dataesr/scientific-paragraphs-categorization huggingface dataset:
3 ). Each preprocessing output was visually inspected. Automatic brain extraction generated plausible brain masks. Normalisation was carried out using the AIBS template (Supplementary Fig. 4 ). Spatial coverage along the anterior-posterior axis varied across datasets. The following analysis is thus restricted to areas fully covered by all scans, corresponding to approximately 2. 96 and À2. 92 mm relative to Bregma. Moreover, distortions made it impossible to cover the amygdala region in full. No marked difference in the performance of each pre-processing step was identified between datasets. The brain-masked, spatially smoothed, temporally filtered, and normalised scans were further processed as follows.
, 2014) , b) they reflect separable electrophysiological signatures in mammals  (Popa et al. , 2009) , and c) they belong to separable functional communities  (Liska et al. , 2015)  in the mouse brain and are similarly characterized by the absence of significant positive correlation in the corresponding human RSN  (Fox et al. , 2005). Detailed FC within the specific ROI for the GSR and VV nuisance model are shown as a function of FC within the corresponding nonspecific ROI at the single-subject level (Supplementary Fig. 5 ). In the VV condition, 98/255 (i. e. 38%) of individual scans fell into the "specific FC" category, while both MC and GSR conditions reach the lowest percentage (30%) of scans exhibiting "specific FC" relative to the ACA seed (Fig. 2c ). Out of 98/255 scans categorised as presenting "specific FC" relative to the ACA seed, up to 14/15 scans originated from the same dataset (median ¼ 6/15).
Two datasets did not contain scans that met the definition. The 98 scans were also unevenly distributed according to the different acquisition parameters, including field strength (4. 7 T N ¼ 1/ 15, 7 T N ¼ 41/120, 9. 4 T N ¼ 38/90, 11. 7 T ¼ 18/30, Х 2 ¼ 13. 76, df ¼ 3, p-value ¼ 0. 0032), coil type (room-temperature N ¼ 26/105, cryoprobe N ¼ 72/150, Х 2 ¼ 13. 13, df ¼ 1, p-value ¼ 0. 00029), breathing condition (free-breathing N ¼ 58/180, ventilated N ¼ 40/75, Х 2 ¼ 9. 10, df ¼ 1, p-value ¼ 0. 0026), and sedation condition (awake N ¼ 7/ 15, isoflurane/halothane N ¼ 18/90, medetomidine N ¼ 26/75, medetomidine þ isoflurane N ¼ 47/75, Х 2 ¼ 32. 42, df ¼ 3, p-value ¼ 4. 28e-07). Hence, scans presenting "specific FC" patterns were more often found in datasets acquired at higher field strengths, with cryoprobes, in ventilated animals, and under medetomidine þ isoflurane combination sedation.
Seed-based analysis identifies common and reproducible mouse resting-state networks We sought to identify common RSNs by employing SBA and to compare reproducibility across datasets. Seeds positioned in representative anatomical regions of the left hemisphere (Supplementary Fig. 1a ) were used to reveal the spatial extent of previously described mouse resting-state networks. The seeds were selected to represent different cortical (somatosensory, motor, high order processing), as well as subcortical systems (striatum, hippocampal formation, thalamus). To obtain high-specificity and high-confidence group-level SBA maps, we first probed only 98/255 scans listed as containing "specific FC" in the previous analysis. We next extended these analyses to include all 255/ 255 scans (Supplementary Fig.
7 ). For the within-dataset comparisons, all 15/15 scans in each dataset were included to reflect inter-dataset variability. All group-level SBA maps exhibited a strong bilateral and homotopic extension (Fig. 3a , Supplementary Fig. 6 ). An ACA seed revealed a network involving the prefrontal cortex, RSP, dorsal striatum, dorsal thalamus and peri-hippocampal areas. This recapitulates anatomical features reminiscent of the human, primate, and rat DMN  (Gozzi and Schwarz, 2016; Hutchison and Everling, 2012; Sforazzini et al. , 2014; Stafford et al. , 2014). Comparable regions were observed for the RSP seed, a region evolutionarily related to the posterior cingulate cortex of the human DMN (Supplementary Fig. 6 ). The anterior insular seed was found to be co-activated with the dorsal cingulate and the amygdalar areas, corresponding to the putative rodent salience network  (Gozzi and Schwarz, 2016) , while the primary somato-motor region (MO) defined a previously described latero-cortical network that appears to be antagonistic to midline DMN regions.
