dataesr/scientific-paragraphs-categorization huggingface dataset:
05, false discovery rate [FDR]-corrected). We also found that more than 90% of the cognitive tasks were significantly decoded using only cerebellar voxels (decoding accuracy, mean ± SD, 0. 918 ± 0. 015; 97. 1% ± 1. 9% of the tasks were significant; Fig. 5b middle ) or subcortical voxels (decoding accuracy, mean ± SD, 0. 856 ± 0. 013; 92. 7% ± 2. 2% of the tasks were significant; Fig. 5b bottom ), although the overall decoding accuracies were smaller than that of the decoding model based on the cortical voxels. To check the robustness of our decoding results, we performed permutation tests by randomly shuffling task labels in the test dataset for a total of 5,000 times. This analysis showed that tasks were significantly decoded in all three brain regions for all subjects (P < 0. 001). The above one-vs.
-one method provided very high decoding accuracy for all three brain parts; thus, the variability of decoding performances across tasks was unclear because of this ceiling effect. To examine such variability, we quantified decoding accuracy using task score (see Methods for details). We found that 99. 5% ± 0. 5% of tasks were significantly decoded using cortical activity (decoding accuracy, mean ± SD, 0. 592 ± 0. 018; Fig. 5c  top, Supplementary Tables  6 7 ), 98. 7% ± 0. 5% of tasks were significantly decoded using cerebellar activity (decoding accuracy, 0. 520 ± 0. 027; Fig. 5c middle ), and 95. 5% ± 2. 3% of tasks were significantly decoded using subcortical activity (decoding accuracy, 0. 404 ± 0. 020; Fig. 5c bottom) (See Supplementary Fig. 7  for the data of the other subjects). Permutation tests also showed that tasks were significantly decoded in all three brain regions for all subjects (P < 0.
No explanation of the tasks was given to the subjects prior to the experiment. During the fMRI experiment, subjects were instructed on how to perform each task by the instruction text that was shown as a part of the stimuli. Subjects only underwent a short training session on how to use the buttons used to respond. MRI data acquisition. The experiment was conducted using a 3. 0 T scanner (TIM Trio; Siemens, Erlangen, Germany) with a 32-channel head coil. We scanned 72 interleaved axial slices that were 2. 0-mm thick without a gap, parallel to the anterior and posterior commissure line, using a T2*-weighted gradient echo multiband echo-planar imaging sequence [repetition time (TR) = 2,000 ms, echo time (TE) = 30 ms, flip angle (FA) = 62°, field of view (FOV) = 192 × 192 mm 2 , resolution = 2 × 2 mm 2 , MB factor = 3].
We obtained 275 volumes for each run, with each following three dummy images. For anatomical reference, highresolution T1-weighted images of the whole brain were also acquired from all subjects with a magnetization-prepared rapid acquisition gradient echo sequence (MPRAGE, TR = 2,530 ms, TE = 3. 26 ms, FA = 9°, FOV = 256 × 256 mm 2 , voxel size = 1 × 1 × 1 mm 3 ). fMRI data preprocessing. Motion correction in each run was performed using the statistical parametric mapping toolbox (SPM12; Wellcome Trust Centre for Neuroimaging, London, UK; http://www. fil. ion. ucl. ac. uk/spm/). All volumes were aligned to the first echo planar image for each subject. Low-frequency drift was removed using a median filter with a 120-s window. The slice timing correction was performed to the first slice of each scan.
The response for each voxel was then normalized by subtracting the mean response and scaling it to the unit variance. We used FreeSurfer  59, 60  to identify the cortical surfaces from the anatomical data and register them to the voxels of the functional data. For each subject, the voxels identified in the cerebral cortex (53,345-66,695 voxels per subject), cerebellum (12,505-15,262 voxels per subject), and subcortex were used in the analysis (5,384-6,622 voxels per subject). For the subcortex, voxels in the bilateral hippocampus, caudate, amygdala, accumbens, pallidum, putamen, and thalamus were included. Note that we refined preprocessing parameters (e. g. , using SPM12 instead of SPM8 and adding slice timing correction) compared with our previous study  45  , which resulted in a slightly different distribution of representational similarities and decoding accuracies in the cerebral cortex.
