dataesr/scientific-paragraphs-categorization huggingface dataset:
Human movements with or without vision exhibit timing (i. e. speed and duration) and variability characteristics which are not well captured by existing computational models. Here, we introduce a stochastic optimal feedforward-feedback control (SFFC) model that can predict the nominal timing and trial-by-trial variability of self-paced arm reaching movements carried out with or without online visual feedback of the hand. In SFFC, movement timing results from the minimization of the intrinsic factors of effort and variance due to constant and signal-dependent motor noise, and movement variability depends on the integration of visual feedback. Reaching arm movements data are used to examine the effect of online vision on movement timing and variability, and test the model. This modelling suggests that the central nervous system predicts the effects of sensorimotor noise to generate an optimal feedforward motor command, and triggers optimal feedback corrections to task-related errors based on the available limb state estimate.
C-F. Simulation of the data of  [35, 36]. Hand paths of 20 trials are shown in panels C and E for the different directions and different distances for the N-E direction. 90% confidence ellipses of the end point are depicted (estimated from 1,000 samples). The corresponding durations are reported in panels D and F. In These simulations suggest that the model can reproduce the basic characteristics of planar arm reaching movements without visual feedback, showing typical dependencies on distance and direction. Next, we compare movements carried out with and without vision. In particular, movements with vision are known to exhibit a smaller endpoint variability than analog movements without vision  [45, 46]. Comparison to data of reaching movements with and without vision. We asked healthy participants to perform arm pointing movements to test the impact of online visual feedback of the hand on the preferred timing and variance of goal-directed movements.
We wanted to estimate the extent to which movements with and without visual feedback differed in terms of timing and trial-by-trial variability, and whether these data could be replicated by the proposed SFFC model. Horizontal arm pointing movements of various directions {E, N-E, N-W, W} and amplitudes {0. 06, 0. 12, 0. 18, 0. 24} m both with and without vision of the moving hand (represented as a cursor on the screen, the actual arm being hidden) were recorded. Details about the task can be found in the Materials and methods section. Two-way repeated measures ANOVAs confirmed a main effect of the visual condition (F 1,20 = 426. 83, p < 0. 001) with movements without vision exhibiting much more endpoint variance. A main effect of distance was also found (F 3,60 = 12. 55, p < 0. 001) and there was a panel D, the acceleration predicted from the hand mobility matrix is depicted (and calculated as in  [36] ).
Every 10 trials a break of approximately 1 minute was scheduled, during which they could place the forearm on the tablet or the desk. Data acquisition and parameters of interest. The stylus position was recorded at 125 Hz with MATLAB (The MathWorks, Inc. ), and the Psychtoolbox  [76]  was used to display the stimuli on the screen. The system was calibrated so that a movement of the stylus on the tablet corresponded to a movement of the same length of the cursor on screen. The raw data were smoothened for further analysis using a 5th-order Butterworth low-pass filter with 12. 5 Hz cutoff frequency and without delay. Velocity was computed via numerical differentiation. Among the parameters of interest, we computed the movement duration using a velocity threshold of 1 cm/s, the peak velocity of the maximal value of velocity profiles (in cm/s), and the endpoint variance (in log(mm 2 )).
We verified that the same results and conclusions were obtained by limiting the observation matrix to the position and velocity components only. The observation noise matrix D was taken of the form D = β I 6 where β specifies the overall magnitude of observation noise. This parameter can be varied depending on whether vision of the cursor is available or not during the movement. Finally, for the feedback cost function, we set R = ρdiag(1, 1, 0, 0, 0, 0) such that only the deviations about the final arm posture defined by the target location were penalized during the post-movement interval. The SOOC solutions were obtained with the GPOPS optimal control software that approximates the continuous-time optimal control problem as a sparse nonlinear programming problem  [40]. To compute the SFFC solutions, we considered a discrete time approximation of the linear-quadratic-Gaussian sub-problem around the SOOC solution with a time step of dt = 0.
