dataesr/scientific-paragraphs-categorization huggingface dataset:
10 states were identified for the third database In Fig. 4  , we illustrate the results obtained in the alpha band for the third dataset showing 10 modules derived from the 61 subjects: DMN (present in 100% of subjects), POST-DMN (present in 95% of subjects), VIS (present in 88% of subjects), L-TEMP (present in 78% of subjects), SAN (present in 72% of subjects), DAN (present in 79% of subjects), SMN (present in 62% of subjects), AUD + VIS (present in 49% of subjects), DAN + VIS (present in 43% of subjects) and CCN (present in 18% of subjects). Results were consistent among several threshold values of functional connectivity matrices (see Table  S3 ) and within the beta frequency band (see Table  S6 ). In summary, results obtained for the three datasets revealed fluctuating modules concordant with the well-known RSNs.
In particular, the default mode network was the most consistent network among subjects in all datasets (reflected by the highest percentage of presence over subjects). Results also showed that some RSNs present various modular topologies over time such as DMN, temporal and the visual networks. In addition, modules that combine several RSNs are observed over time, reflecting cross-network interactions, as discussed below in more details. Fig. 5  presents a step-by-step example of results obtained for a typical subject, where 8 MSs are derived. The similarities between the 30 modules extracted from all MSs and RSNs templates are assessed ( Fig. 5. C). Among the 30 modules, 18 modules have survived the 80% threshold on the nodal overlap.
Dwell time and the fractional occupancy In order to quantify the temporal characteristics of each module, two metrics were computed: dwell time (DT), i. e. the average number of consecutive windows spent in a module; and the fractional occupancy (FO), reflecting the proportion of time spent in each module. As an example, the modules obtained in Fig. 5   In summary, results obtained from all datasets points at the importance (and stability) of DMN and its role as a functional core network during rest, as detailed in Discussion.
Correlation between the derived modules and mental imagery Finally, we seek at understanding if there is any correlation between the derived modules and the subject internal thoughts experienced during resting-state acquisition measured by the Resting-State Questionnaire (rsQ). Only such data was available for dataset 2. More specifically, the five main indices derived from the rsQ (i. e. visual mental imagery, inner language, somatosensory awareness, inner musical experience, and mental manipulation of numbers) were correlated with   S3 ). Results were also consistent across other threshold values (see Figure  S2 ). In summary, these results showed that individual variability in the visual imagery experienced during acquisition was positively related to the occupancy of specific modules, mainly VIS, DAN and VIS + AUD.
Discussion Here, we have shown how fast changes in the modular architecture of large-scale electrophysiological networks shape spontaneous brain activity. We used a recently developed algorithm that extracts repetitive modular brain states alternating over time. As opposed to traditional approaches, the distinctive feature of the applied method resides in tracking modular variations of brain networks. The framework was applied on three independent EEG/MEG datasets, and revealed that RSNs experience continuous modular changes reflected by a process of separation and merging within-and between-the resting networks. In particular, DMN switches dynamically its modular topology, in line with many previous studies suggesting that the DMN can actually be decomposed into subcomponents, mainly anterior and posterior  ( Andrews-Hanna et al.
, 2007 ; Moussa et al. , 2011 ; Wens et al. , 2019 ). The process of association and dissociation within DMN components was also revealed by Allen et al. ( E. A. Allen et al. , 2014 ) , where brain states were described using K-means clustering. More importantly, sev-eral studies have also showed that the dynamic states transition leads to the inclusion of some FPN regions in the DMN in some brain states ( E. A. Allen et al. , 2014 ; Liu et al. , 2019 ) , which was also obtained in our study (results of dataset 1). Similarly, the temporal network alternates its reconfiguration between left, right and complete modules. This finding is in line with previous results depicting the left part of the temporal network as an independent network state  ( Baker et al. , 2014 ). The dynamic modular behavior of the resting brain was also revealed by the occurrence of modules integrating different RSNs.
To test whether there may be a sort of hierarchy in terms of the consistency of brain regions, we also reported, for each RSN, the regions included and their contributions across subjects of all datasets (in%). Once a module was associated to a specific RSN, the overlapping nodes were identified to ultimately compute the rate of their inclusion across all subjects and datasets. Figure  S1  shows that despite the inter-subject variability, some specific brain regions contribute more consistently to a specific RSN than others. The DMN (in its different configurations) was the most consistent module obtained across subjects, since it had the highest percentage of   presence over subjects/datasets, with high fractional occupancy values. These findings highlight the key role of DMN in integrating information in spontaneous brain activity, in accordance with the presence of a large proportion of hubs associated to this network  ( de Pasquale et al.
