dataesr/scientific-paragraphs-categorization huggingface dataset:
Tractography -fixel based analysis Though the DTI model is widely used, it is limited in its ability to distinguish between different fiber populations within a voxel  (Alexander et al. , 2002). We thus performed whole brain statistical analyses on specific fiber populations in a voxel known as fixel  (Raffelt et al. , 2015; 2017). The processing steps have previously been fully detailed  (Raffelt et al. , 2015; 2017)  and were performed using the MRtrix software version 0. 3. 15. The constrained spherical deconvolution (CSD) approach that accounts for non-white matter tissue composition  (Dhollander et al. , 2016)  was used to estimate the fiber orientation distribution (FOD) from the averaged response function of all subjects. Twenty controls and twenty patients were used to generate a population template to which all subjects' FOD was registered.
Cross-sectional analysis Cross-sectional analyses were conducted on data acquired during the second visit (follow-up), including diffusion weighted imaging not acquired at baseline. Between group differences of clinical and imaging parameters were evaluated using analysis of covariance whilst controlling for age, and when significant, step-down Bonferonni correction was performed to correct for multiple comparison. For each SCA group also, Pearson correlation was performed between diffusion imaging parameters and conventional markers (clinical scores and volumes) with the step-down Bonferroni multiple correction approach; p values < 0. 05 were considered significant. Finally, the effect sizes of FA and FDC in SCA groups compared to controls were assessed by the Cohen's d  (Cohen, 1988).
Fixel based analysis The preprocessing steps yielded improved SNR and contrast in the datasets (Supplementary eFig. 3). A visual inspection of the FODs already showed differences between controls and patients with SCA, controls having more fiber orientations in the CST than SCA patients (Supplementary eFig. 4). Using the template fixel mask and template tractogram followed by connectivity-based fixel enhancement statistics (Fig. 5A ), we found significantly decreased FD, FC and FDC in the CST of all SCA patients compared to controls (p < 0. 05) (Fig. 5B ). A significant decrease in FD was also observed in the corpus callosum of patients with SCA7 (p < 0. 05) (Fig. 5B ). FDC correlated with SARA scores in SCA1 (r = -0. 92, p < 0. 001) and SCA3 (r = -0. 752, p = 0. 004) and with CCFS in SCA1 (r = -0.
Though we could not calculate effect sizes on longitudinal DTI and FBA metrics, the current observations indicate that longitudinal effect sizes of FBA metrics will be greater than those of DTI metrics, highlighting their usefulness in clinical trials. Since a single biomarker is likely to fail due to the complexity of the neurodegenerative cascades leading to the onset and progression of SCA, a multimodal biomarkers approach that aims at the integration and visualization of multivariate datasets, could also be applied as we recently showed in our SCA cohort at baseline  (Garali et al. , 2017). In addition, although significant, the longitudinal volumetric changes reported here were relatively small. A multimodal approach may enhance sensitivity to change, especially when structural changes would not be detectable over short follow-up intervals  (D'Abreu et al.
Acknowledgements We are very grateful to the patients and volunteers who participated in this study. We will also like to thank the  Centre d'Investigation Clinique Pitié Neurosciences ,  CIC-1422  and  Melanie Didier  from the  Center for NeuroImaging Research (CENIR)  for their assistance with patients. Full financial disclosure No disclosure:  Adanyeguh, Perlbarg, Rinaldi, Petit ,  Valabregue. Dr. Henry  received research support by grants from  NIH  ( P41 EB015894 ,  P30 NS076408 ),  Bob Allison Ataxia Research Center at the University of Minnesota and Friedreich's Ataxia Research Alliance. Prof Brice  has honoraria from the  Wolfson Foundation  for reviewing the scientific project and from  Lundbeck  for giving a talk, and received research support by grants from the  French Agency for Research and European Union.