005 s. Standard discrete-time algorithms for linear-quadratic-Gaussian control were then used to compute the gains  [19]. In our simulations, we extended the time horizon by 1 s (T 0 = T+1) to consider movements longer than the planned duration T. We tested different extended horizon between 0. 5 s and 2 s and it did not change the results. It is worth noting that sensory feedback delays can be easily handled at this stage due to the discrete time approximation. All the simulations were performed with MATLAB (Mathworks, Natick, MA). Selection of model parameters. The arm parameters used in the simulations (from  [42] , in SI units) are given in Fig  8C. The remaining parameters of the model are related to cost functions (α, r and ρ) and noise magnitudes ({σ i }, {d i } and β). Some of these parameters affect the design of the feedforward command (α, r, {σ i }, {d i }) and the others affect the design of the feedback command (ρ, β).
This work was in part funded by the  EC  under grants  H2020 PH-CODING (FETOPEN 829186 ),  INTUITIVE  ( ITN 861166 ),  REHYB (ICT 871767 ) [EB], by  NIH NINDS   1R56NS100528 ,  NIH   NINDS R21NS120274  grants [NS], and by the  French National Agency for Research  (grant  ANR-19-CE33-0009 ) [BB]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
It ends by giving the internet address of the association in charge of the design, to allow participants to obtain more information if they wish 2. 4. Procedure Data were collected using two separate surveys (one for patients with epilepsy and another for caregivers) created using Qualtrics survey software. After expressing their consent to participate, participants responded to questions about their sociodemographic characteristics, and about the occurrence of seizures, to the QOLIE-31, and to the MCSES. Secondly, they watched the video presenting the technological development project. Finally, they had to answer the UTAUT questionnaire.
Data analysis All the analyses were conducted on SPSS 25. 0 software. A Kmeans clustering hierarchical analysis by dynamic cloud was performed on the dataset concerning the acceptance factors for the connected patch, in order to determine groups that stand out according to the differing data models. Analysis of the final central clusters by means of the elbow method have suggested that solutions of three groupings were the most significant and parsimonious for patients and also for caregivers. Kruskal-Wallis analysis of variance for nonparametric data were also used to compare the three classes for the scores obtained in the QoL-31, the MCSES, and the perception of the frequency of epileptic seizures.
Data Acquisition and Signal Processing The electroencephalographic (EEG) signals were recorded using the following electrode positions: Fz, Cz, Pz, Oz, P3, P4, PO7, and PO8, according to the 10-20 international system. All channels were referenced to the left earlobe, using Fpz as the ground. The signal was amplified through a sixteen-channel biosignal amplifier (gUSBamp, Guger Technologies, Schiedberg, Austria). The amplifier settings were 0. 5-100 Hz for the band-pass filter, the notch (50 Hz) was on, and the sensitivity was 500 µV. The signal was then digitized at a rate of 256 Hz. EEG data collection and processing were controlled by BCI2000 software  [31]. A stepwise linear discriminant analysis (SWLDA) of the data was performed to obtain the weights for the P300 classifier and to calculate the accuracy (using the BCI2000 tool called P300Classifier).
P300 Component The P300 component is a positive deflection produced by the perception of an infrequent expected stimulus. The maximum amplitude is usually reached on central to parietal regions between 250 and 500 ms. However, the auditory P300 has shown a larger latency than that of the visual modality in BCI  [36]. In order to proceed with the statistical analysis for P300 and to study how it may be affected under different conditions, a topographical analysis was carried out. The specific time intervals for this component were chosen based on the specific results of previous works on an auditory ERP-BCI (e. g. , Huang et al. [33] , Hübner et al. [34]  and Onishi et al. [37] ). The program used for the analysis of the EEG signal was EEGLAB (v13. 6. 5b) [38]. One participant was eliminated for waveform ERP analysis because his/her signal was too noisy to be included without altering the grand average.
Finally, for the attentional verification test, a paired t-test was employed to check whether there was a significant difference between conditions C2 and C3 with regard to the amount of information retrieved from the audios. The statistical analyses of performance, workload, and attentional verification were carried out using JASP software  [41]. The analysis of the ERP waveform was carried out with EEGLAB  [38]. The standard error was the measure of variance used to present the results. Brain Sci. 2021, 11, 39 7 of 14.