, 2015 ; Kabbara et al. , 2017 ; van den Heuvel and Sporns, 2013 ). Furthermore, DMN configurations had the greatest stability over time (reflected by the highest mean dwell duration). Such findings support previous studies showing that functional connections within the richclub core (where most regions are affiliated to the DMN) exhibit the greatest stability over time. In these studies, the high temporal stability of DMN has been associated with high dependency on the underlying structural brain topology, since high similarity was obtained between resting-state functional and structural networks when the sample duration increases. In contrast to this, modules with low dwell time were transient and exhibited greatest variability during time reflecting the dynamic functional coordination. In our study, the derived transient modules depend on each subject and database.
Mostly, they belong to high-level cognitive networks, attentional networks and the sensory networks. In all databases, these transient modules showing significant lower dwell times are those integrating multiple RSNs (DAN + VIS, AUD + VIS…). One possible explanation is that individuals dynamically engage in several mental thoughts during resting periods, while imagery and mind-wandering remain the predominant activities  ( Delamillieure et al. , 2010 ; Doucet et al. , 2012 ). Thus, the perspective of looking at the brain as a dynamic system where stable activity is intertwined by transient functional variabilities is supported by many studies  ( Honey et al. , 2007 ; Liu et al. , 2019 ; Van De Ville et al. , 2010 ). In addition, our study highlights the significant presence of the visual network showing a high occupancy rate during time (results of datasets 1 and 2).
Such observation can be associated with the dominance of the visual imagery activity exhibited by most subjects during resting state acquisition  ( Delamillieure et al. , 2010 ). More interestingly, the individual variability in the visual imagery experienced during acquisition was revealed to be positively correlated to the fractional occupancy of VIS network ( Fig. 7  ). Similar correlations were reported by previous studies  ( Pipinis et al. , 2017 ; Stoffers et al. , 2015 ). In addition, the significant relationships assessed between the mental imagery with AUD, DAN and VIS might explain the cross-interactions observed between these networks forming one large module over time (the presence of DAN + VIS + AUD observed in the results of dataset 1, Fig. 2  ). Across the three datasets, our results showed 9 common RSNs: DMN, POST-DMN, VIS, SMN, SAN, LTEMP, VIS + AUD, DAN + VIS.
These striking consistent results have been obtained independently from the technique used to record signals (EEG or MEG), preprocessing steps (automatic in dataset 1 vs. manual in dataset 2 and 3), source reconstruction (wMNE vs. Beamforming), adjacency matrices thresholding value, EEG/MEG frequency bands (alpha vs. beta), atlas parcellation (68 Desikan Killiany vs 78 AAL) and functional connectivity measures (phase vs. envelope couplings), as well as either with or without correcting the zero-lag correlations. However, other modules arise from each dataset ( Figs. 2 , 3 , 4  ). The inter-subject variability was also revealed by the percentage of subjects showing each derived module. Among the same dataset, these individual differences are thought to be associated with variability in cognitive and behavioral functions.
This has been supported by different studies showing that the dynamic network characteristics significantly correlate with intelligence, creativity and executive function  ( Bassett et al. , 2015 ; Kenett et al. , 2020 ; Tompson et al. , 2018 ). Here, between-subjects variation in the temporal characteristics of specific modules, mainly VIS, AUD and DAN, was associated with self-report rating of mental visual imagery as measured by the resting-state questionnaire. The dependence of observed brain activity on the inner thoughts and feeling experienced during resting acquisition was emphasized by multiple studies  ( Diaz et al. , 2016 ; Pipinis et al. , 2017 ; Stoffers et al. , 2015 ). Despite the overall consistency of our findings, results over the three datasets were not perfectly the same.
