dataesr/scientific-paragraphs-categorization huggingface dataset:
fMRI processing of main dataset. The fMRI time series were subjected to time slicing followed by spatial realignment using the SPM12 toolbox (revision 6906, http://www. fil. ion. ucl. ac. uk/ spm/spm/software/spm12). The subjects' T1 image and Destrieux atlas were coregistered to the fMRI images. Average cerebrospinal fluid (CSF) and white matter signal from manually defined regions of interest (5-mm sphere, Marsbar Toolbox 0. 44, http://marsbar. sourceforge. net) were extracted and were regressed out of the BOLD time series along with six rotation and translation motion parameters and global gray matter signal. Time series were bandpass filtered at 0. 009-0. 08 Hz  (Power et al. , 2014)  and scrubbed using frame wise displacement (threshold 0. 5) defined by  Power et al. (2012). The Pearson correlation between all region pairs' remaining time courses were used to build a functional connectivity matrix (FC fMRI ).
EEG processing of main dataset. The gradient artifact induced by the scanner on the EEG signal was removed using the template subtraction and adaptive noise cancellation followed by low-pass filtering at 75 Hz, downsampling to 250 Hz  (Allen et al. , 1998). Then, a cardiobalistic artifact template subtraction  (Allen et al. , 2000)  was carried out using EEGlab v. 7 (http:// sccn. ucsd. edu/eeglab) and the FMRIB plug-in (https://fsl. fmrib. ox. ac. uk/eeglab/fmribplugin/). Data was then analyzed with Brainstorm software  (Tadel et al. , 2011) , which is documented and freely available under the GNU general public license (http://neuroimage. usc. edu/brainstorm, version August 2017). Data was bandpass filtered at 0. 3-70 Hz and segmented according to the TR of the fMRI acquisition (2-s epochs, based on a window length established by  Colclough et al.
Subjects and data of generalization dataset. MR was acquired in 14 healthy subjects (five females, mean age: 30. 9, std: 8,6, Min/Max age: 20-55) with no history of neurological or psychiatric illness (see  Wirsich et al. , 2017). Ethical approval has been obtained from the local Research Ethics Committee (CPP Marseille 2), and informed consent has been obtained from all subjects. A session of 21-min eyes-closed resting state was acquired using concurrent EEG-fMRI (Siemens Magnetom Verio 3T MRI-Scanner, 50 slices, TR = 3. 6, 2. 0 × 2. 0 × 2. 5 mm, TE = 27 ms, FA = 90 • , a total of 350 volumes). EEG-fMRI were acquired during eyes-closed resting state, and the subjects were wearing a 64-channel EEG-cap (BrainCap-MR 3-0, Easycap, Hersching, Germany, according to the 10-20 system with one ECG channel and a reference at the mid-frontal FCz position).
fMRI processing of generalization dataset. fMRI data processing was carried out as described in  Wirsich et al. (2017). In brief, the data was processed as described above (using SPM12 6685, Marsbar 0. 43). After regressing out movement, CSF, white matter and global gray matter signal, instead of bandpass filtering as used in the main dataset, fMRI data was filtered by wavelet analysis using the Brainwaver toolbox (version 1. 6, http://cran. r-project. org/web/packages/ brainwaver/index. html)  (Achard et al. , 2006; Wirsich et al. , 2017). Equivalent to the main dataset, fMRI data points were scrubbed using framewise displacement (threshold 0. 5) as defined by  Power et al. (2012). The Pearson correlation of the remaining wavelet coefficients of scale two (equivalent to a frequency band 0.
EEG processing of generalization dataset. EEG artifact correction was carried out using the Brain Vision Analyzer 2 software (Brain Products, Munich, Germany). To correct for the gradient artifact, a gradient template was subtracted followed by adaptive noise cancellation with 70-Hz low-pass filtering and downsampling to 250 Hz  (Allen et al. , 2000). Peaks from the ECG signal were extracted to generate a cardiac pulse artifact template averaged over the 100 last pulses. This template was then subtracted from the EEG signal  (Allen et al. , 1998). Eye movement was manually rejected using ICA and high-pass filtered at 0. 3 Hz. Data was segmented into 3. 6-s segments (according to one TR of the fMRI sequence and equivalent to  Wirsich et al. , 2017). Segments with obvious movement artifacts were manually excluded from the analysis.
