDA. However, for the cervical cancer data, we obtain better performances except for 20 and 200 genes selected in gene selection process.
Well received are the social and embodied aspects of the performance, forming the biggest category (18). Even if the interaction with strangers is at times perceived as uncomfortable (10), the experience invited participants to interact with the group using their whole body (10), "relying on my body language" (P13), and worked as an ice-breaker (5). The experience was fun (8) and novel (8), and the discovery of new features of the AAR technology (  14 ) contributes to its enjoyment. Though, participants often criticize the storytelling (6) or their lack of agency (3) within the performance. Audio is a mixed category, with almost as many positive as negative statements, regarding the sound design as well as the spatial audio features. Many participants were confused, mainly due to unclear instructions (9) and lack of audio feedback (5), often leading to cognitive load (7).
How can AAR Contribute to Developing New Kinds of Interactions in Participatory Performances? Real-time data was used to coordinate storylines based on the choices and actions of all participants, thus creating a multiplayer experience. This has previously not been possible to achieve in real time, unless humans are present to observe the actions and direct the narrative as in the Binaural Dinner Date experience 8  , or the narrative evolves linearly, where one element always follows another 9  or actions and recordings are tied to specific GPS locations and have to be placed there before an audience arrives 10. In this experiment this group co-ordinated audio was explored in three out of four games. In Mirroring this allowed people to choose and pair up with another participant and receive the complimentary audio tracks in order to perform as a pair.
ACKNOWLEDGMENTS Thanks to BBC R&D IRFS and Audio teams, in particular  Joanna Rusznica  and  Holly Challenger  for helping with UX research;  Joseph Turner  for documenting the study and  Thomas Kelly  for technical support. Many thanks to all participants, without whom this research would not have been possible. Special thanks to  Brian Eno ,  Antonia Folguera  and  Emergency Chorus  for feedback on our project.
Methods All methods were carried out in accordance with the relevant guidelines and regulations. The OCT and angiography data used in the experimental and clinical studies of this work were obtained from the PROPOT trial (randomized trial of the proximal optimization technique in coronary bifurcation lesions). The study was approved by the ethics committee of Teikyo University (IRB approval number 15-159-2) and informed consent was obtained from all study participants. Experimental studies. Experimental coronary artery models, flow chamber studies and imaging procedures. We created four patient-specific silicone models of coronary artery lumen (Fig. 1 ), using our in-house technique as previously described  14. We included both straight and curved coronary artery segments. In brief, the 3D lumen geometries were created in 3D CAAS Workstation 8.
All the steps were performed in Grasshopper 3D, a visual programming language and environment that runs within Rhinoceros 3D (Robert McNeel & Associates, Seattle, WA, USA). In each OCT frame used for the lumen reconstruction, we manually identified the stent contours and strut points (Fig. 2a, b ). The segmented stent contours and strut points were imported into Grasshopper 3D. The segmented stent contours and strut points were packaged in a straight line along the OCT catheter center, rotated at the same angle with the corresponding lumen contour, and straightened, taking as reference the centroid of the distal stent contour (Fig. 2c, d ). Finally, the stent contours and strut points were unrolled on a 2D plane (Fig. 2e, f ). Planar reconstruction of stent wireframe. Following the planar flattening of stent struts, the 2D stent wireframe was reconstructed according to the corresponding planar computer-aided wireframe of the stent.
To keep the analysis steps blinded, different operators performed the 3D reconstruction from OCT, 3D reconstruction from μCT, and comparison between OCT-and μCT-based models to minimize possible biases. Reproducibility. To calculate the reproducibility of the OCT-based 3D reconstruction method, the stents were 3D reconstructed by two independent operators. The 3D reconstructed stents were compared in terms of MSD (quantitatively) and stent shape (qualitatively). Clinical Studies. Clinical Feasibility, Processing Times, and CFD Studies. The clinical feasibility and processing times of our stent reconstruction method were assessed in n = 3 patient coronary artery bifurcations with varying disease burden (Supplementary Table  1 ). All these cases underwent stenting with a single stent. OCT and angiography data were acquired according to the imaging protocols mentioned above.
3 ). Finally, all the steps in our method were automatic or semi-automatic except for the manual step to reconstruct the planar stent wireframe. Our code coordinated all these steps effectively, thereby reducing the human interaction and processing times and improving the accuracy and reproducibility of the proposed technique. The proposed methodology has several scientific and clinical applications. It can be used at the cardiac catheterization laboratory to inform the interventional cardiologist on the spatial configuration of the deployed stent in relation to the main vessel lumen and side branches (when applicable), identifying areas of stent under-expansion, strut malapposition, and floating struts at the carina and side branch ostia. This information is essential  for stenting optimization, which is directly related to favorable clinical outcomes  17, 18.
