dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008). The strength and stability of this network in mouse lemurs under anaesthesia is consistent with the discovery of this network in many other anesthetized animals (J. L. Vincent et al. , 2007). This suggests that it is an essential element of brain functional organization and that it may be dedicated to other tasks too. In the visual network, occipital cortex was detected in both species. Additional more anterior-parietal regions such as the paracentral lobule and the postcentral were highlighted in humans. We cannot rule out that this wider extension in human dataset is not related to the wakefulness state as it induces a richer repertoire of functional configurations  (Barttfeld et al. , 2015). In mouse lemurs, a network involving the anterior cingulum, frontal and temporal regions was classified as the fronto-temporal network.
Finally, critical hubs were detected and involved the posterior and anterior cingulate cortices, the central prefrontal cortex, and the dorsal thalamus. The mouse lemur is an interesting primate because of its key position in the phylogenetic tree, rodent-like small size and nocturnal and arboreal lifestyle. The 3D functional atlas and resting state network maps are freely available at https://www. nitrc. org/projects/fmri_mouselemur/. The imaging tools used to create and manipulate the template are also available (https://sammba-mri. github. io).
Animal preparation and MRI acquisition Each animal was scanned twice with an interval of 6 months. All scanning was under isoflurane anaesthesia at 1. 25-1. 5% in air, with respiratory rate monitored to confirm animal stability until the end of the experiment. Body temperature was maintained by an air heating system at 32°C, inducing a natural torpor in mouse lemurs  (Aujard and Vasseur, 2001). This has the advantage of allowing a low anaesthesia level without reawakening. The MRI system was an 11. Then spatial pre-processing was performed using the python module sammba-mri (SmAll MaMmals BrAin MRI; http://sammba-mri. github. io) which, using nipype for pipelining  (Gorgolewski et al. , 2011) , leverages AFNI  (Cox, 1996)  for most steps and RATS  (Oguz et al. , 2014)  for brain extraction.
Human data Artefacts were inspected in individual datasets using the TSDiffAna routines (http://imaging. mrc-cbu. cam. ac. uk/imaging/DataDiagnostics). Datasets displaying significant movements (> 1. 5° rotation or > 3 mm translation) and abnormal variance distribution and/or artefacted were excluded from the analysis. Data were then preprocessed as defined in Landeau et al. (Landeau et al. , 2017)  with slice timing correction, realignment to the first volume and spatial normalization within native space to correct for distortion effects. EPI volumes were registered to their own high resolution anatomical image and then registered and normalized to MNI template space. Nuisance signal regression was applied including a linear trend as well as 24-motion confounds (6 motion parameters, those of the preceding volume, plus each of their squares  (Friston et al.
Modularity and large scale network identification by graph theory analysis The modularity of a partition (Q) is the degree to which a network can be subdivided into non-overlapping groups of nodes (D. B. Vincent et al. , 2008). The modularity of a partition as well as an optimal segregation of the whole brain network into modules were calculated using Gephi 0. 9. 2  (Bastian et al. , 2009). 5. 6. 3. Large scale network identification by dictionary learning analysis A second dictionary analysis was performed in mouse lemurs and humans using a smaller number of SCs in order to highlight large networks and to compare them. Six SCs were used based on the 6 modules found with the graph theory analysis (see Results). In humans, a mask excluding the hindbrain and the white matter was used prior to the analysis to compare the dictionary learning of the two species in a similar space.
Data collection Age, sex and time between dizziness onset and ED visit were collected using Resurgences® software (Intuitive, Berger-Levrault, Boulogne -Billancourt, France). PS100 concentrations were measured on serum samples by electro-chemiluminescence assay (Roche, Mannheim, Germany). Copeptin concentrations were measured on serum samples by the Kryptor method (Thermo Scientific, Hennigsdorf, Germany). The positivity threshold for copeptin was set, in accordance with the laboratory standard, at strictly above 10 pmol/L and that of PS100 was set at strictly above 0. 105 μmol/L.
Statistical analyses All data were anonymized and analyzed with Excel® software (Microsoft corporation, Redmond, WA, USA). Quantitative variables were described by their median and interquartile and compared using Mann Whitney U test. Qualitative variables were described by their gross number and percentage and compared by a Chi-2 test. A value of p < 0. 05 was considered significant. Sensitivity, specificity, and positive and negative predictive values predicting stroke as the cause of dizziness were calculated for each biomarker alone or in combination. As this is an exploratory study, we included as many consecutive patients as possible during the inclusion period.