Acknowledgement This work was financially supported by grants from the  Capital Health Research and Development of Special  (Grant No. 2014-2-1073 , Beijing, China) and the  National Natural Science Foundation of China  (Grant No. 31440051 , Beijing, China). Abbreviations: AUC: area under the curve; BES: brief electrical stimulation; EMS: electrical myostimulation; HN: hypoglossal nerve; FN: facial nerve; MAP: muscle action potential; PFA: paraformaldehyde; PNG: predegenerated nerve graft.
The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models.
To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements.
Furthermore, multiple repetitions (30) of the movements are recorded, which allows considering within-task variations and contrasting them with between-task variations in synergy space. In particular, we apply a generic model of modularity, named space-bytime decomposition, which assumes the concurrent existence of spatial and temporal modules  (Delis et al. , 2014). The use of such a unifying model limits the dependence of conclusions upon the decomposition model used, as temporal, spatial or spatiotemporal modular decompositions have been commonly assumed separately before  (Ivanenko et al. , 2005; d'Avella et al. , 2006; Bizzi et al. , 2008). To compute task decoding scores, we employ a single-trial task decoding analysis described in depth in previous studies  (Delis et al. , 2013a,b)  to assess how well we can distinguish the task performed in synergy space, which would be impossible to estimate if trial-averaged data or a limited set of tasks were considered.
Kinematics and EMG Recording and Preprocessing Kinematics We recorded the 3D positions of 20 retroreflective markers (diameter = 20 mm) using an optoelectronic measuring device (Vicon Motion System, Oxford, UK) at a sampling frequency of 100 Hz. 16 passive markers were fixed symmetrically on the two hemibodies (acromial process, humeral lateral condyle, ulnar styloid process, apex of the index finger, greater trochanter, knee interstitial joint space, external malleolus, and fifth metatarsal head of foot). We added external cantus of the eye on the right face, auditory meatus on the left, and head apex and the first thoracic vertebra (T1) at the middle. The kinematic data were low-pass filtered (Butterworth filter, cut-off frequency of 20 Hz) and numerically differentiated to compute tangential velocity and acceleration of each marker.
e. , we binned the timecourse of the signal into 50 bins and computed the area under the curve in each bin. Movement artifacts were visually removed by discarding the associated trials (<2% of the total number of trials). The data were then normalized in amplitude on a muscle-per-muscle basis by dividing each single-trial muscle signal by its maximal value attained throughout the experiment. A potential detachment of EMG electrodes was assessed, for each subject, by visually checking a posteriori that none of the recorded muscles showed an abnormal change in signal amplitude across trials. For each subject, we finally formed an EMG matrix of (50 time steps × 30 muscles) in rows and 2,160 trials in columns consisting of all the movement-related EMG activity (rectified and filtered) of the 30 muscles for all recorded trials.
Variance Accounted for (VAF) To assess how well the space-by-time decomposition approximates the recorded EMG activity, we computed the variance accounted for (VAF) by the space-by-time decomposition. VAF is defined as the total reconstruction error normalized by the total variance of the dataset as follows: VAF = 1 - s ||m s (t) - P i=1 N j=1 w i (t)a s ij w j || 2 s ||m s (t) -m|| 2 , ( 2 ) where S is the total number of trials, m is the mean muscle activity across all trials, time steps and muscles and ||. || denotes the Frobenius norm. Note that different VAFs could be defined by replacing m by zero or any other reference value  (Torres-Oviedo et al. , 2006) , indicating that VAF values may differ depending on the precise definition of m.
We considered spatial modules as M-dimensional vectors and computed correlation coefficients between all pairs of modules across all pairs of subjects. Using R i,j as distance measure, we created a hierarchical cluster tree from all module pairs (Matlab function "linkage" with the "average" distance method, i. e. , using as distance between two clusters the average distance between all pairs of objects across the two clusters). The number of clusters was set to the maximum number of spatial modules across subjects (i. e. , 7 here). The correlation between modules was then computed as the mean pairwise correlation between all pairs of modules within each cluster.
