dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017)  or cerebellum lobules  (Carass et al. , 2018). We measured the results of the proposed pipeline using a LOO methodology and achieved an average dice value of 0. 8262. This result was obtained from four different sub-datasets ranking from 0. 7831 to 0. 8353 showing a good generalization of the proposed method. This result was quite close to the manual intraobserver accuracy that was estimated as 0. 8363 using a reduced dataset. We also compared the proposed method with a related currently available state-of-the-art method for full brain labeling. We demonstrated that vol2Brain was not only far superior to the linear (0. 8262 vs. 0. 7369) and nonlinear (0. 8262 vs. 0. 7591) versions of JLF method but also more efficient with a temporal cost of minutes compared with hours. The proposed vol2Brain pipeline is already available through our volBrain platform (https://volbrain.
FUNDING This research was supported by the  Spanish   DPI2017-87743-R grant  from the  Ministerio de Economia, Industria y Competitividad of Spain. This work was benefited from the support of the project DeepvolBrain of the  French National Research Agency  ( ANR-18-CE45-0013 ). This study was achieved within the context of the  Laboratory of Excellence TRAIL   ANR-10-LABX-57  for the  BigDataBrain  project.
Significant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that different brain states are underpinned by dissociable spatiotemporal dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep and disorders of consciousness after coma). The model-free approach was based on Kuramoto's turbulence framework using coupled oscillators. This was extended by a measure of the information cascade across spatial scales. Complementarily, the model-based approach used exhaustive in silico perturbations of whole-brain models fitted to these measures.
Results We used model-free and model-based frameworks to explore information transmission flow in whole-brain dynamics across different brain states. Specifically, we compared brain measures on three independent resting-state fMRI datasets. The meditation dataset comprised experienced Vipassana meditators (N = 19) during both focused attention meditation (M) and resting state (R). The sleep dataset comprised healthy subjects during deep sleep, i. e. , stage 3 (DS, N = 13) and resting state (R, N = 13) states. Finally, the disorders of consciousness (DOC) dataset were acquired in two independent research sites (Liège and Paris), comprised of healthy volunteers (R CNT : N = 49) and DOC patients diagnosed in a minimally conscious state (R MCS : N = 66) or an unresponsive wakefulness state (R UWS : N = 39).
First, we applied the model-free approach to measuring information transmission flow across spacetime scales based on the recent finding demonstrating turbulence in human brain dynamics (Fig. 1a )  38. This analysis was based on the local Kuramoto order parameter that describes the local level of synchronisation of a brain area, n, as a function of space, x, and time, t, at a given scale, λ. The scale of the local synchronisation is defined by the parameter λ, which determines the size of the spatial distances where the synchronisation is evaluated, where high values of λ stand for short distances, and vice versa (Fig. 1b ). In particular, we computed for each dataset the amplitude turbulence defined by Kuramoto as the space and time variability of the local level of synchronisation  28, 40, 54  (referred here as Kuramoto amplitude turbulence), and three measures quantifying the information transmission in terms of scale, space and time correlation of the local level of synchronisation that we defined as transfer correlation, information cascade flow, and information cascade (Fig.
1  and see more details in Methods and ref. 39  ). Second, we applied the model-based approach based on the sensitivity of these models to react to external in silico perturbations (Fig. 1c  and Methods). For each brain state, we constructed a whole-brain dynamical model based on the normal form of a supercritical Hopf bifurcation coupled with the dMRI structural connectivity and the exponential distance rule (EDR). Finally, to evaluate how each model fitted reacts to external stimuli, we applied in silico perturbations by quantifying the susceptibility and information encoding capability measures. Model-free framework. We computed the information transmission flow measures on the three datasets in terms of Kuramoto amplitude turbulence and transfer correlation within the 0. 008-0. 08 Hz frequency range (see Methods).