Setting Notations. We denote matrices with bold capital letters, vectors with bold small letters, scalars or indexes with standard small letters. For any matrix A, A Fro will denote its Frobenius norm (the square root of the sum of its squared coefficients). ,. denotes the scalar product between vectors or matrix multiplication. The data. We consider a setting in which rest-and task-fMRI datasets are observed in a populations of subjects, as in  Shafto et al. (2014); van Essen et al. (2012) ;  Gordon et al. (2017). We denote the rest-fMRI datasets X s ∈ R p×ns , as p × n s matrices, where s indexes the subjects, p is the number of voxels in the brain and n s is the number of 3D volumes of a run. Task-fMRI datasets are assumed to be in the form of statistical parametric maps, displaying some contrasts of interest.
Setup Our experiments were conducted on task-fMRI data of 500 subjects from the HCP dataset  (van Essen et al. , 2012; Barch et al. , 2013). This dataset was developed as an attempt to assess fundamental cognitive domains that sample the diversity of neural systems, namely language processing (including semantic and phonological processing), working-memory, social cognition, relational processing, motor responses, reward and emotion processing. We used the preprocessed data from the HCP500 release. Task-fMRI data. We consider task activation maps obtained from General Linear Models (GLMs)  (Friston et al. , 1994)  that show the activation of each brain voxel to different functional contrasts, for each subject. For instance, the working memory task refers to 19 functional activation maps -each containing p ∼ 2 × 10 5 voxels-per subject.
This is meant to mitigate the possible impact of noise per run (LR/RL phase encoding-related distortions etc. ). Procedure. N train = 200 subjects were used in Algorithm 1 to fit an ensemble of models. As a baseline method, we used parcellations in which each parcel comprised about 4000 voxels, for a total of about 60 parcels. The parcellation was obtained using Ward algorithm with spatial  connectivity constraints  (Thirion et al. , 2014a). Note that the number of parcels is a user-specified parameter, hence it can vary freely. While this algorithm is deterministic, i. e. it returns a fixed parcellation for a given input dataset (in the present case, the input dataset is rest-fMRI data), some randomness can be introduced by varying the input data, e. g. by Bootstrap  (Hoyos-Idrobo et al. , 2018).
Individual Brain Charting (IBC) dataset We reproduced the analysis on the IBC dataset. Although this dataset has a limited number of participants (twelve) data from many tasks were collected for each of them, thus providing a comprehensive coverage of cognitive functions. The task-fMRI data related to this dataset are described in  Pinho et al. (2018) ; they are available on NeuroVault, under the collection 4438 (https://neurovault. org/collections/4438). In total, 53 functional contrasts were studied, namely those listed in  Pinho et al. (2018). Unlike HCP, these contrasts are independent; they include the tasks of the HCP protocol, plus another battery assessing brain responses for mental arithmetics, spatial abilities, social cognition, emotional responses and a rapid-serialvisual-presentation language localizer.
The dataset also comprises resting-state data, with 4 acquisitions obtained on a 3T Siemens Prisma scanner. The imaging parameters for resting-state fMRI are different from those of task-fMRI: 2. 2mm (vs 1. 5mm) isotropic resolution, Multiband factor of 6 (vs 3), TR=. 76s (vs 2. 0s); the phase encoding direction was AP then PA. Four runs were acquired per subject in two sessions (one AP and one PA acquisition per session). The duration of each rest-fMRI data run is 14 minutes 12 seconds, amounting to 1120 scans. Acquisition details for task-fMRI data are available in  Pinho et al. (2018)  and a detailed documentation of the data is available at https://project. inria. fr/ IBC/data. For the sake of the analysis presented here, all images (task and rest) were resampled to 3mm resolution after motion estimation, motion correction, coregistration to the individual anatomical scan and spatial normalization to MNI space.