Significance of Identified Decompositions We used a permutation test to assess the ability of the identified decompositions to uncover meaningful structure in the data. We compared the VAF and decoding performance of the identified decompositions with the VAF and decoding performance values obtained when decomposing structureless data. We generated structureless data from the recorded data by randomly permuting the muscles for each time step of each trial in every movement. The input matrix thus had exactly the same numerical values but was devoid of biomechanical significance. For each subject, we performed 10 different permutations, which resulted in 10 simulated datasets on which we applied sNM3F to extract space-by-time decompositions and computed VAF and decoding performance of the resulting decompositions.
Note that the VAF could not be evaluated from some these non-modular decompositions because they involve only a subset of the recorded muscles; thus, they can reconstruct only a part of the EMG recordings. To resolve this issue, we then quantified the maximal decoding and maximal VAF that can be achieved by the dataset under investigation using non-modular descriptions of the data involving all recorded muscles. We described EMG activity in single trials using the rms values from the single-trial recordings of all M = 30 muscles, binned into increasing numbers of temporal bins (B varying from 1 to 50). We input these values to LDA and computed the maximal "non-modular" decoding performance (for B = 50 the non-modular description was identical to the one given by the original data set). Computing the VAF was also possible here, using the following procedure: the reconstructed EMG matrix of this non-modular decomposition was obtained by assuming all time points within each bin to be equal to the rms value of that bin (i.
This suggests that the extracted spatial modules represent functional muscle couplings for performing the movement at hand rather than purely anatomical groupings of muscles controlling the same joints or body parts. To quantify whether the extracted modules were able to generate muscle patterns underlying new tasks, we ran two  additional tests. We extracted spatial and temporal modules using only a subset of the motor tasks (75% of all tasks) and then quantified the similarity of these modules with those extracted from the full dataset. To simplify these tests, we ran the decomposition on averaged data (trial-averaged for each task) for each subject and for the optimal number of modules. We first performed the decomposition on a task subset selected randomly to encompass 75% of the total number of tasks (i.
e. , 54 tasks out of 72). We repeated this process 10 times for each subject. The results showed a high correlation between the modules extracted from subsets of tasks and the initial decomposition (containing all tasks): averaged r values across best-matching pairs of modules and subjects was 0. 96 ± 0. 03 for the spatial modules, and 0. 99 ± 0. 01 for the temporal modules. Accordingly, the modular decompositions of the task subsets approximated the test EMG data equally well as the original decomposition that included all tasks (VAF = 90% ± 6%). In our second test, we chose specific subsets of tasks to assess the effect of target location on the extracted modules. For each subject, we tested four different subsets (of 54 tasks out of 72): (1) discarding all tasks ending at any target of the top row: r = 0.
DISCUSSION In this study, we analyzed the effectiveness of space-bytime modularity in describing muscle patterns underlying a set of complex goal-directed movements. We designed a comprehensive experiment comprising 72 distinct wholebody pointing movements and collected 30 EMG channels. We assessed the extent to which a space-by-time modular decomposition of muscle activity could (a) approximate the rectified/filtered EMG data on single trials and (b) convey task-relevant information. Although descriptive, the present study shows that space-by-time modularity provides a relevant overview of how muscle patterns may be formed during wholebody voluntary movements, as discussed below. The different sets of parameters were compared in terms of their decoding power for three different dimensionalities (9, 16, and 25 parameters corresponding to (P, N) = (3, 3),(4, 4),(5, 5) spatial and temporal modules respectively).
In this vein, we propose the design of an experiment that comprises as many movements as possible (with numerous repetitions of the same movement) and involves time-varying EMG recordings of as many muscles as possible  (Steele et al. , 2013 (Steele et al. , , 2015)). Our experiment here comprises more tasks (understand movement types here) than most other studies and also considers a complex daily-life motor behavior of whole-body reaching while standing for which a large interindividual and intra-individual variability may exist  (Berret et al. , 2009; Hilt et al. , 2016). Second, we evaluated the functional role of the model by looking at the extent to which it could represent such a wide variety of muscle patterns with betweentask and within-task variabilities. Third, we compared the performance of the modular model with alternative non-modular models in terms of task decoding, data approximation and dimensionality reduction (i.