First, we explored the level of Kuramoto amplitude turbulence over different λ Fig. 1  Overview of framework. a Turbulence in fluids is one the most common dynamical regime where the mixing motion governs (left panel). The energy cascade, i. e. , how the energy travels across scale while dissipated and the statistical properties defined as power laws on the energy levels and structure functions (bottom panel) determine the turbulent behaviour of the fluid. The analogy between brain activity and Turbulence has been recently demonstrated using resting state data from a large dataset of 1,003 healthy human participants. b Model-free approach. The turbulent behaviour of brain activity is reflected in the similarity between the local level of synchronisation, determined by the local Kuramoto order parameter (R) at different scales (λ), and vortex with different spatial scales in fluid dynamics.
We found that the meditation state increases Kuramoto amplitude turbulence levels in higher spatial scales, i. e. , short distances in the brain, compared to the resting state. On the other hand, the deep sleep state shows lower Kuramoto amplitude turbulence levels than the resting state across all the spatial scales. Finally, the Kuramoto amplitude turbulence levels decrease for DOC patients (R MCS and R UWS ) compared to healthy controls during resting state in lower lambda values, i. e. , long distances, but increases in higher lambda scales; differentiating, also, between the R UWS and R MCS groups. The results of Kuramoto amplitude turbulence levels in each state are displayed in Fig. 2a. We included in the supplemental material seven videos (Supplementary Videos 1 to 7) of the full spatiotemporal evolution of Kuramoto amplitude turbulence in one hemisphere across time of the full resting state of a single participant for each brain state within each dataset.
01, λ = 0. 12 and λ = 0. 3. The meditation state showed significant increases in Kuramoto amplitude turbulence compared to the resting state only on higher scales. The DS shows significantly lower Kuramoto amplitude turbulence than the resting state across all spatial scales. By contrast, the Kuramoto amplitude turbulence showed significant decreases in R MCS and R UWS states in lower lambda scales but significant increases in higher scales compared to R CNT. b The plots were computed as the linear fit of the mean level of Kuramoto amplitude turbulence at each scale for the three brain states for the DOC dataset (i. e. , R CNT , R MCS , and R UWS ) and two brain states for sleep and meditation datasets (i. e. , W, DS, and R, M, respectively). The plots display the obtained slopes as a function of the scale.
In particular, DOC showed negative slopes at lower scales and increased with the scales up to positive slopes. The sleep dataset presented negative slopes at lower scales, increased up to λ = 0. 12, and a negative slope value was kept almost constant. The meditation dataset also increased with scale but presented less variability than the other datasets. Dashed vertical lines indicate the scales displayed in A and the horizontal red dashed line highlights the zero slope. c We computed the transfer correlation (|A λ |), which measures how the information travels across space at different spatial scales, i. e. , we show the results as a constant k - |A λ |, with k = 3 |A λ |. The meditation state presents no significant differences on any scale compared to the resting state. In contrast, the transfer correlation significantly decreased for DS and R MCS , R UWS states compared to the resting state across all scales.
d We performed the same computation as in panel B for the transfer correlation measure. In this case, DOC and sleep datasets presented a similar slope-scale relationship, whereas the meditation dataset presented less variability across scales. In the figure, P-values were assessed using the Wilcoxon rank-sum test and corrected for multiple comparisons, *P < 0. 05, **P < 0. 01 and ***P < 0. 001. slopes and scales for each dataset. The meditation dataset presents similar behaviour but is less sensitive to this measure, i. e. , lower variability of the slope values across scales. By contrast, the sleep dataset shows a monotonical increase of slope values from negative values at low scales up to λ = 0. 12, where it remains almost constant for higher scales. Finally, DOC states present the same behaviour: the slopes monotonically increase from negative values at low λ scales towards positive values at high λ.