For instance, the DMN were present 96%, 98% and 100% over dataset 1, 2 and 3 respectively while the VIS network was present 86%, 94% and 88%. Also results showed the absence of the ANT-DMN in dataset 3, the absence of FPN in dataset 2 and 3 and the absence of CCN in dataset1. The discrepancy of results obtained from different datasets may be related to some differences in the datasets such as the sample size, age of subjects and the conditions of experiments (i. e. eyes closed/eyes opened). In fact, while RSNs have been successfully extracted in both eyes-opened ( E. A. Allen et al. , 2014 ; Baker et al. , 2014 ; de Pasquale et al. , 2018 )  and eyes-closed conditions  ( Bernas et al. , 2018 ; Kabbara et al. , 2017 ; Owen et al. , 2013 ) , many studies have been conducted to investigate the functional connectivity differences in RSNs between eyes closed and eyes opened conditions  ( Agcaoglu et al.
, 2019 ; Patriat et al. , 2013 ; Van Dijk et al. , 2010 ; Yan et al. , 2009 ). Our results on dataset 3 (acquired in eyes opened condition) showing the highest DMN occurrence (100%) among other datasets (acquired in eyes closed condition) can be related with the studies revealing higher DMN functional connectivity in the eyes opened as compared to the eyes closed condition  ( Van Dijk et al. , 2010 ; Yan et al. , 2009  ). An additional cause for the discrepancy of results between datasets is the use of different modalities (MEG/EEG). More precisely, MEG/EEG differences proved to arise particularly when investigating transient resting-state functional connectivity patterns  ( Coquelet et al. , 2020 ). The relevance of the alpha to beta frequency range (8-30 Hz) in driving spontaneous large-scale neuronal interactions was revealed by multiple EEG/MEG studies  ( Brookes et al.
Nevertheless, slight differences were observed in the derived modules and their temporal characteristics between the two frequency bands. From a methodological viewpoint, we have adopted in each dataset the pipeline (from data processing to networks construction) used by the previous studies dealing with the same datasets. Therefore, for EEG datasets, we used the wMNE/PLV combination to reconstruct dynamic networks, since it is supported by several studies on resting EEG and two comparative studies  ( Hassan et al. , 2016 ; M. 2014 ). For the MEG dataset, beamforming construction combined with amplitude correlation (and orthogonalization) between band-limited power envelops was used by multiple studies using the MEG HCP data  ( Brookes et al. , 2012 ; Colclough et al. , 2015 ). The suitable window width is a crucial issue in reconstructing dynamic functional networks.
On the one hand, short windows do not contain sufficient information to accurately estimate connectivity. On the other hand, large windows might fail to capture the temporal changes of brain networks. Hence, the ideal is to choose the shortest window that guarantees a sufficient number of data points over which connectivity is computed. This depends on the frequency band of interest that affects the degree of freedom in time series. It also depends on the correlation measure used. In EEG datasets, we adopted the recommendation of Lachaux et al. ( Lachaux et al. , 2000 )  in selecting the smallest appropriate window length offering 6 number of 'cycles' at the given frequency band. The reproducibility of resting state results whilst changing the size of the sliding window was validated in a previous study  ( Kabbara et al.
, 2017 ). In MEG, we used the same correlation method with the corresponding sliding window size (0. 5 s) used in previous stud-ies dealing with the same dataset  ( Colclough et al. , 2016a ; O'Neill et al. , 2017b ). While EEG/MEG modalities present a key advantage for tracking dynamic brain processes in the time frame in which these processes occur (namely in the sub-second range), they are limited in terms of spatial coverage. Basically, the main cause of this poor spatial resolution is the "volume conduction " problem which distorts signals, inducing, at each scalp position, a mixture effect of the underlying sources. Readers may refer to  ( Schoffelen and Gross, 2009 )  for more details about this issue. Although the EEG/MEG source connectivity method contributes to enhance the spatial resolution by reducing the effects of the volume conduction problem, it remains unable to completely remove source leakage effects  ( Hassan and Wendling, 2018 ).
More precisely, it is difficult to pinpoint the activity originating from distinct but closely adjacent sources. For this reason, in our study, we avoided the use of a high number of ROIs. Thus, we used 68 anatomical ROIs in EEG datasets, and 78 ROIs in MEG dataset as done in previous resting-state studies showed that these ROIs are sufficient in extracting the global characteristics of the brain networks with acceptable spatial resolution while minimizing the problem of spurious connections between "very close sources "  ( Kabbara et al. , 2017 ). The Desikan_Killiany atlas (68 ROIs) was also used to extract useful information focusing on investigating the "large-scale " networks derived from spontaneous activity in the context of personality traits (Aya  Kabbara et al. , 2019 ) , and brain disorders (A.