, 2015). The validation of connectivity patterns largely relies on TRT reliability of graph metrics  (Nakagawa and Schielzeth, 2010)  across different sessions and subjects. In the past decade, several rsfMRI studies have been conducted to investigate the effect of different connectivity metrics and scan duration on the functional structure of brain networks and the reliability of graph metrics  (Smith et al. , 2011; Whitlow et al. , 2011; Liang et al. , 2012; Birn et al. , 2013; Liao et al. , 2013; Cao et al. , 2014). So far, a few studies have investigated the reliability of resting-state functional connectivity (RSFC) patterns and global and regional graph metrics using rsfNIRS data  (Zhang et al. , 2011; Niu et al. , 2013; Blasi et al. , 2014; Geng et al. , 2017). Overall, it is still not well understood how the methodological choices including connectivity metrics and data length can affect the topological properties of rsfNIRS-based RSFC.
Concerning connectivity metrics, most fNIRS studies of the brain's intrinsic functional connectivity have used Pearson's correlation or cross correlation to measure linear associations between brain regions  (Mesquita et al. , 2010; Niu et al. , 2013; Li et al. , 2015; Novi et al. , 2016; Geng et al. , 2017). These metrics are not capable of capturing the general linear/nonlinear dependency or the direct interdependence between brain regions after ruling out the effect of other regions  (Liang et al. , 2012 , Smith et al. , 2011). With regard to the scan length, to the best of our knowledge, there has been only one study  (Geng et al. 2017 ) so far conducted to evaluate the effect of data length on the reliability and stability of a few graph metrics using rsfNIRS data. They have performed a reliability analysis on binarized brain networks constructed using whole-brain rsfNIRS data and the linear correlation analysis.
Thresholding has shown to seriously affect network metrics values especially in networks with limited number of nodes  (De Vico Fallani et al. , 2014; Garrison et al. , 2015; Hahn et al. , 2016). In this study, we performed a comprehensive assessment of the effect of data length on the reliability and stability of four functional connectivity metrics (PC, BC, MI and PtC) using rsfNIRS data. We further investigated the intersession reliability of twelve global and regional graph measures commonly used for characterizing functional integration and segregation of information of brain networks with different data lengths.
Subjects and experimental setup The resting state fNIRS data collected from 13 healthy adult subjects (35 ± 12 years old) were included in this study. The experimental setup and dataset have been reported in other studies  (Mesquita et al. , 2010; Novi, et al. , 2016; Aarabi and Huppert, 2016). To collect the data, subjects have been instructed to remain still in a quiet dark room. From each subject, 300-sec rsfNIRS data have been collected in two separate sessions. All procedures have been approved by the Institutional Review Board at the Massachusetts General Hospital, where written informed consents were obtained from all participants. All measurements have been collected with a continuous-wave near-infrared optical system (CW5, TechEn Inc. , Milford, MA) using 32 laser diodes at 2 wavelengths (690 and 830 nm), 32 avalanche photodiodes (APD) and a sampling rate of 10 Hz  (Mesquita et al.
Data preprocessing The optical data were converted to relative changes in optical density as described in  Huppert et al. (2009). The modified Beer-Lambert law was used to calculate the concentration change of HbR and HbO based on the differences in the absorption for the two wavelengths. The concentration in HbT was calculated by summing the concentrations in HbO and HbR. The average differential path-length factor was assumed to be 6 for both wavelengths  (Duncan et al. , 1995). The time courses of HbO, HbR and HbT signals were off-line band-pass filtered between 0. 01 and 0. 1 Hz using a second-order Butterworth filter to remove baseline drift and systemic physiological noise including respiration and cardiac activity. For each subject, we further removed motion artifacts by using the spatial ICA and excluded noisy channels as described in  (Aarabi and Huppert, 2016).
Stability analysis We used non-parametric Friedman tests (with p<0. 05) using post-hoc multiple comparison tests to identify stability zones, within which the FC measures and graph metrics show stabilization with increasing/decreasing data lengths  (Fraschini et al. , 2016). To find stability zones, the whole data length range was searched for each FC measure or graph metric to identify the time interval(s), within which no significant differences were observed in correlation or graph metric values with increasing or decreasing data lengths in the interval(s). We also assessed the statistical differences between the correlation values and graph metrics derived by each of the four connectivity measures using repeated measures analysis of variance (ANOVA, with p<0. 05). Post-hoc paired t-tests were also used to characterize differences between the FC measures (with p<0.
