dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, for each study we apply an image-based meta-analysis procedure recently used in  Botvinik-Nezer et al. (2020)  to all of our analysis results, aggregating the information acquired from running one dataset through multiple pipelines to obtain a consensus map of activated brain regions. The remainder of the manuscript is organised as follows: First, we provide a brief summary of the three original published studies from which we sourced our selected datasets. We then describe the pipelines implemented for our reanalyses of the data, and detail the quantitative and qualitative metrics and image-based meta-analysis procedure applied to our analysis results. Finally, we evaluate our findings to assess the magnitude of variation between fMRI analysis software at each stage of the analysis workflow, and discuss the repercussions of these results on the functional neuroimaging literature.
Methods We first provide an overview of the original study paradigms for the three published task-fMRI works from which we sourced the three datasets, before we go on to detail the reanalysis methods carried out in this work. Most notably, while the original studies' analyses were carried out on 16, 29 and 30 participants task-fMRI data respectively, for the latter two studies only 21 and 17 participants' data were available for reanalyses. Alongside this, due to preprocessing failing for one individual in the ds000001 dataset, we ultimately reanalyzed 15 subjects rather than the complete sample of 16 whose data were shared (see the start of Section 3 for more details).
Study Description and Data Source We selected three task-fMRI studies from the publicly accessible OpenfMRI (now upgraded to OpenNeuro, RRID:SCR 005031) data repository  (Gorgolewski et al. , 2017) , OpenfMRI dataset accession numbers: ds000001 (Revision: 2. 0. 4; Schonberg et al. , 2012) , ds000109 (Revision 2. 0. 2; Moran et al. , 2012) , and ds000120 (Revision 1. 0. 0; Padmanabhan et al. , 2011). Each of the datasets had been organized in compliance with the Brain Imaging Data Structure (BIDS, RRID:SCR 016124;  Gorgolewski et al. , 2016). These datasets were chosen following an extensive selection procedure (carried out between May 2016 and November 2016), whereby we vetted the associated publication for each dataset stored in the repository. We sought to find studies with simple analysis pipelines and clearly reported regions of brain activation that would be easily comparable to our own results.
Here, our first aim was to isolate whether the largest variation between software occurs during the preprocessing or statistical modelling of the functional data. To do this, we repeated the BMN analyses, except this time implementing the same preprocessing strategy to the three datasets before carrying out the rest of the analyses in the three packages. A common minimal preprocessing workflow was applied to each of the datasets using fMRIPrep 20. 0. 02  (Esteban et al. , 2019 (Esteban et al. , , 2020b ; RRID:SCR 016216), which is based on Nipype 1. 4. 2 (Gorgolewski et al. , 2011 , Esteban et al. , 2020a; RRID:SCR 002502). The fMRIPrep pipeline combines procedures from a range of software packages to provide the optimal implementation at each stage of preprocessing. We will now describe the preprocessing sub-workflows that were applied to all three datasets' anatomical and functional data within fMRIPrep.
Anatomical Data Preprocessing in fMRIPrep For each of the three datasets, the preprocessing of the anatomical data was carried out within fMRIPrep as follows. The T1w image was corrected for intensity non-uniformity  (INU)  with  N4BiasFieldCorrection (Tustison et al. , 2010) , distributed with ANTs 2. 2. 0 (Avants et al. , 2008, RRID:SCR 004757), and used as T1w-reference throughout the workflow. The T1wreference was then skull-stripped with a Nipype implementation of the antsBrainExtraction. sh workflow (from ANTs), using OASIS30ANTs as a target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast (FSL 5. 0. 9, RRID:SCR 002823,  Zhang et al. , 2001). Brain surfaces were reconstructed using recon-all (FreeSurfer 6.
Functional Data Preprocessing in fMRIPrep For each of the the three datasets, the preprocessing of the functional data was carried out within fMRIPrep as follows. For each of the BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration  (Greve and Fischl, 2009). Co-registration was configured with six degrees of freedom. Table  1 : fMRIPrep Processing Pipeline. For all three studies, a subset of the workflows applied the same fMRIPrep preprocessing pipeline.