Of note, our method has the potential to use other intracoronary imaging modalities, such as high definition IVUS, as anatomical input. The relatively simple steps of our method can allow operators without technical or engineering background (e. g. , medical students, fellows) to use it, making it widely applicable. This study has several limitations. First, the processing times were not optimal. The most time-consuming steps in the process were the: (1) Manual strut segmentation of OCT images (average time 60 ± 10 min), (2) Manual planar reconstruction of the stent wireframe (average time 87 ± 18 min), and the data transfer between different software used for the angiography and OCT image processing (i. e. , CAAS, EchoPlaque, VMTK). Our current efforts focus on the development of a fully automated strut segmentation algorithm requiring limited manual interventions, a smart algorithm for planar stent wireframe reconstruction that limits the manual intervention to the highly challenging cases, and a new Grasshopper code to streamline the cross-talk between different software.
Here we introduce a novel method and a software: the Functionnectome, that unlocks the function of the white matter. Functional white matter analysis is derived from the combination of task-related functional MRI and new anatomical priors for brain circuits. In doing so, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome is available as a companion open-source software (https://github. com/NotaCS/Functionnectome). In fMRI studies, patterns of task-based neural activation are classically derived from the statistical analysis of each voxel's time-course, usually using the general linear model (GLM) with the predicted hemodynamic response from the task. In order to evaluate the participation of white matter pathways in these tasks, we designed a new method, the Functionnectome, which combines the functional signal from distant voxels of the classical fMRI 4D volume (Fig.
1a ) using their probabilistic structural relationship given by anatomical priors of the involved brain circuits (Fig. 1b ). These priors are probability maps derived from high resolution tractography (Fig. 1d ). The resulting 4D volume, a "functionnectome" (Fig. 1c ), can then be analysed with the usual statistical tools to reveal the activation patterns emerging from the signal (Fig. 1e ). Here, we illustrated this process by applying the Functionnectome to motor (n=46), working memory (n=45), and language (n=44) functional volumes of the test-retest Human Connectome Project (HCP) dataset. Results from the functionnectome maps (a, c) are presented side by side with classical task activation analysis (b, d). The white matter "activations" displayed on the functionnectome zmaps correspond to significant involvement of the associated pathways during the task.
In addition to cortico-cortical pathways, the cortico-ponto-cerebellar tract was also activated and connects the classical fMRI (Figure  4b ) fronto-parietal working memory network with the cerebellum. The Functionnectome analysis for a language task (semantic) is displayed in Figure  5a  and revealed the involvement of the language "ventral stream". The ventral stream is composed of the uncinate and the inferior fronto-occipital fasciculus connecting the inferior frontal gyrus, and the temporal lobe, and the occipital lobe  25, 26. In addition, the analysis also showed the middle longitudinal fasciculus which links the temporal pole to the inferior parietal lobe  27  as well as the posterior segment of the arcuate fasciculus which links posterior temporal regions to the parietal lobe  16.
Language (semantics) As expected, the classic fMRI maps displayed activation in the posterior inferior frontal gyrus, the superior temporal gyrus, and the inferior parietal lobe, forming the semantic network  16, 28. The anterior part of the temporal lobe was activated (Figure  5b ), which is in line with the literature highlighting it as a central hub for semantic processing  29. The orbitofrontal cortex has previously been linked to language and semantic cognition  29. Lastly, activation of the amygdala, mammillary bodies, and hippocampus reflects the involvement of a limbic network, integrating emotions with semantic memory  30. For each analysis reported above, we repeated the analysis in a replication dataset of the same participants (same session, opposite phase of acquisition). Table  1  indicates the reproducibility rate of the results.
Discussion We propose a new method, the Functionnectome, to investigate the functional involvement of brain circuits during task-related cerebral processes. Applying our method to a high-quality functional neuroimaging dataset revealed -for the first time -the white matter circuits supporting motor, working memory, and language activations. Results also indicated a higher reproducibility of the Functionnectome maps compared to classical task-related activation methods suggesting that the functioning of the brain is mediated by interactions via anatomical circuits rather than the isolated contribution of brain regions. To support the broad uptake of this method and facilitate its application to a wide range of datasets, including in the clinic, we provide a GUI and terminal-based companion software.