ICC varies between 0 (low reliability) and 1 (high reliability). Since ICC might have negative values indicating non-reliable estimation when the within-subject variance was larger than the between-subject variance, we set negative values of ICC to zero to avoid misinterpretation  (Rousson et al. , 2002; Zhang et al. , 2011). All analyses were performed using custom codes written in Matlab (MATLAB R2014a, The MathWorks Inc. ), the Brain Connectivity Toolbox  (Rubinov and Sporns, 2010) , Graphvar toolbox  (Kruschwitz et al. , 2015).
Stability and reliability of network metrics Figure  4  shows the boxplots of degree (k w ) and shortest path length (l w ) as a function of data length for HbO, HbR and HbT across all nodes and subjects for both sessions. The weighted graph metrics were derived by each FC measure. The results for other graph metrics studied can be found in Figure  S1. As shown, the average degree (K w ) decreased with increasing data length. The repeatedmeasures ANOVA revealed no significant differences (p>0. 05) between PC and BC. MI and PtC showed significantly lower values in comparison with PC and BC. Global efficiency ( W G E ), average clustering coefficient (C w ) and transitivity (T w ) also showed similar decreases with increasing data length (Figure  S1 ). An inverse trend was observed for characteristic path length (L w ), graph radius (r w ) and diameter (ρ w ), modularity (Q w ), betweenness (B w ) and vulnerability (V w ) (Figure  S1 ).
Discussion In this study, we investigated the effect of data length on the reliability and stability of four functional connectivity measures and twelve graph theoretical metrics commonly used to study functional brain networks. We performed the test-retest reliability analysis using nine data lengths ranging from 0. 5 to 4. 5 min. Our results show that data length can significantly affect the results of FC analysis and the topological properties of functional brain networks derived from rsfNIRS data. The stability analysis showed that PC and BC stabilized with data lengths longer than 1-2. 5 min depending on concentration signals. Compared to PC and BC, the stabilization for MI occurred with medium to long-range data lengths (longer than 2. 5 min). The partial correlation showed stability only for short to medium-range data lengths (2.
Technical considerations Several technical issues might affect the accuracy of the TRT reliability analysis performed in our study. First, we used the weighted network connectivity analysis (van Wijk et al 2010). It has been suggested that compared to binarized networks, weighted networks could better characterize subtle changes in network topology  (Barrat et al. , 2004). However, the weighted approach could bias the connectivity results due to weak connections related to noise  (Ivkovic et al. , 2012). Since non-significant connectivity values may have minor effects on the network topology  (van Wijk et al. , 2010) , one can construct soft-thresholded networks with a continuous mapping of all correlation values to edge weights  (Schwarz and McGonigle, 2011). Second, we investigated the TRT reliability of functional brain network metrics using rsfNIRS data collected with relatively short time intervals between different sessions for the same subjects.
Thereafter, we acquired turbo inversion recovery magnitude (TIRM) images with a similar FOV, resolution, and slices (TR 4,000 milliseconds, echo time 41 milliseconds, inversion time 220 milliseconds). Furthermore, we collected data using 3D Dixon sequences (TR 10 milliseconds, flip angle 3°, FOV 256 × 192 mm, voxel size 1 × 1 × 5 mm, number slices 32) in either a 2-point version (TE1/TE2 = 2. 45/3. 675 milliseconds) or 3-point version (TE1/TE2/TE3 = 2. 31/3. 68/5. 07 milliseconds). Finally, at the Nijmegen site, multi-echo spin-echo images were acquired (TR 3,720 milliseconds, echo train length 17, echo-spacing 8 milliseconds, voxel size 1. 5 × 1. 5 × 10 mm, number of slices 5, slice gap 20 mm).
Data analysis Semiquantitative MRI An experienced radiologist (M. J. P. ) scored 7 lower leg and 12 upper leg muscles (figure  1, B  and C ) semiquantitatively for signs of fat infiltration and putative edema on T1-weighted spin echo and TIRM images, respectively. For the T1weighted images, the ordinal Lamminen score (range 1-4) was used, in which 1 represents normal muscle signal intensity and 4 reflects a total, homogeneous hyperintense signal change in the entire muscle. 17 The TIRM images were scored with the ordinal Malattia score (range 0-2), with 0 meaning no muscle signal abnormalities and 2 indicating a high degree of signal hyperintensity. 18 The Lamminen and Malattia individual muscle scores of each participant were summed over all lower extremity muscles for comparison between patients with DM1 and unaffected controls.