This positive slopes at high λ can be associated with an increase in the short-range information transmission with a lack of a global broadcasting due to the long-range transmission diminution (negative slopes at low λ). It is noticeable that with this quantification it is possible to differentiate between datasets that involve a reduction of consciousness, i. e. , despite that sleep and DOC patients present a reduction of the information processing in many scales, the behaviour across scales captures differences between sleep and DOC states. Secondly, we explored how the information is transferred across space in terms of the time correlation between the level of local synchronisation at each scale (see Methods). This measure indicates how the information travels across space at a given spatial scale, λ.
We found that the transfer correlation in the meditation state did not significantly differ across any scale compared to the resting state. By contrast, this measure significantly decreases for deep sleep and DOC states across all λ scales compared to the resting state, and interestingly, differentiating the R MCS and R UWS groups across all scales (Fig. 2c ). Furthermore, to summarise the behaviour of transfer correlation at different scales, we quantified the changes at each λ across brain states. Conversely, the evolution of the slopes across scales for the transfer correlation presents the same behaviour across all datasets (Fig. 2d ). We measured how the information travels across scales by defining the information cascade flow, as the predictability in terms of time correlation of a given level of local synchronisation at scale λ from the level of local synchronisation at scale λ-Δλ, in consecutive time steps, t and t + Δt (where Δλ and Δt are the discretisation of scale and time, respectively).
In contrast, the deep sleep and DOC states present less transfer correlation across the scales than the resting state, moreover, the information cascade clearly differentiate between R CNT and R UWS states (Fig. 3b ). To assess the regional heterogeneity of the synchronisation time variability at a given scale, we defined the node-level metastability as the standard deviation over time of the local Kuramoto order parameter for each brain state in each dataset. This measure indicates how changes the level of local synchronisation across time. We quantified this difference by computing the Kolmogorov-Smirnov distance (KSD) between the distributions of node-level metastability, where larger values mean more different distributions (see Methods). We found that the KSD for all datasets monotonically decreases between brain states across scales, whereas the value of λ increases.
P-values were assessed using the Wilcoxon rank-sum test and corrected for multiple comparisons, *P < 0. 05, **P < 0. 01 and ***P < 0. 001. absolute difference between the node-level metastability between brain states in each dataset at λ = 0. 12 rendered onto the brain cortex (Fig. 4b ). Then, we identified the resting state networks to which they mainly belong and quantified the number of nodes per network by selecting the nodes for each comparison of the top 15% quantile. We found that differences between meditation and resting state are mainly in the limbic and default-mode networks. In contrast, the highest differences between deep sleep and resting state are observed in the nodes of the visual-and default-modenetworks. Finally, the highest differences in local synchronisation are found between controls during resting state and DOC patients (R MCS and R UWS ) in the somatomotor-, salience-, control-, and default-mode-networks.
The perturbation consisted of applying an external periodical force equally for all brain regions. This stimulus was represented as an external additive periodic forcing term, given by F j = F 0j cos(ω 0 t) + iF 0j sin(ω 0 t) with F 0j = 5 × 10 -4 , in the corresponding real and imaginary part of the node j equation (Eqs. 10 and 11 see Methods), with frequency ω 0 equal to the average across node of the empirical node frequency (Fig. 1c ). Finally, we computed the susceptibility Fig. 4  Local node-level metastability was significantly different between brain states and revealed distinct signatures of network involvement. We computed the node-level metastability as the standard deviation across time of the local Kuramoto order parameter (see Methods). a We performed the KSD between distributions of the node-level of metastability of each brain state within each dataset for each scale.
The KSD for all datasets monotonically decreases, whereas the value of λ increases for all comparisons. b Render brains represent the absolute difference of the node-level metastability between each brain state for scale λ = 0. 12, indicated with vertical dashed lines in panel A. We selected the top 15% quantile of absolute differences between conditions, identified the resting state networks to which they belong and quantified the number of nodes per network. c Radar plots represent the number of nodes on the top 15% quantile of the absolute difference by each comparison and resting-state network (CON: control; DMN: default mode; TP: temporal-parietal; VIS; visual; SOM: somatomotor; ATT: attentional; SAL: salience; LIM: limbic). The networks showing the highest differences between resting and meditation states were the limbic and default-mode networks.