Task-type features. The task-type features were composed of one-hot vectors, which were assigned 1 or 0 for each time bin, indicating whether one of the 103 tasks was performed in that period. The total number of task-type features was thus 103. Encoding model fitting. In the encoding model, cortical activity in each voxel was fitted with a finite impulse response model that captured the slow hemodynamic response and its coupling with neural activity  35, 61. The feature matrix F E [T × 3 N] was modeled by concatenating sets of [T × N] feature matrices with three temporal delays of 2, 4, and 6 s (T = # of samples; N = # of features). The cortical response R E [T × V] was then modeled by multiplying the feature matrix F E with the weight matrix W E [3 N × V] (V = # of voxels): RE ¼ F E W E ð1Þ We used an L2-regularized linear regression using the training dataset to obtain the weight matrix W E.
The training dataset consisted of 3,336 samples (6,672 s). The optimal regularization parameter was assessed using 10-fold cross-validation, with the 18 different regularization parameters ranging from 1 to 2  17. The test dataset consisted of 412 samples (824 s, repeated four times). To reshape the data spanning over six test runs into the four times-repeated dataset, we discarded 6 s of the no-task period at the end of each run as well as the 2-s feedback periods at the end of the third and sixth test runs. Four repetitions of the test dataset were averaged to increase the signal-to-noise ratio. Prediction accuracy was calculated using Pearson's correlation coefficient between the predicted signal and measured signal in the test dataset. Evaluation of optimal regularization parameters. To keep the scale of the weight values consistent across subjects, we performed a resampling procedure to assess the optimal regularization parameter used for group RSA and PCA  38.
To this end, we randomly divided the training dataset into training samples (80%) and validation samples (20%) for each subject and performed model fitting using an L2regularized linear regression. This procedure was repeated 50 times. The resultant prediction accuracies were averaged across the six subjects for each parameter. We selected the optimal regularization parameter that provided the highest mean prediction accuracy across subjects. This regularization parameter was used for model fitting for group RSA and PCA. Representational similarity analysis. To examine hierarchical relations across tasks, we conducted an RSA. First, we concatenated the weight matrix of predictive voxels of the task-type model across six subjects. Concatenation of the estimated weights was performed to obtain a group-level representation that provides a common basis that is comparable across subjects  37, 38.
We used Neurosynth as a metadata reference of the past neuroimaging literature  46. From the approximately 3,000 terms in the database, we manually selected 715 terms that covered the comprehensive cognitive factors while also avoiding redundancy. In particular, we removed several plural terms that also had their singular counterpart (e. g. , "concept" and "concepts") and past tense verbs that also had their present counterpart (e. g. , "judge" and "judged") from the dataset. We also excluded those terms that indicated anatomical regions (e. g. , "parietal"). We used the reverse inference map of the Neurosynth database for each of the 715 selected terms. The reverse inference map indicated the likelihood of a given term being used in a study if the activity was observed at a particular voxel.
Each reverse inference map in the MNI152 space was then registered to the subjects' reference echo planar imaging (EPI) data using FreeSurfer  59, 60. For each of the PCA score maps, we calculated Pearson's correlation coefficients with the 715 registered reverse inference maps, which resulted in a cognitive factor vector with 715 elements. Terms with higher correlation coefficient values were regarded as contributing more to the target PC. Cognitive factor features. To obtain task representations using continuous features in the human cognitive space, we transformed sparse task-type features into latent cognitive factor feature space. We used the reverse inference map of the Neurosynth database  46  for each of the 715 terms selected. Each reverse inference map in the Neurosynth database in MNI152 space was registered to the subjects' reference EPI data using FreeSurfer  59, 60.
To exclude the possible effect of low-level sensorimotor features on prediction and decoding performances, we performed an additional encoding model fitting using sensorimotor components. Sensorimotor features were obtained by concatenating ME features (visual), MTF features (auditory), and BR features (motor) (see the following subsections for details). Our previous studies validated the efficacy of these feature in controlling sensorimotor information  33, 44, 45. Moreover, ME and MTF features are easier to interpret than neural network features because they explicitly modeled neuronal response patterns in the early visual and auditory cortices. For each subject, we randomly divided the training dataset into training (80%) and validation samples (20%) and performed model fitting using an L2-regularized linear regression.