EEG datasets 4. 1. 1. Dataset 1 (HBN) 4. 1. 1. 1. Participants. As part of the Healthy Brain Network (HBN) Biobank release 1  ( Alexander et al. , 2017 )  http://fcon_1000. projects. nitrc. org/indi/cmi_healthy_brain_network/sharing_neuro. html , restingstate EEG data were collected from 444 healthy subjects (239 female). The release originally included 603 subjects, but data from 159 subjects were rejected after pre-processing and visual inspection. Subjects are healthy and aged between 5 and 21 years old. The Ids of the 444 participants are listed in Table  S7  (see Supplementary materials). 4. 1. 1. 2. Data acquisition and pre-processing. High-density EEG data are recorded in a sound-shielded room at a sampling rate of 500 Hz with a bandpass of 0. 1 to 100 Hz, using a 128-channel EEG geodesic hydrocel system by EGI.
The recording reference is at Cz (vertex of the head). The impedance of each electrode is checked prior to recording, to ensure good contact, and is kept below 40 kOhm. Each EEG session consisted of 5 min resting period (in eyes-closed condition). As provided by the HBN, EEG signals were preprocessed using Automagic Matlab toolbox  ( Pedroni et al. , 2019 ) , visual inspection was also done on the data after automatic preprocessing. Briefly, it consists of interpolating the noisy, flat or outlier channels. The Multiple Artifact Rejection Algorithm (MARA) which automatizes the process of independent component analysis (ICA) was used to detect and reject artifacts such as the eye blinks and the movement artifacts  ( Winkler et al. , 2011 ). Then, four artifact-free epochs of 40-s length were selected for each participant.
This epoch length was used in a previous study, and was considered as a good compromise between the needed temporal resolution and the results reproducibility  ( Kabbara et al. , 2017 ). 4. 1. 2. Dataset 2 4. 1. 2. 1. Participants. A total of 56 healthy subjects were recruited (29 female). The mean age was 34. 7 years old ( SD = 9. 1 years, range = 18-55). Education ranged from 10 years of schooling to a PhD degree. None of the volunteers reported taking any medication or drugs, nor suffered from any past or present neurological or psychiatric disease. The study was approved by the "Comité de Protection des Personnes Sud Méditerranée " (agreement n°10-41). Same data were used in previous studies ((  Kabbara et al. , 2020 ) Aya  Kabbara et al. , 2019 ; Paban et al. , 2019 ). After EEG acquisition, all participants have completed the resting-state questionnaire (ReSQ).
Data acquisition and preprocessing. Each EEG session consisted in a 10-min resting period with the participant's eyes closed. Participants were seated in a dimly lit room, were instructed to close their eyes, and then to simply relax until they were informed that they could open their eyes. Participants were informed that the resting period would last approximately 10 min. The eyes-closed resting EEG recordings protocol was chosen to minimize movement and sensory input effects on electrical brain activity. EEG data were collected using a 64-channel Biosemi ActiveTwo system (Biosemi Instruments, Amsterdam, The Netherlands) positioned according to the standard 10-20 system montage, one electrocardiogram, and two bilateral electro-oculogram electrodes (EOG) for horizontal movements. Nasion-inion and preauricular anatomical measurements were made to locate each individual's vertex site.
Dynamic brain networks construction For the two EEG datasets, dynamic brain networks were reconstructed using the "EEG source connectivity " method  ( Hassan and Wendling, 2018 )  combined with a sliding window approach as detailed in (A. Kabbara al. , 2018 Kabbara al. , , 2017 ) ). "EEG source connectivity " involves two main steps: i) solving the inverse problem in order to estimate the cortical sources and reconstruct their temporal dynamics, and ii) measuring the functional connectivity between the reconstructed timeseries. Briefly, the steps performed were the following: 1 EEGs and MRI template (ICBM152) were coregistered through the identification of anatomical landmarks by using Brainstorm  ( Tadel et al. , 2011 ). 2 A realistic head model was built using the OpenMEEG  ( Gramfort et al.