Hence it has been postulated to serve as a possible rodent homologue of the primate task-positive network (Fig. 3a )  (Liska et al. , 2015; Sforazzini et al. , 2014). The corresponding networks estimated across all scans (255/255) recapitulated features identified in the 98/255 scans listed as containing "specific FC" but appeared to have a much lower spatial specificity (Supplementary Fig. 7 ). Overlap maps summarising the results from each individual dataset revealed that 70% (12/17) of the datasets presented the features listed above (Fig. 3b ). Overlap maps indicate, on a voxel basis, the percentage of the dataset presenting a significant FC following a one-sample t-test performed on its 15 scans. The one-sample t-test maps relative to each dataset are detailed in Supplementary Fig. 8.
Sedation protocol and SNR affect connectivity strength The datasets analysed here were acquired at varying field strengths, and with different coil designs, EPI sequence parameters, animal handling procedures, and anaesthesia protocols, i. e. either awake or sedated states. Hence the acquisitions were not purposefully balanced to test specific effects. To identify factors associated with FC strength, a simplified linear model was designed including the following explanatory factors: sedation and breathing conditions, SNR, and motion (mean FWD). Limitations in the orthogonality and representation of specific acquisition factors such as field strength, coil design, EPI sequence parameters, number of volumes, gender, and age prevented designing a more extensive model. Individual-level FC values (z-statistic) were extracted from SBA maps estimated from the ACA seed using an ROI located in the RSP and shown as a function of different acquisition parameters (Fig.
Distributions of FC values may hence provide useful reference points. Connectivity strength between the ACA and RSP, representing a central feature of the rodent DMN, reached z ¼ 2. 77, 5. 71, and 10. 46 at the 50th, 75th, and 95th percentile respectively (Pearson's r ¼ 0. 15, 0. 26, 0. 43, when SBA is carried out using a correlation analysis instead of a general linear model). Additional SBA parameter distributions are provided for other ROI pairs in Supplementary Table  1. The parameters of the acquisitions featured in this analysis offer an objective criterion to evaluate and compare sensitivity to FC in a new dataset or previous publications, insofar as comparable metrics are available.
Network-specific functional connectivity is found in all datasets Evidence for robust distal FC could not be established in all datasets with SBA. To investigate the presence of network-specific FC also in datasets characterized by weaker distal connectivity, a dual regression combined with group-level ICA (drICA) approach was performed  (Filippini et al. , 2009). To obtain an enriched data-driven reference atlas, a group ICA atlas was generated out of the 98 "specific FC" scans selected in the SBA above, using 20 dimensions. This was motivated by the observation of spurious FC in one-sample t-test of SBA maps when all scans were included (Fig. 2b ). The atlas revealed 9 cortical components (Fig. 5a , Supplementary Fig. 9 , Supplementary Table  3 ), 5 overlapping with the latero-cortical network (MO and 4 SSp areas), 3 overlapping with elements of the DMN (prefrontal, cingulate/RSP, and temporal associative areas), and 1 overlapping with the insular area (AI).
Additionally, 5 sub-cortical components were revealed, overlapping with the nucleus accumbens (ACB), caudoputamen (CP), pallidum (PAL), hippocampal region (HIP), and thalamus (TH) (Supplementary Fig. 10 ). The components recapitulate many of the features identified with SBA (Fig. 5b  and c ), namely a homotopic bilateral organisation. The components identified here also presented strong similarities to a previous analysis  (Zerbi et al. , 2015). Due to uneven brain coverages across datasets, rostral and caudal RSNs could not be examined, including olfactory, auditory, and visual networks. To obtain individual-level representations of these components, a dual regression approach was implemented using the reference ICA identified above. These group-level ICA were used as masks to extract time series which were then regressed into all (255/255) individual scans using a general linear model.