The T2 relaxation time of muscle water (T2 water in milliseconds) was determined per voxel by fitting multi-spin echo data with a bicomponent extended-phase graph model (appendix available from Dryad, doi. org/10. 5061/dryad. 4nb96c1). 19, 20 Increased T2 water is thought to reflect putative edema and disease activity. For the quantitative evaluation, we analyzed the same 19 lower extremity muscles as evaluated semiquantitatively, plus the flexor digitorum longus, i. e. , in total 8 calf and 12 thigh muscles. They were delineated on 5 distal, middle, and proximal slices of the Dixon images of the upper and lower leg using Medical Image Processing, Analysis, and Visualization (mipav. cit. nih. gov), thereby avoiding subcutaneous fat contamination (figure  1, A-C ). Furthermore, delineation was performed on the 5 slices of the Dixon images corresponding in position to the T2 water map and transposed to this T2 water map (figure  1A ).
Average fat fraction, T2 water , and estimated muscle volume were determined for each muscle (figure  1D ). Average fat fraction was defined as the mean over all voxels, and muscle volume was defined as the number of voxels multiplied by the voxel volume using the 15 delineated slices of the fat fraction map. In addition, contractile muscle volume, i. e. , the remaining muscle tissue still able to contract, was calculated as follows: contractile muscle volume = muscle volume × ð1 -fat fractionÞ (2) To obtain T2 water , the voxels with severe fat infiltration were excluded (data available from Dryad, doi. org/10. 5061/ dryad. 4nb96c1) because sufficient water signal is required for reliable fitting of T2 water. 21 If the remaining number of voxels was >10% of the total number of voxels in that muscle, T2 water was calculated as the average over these voxels in the 5 T2 water map slices.
Muscles were excluded from the analysis if visual inspection revealed the presence of movement artifacts. For the fat fraction and contractile muscle volume analysis, muscles were also excluded if artifacts in the fat/water reconstruction made the fat fraction estimate unreliable. Outcomes for the 20 individual muscles were combined, resulting in per participant a value for the entire lower extremity and a value for the lower and upper leg level (figure  1D ). For the fat fraction and T2 water , muscles were combined by taking the average over all 20 muscles per participant and the average over the 8 lower leg and 12 upper leg muscles separately. For the muscle volume and contractile muscle volume, we took the sum of these muscles per participant. The outcome measures for the entire lower extremity were calculated only if upper and lower leg muscle data were available.
To investigate whether muscles with signs of fat infiltration differ in T2 water with muscles without fat infiltration, muscles were divided into 2 groups based on their fat fraction. For all 20 muscles, a cutoff value was defined as the average fat fraction + 2 SDs of that muscle in unaffected controls (figure  1D ). It was observed that, in unaffected controls, T2 water differed between muscles; e. g. , the average T2 water was 27. 6 milliseconds in the rectus femoris and 33. 8 milliseconds in the gastrocnemius medialis (Friedman test, p < 0. 001, figure e-4 available from Dryad, doi. org/10. 5061/dryad. 4nb96c1). Therefore, for each muscle, we determined the absolute difference in milliseconds relative to the average T2 water over the 10 unaffected controls in that particular muscle (rT2 water ).
Statistical analysis Statistical analysis was performed with IBM SPSS Statistics version 22 (SPSS, Chicago, IL) or R statistics using the RStudio package (R Foundation for Statistical Computing, Vienna, Austria). 22, 23 Differences between unaffected controls and patients with DM1 were assessed with a 2-tailed Mann-Whitney U test. A 2-tailed 1-sample t test to zero was applied to determine whether rT2 water in DM1 differed from that in unaffected controls. The difference in rT2 water between non-fat-infiltrated and fat-infiltrated muscles was evaluated with a 2-tailed Wilcoxon signed-rank test. Fat fraction and T2 water were correlated with the Lamminen and Malattia scoring using the Spearman correlation, respectively. Multivariate linear regression was applied to predict fat fraction using age, CTG ePAL , and CTG ResSI.