, 2010 )  software. 3 A Desikan-Killiany atlas-based segmentation approach was used to parcellate the cortical surface into 68 regions  ( Desikan et al. , 2006 ). 4 The weighted minimum norm estimate (wMNE) algorithm was used to estimate the regional time series  ( Hamalainen and Ilmoniemi, 1994  ). 5 The reconstructed regional time series were filtered in alpha 8-13 Hz and beta 13-30 Hz frequency bands, shown to be the most involved frequency bands at rest. 6 To compute the functional connectivity between the reconstructed regional time-series, we used the phase locking value (PLV) metric  ( Lachaux et al. , 2000 )  defined by the following equation: 𝑃 𝐿𝑉 ( 𝑡 ) = | | | | | 1 𝛿 𝑡 + 𝛿∕2 ∫ 𝑡 -𝛿∕2 𝑒𝑥𝑝 ( 𝑗 ( 𝜑 𝑦 ( 𝑡 ) -𝜑 𝑥 ( 𝑡 ) ) 𝑑𝜏 | | | | | (1) where   (  ) and   (  ) are the unwrapped phases of the signals x and y at time t.
MEG recordings and pre-processing The acquisition was performed using a whole-head Magnes 3600 scanner (4D Neuroimaging, San Diego, CA, USA). Resting state measurements were taken in three consecutive sessions of 6 min each. During the scan the subject is instructed to relax with eyes open and maintain fixation on a projected crosshair presented on a dark background. Data were provided pre-processed, after passing through a pipeline that removed artefactual segments, identified faulty recording channels, and regressed out artefacts which appear as independent components in an ICA decomposition with clear artefactual temporal signatures (such as eye blinks or cardiac interference).
Dynamic brain networks construction Here, we adopted the same pipeline used by the previous studies dealing with the same dataset  ( Colclough et al. , 2015 ). Thus, to solve the inverse problem, we have applied a linearly constrained minimum variance beamformer  ( Van Veen et al. , 1997 ). Pre-computed singleshell source models are provided by the HCP and the data covariance were computed separately in the 1-30 Hz and 30-48 Hz bands as in  ( Colclough et al. , 2016b ). Data were beamformed onto a 6 mm grid using normalized lead fields. Then, source estimates were normalized by the power of the projected sensor noise. Source space data were filtered in alpha (8-13 Hz) and beta bands (13-30 Hz). After obtaining the regional time series on the basis of the Automated Anatomical labeling atlas (AAL)  ( Tzourio-Mazoyer et al.
Statistical tests In order to investigate whether the observed brain modules are related to subjective internal thoughts and feelings experienced during resting-state acquisition, we have assessed the statistical relationships between the occurrence of modules and the phenotypes of cognition measured by the Resting-State Questionnaire (rsQ). More specifically, Pearson's correlation between the five main indices derived from the rsQ (i. e. visual mental imagery, inner language, somatosensory awareness, inner musical experience, and mental manipulation of numbers) and the fractional occupancies of the derived modules were computed for the 57 participants provided by Dataset 2. To consider the multiple comparisons problem (between the five types of mental activity, and the 11 modules), p -values were corrected using Bonferroni procedure  ( Bland and Altman, 1995 )  yielding an adjusted threshold of  < 0.
ordered in terms of FO are: DMN (FO = 56%), VIS (FO = 43%), DAN + VIS (FO = 43%), SMN (FO = 31%), SAN (FO = 19%), LTEMP (FO = 14%), AUD + VIS (FO = 10%), ANT-DMN(FO = 4%), POSTDMN (FO = 3%), DMN + FPN (FO = 3%), FPN (FO = 3%). In terms of DT, the modules are ordered as follow: DMN (DT = 16%), VIS (DT = 10%), DAN + VIS (DT = 7%), SMN (DT = 7%), AUD + VIS (DT = 7%), SAN (DT = 6%), POSTDMN(DT = 6%), LTEMP (DT = 6%), FPN (DT = 5%), DMN + FPN (DT = 5%). Fig. 6 reports the FOs and the mean DTs of the modules obtained for each dataset. The DMN (or one of its modular configurations) has clearly the highest FO and DT over all datasets. The VIS network is shown as significant in terms of FO in dataset2. According to the DT, SMN and SAN are depicted as significantly stable modules in dataset 1.
Acknowledgments This work was financed by the  Rennes University , the Institute of Clinical Neuroscience of Rennes  (Project named EEGCog). The study was also funded by the  National Council for Scientific Research (CNRS) in Lebanon. The authors would also like to thank the  Lebanese Association for Scientific Research (LASER)  and  Campus France ,  Programme Hubert Curien CEDRE  (Project No. 42257YA  ), for supporting this study.