To investigate specificity relative to a DMN-related component, FC relative to the cingulate/RSP component was extracted from the ACA ROI (Specific ROI, z ¼ 9. 68, 16. 28, and 24. 24, 50th, 75th, and 95th percentiles, Fig. 5e ) and SSp ROI (Unspecific ROI). "Specific FC" was determined in 79% (201/255) of the scans, "Unspecific FC" in 16%, "Spurious FC" in 1. 5%, and "No FC" in 3. 1% (Fig. 5d ). "Specific FC" in 15/15 scans was found in 2 datasets (Median ¼ 12/15). The "Specific FC" category was also more evenly distributed relative to acquisition protocols and Fig. 2. Denoising strategies and their impact on functional connectivity (FC) specificity. a-b, Seed-based analysis for a seed in the anterior cingulate area (ACA) following either global signal regression (GSR, a) or vascular þ ventricle signal regression (VV, b).
While not reliably identified in all datasets and scan conditions, these large-scale networks were found to Fig. 3. Seed-based analyses (SBA) for 3 selected seeds positioned in the left hemisphere. One-sample t-test maps of individual maps reveal the full extent of SBAderived resting-state networks in the mouse brain across 98/255 scans that presented "specific functional connectivity (FC)" following vascular þ ventricle signal regression. FC relative to a seed located in the anterior cingulate area reveals the extent of the mouse default-mode network, including the dorsal caudoputamen, dorsal thalamus, and peri-hippocampal areas. The seed in the insular area reveals significant FC in dorsal cingulate and amygdalar areas, corresponding to areas previously associated with the human salience network.
Inter-hemispheric homotopic FC is found relative to the somato-motor seed, together with lateral striatal FC. Overlap maps, indicating the percentage of datasets presenting significant FC after applying one-sample t-tests (p < 0. 05, uncorrected), reveal that 12/17 of datasets recapitulated the features stated above. Out of these, 5 were not considered to overlap specifically (Supplementary Fig. 8 ). Voxelwise corrected t-statistic for onesample t-tests and overlap maps are shown as a colour-coded overlays on the AIBS reference template. Fig. 4. Functional connectivity (FC) in the retrosplenial cortex relative to a seed located in the anterior cingulate area, as a function of acquisition parameters. A statistically significant association was determined between sedation effect and FC (a, F (247, 251) ¼ 18.
29, p ¼ 3. 5e-13) and between SNR and FC (c, F (247,248) ¼ 12. 39, p ¼ 5. 1e-4). Neither breathing condition nor motion effects were significant with FC (b, d). Due to limitations in the representation of each level within a factor, coil design (e) and magnetic field (f) were omitted from the final statistical model. Free ¼ free-breathing, Vent ¼ mechanically ventilated, Cryo ¼ cryoprobe, RT ¼ room-temperature. colocalize within the well-delineated boundaries in the majority of scans and datasets, recapitulating previous descriptions in rodents  (Gozzi and Schwarz, 2016; Lu et al. , 2012; Sforazzini et al. , 2014; Stafford et al. , 2014) , monkeys  (Hutchison and Everling, 2012) , and humans  (Buckner et al. , 2008). Interestingly, most (12/17) of the datasets converged toward spatially defined common RSNs when distal FC relative to a seed location was assessed.
When the analysis was restricted to local connectivity, i. e. parameter estimates confined within the pre-defined networks, all (17/ 17) datasets converged. These results indicate that group-level, or second-level inferences, might be assessed irrespective of acquisition protocol or animal handling procedures using robust analysis strategies. At the subject level, "specific FC" relative to the DMN was found in 98/ 255 of the scans, indicating that first-level inference on distal FC is within reach in some, but not all datasets. Sedation and equipment performance leading to increased SNR were the major factors associated with both FC sensitivity and specificity, together with breathing conditions. Awake animals presented overall higher FC, however, datasets acquired with medetomidine þ isoflurane combination together with mechanical ventilation were associated with greater specificity within elements of the DMN.
Importantly, the results converged irrespective of sedation or awake protocols. This underlines that all datasets should be examined with the same criteria to further enhance results comparability. Hence, the set of standards provided here (e. g. spatial maps and FC parameter distributions), will allow for the calibration of future multi-centre projects and assist in designing meta-analysis and replication studies, the gold standards in evidence-based research. In addition to acquisition procedures, the adoption of analysis standards must be encouraged. An MRI template  (Dorr et al. , 2008)  transformed into the AIBS standard space provides a common space that extends beyond animal MRI studies, including the seamless implementation of AIBS resources  (Bergmann et al. , 2016; Grandjean et al.