Acknowledgement: All authors developed the study concept, and contributed to the study design. SC created the stimuli, and collected and analyzed data. All authors wrote the article, and approved the final version of the manuscript for submission. This research was funded by the ESRC (Grant RES062231647) and the British Academy (Grant SG131011). Additional support was supplied by ERC grant 241242. We thank Alan Gilchrist, David Foster, and Johan Wagemans for their comments on an earlier version of this article. We also thank Helen Spray and William Martin for conducting the paper-based controls referred to in the supplemental material. Perceiving the lightness of an object or surface is so fundamental to vision that it is easy to believe arguments that it is hard-wired into our sensory physiology (e.
1A ), resulting in 39 stimuli per actor (Audio S1-S78). Healthy participants (n=10) were each scanned in alternating sessions of fMRI and MEG (four sessions each) while performing a simple repetition detection task that ensured appropriate attention to the stimuli while avoiding an explicit focus on emotional attributes. The large amount of multimodal imaging data for each individual (8 sessions each for a total of 80 sessions) was key to adjudicating between overlapping emotion models with robust analyses. Once scanning was complete they rated the perceived dissimilarity of all (within-actor) pairs of stimuli in the absence of instructions that would bias the judgment toward a specific stimulus feature. During the last session, they evaluated the perceived emotional stimulus attributes by categorizing emotions and rating their valence and arousal (cf.
89%, s. e. m. = 1. 65%, T(9) = 10. 28, p = 0. 00002). Thus, the behavioral data indicate that both categories and dimensions influence the perception of the emotional voice stimuli but that categories have a stronger influence. Next, we asked where (cerebral location) and when (peri-stimulus latency) stimulus-evoked cerebral activity was significantly associated with either the categorical or the continuous emotion models. We first built fMRI RDMs reflecting at each cerebral location (voxel) the pairwise stimulus-evoked blood oxygenation level signal difference measured via fMRI, measured within a local sphere centered on that voxel (spatial fMRI searchlight = 6 mm radius). Each fMRI RDM was tested for a significant correlation with each of the three emotion-attribute RDMs (see Fig. 2A -B correlation maps and Fig.
The MEG tubephone system introduced strong spectral coloring of the sound stimuli and suppressed heavily frequencies > 6 kHz. Stimuli for all sessions were consequently low-pass filtered at 5 kHz. Flat-frequency response for the MEG audio stimulation chain was achieved through inverse filtering methods. Neuroimaging data acquisition fMRI scans were acquired with a Siemens 3T Trio scanner, using a 32-channel head coil. Functional multiband echo planar imaging (EPI) volumes were collected with a repetition time (TR) of 1s (echo time TE = 26 ms; flip angle = 60; multiband factor = 4; GRAPPA = 2). Each functional volume included 56 slices of 2. 5 mm thickness (inter-slice gap = 2. 5 mm; interleaved even acquisition order (interleaved even) in an axial orientation along the direction of the temporal lobe, providing nearly whole-brain coverage.
Analysis of behavioral data We initially assessed the effect of voice morphing on perceived emotion attributes (five morphing % parameters describing each experimental stimulus -one for each expressed emotion and one for the neutral vocalization; six measures of perceived emotionfour emotion categorization probabilities plus valence and arousal ratings). The five morphing parameters were not orthogonal because for each stimulus only two at best had a non-zero value (average Spearman correlation between morph parameters = -0. 24; STD = 0. 02). For this reason, the perceptual effect of morph parameters was assessed independently of their shared variance by measuring their Spearman semi-partial correlation (s. p. r) with the measures of perceived emotion. Significance testing for all of the analyses in this study relied on a permutation-based group-level random effects (RFX) approach.