The preprocessing steps were performed with the SPM12 software, called through Nipype and Pypreprocess, using the routines described in  Pinho et al. (2018). These data were preprocessed as follows: band-pass filtering to the [. 01,. 1]Hz range, 5mm smoothing, centering and standardization of the resulting time series. Raw data for this dataset are available in https: //openneuro. org/datasets/ds002685. Here we use a leave-one-subject-out cross-validation due to the limited number of subjects.
Tools used The experiments are based on custom scripts that rely on the Nilearn library (v. 0. 5. 0) for the core structures and visualization tools  Abraham et al. (2013b). The Ridge regression and multi-layer perceptron used as task-to-rest fitting models are those of Scikit-learn v. 0. 20  Pedregosa et al. (2011). Note that the RidgeCV estimator with default parameters was used, meaning that the regularization parameter was set by nested 5-fold cross-validation, among an initial grid of 3 values [0. 1, 1, 10]. Experiments were run initially under Python 2. 7 on Linux workstations and later reproduced under Python 3. 5. Analysis code is available under the following repository: http://github. com/ hbp-brain-charting/public_analysis_code/tree/master/ papers_scripts/gradients 4. Results.
We obtained this information, but considering the cross-subject and cross-contrast average of R 2 voxel maps. The result is shown in Fig. 7 : the Intra-Parietal Sulcus bilaterally, pre-SMA, lateral occipital regions around MT/V5 cortex bilaterally, and bilaterally, spots in the superior frontal Gyrus, medial frontal Gyrus and insula. This topography does not correspond with the first functional gradient of  Margulies et al. (2016) , that outlined the default mode network. Instead, it outlines the visual pathways and dorsal/ventral attention networks, also known as executive control and salience networks. The corresponding R 2 voxel map for the IBC dataset (see below) is displayed in Figure  B. 10 in the appendix.
Reproduction on a smaller scale dataset We eventually reproduced the analysis on the IBC dataset (N=12). We assessed i) whether significantly accurate predictions were possible on small groups of subjects; and The curves display the difference between the accuracy obtained using a variable number of parcellations and then averaging the predictions, compared to using a single fixed parcellation (baseline method). We observe that using multiple parcellations tends to improve performance. The corresponding gains (1. 5% to 10%) are nonnegligible in front of the average accuracy reported in Fig. 8. This means that the prediction problem is dominated by variance, such as that induced by physiological and acquisition noise, which is reduced by bagging. Accordingly, the gain brought by multiple atlases is particularly pronounced in the small-sample regime and it diminishes as the sample size increases.
All the results were obtained using N train = 60 subjects. Figure  9 : Reproduction on a small-scale study. We reused the same modeling approach in the IBC dataset comprising 12 subjects. Prediction-accuracy results, measured as a ratio of explained variance, are displayed on the top for the 12 tasks in  Pinho et al. (2018) ). The vertical green bar indicates the across-contrast-corrected p < 0. 05 performance obtained by a permutation test. The effect of increasing the number of parcellations is illustrated for three tasks in the lower panel: a language-localizer (the RSVP-Language task in  Pinho et al. (2018) , working-memory task, and spatial-localizer task, named ARCHI Spatial. Error-bars represent cross-validation splits. We observe that accurate results are obtained for almost all conditions of all tasks, and that averaging across multiple parcellations systematically improves the outcome.
model instances. The simplicity and the computational efficiency of the linear model lead to the clear result of approximately 200 regions. Moreover, in that regime, the locally linear model outperforms a whole-brain non-linear model, obtained by a multilayer perceptron, confirming the relevance of localized variation model to capture distributed activity patterns. • Third, we introduced for the first time, in this context, a multiple-parcellation estimator and showed that it improves prediction accuracy and reduces variance. For a sample size of N=11, simply bagging results from different parcellations brings an increase of 0. 07 in average R 2. For N=31, i. e. the typical median size of neuroimaging studies, the gain remains above 0. 03, which is not negligible. It is thus especially useful in the low-sample regime, which is well illustrated in the IBC-dataset use case with few subjects.