Hybrid connectivity ICA. We used the hybrid connICA  (Amico et al. , 2017; Amico & Goñi, 2018a) , code available here: https://engineering. purdue. edu/ConnplexityLab/publications/ connICA_hybrid_toolbox_v1. 0. tar. gz) method as a data-driven way to disentangle the main brain network patterns underlying the FCs fMRI and FCs EEG (Figure  2 ). To analyze the data in one hybrid vector, we uniformed the value distribution range of EEG (imaginary coherency original range 0 to 1) and fMRI (Pearson correlation, range -1 to 1) by transforming the EEG imaginary coherency according to the procedure in  (Amico & Goñi, 2018a). In brief, we derived a correlation value from EEG by taking the Pearson correlation between the i th and j th row of the imaginary coherency-based connectome. This procedure results in a new correlation matrix where the values -1 and 1 indicate if the EEG nodes are connected antagonistically to the network or have high similarity (see also matching index in  Rubinov & Sporns, 2010).
of selected independent components (ICs) are an arbitrary choice, parameters were explored for keeping the principal components (PCs) that explain 75% to 90% of the data (in 4 steps of 5% each) and reducing the data from 5 to 20 ICs (in 4 steps of 5 ICs). The ICA algorithm was run (500 times, similarly to  Amico & Goñi, 2018a)  to extract the main hybrid (joint FC EEG -FC fMRI ) components associated with the whole population. Third, the most robust components were selected. Here, robust is defined as appearing at least in 75% of ICA iterations with correlation higher than 0. 75 for both IC connectivity strength and subject mixing weights. This approach has been shown to generate robust connICA estimation  (Amico et al. , 2017). Two consistent hybrid ICs were observed across the majority of parameter pairs (the above described PCA reduction followed by limiting the number of ICs) and across both datasets.
We chose the parameters by maximizing the correlation of the upper triangle of those two ICs between the two datasets (this is the maximum value when averaging the four diagonals in Figure  3 ; for across-parameter stability of the components within each dataset see Supporting Information Figures  S1  and S2 ). Choosing separate parameters for each dataset can result in slightly higher correlation of ICs across datasets (i. e. , keeping PCs that explain 75% of the variance in one dataset and 80% of the variance in the other; see Figure  3  off-diagonals and Supporting Information Table  S1 ). However, in order to avoid overestimation of the correlation between the two datasets, we chose the same parameter configuration for both main and generalization datasets. For some parameter pairs we also observed that in the main dataset, stable components sometimes split up into two components.
Both components are correlated to one component in the generalization dataset and can optionally be merged into one component that correlates even better with the component found in the generalization data. In case we found two components in the main dataset, we kept only the component that correlated more with the generalization dataset (to conservatively avoid adding an additional merging step). Finally, we kept the IC configuration most consistent between the two datasets: the first PCA components explaining 75% of the variance (12 first PCs in case of the main dataset and 14 first PCs for the generalization dataset) were kept for further analysis. We tested all combinations, keeping principal components with 75-90% of the variance, followed by an independent component analysis keeping 5-20 ICs (e.
g. , IC5PCA75 depicts an analysis using 5 ICs and keeping all PCs that cumulatively explain 75% of the data variance). Rows with consistent correlation of zero mean that in one of the datasets, the preceding separate ICC procedure had found no component passing stability requirements for this parameter combination. There is significant (p < 10 -100 ) spatial correlation of IC matrices between datasets for all parameters for which stable ICs had been identified (average correlation for the ICN-FG component: fMRI part r = 0. 27; EEG part r = 0. 35; and for the VIS-FS component: fMRI part r = 0. 45; EEG part r = 0. 44).
Modularity of ICs. Modularity of EEG and fMRI IC matrices were calculated based on the Brain Connectivity Toolbox (version 2019_03_03, https://sites. google. com/site/bctnet/;  Rubinov & Sporns, 2010). To test in how far the networks overlap with canonical ICNs (as proposed by  Amico et al. , 2017) , we calculated modularity based on a predefined module label M for each region derived from the Yeo7 ICN networks  (Yeo et al. , 2011). Using this predefined set of modules enables us to interpret the observed modularity values as a measure of convergence with the well-known canonical ICN organization. For the calculation of modularity Q, negative and positive connectivity values were treated separately. Connectivity values were treated according to the negative asymmetry implementation proposed by  Rubinov and Sporns (2011) , weighting the modularity  (Newman, 2004)  of positive connections more than negative connections (see implementation in function community_louvain.