, 2017b; Oh et al. , 2014; Richiardi et al. , 2015; Stafford et al. , 2014). Moreover, analysis based on robust methods  (Zuo and Xing, 2014) , such as drICA  (Filippini et al. , 2009) , together with considerations for statistical analysis  (Eklund et al. , 2016) , and the sharing of datasets on online repositories  (Nichols et al. , 2017)  provide a comprehensive evidence-based roadmap to improve the comparability of acquisitions carried out between centres and enhance the robustness and reproducibility of future results. In particular, all the datasets analysed in the context of this study have been made fully available and therefore will provide references for scientists developing customized rsfMRI protocols. Several major limitations in this study should be acknowledged. First and foremost, the lack of consensus quality assurance parameters for the estimation of FC led us to devise a strategy to examine FC specificity.
Human studies often use test-retest reliability method to assess protocol stability and reproducibility  (Zuo and Xing, 2014) , however, such metrics require duplicated acquisitions not presently available. Moreover, test-retest reliability might be biased in low-FC datasets, where the absence of FC is a reproducible feature. Because this study combined a set of existing scans, factors were not entirely orthogonal and it was not possible to model potentially relevant effects affecting FC metrics, such as specific sequence parameters (e. g. number of volumes), as well as biologically relevant factors including sex, age, and mouse strain. Additionally, the analysis was based on a fixed pre-processing pipeline. Different pipelines, beyond nuisance regression, have not been compared. Hence, different pre-processing step may favour specific acquisitions, e.
g. adapted bandpass filters for anaesthesia protocols Fig. 5. Group-level independent component analysis (ICA) estimated across 98/255 "specific functional connectivity (FC)" scans reveals canonical mouse components (a). All components presented a marked bilateral organisation. Nine components were found to overlap principally with the isocortex including regions attributed to latero-cortical, salience, and DMN networks by seed-based analyses, three components overlapped with the striatum, one with the hippocampal areas, and one with the thalamus. Detailed representations of the cingulate/retrosplenial area component (Cg/RSP b). One-sample t-tests within datasets indicate that 100% of datasets presented significant FC (p < 0. 05, uncorrected) within the Cg/RSP component (c). Remaining components are presented in Supplementary Figs.
, 2019) , may overcome this limitation in the future. Finally, the lack of distal FC in some datasets could not be attributed to specific animal handling protocols or equipment performance. This indicates that additional experimental factors not considered here might be better predictors in estimating this particular kind of FC. For example, the implementation of procedures to control the arterial level of carbon dioxide may be critical to prevent hypercapnic conditions, a feature that is associated with reduced FC connectivity  (Biswal et al. , 1997)  and that is often observed in freely-breathing anaesthetized rodents. Despite these limitations, the work presented here is likely to enhance the true scientific value of mouse rsfMRI by establishing standards. With these, the field is set to meet its goals toward the establishment and understanding of the cellular and molecular mechanisms of large-scale brain functional reorganisation in the healthy and diseased brain.
Acknowledgements This work was supported by the  Singapore Bioimaging Consortium (SBIC) ,  A*STAR, Singapore. AG acknowledges funding from the  Simons Foundation  ( SFARI 314688  and  400101 ), the  Brain and Behavior Foundation  (2017  NARSAD independent Investigator Grant ) and the  European Research Council  ( ERC, G. A. 802371 ). This work was also supported by the  JSPS KAKENHI  Grant Number  16K07032  to NT,  16K10233  to NY, and  18K18375  to CS, by  Brain/MINDS , the  Strategic Research Program for Brain Sciences (SRPBS)  from the  Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT)  and  Japan Agency for Medical Research and Development (AMED)  to NT and HO, by  AMED  under Grant Number  JP19dm0307007h0002  and  JP19dm0307008h0002  to NY,  JP19dm0307026h0002  to CS, and  17dm0107066h  to IA, by  COI program  by  Japan Science and Technology Agency (JST)  to IA, and by  ERATO JPMJER1801  by  JST  to NY.