Our model-free framework was able to show the role of information cascade in 'vortex space' as a distinguishing feature between brain states (resting state, meditation, deep sleep, R MCS , and R UWS ) as measured by fMRI neuroimaging. As such our results demonstrated that these brain states exhibit significant differences in information cascade across different scales at both the spatial and temporal domains. Equal y, our model-based approach fitted a whole-brain model to the Fig. 5  Model-based framework revealed significant perturbative differences for different brain states. a We show the evolution of the error of the whole-brain model FC fitting to the empirical fMRI data as a function of the global coupling strength, G. The error of the FC fitting was given by the square root of the difference between the simulated and empirical FC matrix.
The optimal working point of the model was defined as the minimum value of the FC fitting, i. e. , where the model shows maximal similarity to the empirical fMRI data. b We show the results of the susceptibility measure, which estimates how these models react to external periodical force perturbations. In all datasets, the resting state was the most susceptible to be perturbed. c We show the information encoding capability of the whole-brain models, which captures how different external stimulations are encoded in the dynamics. Similar to the susceptibility measure, the resting state was more susceptible to react to the perturbations. Susceptibility and information capability measures differentiated each brain state and between R MCS and R UWS groups. These results show that each brain state encodes the whole-brain dynamics with a particular complexity.
Interestingly, while this measure increases with the distance between resting and meditation, this difference is not statistically significant. This suggests that meditation is more similar to the resting state but that there are important significant differences which can be revealed by the other information transmission measures. The information cascade flow monotonically decreased with shorter distances (the increase in spatial scale λ) for all brain states (shown in Fig. 3a ). This measure also discriminated between conditions within each dataset, i. e. , showing lower values for DOC patients than control participants and in the deep sleep stage compared to the resting state in the same participants. The information cascade (i. e. , the average of the information flow across scales) was lower in low-levels states of awareness (deep sleep, R MCS and R UWS ) than in normal resting state (shown in Fig.
Specifically, our external manipulation consisted of a shift towards the bifurcation point of the intrinsic local dynamics of brain areas. We found that the resting state showed significantly higher susceptibility and information encoding capability than in the pairwise comparison in each dataset, i. e. , meditation, deep sleep and DOC. The similar behaviour of both measures (susceptibility and information encoding capability) can be related to the specific features of our perturbative approach. Differences in silico protocols can be assessed to study how different brain states react to external perturbations such as shifting the local dynamics in the opposite direction, node by node perturbation  78, 79  , nonsustained perturbations  47, 80  or perturbing with external strength dependent periodic force  47, 77.
Methods Participants Meditation. A total of 19 experienced meditators with more than 1000 hours of meditation experience were selected from a dataset previously described in Escrichs et al. (  2019 )  83. Meditators were recruited from Vipassana communities of Barcelona, Catalonia, Spain (7 females, mean ± SD, 39. 8 ± 10. 29 years, 9,526. 9 ± 8,619. 8 meditation experience). Participants were asked to practice focused attention on breathing (i. e. , anapanasati in language Pali). In this meditation technique, meditators focus their attention on natural breathing, and when they realize that the mind is wandering, they must refocus their attention back to natural breathing. All participants reported no history of past neurological disorder and gave written informed consent. The study was approved by the Ethics Committee of the Bellvitge University Hospital according to the Helsinki Declaration on ethical research.