This toolbox allows the implementation of the Functionnectome to any previously acquired fMRI dataset and beyond. While the toolbox is flexible and users can integrate their own priors, the current release and the high quality priors accompanying it open up novel avenues for research on the integrative function of white matter. Obtaining significant activation of brain circuits with our method requires the functional involvement of the brain areas at both ends of this circuit. As first examples of such functional exploration of white matter, we investigated the brain circuits supporting prominent activation tasks including motor, memory and language functions. Our knowledge of the fine circuitry of the motor system is mostly derived from animal and lesion studies. The Functionnectome applied to motor tasks offer the unique opportunity to explore these circuits in the healthy human brain.
Here, the Functionnectome revealed the exact portion of the corpus callosum that integrates bilateral contribution to working memory. The language circuitry, and more so its semantic system, offers an even greater challenge as it cannot be explored in animal studies  26. The Functionnectome confirmed, for the first time in the healthy human brain, the structural-functional circuitry supporting semantic processes originally suggested by lesion studies  25  or intraoperative stimulation  26  in humans (i. e. the uncinate fasciculus, the inferior frontooccipital fasciculus, the middle longitudinal fasciculus, and the posterior segment of the arcuate fasciculus). The Functionnectome also revealed the involvement of the anterior corpus callosum in story comprehension. Whilst some language processes require the integration of the left and right hemispheres via the posterior corpus callosum  42  , the anterior corpus callosum has been implicated in semantic disorders (e.
In this regard, we provide an open-source software that will allow easy application of the Functionnectome method to revisit already acquired fMRI datasets, either private or publicly available (e. g. HCP  45  , UK Biobank 46 , ABIDE  47  ). The use of the Functionnectome is also not bound to the activation paradigm and can be combined with advanced fMRI statistics  7  to reveal the dynamic causal interaction between brain circuits. Additionally, the Functionnectome can leverage the wealth of MRI modalities to explore the involvement of white matter circuits in different aspects of brain dynamics. For example, it could be applied to resting-state functional connectivity or cortical thickness to open up new perspectives in the study of functional synchronisation, cortical changes during development, and brain pathologies.
Similarly, although we provide high-quality anatomical priors based on best white matter mapping derived from the Human Connectome Project 7T  49  in the software, other research teams are welcome to use homemade priors. Future developments of the priors might include a separation in interhemispheric (i. e. commissural circuits), cortico-subcortical (i. e. projection circuits) and cortico-cortical connectivity (i. e. association circuits) to better disentangle brain circuitries. Finally, the computation of the Functionnectome using every brain voxel can be very computationally expensive and thus time-consuming. While we recommend using this procedure, we acknowledge that not all research teams have access to the computing power required to compute the "voxel-wise" Functionnectome of several subjects in a reasonable amount of time.
To circumvent this potential constraint, we provide an option within the software allowing the use of an atlas and its parcels instead of all voxels, for a less computationally intensive "region-wise" analysis. The anatomical priors come with this alternative option using the recently published multimodal parcellation atlas  50  , should you choose to use this option (see supplementary materials). Overall, we introduced and demonstrated the potential of the Functionnectome method, opening the field of in-vivo study of the function of white matter in healthy humans. In this context, the Functionnectome promotes a paradigm shift in the study of the brain, focusing on the interaction of brain regions in the support of a brain function, rather than the fractionated contribution of independent regions.
Acquisition parameters and preprocessing EPI acquisitions: Full description of the acquisition parameters have been described elsewhere  51. In brief, the data were acquired on a 3 Tesla Siemens Skyra scanner using a whole-brain EPI acquisition with a 32 channel head coil. The parameters were as follows: TE = 33. 1 ms, TR = 720 ms, flip angle = 52°, BW = 2290 Hz/Px, inplane FOV = 208 × 180 mm, 72 slices, 2. 0 mm isotropic voxels, and a multi-band acceleration factor of 8. Each task was acquired twice using a right-to-left and a leftto-right phase encoding. The EPI 4D acquisitions were then preprocessed through the "Minimal preprocessing pipeline" fMRIVolume  52  , which applies movement and distortion corrections and performs a registration to the MNI152 space.
DWI acquisitions and tractography Structural connectome data were downloaded here (subset of 100 subjects; available at https://osf. io/5zqwg/ and http://www. bcblab. com/BCB/Opendata. html) This dataset was derived from the diffusion-weighted imaging dataset of 100 participants acquired at 7 Tesla by the Human Connectome Project Team 49 The scanning parameters have previously been described in  49. In brief, each diffusion-weighted imaging consisted of a total of 132 near-axial slices acquired with an acceleration factor of 3  53  , isotropic (1. 05 mm3) resolution and coverage of the whole head with a TE of 71. 2 ms and with a TR of 7000 ms. At each slice location, diffusion-weighted images were acquired with 65 uniformly distributed gradients in multiple Q-space shells  54  and 6 images with no diffusion gradient applied.