RESULTS We observed correlations of connection strength for fMRI versus δ/θ/α/β/γ of r = 0. 34/0. 34/ 0. 33/0. 36/0. 29 in the main dataset (replication dataset correlation were fMRI vs. δ/θ/α/β/γ: r = 0. 34/0. 32/0. 33/0. 37/0. 16 as reported in  Wirsich et al. , 2017). Next, we sought to move beyond the prior correlational approaches by using joint fMRI-EEG decomposition of brain FC.
Outcome of connICA After Determination of Free Parameters When applying the connICA method to the EEG-fMRI connectivity matrices, as a function of the choice of the free parameters in the framework (PCA reduction and number of selected ICs; cf. Figure  3 ) we observed two to four stable reoccurring components in the main dataset, whereas two stable components reoccurred in the generalization dataset (using PCs explaining 75% of the variance followed by an ICA with 10 components; see Methods section). Two stable components were appearing in both datasets (Figure  3 , Supporting Information Figures  S1  and S2 ): 1. We observed an Intrinsic Connectivity Network-Frequency-General component (ICN-FG) that captures all the main within-network connectivity in the ICNs described by  Yeo et al. (2011)  (Figure  4  and Figure  5 ).
The ICN-Frequency-General Component The ICN-FG component was identified in both the main and generalization datasets, with spatial similarity across datasets observed for both the fMRI IC weight matrix (i. e. , the spatial pattern in Figure  4B  compared to that in Supporting Information Figure  S3 ; r = 0. 36, p < 1. 0 -300 ) and the EEG IC weight matrix (Figure  4C  vs. Supporting Information Figure  S3C ; r = 0. 51, p < 1. 0 × 10 -300 ). Lifting our requirement of keeping parameters identical across the two datasets would further improve the correlations across datasets (cf. off-diagonals in Figure  3 ); for the fMRI part, the correlation of IC weights would be maximized to r = 0. 41 (p = <1. 0 × 10 -300 ) in case of a PCA selecting 75% of the variance followed by an ICA limited to 10 ICs (IC10PCA75) for the main dataset, while using IC10PCA80 for the generalization dataset.
Conversely, the correlation would be minimized to r = 0. 12, (p = 4. 4 × 10 -34 ) by combining IC10PCA90 (main) vs. IC5PCA75 (generalization). The mean correlation between all possible combinations of parameters was 0. 27 (p = 5. 5 × 10 -183 ). For the EEG part, the IC weight correlation is maximized to r = 0. 54 (p = <1. 0 × 10 -300 ) when combining IC10PCA80 (main) with IC20PCA90 (generalization), and minimized to r = 0. 22 (p = 3. 8 × 10 -123 ) for IC10PCA85 (main) combined with IC20PCA85 (generalization). The mean cross-dataset correlation over all parameter combinations was r = 0. 35 (p = <1. 0 × 10 -300 ). This similarity confirms the robustness of the hybrid component across datasets and across a variety of parameter combinations. On visual inspection, EEG and fMRI parts of the IC (Figure  4B  and 4C) share partial spatial similarity.
This is in line with small but significant correlation between EEG and fMRI parts of the ICN-FG independent component (main dataset: r = 0. 11, p = 3. 66 × 10 -30 ; generalization dataset r = 0. 37, p = 1. 42 × 10 -251 ). Assessed visually, both EEG and fMRI parts of the IC express a strong organization into ICNs. We empirically tested this observation by calculating the modularity of the network given the predetermined labels of the seven ICNs as defined by  Yeo et al. (2011). The mapping of the ICN-FG component to the Yeo ICNs is visualized in Figure  5A , 5B, 5D, and 5E. For FC fMRI , we found significantly higher modularity than in randomized networks (5,000 iterations with preserved weighted degree distribution and weighted degree sequence)  (Maslov & Sneppen, 2002; Rubinov & Sporns, 2011 ) (q fMRI = 0.
22, p < 0. 0002; generalization q fMRI = 0. 37, p < 0. 0002). Similarly, we found significant moderate modularity for FC EEG (q EEG = 0. 093, p < 0. 0002/generalization q = 0. 14, p < 0. 0002, same null model as ndom network with FC fMRI , 5,000 iterations). The significant modularity implies that connectivity is generally stronger within ICN modules than across them, confirming the relative spatial closeness of the hybrid component to the well-known ICN organization. In addition to supporting the ICN backbone of the intrinsic connectivity architecture, the IC also showed a high subject-specific fingerprint on all its mixing weights (Figure  4A , ICC subject = 0. 85, p < 1. 0 × 10 -10 ; generalization ICC subject = 0. 48, p = 2. 18 × 10 -6 ), but importantly nonsignificant (main dataset, ICC subject = -0.