It was further supported as part of the  Excellence Cluster 'BrainLinks-Brain-Tools ' by the  German Research Foundation , grant  EXC1086. AH acknowledges funding from the  German BMBF (NeuroImpa ,  01EC1403C  and  NeuroRad 02NUK034D ). MD acknowledges funding from  France-Alzheimer Association, Plan Alzheimer Foundation  and the  French Public Investment Bank's "ROMANE" program. This work was also supported by the  Fund for Scientific Research Flanders (FWO)  (grant agreements  G057615N  and  12S4815N -AvL ), the  Stichting Alzheimer Onderzoek (SAO-FRA , grant agreement  13026-AvL ), the interdisciplinary PhD grant BOF DOCPRO 2014 -MV). NG acknowledges  NEWMEDS  project funded from the  Innovative Medicine Initiative Joint Undertaking  under Grant Agreement no. 115008  of which resources are composed of  European Federation of Pharmaceutical Industries and Associations (EFPIA)  inkind contribution and financial contribution from the  European Union 's  Seventh Framework Programme  (FP7/2007-2013); as well as funding from the  German Research Foundation (Deutsche Forschungsgemeinschaft) :  DFG SA 1869/15-1  and  DFG GA 2109/2-1.
Background: State-space models are important tools for quality control and analysis of error-prone animal movement data. The near real-time (within 24 h) capability of the Argos satellite system can aid dynamic ocean management of human activities by informing when animals enter wind farms, shipping lanes, and other intensive use zones. This capability also facilitates the use of ocean observations from animal-borne sensors in operational ocean forecasting models. Such near real-time data provision requires rapid, reliable quality control to deal with error-prone Argos locations. Methods: We formulate a continuous-time state-space model to filter the three types of Argos location data (Least-Squares, Kalman filter, and Kalman smoother), accounting for irregular timing of observations. Our model is deliberately simple to ensure speed and reliability for automated, near real-time quality control of Argos location data.
We validate the model by fitting to Argos locations collected from 61 individuals across 7 marine vertebrates and compare model-estimated locations to contemporaneous GPS locations. We then test assumptions that Argos Kalman filter/smoother error ellipses are unbiased, and that Argos Kalman smoother location accuracy cannot be improved by subsequent state-space modelling. Results: Estimation accuracy varied among species with median Root Mean Squared Errors usually < 5 km, and decreased with increasing data sampling rate and precision of Argos locations. Including a model parameter to inflate Argos error ellipse sizes in the north -south direction resulted in more accurate location estimates. Finally, in some cases the model appreciably improved the accuracy of the Argos Kalman smoother locations, which should not be possible if the smoother is using all available information.
Background State-space models have emerged as important tools both for quality control and ecological analysis of error-prone animal movement data  [1] [2] [3] [4] [5]. Analysis of these data with discrete-time models is simple in principle, breaking down animal movement into a series of discrete steps that occur on some fixed time interval (e. g. ,  [1, 6] ). Yet animal movement is a process that unfolds continuously through time, usually absent of clear breaks that could delineate discrete steps. We merely measure the movements from locations obtained over discrete, often irregular intervals in time. In this sense, a continuous-time model can more naturally handle temporally irregular observations while mimicking the true underlying continuous movement process(es)  [2, 7]. In the marine realm, air-breathing animal locations are typically measured by satellite-linked electronic tags at irregular time intervals dictated by a combination of satellite availability and an animal's surface behaviour.
The Argos satellite telemetry system is one of the most common platforms used to track animals at sea, with over 40,000 individuals tracked since 2007 (S. Baudel, pers. comm. ). In this system, transmissions from electronic tags are received by one of several polar-orbiting satellites as they pass overhead, and the Doppler shift in transmission frequency along with other information is used to geolocate the tags  [8]. The polar orbits of Argos satellites result in more dense coverage and potentially higher temporal, resolution data closer to the poles than at the equator. From inception in 1978 to 2011, CLS (Collecte Localisation Satellites) has used a Least-Squares algorithm to geolocate the tag transmissions. This approach does not quantify location uncertainty but rather provides location quality classes based on information including the number of transmissions received  [8].
State-space models developed for Argos Least-Squares locations have relied on independent, ground-truth data (e. g. ,  [9] ) to quantify location uncertainty for each of the location quality classes  [1, 2]. However, independently quantified uncertainties, based on a single or small number of data sets, are unlikely to be appropriate for all species in all locations. For example, Lowther et al. [10]  found that modifications to assumed Least-Square error variances can influence the accuracy of locations predicted by different state-space models. In 2011, CLS replaced their Least-Squares algorithm with a state-space model, based on a multiple model Kalman filter algorithm, to estimate locations and their uncertainty  [11]. This approach provides more location estimates, each with a corresponding estimated error ellipse, and with greater accuracy compared to the original Least-Squares method.