05 level. Subsequent modeling of behavioral data assessed the association of perceived stimulus dissimilarity (pairwise dissimilarity ratings) with perceived emotion categories, valence and arousal (each transformed to a pairwise stimulus distance -emotion representational dissimilarity matrix RDM). The valence and arousal RDMs measured the absolute pairwise difference in valence and arousal ratings, respectively. The category RDM was defined as the Euclidean distance between stimulus-specific categorization response profiles (e. g. , categorization profile consisting of 10, 2, 1 and 0 anger, disgust, fear and pleasure responses). All emotion RDMs were computed independently for each participant and speaker. Significance testing relied on a similar approach as for the analysis of the effect of morph parameters on perceived emotion attributes (N permutations = 100,000; reshuffling of rows and columns of distance matrices).
Preprocessing of neuroimaging data Analyses were carried out in Matlab using SPM12, Fieldtrip  (44) , GLMdenoise  (45)  and custom code. The initial preprocessing of fMRI and MEG data produced for each participant the stimulus-specific responses further analyzed to assess the encoding of emotion attributes (see below). Functional MRI images from all runs were realigned to the first image in the first run and unwarped to correct for movement-by-distortion interactions (full width at half maximum -FWHM = 5 and 4 mm for realignment and unwarp, respectively; for both 7th degree B-spline for interpolation), and slice time corrected to the onset of the temporally central slice. Anatomical volumes were co-registered to the grand-average of the preprocessed functional volumes and segmented into grey matter, white matter, and cerebro-spinal fluid.
Visualization The non-metric MDS models in Fig. 1  and 2  were computed using the R-package SMACOF  (51). We modeled the dissimilarity ratings and the emotion RDMs using an interindividual difference scaling model (INDSCAL), avoiding well known distortions of the representational geometry associated with group averaging of distance data  (52). The 3D glass brains in Fig. 3  and S3 comprised two components:  [1]  a mesh of the ICBM 152 2009c Nonlinear Asymmetric template (53);  [2]  the functional blobs, rendered by first modeling the surface of each blob with a 3D mesh, and then projecting onto it the volumetric statistical map it circumscribed (maximum projection within 7 mm radius sphere centered at mesh vertex). All meshes and projections were computed within BrainVISA (http://brainvisa.
Acknowledgments: Supported by  UK's Biotechnology and Biological Sciences Research Council  (grants  BB/M009742/1  to JG, BLG, SAK, and PB, and  BB/L023288/1  to PB and JG), by the  French Fondation pour la Recherche Médicale  (grant  AJE201214  to PB), and by Research supported by grants  ANR-16-CONV-0002  ( ILCB ),  ANR-11-LABX-0036  (BLRI), and the  Excellence Initiative of Aix-Marseille University  (A*MIDEX). Conceptualization: BLG, PB; Methodology: BLG, CW, NK, SAK, PB, JG; Software: BLG; Validation: BLG; Formal Analysis: BLG, CW, JG; Investigation: BLG, CW; Resources: BLG, PB; Data Curation: BLG, CW; Writing -Original Draft: BLG, CW, SAK, PB, JG; Writing -Review & Editing: BLG, CW, NK, SAK, PB, JG; Visualization: BLG; Supervision: BLG, PB, JG; Project Administration: JG; Funding Acquisition: BLG, SAK, PB, JG.
Study overview The present study aims to examine the effect of cognitive load on reactions to thin-ideal exposure, as manifested on an implicit dependent measure. Female participants were exposed to images of the thin ideal or women's fashion accessories. A classic implicit measure, i. e. a lexical decision task, was used to assess the accessibility of negative concepts following thin-ideal exposure. Half of the participants were allocated to a low cognitive load condition and the other half under high cognitive load condition. According to Payne  [36] , a process can be considered automatic if it occurs as strongly, or more strongly, under cognitive load as it does in the absence of load. Following this reasoning, the data would be consistent with our hypothesis if we found a main effect of thin-ideal exposure such that it increased negative thought accessibility regardless of cognitive load, or if we found an interaction such that thin-ideal exposure increased negative thought accessibility to a higher degree under high than low cognitive load.