The Visual-Frequency-Sensitive Component The VIS-FS component was found in both datasets with spatial similarity across datasets of r = 0. 50 (p < 10 -300 ; Figure  4E  vs. Supporting Information Figure  S3 ) for the fMRI IC matrix and r = 0. 50 (p < 10 -300 ; Figure  4F  vs. Supporting Information Figure  3F ) for the EEG IC matrix. When permitting parameters to differ across the two datasets (off-diagonals in Figure  3 ), this cross-dataset correlation of the IC matrix is maximized for the fMRI part to r = 0. 62 (p < 10 -300 ) by combining a PCA selecting 75% of the variance followed by and ICA limited to 10 ICs (IC10PCA75) for the main dataset and IC10PCA85 for the generalization dataset. The correlation is minimized to r = 0. 27 (p = 5. 5 × 10 -183 ) for IC5PCA75 (main) and IC20PC85 (generalization).
The mean correlation of all parameter configurations is r = 0. 45 (p = <1. 0 × 10 -300 ). The correlation of the EEG component is maximized to r = 0. 53 (p = <1. 0 × 10 -300 ) in case of combining IC20PCA85 (main) with IC5PCA75 (generalization) and minimized to r = 0. 35 (p = <1. 0 × 10 -300 ) when combining IC10PCA80 (main) and IC20PCA90 (generalization). The mean cross-dataset correlation of all parameter configurations is r = 0. 44 (p = <1. 0 × 10 -300 ). Again, this similarity confirms the robustness of the identified hybrid component across datasets. Compared with the ICN-FG component, EEG and fMRI parts of the VIS-FS component diverge considerably in their spatial pattern  (Figure 4E and 4F). In line with this divergence, EEG and fMRI matrices showed a very low but significant anticorrelation (main dataset r = -0.
10, p = 1. 09 × 10 -26 ; generalization dataset r = -0. 15, p = 5. 79 × 10 -58 ). The mapping of the VIS-FS component to the Yeo ICNs is visualized in Figure  6 (A, B, D, E ). The fMRI pattern of this component is dominated by ICN organization (Figure  4E ) similar to the observation in the ICN-FG component. Supporting this observation, fMRI again showed significant modularity when mapped to Yeo's seven ICNs (compared to 5,000 iterations of random networks with preserved weight and strength distribution of FC fMRI /FC EEG ; Rubinov & Sporns, 2011: q fMRI = 0. 32, p < 0. 0002; generalization dataset q fMRI = 0. 30, p < 0. 0002). However, the visual (VIS) ICN, and to a lesser degree the somatomotor (SM) ICN, are more strongly expressed than other ICNs. The EEG part (Figure  4F ) co-occurring with the aforementioned fMRI pattern depicts a more diverse connectivity profile diverging from the ICN architecture.
This divergence is confirmed by lack of significant modularity when mapped to the seven canonical ICNs (q EEG -0. 012, p = 0. 95; generalization q EEG = -0. 018, p = 0. 97). The most salient characteristics of the EEG pattern are strong connectivity among default mode network (DMN), S, M and dorsal attention (DA) networks  (Figure 4F and 6D and 6F). Interestingly, these joint EEG-fMRI patterns are sensitive to different frequency bands under consideration. In other words, the mixing weights associated with the VIS-FS component change in magnitude as a function of frequency (Figure  4D , ICC freq = 0. 69, p < 1. 0 × 10 -10 ; generalization dataset ICC freq = 0. 48, p = 3. 51 × 10 -8 ), and do not represent a subject-specific fingerprint (ICC subject = -0. 11, p = 0. 98, generalization dataset ICC subject = 0.
Relationship Between Components and Head Movement To test if subject-specific mixing weights are driven by head movement, we checked for a monotonous relationship between the mixing weights and mean framewise displacement (FD), as well as between mixing weights and the number of scrubbed volumes by using Spearman ranked sum correlation. For the ICN-FG component, we found an negative correlation between subject-specific mixing weights and motion across all bands in the main dataset but not in the generalization dataset (main dataset: FD vs. mixing weights (Figure  4A ): rho = -0. 44, p = 1. 9 × 10 -7 ; no. of scrubbed volumes vs. ICA: rho = -0. 37, p = 1. 4 × 10 -5 /generalization dataset: FD vs. mixing weights: rho = 0. 17, p = 0. 14; no. of scrubbed volumes vs. mixing weights: rho = 0. 042, p = 0.