This acquisition was repeated four times with a b-value of 1000 and 2000 s mm-2 in pairs with anterior-to-posterior and posterior-to-anterior phase-encoding directions. The default HCP preprocessing pipeline (v3. 19. 0)  52  was applied to the data  55. In short, the susceptibility-induced off-resonance field was estimated from pairs of images with diffusion gradient applied with distortions going in opposite directions  56  and corrected for the whole diffusion-weighted dataset using TOPUP  57. Subsequently, motion and geometrical distortion were corrected using the EDDY tool as implemented in FSL. Next, we discarded the volumes with a b-value of 1000 s/mm-2 and whole-brain deterministic tractography was subsequently performed in the native DWI space using StarTrack software (https://www.
mr-startrack. com). A damped Richardson-Lucy algorithm was applied for spherical deconvolutions  57, 58. A fixed fibre response corresponding to a shape factor of α = 1. 5 × 10-3 mm2 s-1 was adopted, coupled with the geometric damping parameter of 8. Two hundred algorithm iterations were run. The absolute threshold was defined as three times the spherical fibre orientation distribution (FOD) of a grey matter isotropic voxel and the relative threshold as 8% of the maximum amplitude of the FOD  59. A modified Euler algorithm  60  was used to perform the whole-brain streamline tractography, with an angle threshold of 35°, a step size of 0. 5 mm and a minimum streamline length of 15 mm. We co-registered the structural connectome data to the standard MNI 2 mm space using the following steps: first, whole-brain streamline tractography was converted into streamline density volumes where the intensities corresponded to the number of streamlines crossing each voxel.
Second, a study-specific template of streamline density volumes was generated using the Greedy symmetric diffeomorphic normalisation (GreedySyN) pipeline distributed with ANTs  60, 61. This provided an average template of the streamline density volumes for all subjects. The template was then co-registered with a standard 2 mm MNI152 template using flirt as implemented in FSL. This step produced a streamline density template in the MNI152 space. Third, individual streamline density volumes were registered to the streamline density template in the MNI152 space template and the same transformation was applied to the individual whole-brain streamline tractography using the trackmath tool distributed with the software package Tract Querier 62 using ANTs GreedySyn. This step produced a whole-brain streamline tractography in the standard MNI152 space.
Functional data pre-processing To compare our method with standard analyses, we explored the activation patterns of HCP fMRI paradigms for motor (n=46), working memory (n=45), and language (n=44) tasks (see https://protocols. humanconnectome. org/HCP/3T/task-fMRIprotocol-details. html and  64  for a full description of the taks protocol). Briefly, the motor tasks consisted of finger tapping (left or right hand) and toes clenching (left or right foot); the working memory task was a 2-back task; and the language task corresponded to the comparison of comprehension of high and low semantic content (stories, math problems).
Processing of neuroimaging data The activation analysis was applied to both the original functional dataset and the functionnectome 4D volumes. Processing was identical except for the application of a traditional spatial smoothing (FWHM = 4 mm) on the functional dataset (i. e. as a usual step to improve the signal/noise ratio and misalignment). Specifically, no spatial smoothing is required for the functionnectomes 4D volumes. Usual smoothing aims at improving the signal/noise ratio (SNR) using a weighted average of the local signal, assuming that neighbouring voxels share some signal of interest. The Functionnectome method combines the signal from distant yet structurally linked voxels, which has an analogous effect of improving the SNR, but is guided by actual brain circuits. The processing was done using FEAT (FMRI Expert Analysis Tool, v6.
00) from FSL (FMRIB's Software Library). First-level statistical analysis was carried out on each participant using FILM (FMRIB's Improved Linear Model)  65  with prewhitening. For each task, we used the main contrasts provided with the HCP dataset. Then, a group-level analysis was performed on all participants using the first stage of FLAME (FMRIB's Local Analysis of Mixed Effects)  66  , resulting in the z-maps (z-transformed tmaps) presented in the results (fig. 1 2 3 4 ).
Introduction The Functionnectome is a Python-based tool to allow projecting functional signals (or more generally grey matter information) onto the white matter. As input, the software accepts NifTI 4D volumes (usually fMRI files), uses predefined (but modifiable if needed) white matter priors for the computation, and outputs functionnectome 4D volumes. The user can choose between navigation through commands or a GUI (Graphical User Interface). The generated functionnectomes 4D volumes can then be processed with the same statistical tools used to analyze fMRI. For example, the functionnectome generated from a classic task fMRI can be analyzed with the usual tools (e. g. generalized linear model) to reveal the whitematter pathways involved with the task.