Participants The age and fraction of men to women were not different between the 33 patients with DM1 (24 in Nijmegen, 9 in Paris) and 10 unaffected controls (table  1 ). The clinical forms by which the patients with DM1 were affected are presented in table  1. According to the clinical scores such as the MIRS, DM1-Activ-c, 6MWT, and Checklist Individual Strengthfatigue, patients varied from mildly to severely affected and exhibited a wide range of CTG repeat lengths (table  1 ). Semiquantitative evaluation of the MRI data included 615 muscles in patients with DM1 and 190 muscles in unaffected controls; quantitative evaluation included 633 muscles in patients with DM1 and 200 muscles in unaffected controls. Twelve upper leg muscles were not analyzed because 1 patient could not lie in the supine position long enough to acquire data with all MRI sequences.
6; controls, 0. 4 ± 0. 7, p < 0. 001; table  2 ). Most TIRM-positive lesions were observed in the lower leg. In the 33 scored patients, the number of lower leg muscles with these lesions ranged from 20 tibialis posterior muscles (Malattia score >0) to 25 tibialis anterior and gastrocnemius medialis muscles. In the upper leg, the vastus lateralis and intermedius were most often TIRM positive (12 and 14 muscles, respectively), and only 4 of the scored gracilis, sartorius, and adductor longus muscles were TIRM positive. Of the 422 muscles that showed no fat infiltration (Lamminen score 1), 83 muscles (20%) showed hyperintense areas on the TIRM images (i. e. , Malattia score 1 or 2). Detailed results on the individual Lamminen and Malattia scores are presented in table e-1 and figure e-1 (available from Dryad, doi.
org/10. 5061/dryad. 4nb96c1). For the quantitative determination of muscle fat fraction, muscle volume, and contractile muscle volume, we analyzed the MRIs obtained with the Dixon method (figure  3  and table  2 ). These analyses revealed an increased fat fraction for patients with DM1 compared to unaffected controls: 15. 6 ± 11. 1% vs 3. 7 ± 1. 5%, respectively (p < 0. 001; figure  3A ). Analysis of these images also revealed that muscle volume was reduced in patients with DM1 (DM1: 902 ± 232 cm 3 ; controls: 1,097 ± 251 cm 3 ; p = 0. 028), as well as contractile muscle volume (DM1: 779 ± 247 cm 3 ; controls: 1,054 ± 246 cm  3  ; p = 0. 008) (figure  3, B  and C ). To determine T2 water values as a quantitative parameter for putative edema-associated disease activity, we analyzed MRIs of the lower extremity obtained with a multi-echo spin-echo sequence.
The quantitative values for fat fraction and T2 water in DM1 muscles correlate well with the corresponding semiquantitative scores (Lamminen score vs fat fraction: ρ = 0. 631, p < 0. 001; Malattia score vs T2 water : ρ = 0. 400, p < 0. 001). Average values for quantitative MRI measures per individual muscle are presented in figures e-2 through e-5 (available from Dryad, doi. org/10. 5061/dryad. 4nb96c1). To assess the prevalence of fat infiltration in individual muscles of patients with DM1, we analyzed the fat fractions per muscle (figure  4 , A-D). Fat fraction was higher in lower leg muscles compared to upper leg muscles (lower leg 22. 5 ± 18. 2%, upper leg 10. 3 ± 7. 6%; Wilcoxon signed rank test, p = 0. 001). Among the lower leg muscles, the gastrocnemius medialis and soleus exhibited the highest fat fractions, while the vastus intermedius had the highest fat fraction in the upper leg (figure  4 , E and F).
28 Furthermore, the patients with DM1 who carried AciI-sensitive variant repeats, most likely CCG or CGG variants, exhibited a slower increase in fat fractions than patients with DM1 without detectable variant repeats. 5] [16]  Individual parameters or interactions in the multivariate regression models were not significant, and only 2 patients with AciI-sensitive variant repeats were assessed. Nevertheless, our data highlight the value of genetic profiling for future randomized controlled trials, because age and CTG repeat length determine the severity of muscle involvement at baseline and most likely its progression over time. Finally, we investigated whether the quantitative MRI outcome measures reflect disease severity and physical capacity of the patients with DM1. We observed that the 6MWT results correlate with fat fraction and contractile muscle volume and that the MIRS disease score correlates with fat fraction.
stepwise model comparison using the Akaike information criterion confirmed this as the most informative model. The 2 participants with AciI-sensitive variant repeats and a successful fat fraction calculation had lower mean fat fractions than the other patients with DM1 (3. 7% vs 16. 0%, Welch t test, p = 0. 002; figure e-5, available from Dryad, doi. org/10. 5061/ dryad. 4nb96c1).