This procedure was repeated 50 times. The reliably predicted voxels by this analysis (having a mean prediction accuracy of at least 0. 3) were called as sensorimotor voxels and were excluded from some of the analyses as described in the Result section. Motion energy features. We used the ME model that has been used in previous studies  35, 44, 45  and provided in a public repository (https://github. com/gallantlab/ motion_energy_matlab). First, movie frames and pictures were spatially downsampled to 96 × 96 pixels. The RGB pixel values were then converted into the Commission International de l'Eclairage (CIE) LAB color space, and the color information was subsequently discarded. The luminance (L*) pattern was passed through a bank of three-dimensional spatiotemporal Gabor wavelet filters. The outputs of the two filters with orthogonal phases (quadrature pairs) were squared and summed to yield local ME.
The total number of BR features was four. FD ¼ R D W D ð2Þ The weight matrix W D was estimated using an L2-regularized linear regression with the training dataset, following the same procedure for the encoding model fitting. Decoding with novel tasks. In order to examine the generalizability of our models, we performed encoding and decoding analyses with novel tasks not used during model training (Fig. 5a ). We randomly divided the 103 tasks into five task groups. A single task group contained 20-21 tasks. We performed five independent model fittings, each with a different task group as the target. From the training dataset, we excluded the time points during which the target tasks were performed and those within 6 s after the presentation of the target tasks. In the test dataset, we used only the time points during which the target tasks were performed and those within 6 s after the presentation of the target tasks.
Statistics and reproducibility. Statistical significance of encoding models (412 samples, one-sided) was computed by comparing estimated correlations with the null distribution of correlations between the two independent Gaussian random vectors with the same length as the test dataset  38. The statistical threshold was set at P < 0. 05 and corrected for multiple comparisons using the FDR procedure  63. The statistical significance of the decoding accuracy (for both one-vs-one and task score-based methods) was tested for each task using the one-sided sign test (102 samples; P < 0. 05, with FDR correction)  64  , which is a nonparametric statistical test used in previous decoding studies  45, [65] [66] [67]. The significance of average decoding accuracy (across tasks) was further tested using the permutation test (P < 0.
05). Specifically, task labels in the test dataset were randomly shuffled 5,000 times, and p-values were calculated (for both one-vs-one and task score-based methods) based on the resultant null distribution of average decoding accuracy. Results from the six subjects were considered as replications of the analyses. All model fitting and analyses were conducted using custom software written on MATLAB. For data visualization on the cortical maps, pycortex was used  68. Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Acknowledgements We thank  MEXT/JSPS KAKENHI  (grant numbers  JP17K13083  and  JP18H05091  in # 4903  ( Evolinguistics ) for T. N. ,  JP15H05311  and  JP18H05522  for S. N. ) as well as  JST CREST JPMJCR18A5  and  ERATO JPMJER1801  (for S. N. ) for the partial financial support to this study. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data availability The raw MRI data are available at the OpenNeuro. org (https://openneuro. org/datasets/ ds002306)  69. Source data underlying Figs. 2d-f, 3b, 4b, 5b-e, and 6f are provided in Supplementary Data 1, 2, 3, 4, and 5, respectively. Other data are available from the corresponding author upon reasonable request.
The result is the impoverishment of benthic species from the western approach, the richest, to the eastern approach, the poorest. Cabioch et al. [10]  hence described the importance of the edaphic-climatic gradients for the benthic species distribution. Moreover, the authors in  [7, 8]  described the presence of Sarnian species occurring in the hydrological isolated Normand-Breton Gulf. Holme  [8]  was the first to collect quantitative data for the macrobenthic communities in some soft-bottom communities at the scale of the whole EC. He described the difficulties of sampling coarse sand, gravel and pebbles using quantitative sampling gears such as grabs. Most quantitative data were then collected near the shore in sandy and muddy sediments, which were much easier to collect with the type of grabs available at the time.