Regarding the comparison with the Multi-Layer Perceptron (MLP), let us point out that the model has been grossly optimized for the numbers of units but not for all architecture aspects (number of of layers etc. ), as performing such optimization on a given dataset unavoidably results in overfit. The performance of complex non-linear models is a topic of investigation in the field He et al. (  2020 ), but the current consensus is that noisy and samplelimited data used in neuroimaging may not benefit as much from such models as e. g. computer vision applications. On quantitative analysis of local gradients. Recent studies have embraced the goal to describe individualized brain features  (DiNicola et al. , 2020; Gordon et al. , 2017; Braga et al. , 2019; Pinho et al. , 2018). Nevertheless, the present study departs from those by taking a quantitative point of view, in order to establish the significance of the organization described in front of the noise level.
Here, we focused on the limited set of contrasts made available in the datasets that we used. Probing more general families of contrasts will require multiple datasets, which goes beyond the scope of the present paper. An important related question is what cognitive domains are best predicted by resting-state features. Worthy of note is the fact that the definition of individual topographies from resting-state might require specific methods. We use herein Dictionary Learning; yet, the popular Independent Components Analysis, with the same number of components, yields similar results  (Bzdok et al. , 2016; Dohmatob et al. , 2016) , although we observed slightly lower accuracy (about 3%). Indeed, Sparse Dictionary Learning and Independent Components Analysis are deeply related  (Daubechies et al.
, 2009) , but the former yields sharper components. Fourth, as noted by one reviewer, the family structure of the HCP sample was not taken into account: it is thus possible that members of the same family were included in the train and test set respectively. The family information was not available to the authors when the experiments were performed. We conjecture that this has little to no impact on the results for two reasons: all the models combine equal contributions from all the HCP population, mitigating any bias related to one genetically similar sample. Second, the IBC dataset comprises unrelated individuals yet shows similar effects. Future work. Given the above limitations, the present work calls for extensions to more functional datasets and modalities, to ascertain and clarify the role of functional gradients and their relevance to brain cognitive architecture.
g. in cross-species comparison  (Xu et al. , 2019). Making sense of the observed gradients will ultimately require the use of dedicated datasets that overcome the low signal-to-noise ratio of each acquisition, using multiple acquisitions in fixed cohorts of participants (e. g. developing deep phenotyping framework)  Gordon et al. (2017) ;  Pinho et al. (2018). In particular, the higher prediction accuracy found for multimodal regions calls for more studies. Varying the conditions and modalities will be essential to formally validate the relevance of local and global brainorganization gradients. Our core assumption is that these loadings are a local marker of brain function. Based on this, we postulate the existence of link function f : R k → R c that maps voxelwise resting-state topographies to task-evoked brain activity.
Acknowledgment Appendix A. 2. Estimation of group-level resting-state topographies Several models have been proposed for group restingstate modeling  (Smith et al. , 2014; Varoquaux et al. , 2010 Varoquaux et al. , , 2011;; Abraham et al. , 2013a; Mensch et al. , 2016; Dohmatob et al. , 2016) , yet some of them are costly, as they require the iterative estimation of group-level and individual parameters. Since the resting-state time-series data are large (for example, 1200 3D volumes of 2 × 10 5 voxels in each subject of the HCP dataset  (van Essen et al. , 2012) ), a decomposition method that scales well is required. Incremental Principal Component Analysis/ICAbased methods  (Smith et al. , 2014; Varoquaux et al. , 2010)  have been designed for that purpose. Yet, the structured online dictionary-learning  (Mairal et al.
This project has received funding from the European Union's Horizon 2020 Framework Program for Research and Innovation under Grant Agreement No 945539 (Human Brain Project SGA3) and the KARAIB AI chair (ANR-20-CHIA-0025-01). 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.
GPS18169 at hand, the compound entered an in vivo experiment in t model of high fat regimen. GPS18169 showed similar stability in plasma, as compared to GPS15290 (Table  5 ). We measured about 50% of the initial dose remaining after incubation in mice plasma in the case of GPS18169, while GPS15290 remained in the low 20% range. With these data on GPS18169 at hand, the compound entered an in vivo experiment in the classical obesity model of high fat regimen.