73). For the VIS-FS component, we did not find a relationship between subject-specific mixing weights and motion across any band in either dataset (main dataset: FD vs. mixing weights (Figure  4D ): rho = 0. 07, p = 0. 40; no. of scrubbed volumes vs. ICA: rho = 0. 12, p = 0. 14/generalization dataset: FD vs. mixing weights: rho = 0. 11, p = 0. 36; no. of scrubbed volumes vs. mixing weights: rho = 0. 04, p = 0. 74). To summarize, we found a negative relationship between movement and mixing weights of the ICN-FG component in the main dataset. This outcome indicates that for the main dataset, subjects with movement contribute less to the ICN-FG component. Of note, as mentioned in the Methods section, we observed a second component with similar characteristics to the ICN-FG component in the main dataset.
DISCUSSION Since BOLD fMRI measures neural energy consumption, FC fMRI collapses over a mixture of electrophysiological FC (FC EEG ) mechanisms in various oscillatory frequency bands. To disentangle the complex relationship between FC fMRI and multifrequency FC EEG , we applied a data-driven decomposition of bimodal connectomes for simultaneous EEG-fMRI recordings. Two robust spatially independent bimodal connectome components, each representing FC fMRI and FC EEG , were found across all frequency bands and both independent datasets. The first component, which we call the Intrinsic Connectivity Network-Frequency-General component (ICN-FG), has a uniformly distributed frequency fingerprint and is linked to ICNs in both modalities (Figures  4A  and 5 ). Conversely, the second component, which we call the Visual-Frequency-Sensitive component (VIS-FS), spatially diverges between EEG and fMRI and is sensitive to different frequency bands under consideration (i.
ICN-FG Component The ICN-FG component captures the main within-network connectivity in well-known neurocognitive networks for EEG and fMRI. The EEG and fMRI patterns are co-expressed over subjects and quantified by subject-specific mixing weights. This result corroborates previous findings extracting the same robust ICN-FG components from different fMRI datasets  (Amico et al. , 2017; Contreras et al. , 2017). The fact that we found this ICN-conform stable hybrid component across modalities might suggest that the within-network connectivity dynamics are "scale-independent," that is, to some extent, insensitive to whether one considers the millisecond-scale of the FC EEG connectome in a specific frequency, or the infraslow-scale of the FC fMRI connectome. Previous work on microstates have indeed demonstrated temporal scale-free behavior in EEG ranging from fast millisecond to slow second time range (Van de  Ville et al.
, 2010). Such timescale invariance is also in line with more recent findings linking fMRI to EEG  (Deligianni et al. , 2014; Wirsich et al. , 2017)  and to MEG connectivity  (Brookes et al. , 2011; Colclough et al. , 2016). We found strong ICC at the subject level, suggesting a strong fingerprint for each subject  (Amico & Goñi, 2018b). This result should be interpreted very carefully, however, as we observed in the main dataset (but not in the replication dataset) that those subject-specific mixing weights are negatively correlated with head movement for the ICN-FG component. As such, the subject-specific mixing weights should be interpreted as subject-specific contributions to the hybrid component that might also be weighted by session-specific individual conditions such as vigilance state and movement of the subject.
This possibility also implies that the final connectivity component is "corrected" for those subject-specific (linear) contributions, consequently representing a clean component common among all subjects. As we showed in the main dataset, the ICN-FG component can split into two parts, one being anticorrelated to movement and one being uncorrelated to movement. This demonstrates the ability of ICA to decompose the connectivity matrix into behaviorally relevant parts provided a sufficient number of subjects (the larger main dataset of 26 subjects was able to detect a split of the ICN-FG component that could not be observed in the smaller generalization dataset). In conclusion, the ICN-FG component suggests that the presence of strong electrophysiological communication in all frequencies (widely distributed, with ventral dominance) contributes to strong ICN architecture in fMRI.
VIS-FS Component The VIS-FS hybrid component shows two spatially divergent patterns for fMRI and EEG  (Figure 4C and 4F). The fMRI part mainly captures the connectivity within visual network (VIS) and the connectivity between visual and somatomotor (SM) networks but also other ICNs. On the other hand, the EEG part co-occurring with the aforementioned fMRI pattern depicts a more diverse connectivity profile that captures between-network connectivity, especially between DMN. Although the pattern has not been described before, it reproduces across both datasets (correlation between EEG IC strength of the two datasets r = 0. 50; see Figure  3 ). The observed difference across EEG and fMRI parts of the VIS-FS component suggests that the hybrid connICA is able to detect the complementary information carried over the two different timescales measured by FC fMRI and FC EEG.