To increase our knowledge of the benthic habitats in the central deeper areas of the EC, two VIDEOCHARM surveys were carried out in 2010 and 2011. The study area covered a profile across the EC and was designed in the continuity of the CHARM II sampling grid to expand existing databases and to complement the UK work carried out in English waters  [11, [23] [24] [25] [26]. During the surveys, benthic habitats were studied using acoustic remote sensing techniques, coupled with in situ grab sampling and collections of video footage using a small remotely operated vehicle (ROV), for a total of seven methods. The aims of this paper were:  (1)  to characterise the benthic habitats along the VIDEOCHARM profile using the seven available methods;  (2)  to provide quantitative macrobenthic data in this offshore area of the EC; and (3) to propose EUNIS classification of the benthic habitats and communities in the central and eastern parts of the EC.
Side Scan Sonar Observations A complete acoustic coverage of each sampling box was carried out during the VIDEOCHARM surveys in 2010 and 2011 using side scan sonar (Figure  2 ) (for the methodology of the acoustic survey, see  [33] ). A visual analysis of the side scan sonar mosaic was conducted on board, and observations of the sediment types from grab samples and ROV footage were used to describe acoustic facies. Side scan sonar technology has been used successfully for many years to produce high-resolution acoustic maps of the seabed  [17] [18] [19] [33] [34] [35]. Typically, side scan sonar data are produced using a pair of transducers mounted on either side of a tow fish which is connected to a survey vessel by means of a cable. The sound emitted from the transducers ensonifies a continuous surface of seabed either side of the transducers  [36].
Reflected sound received by the transducers from the surface of the seabed provides information on the nature (e. g. , hardness, roughness, texture) of the sediments and the presence and disposition of seabed features (e. g. , sand waves, rock outcrops, algae cover, and anthropogenic features) across the swathe  [37]. For the VIDEOCHARM surveys, side scan sonar data were collected using the DF 1000 Edgetech (100-400 kHz) side scan sonar system in conjunction with data acquisition software. Data were processed, georeferenced and mosaiced using the 'Caraïbes' software package from IFREMER to produce continuous acoustic maps of the area surveyed. The vessel position was provided by a Thales 3011/Fugro SeaStar DGPS system, and the position of the side scan tow fish was calculated by using vessel heading, vessel offsets, tow cable layback and tow fish depth.
Grab Sample Collection Sediment and macrofauna samples were collected with a 0. 25 m² Hamon grab to ground-truth the acoustic surveys through the provision of information on sediment particle size distributions and macrofaunal communities. The grab sampling was also in the spatial continuity of the benthic sampling program CHARM II, to complement existing databases in the easternmost part of the Channel  [25, 38]. One replicate was used for sediment characterisation, and two replicates were used for macrofauna analyses. Snapshots of each replicate were made directly after the grabs were recovered. The two macrofauna grabs were then washed onboard the RV 'Côtes de la Manche' over two circular superposed mesh sieves (1 mm and 2 mm) to remove fine sediments. The biological and sediment content was fixed in containers with 10% buffered formaldehyde solution.
Data species richness and abundances from both replicates (0. 5 m²) were pooled for ecological analyses, and colonial epifaunal taxa were reported only as present. A subsample from the sediment grab was analysed for particle size distribution. First, sediment was wet sieved over a 50 μm mesh. The sieved sediment fraction (50 μm) was kept still and left to deposit for 48 h and then dried after the supernatant was removed. The rest of the sediment (>50 μm) was dried at 70 °C and then sieved using 32 sievecolumn (50; 63; 80; 100; 125; 160; 200; 250; 315; 400; 500; 630; 800; 1000; 1250; 1;600; 2000; 2500; 3150; 4000; 5000; 6300; 8000; 10,000; 12,500; 16,000; 20,000; 25,000; 31,500; 40,000; 50,000; 63,000 μm), and the total weight of each fraction was recorded. The sieve choice followed the modified Wentworth's classification to determine the sediment type of each station using a Folk diagram  [25, 26, 38, 39].