Sleep. A total of 63 healthy subjects (36 females, mean ± SD, 23 ± 43. 3 years) were selected from a dataset previously described in Tagliazucchi and Laufs  84. On the day of the study, participants reported a wake-up time between 5:00 AM and 11:00 AM and a sleep onset time between 10:00 PM and 2:00 AM for the night before the experiment. Within half an hour of 7 PM, participants entered the scanner and were asked to relax, close their eyes, and not fight the sleep onset. Their resting state activity was measured for 52 minutes with a simultaneous combination of EEG and fMRI. According to the rules of the American Academy of Sleep Medicine  85  , the scalp potentials measured with EEG determine the classification of sleep into four stages (resting state, N1, N2 and N3 sleep). We selected 13 subjects who reached the deep sleep stage (DS, i.
The patient's family gave their informed consent for the participation of their relative, and all investigations were conducted according to the Declaration of Helsinki and the French regulations. Disorders of consciousness, Liège. A total of 35 healthy controls (14 females, mean age ± SD, 40 ± 14 years) and 48 patients with disorders of consciousness (DOC) were included in the study based on a dataset previously described in López-González et al  53. The diagnosis was made after at least 5 CRS-R by trained clinicians. The highest diagnosis of the level of consciousness was taken as the final diagnosis, which was also confirmed with Positron Emission Tomography (PET) (i. e. , patients in MCS presented a relatively preserved metabolism in the frontoparietal network while patients with UWS had a bilateral hypometabolism in this network).
4375 × 3. 4375 × 3. 4375 mm, flip angle: 90°, FOV: 220 mm 2 ). An anatomical volume was also acquired using a T1-weighted MPRAGE sequence in the same acquisition session (154 slices, slice thickness: 1. 2 mm, TR/TE: 7. 112 ms/ 3. 084 ms, voxel size: 1 × 1 × 1 mm, flip angle: 15°). In the second protocol, MRI data of 51 patients were acquired on a 3 T Siemens Skyra System. T2*-weighted whole brain resting state images were recorded with a gradient-echo EPI sequence using axial orientation (180 volumes, 62 slices, slice thickness: 2. 5 mm, TR/TE: 2000 ms/30 ms, voxel size: 2 × 2 × 2 mm, flip angle: 90°, FOV: 240 mm 2 , multiband factor: 2). An anatomical volume was acquired in the same session using a T1-weighted MPRAGE sequence (208 slices, slice thickness: Brain parcellation. We used the Schaefer parcellation with 1000 brain areas, based on estimation from a large dataset (n = 1489)  86  , to extract the time series from each subject.
Furthermore, we estimated the Euclidean distances from the Schaefer parcellation in MNI space. Resting-state pre-processing For meditation, Paris, Liège datasets. The pre-processing of resting-state data was performed using FSL (http://fsl. fmrib. ox. ac. uk/fsl) as described in our previous study  53. In brief, resting-state fMRI was computed using MELODIC (Multivariate Exploratory Linear Optimised Decomposition into Independent Components)  87. Steps included discarding the first five volumes, motion correction using MCFLIRT  88  , Brain Extraction Tool (BET)  89  , spatial smoothing with 5 mm FWHM Gaussian Kernel, rigid-body registration, high pass filter cutoff = 100. 0 s, and single-session ICA with automatic dimensionality estimation. Then, lesion-driven artifacts (for patients) and noise components were regressed out independently for each subject using FIX (FMRIB's ICA-based X-noiseifier)  90.
Finally, FSL tools were used to coregister the images and extract the time-series between 1000 cortical brain areas for each subject in MNI space from the Schaefer parcellation  86. For the sleep dataset. The pre-processing of resting-state data was performed using FSL (http://fsl. fmrib. ox. ac. uk/fsl). In brief, steps included discarding the first five volumes, motion correction using MCFLIRT  88  , BET  89  , spatial smoothing with 5 mm FWHM Gaussian Kernel, rigid-body registration, bandpass filtering between 0. 01 -0. 1 Hz. Finally, FSL tools were used to co-register the images and extract the time-series between 1000 cortical brain areas for each subject in MNI space from the Schaefer parcellation  86. Previous publications based on this dataset can be consulted for further details  77.