Database and Statistical Analyses Appendix A reports all the operations realised during the VIDEOCHARM surveys in 2010 and 2011. The different operations permit to obtain seven data tables, corresponding to seven habitat sampling methods: (1 and 2) endofauna collected at the 40 grab stations after sieving on 2 mm and then on 1 mm (in most cases two replicates per station, apart from four stations B21, B32, B109, B129 where only one grab was available; in these cases, the data were doubled to obtain a total surface of 0. 5 m²); (3) nondenumerable epifauna identified on pebbles and blocks in the 40 stations; (4) Hamon grab snapshot of sediment collected for all replicates (see Appendix A); (5) sediment particle size distribution, (6) video footage of the ROV SeaBotix at 30 stations;  (7)  interpretation of the side scan sonar profiles to identify acoustic facies.
Faunal data were used to calculate the taxonomic richness (TR, number of taxa per 0. 5 m 2 ), abundance (number of individuals per 0. 25 m 2 ), and diversity indices for each station. The Shannon-Weaver diversity index (H') in log2 and Pielou's evenness (J') were calculated. Ecological status was estimated from diversity indices H' and J values according to the thresholds defined previously and resumed in  [39] : 0-1, bad; 1-2: poor; 2-3: moderate; 3-4: good and >4: high. For J, the thresholds are <0. 2: bad; 0. 2-0. 4: poor; 0. 4-0. 6: moderate; 0. 6-0. 8: good and >0. 8: high, which were independent from the organic matter concentration in the sediment. Data analysis was performed using the PRIMER version 6 software package (Plymouth Routines in Multivariate Ecological Research)  [40]. Hierarchical cluster analysis (HCA) was carried out on the matrices based on Sorensen's coefficient for the presence/absence (all the taxa) and on Log10(x + 1) or square root transformed abundances per 0.
5 m 2 used to down-weight the importance of very abundant denumerable taxa with the Bray-Curtis similarity using group average linking of the species found in the different stations, with the construction of dendrograms using the group average algorithm generated from the PRIMER-6 software package (Plymouth Routines in Multivariate Ecological Research). To identify those species within different groups which primarily account for the observed assemblage differences, SIMPER (SIMilarity PERcentage) routines were performed  [40].
15 habitat (deep circalittoral coarse sediment, i. e. , MD2 offshore circalittoral coarse sediment of the EUNIS 2022 classification) (https://www. eea. europa. eu/data-andmaps/data/eunis-habitat-classification-1). The EUNIS habitat classification was first published in the early 2000s and was developed according to the data available at the time. The classification was then better suited to describe shallow bays and estuarine environments (mobile muddy fine sediments) such as the Bay of Seine in the English Channel  (42)  rather than in coarser sediment habitats occurring in deeper areas such as those found in the central and western part of EC. The EUNIS habitat classification is now widely used to describe the diversity of marine benthic habitats of the European marine seabed  [28, 41]. The combination of approaches used in the VIDEOCHARM surveys (side scan sonar, video, photography, and sediment particle size and taxa identification) identified the offshore circalittoral coarse habitat to levels 5 and 6 of the EUNIS 2022 classification.
The successive HCA proposed in this paper showed that most of the stations were always classified into the same cluster groups, whatever data source used, and only a few stations changed cluster groups according to the recorded taxa (Table  7 ). The cross analysis of these faunal groups with sediment data led to the allocation of the grab sampling stations to five EUNIS habitats. Table  7. Summary information on the sediment types according to the photos (expert assessment) and sediment analyses for the 40 stations according to their location in the sampled boxes in June 2010 and June 2011. Positions of the stations according to the groups identified in the cluster dendrograms (see Figures  5 6 7 8 and Tables  3 4 5 6 ). The colours indicate the classification of each station into the five EUNIS 2022 habitat classifications.
Magnetoencephalography As reported in our previous study  (Experiment 4, Weiss et al. , 2021) , we recorded MEG data using a whole-head Elekta Neuromag TRIUX system (Elekta Instrument AB, Stockholm, Sweden) composed of 204 planar gradiometers and 102 magnetometers using a sampling frequency of 1000 Hz. Prior to the experiment, each participant's head shape was digitized in the MEG coordinate frame. We used four additional head position indicator (HPI) coils, whose positions were also digitized, to monitor and correct for small head movements across blocks. The movement of HPI coils between blocks remained small (mean: 2. 3 mm) was similar across blocks and across conditions (t 23 = 1. 6, p=0. 128). We first removed magnetic noise from external sources using temporal signal space separation, after manually removing detected nonphysiological jumps in MEG signals.