Manipulation of Modelling Methods and Hybrid Pipeline Generation A 2 AF 3 AF 4 AF 5 AF 6 AF 7 F 1 S 2 SF 3 SF 4 SF 5 SF 6 SF A AS AS AS AS AS S Figure 1: Diagrams to enumerate the complete set of 59 pipelines that were carried out on the three datasets. For ds000001 and ds000109, 26 pipelines were implemented for each dataset (13 workflows using parametric inference at the group-level, and 13 parallel workflows using nonparametric inference displayed by the purple lines). For these two datasets, FSL was used as the reference software package, and hybrid pipelines were generated by interchanging procedures from either AFNI and FSL (AF pipelines) or SPM and FSL (SF pipelines) across the analysis workflow. A total of 7 pipelines were carried out on the ds000120 dataset, where it was not feasible to analyze the data in FSL and nonparametric inference was unavailable in AFNI.
However, exchanging the fMRI signal and low-frequency drift models between software could be done easily, by simply interchanging the relevant regressors in the design matrix. Because of this, for each of our hybrid analysis pipelines the choice of software used for modelling the noise ultimately determined the package through which the subject-level analyses were conducted. For example, a hybrid pipeline using FSL's first-level noise model with AFNI's first-level fMRI signal model and drift model would be implemented within FSL, except the regressors in the design matrix for modelling the fMRI signal and lowfrequency drifts were then interchanged with the corresponding regressors from the design matrix generated by running the complete analysis within AFNI. In addition to this, six motion parameters (translations and rotations) estimated by fMRIPrep as part of the preprocessing workflow were included in all first-level analysis models as nuisance regressors.
Finally, for each hybrid pipeline the subject-level contrast of parameter estimate maps were inputted into the software package specified by the workflow for group-level analysis and inference. Taking all combinations of software procedures considered across the the three datasets yielded a total of 59 unique workflows, shown diagrammatically in Figure  1. The diagrams labelled 1 (far-left) and 7 (far-right) for each study display the pipelines that were carried out in BMN, where a single software package was used to conduct the entire analysis workflow. Pipelines labelled 2 and 6 are similar, except that preprocessing was carried out using the fMRIPrep workflow described in the previous section. Pipelines labelled 3 to 5 include further manipulations, each step interchanging one aspect of the subject-and group-level modelling as described above.
With each Dice coe cient, the percentage of 'spill-over' activation was also computed, that is, the percentage of activation in one pipeline's thresholded statistic map that fell outside of the analysis mask of the other pipeline. Finally, we applied a recently proposed image-based meta-analysis method that aggregated information across all pipelines (for a given dataset) to yield a 'consensus' activation map, i. e. the set of brain regions where significant activation was unanimous across all analysis pipelines applied to the data. The consensus analysis was performed on the collection of unthresholded group-level z-statistic images obtained across all pipelines, accounting for the correlations between pipelines owing to the same underlying data and identical procedures applied across parts of the analysis workflow.
The method was originally proposed in  Botvinik-Nezer et al. , 2020 , where it was used to infer a consensus across results obtained by many analysis teams for a single task-fMRI dataset. Full details of the method are provided in Supplementary Section 8. 1. We applied the consensus analysis methods to further examine the robustness of the individual results obtained for each dataset after accounting for the inter-pipeline variation. For new analyses (all pipelines in Fig. 1  excluding diagrams labelled 1 and 7 that were carried out as part of BMN), AFNI and FSL scripts were written in Python 3. 7. 6 and SPM scripts were written in GNU Octave (version 4. 4. 1). Scripts were made generalizable using a series of templates to extract the stimulus timings from the raw data, carry out the fMRIPrep preprocessing workflow, and subsequently conduct subject-and group-level analyses.
Results All analysis scripts and results have been made available, see Section 5 for more details. The preprocessing of each subject's data for all three studies was assessed using the summary reports provided as part of the fMRIPrep workflow. This included checking that each particant's functional data had been correctly masked and successfully registered to the MNI template image. Inspection of these reports confirmed that preprocessing had been successful for all-butone subject, subject 4 from the ds000001 dataset. Exceptionally high intensities found in this subject's raw T1w anatomical image (potentially due to an erroneous brain-extraction applied to the anatomical data before it was shared) caused drop-out in sizable regions of the brain during bias-field correction in the fMRIPrep preprocessing pipeline.
Main Sources of Pipeline-Variability A detailed review of the regions of activation found in the thresholded maps across all pipelines for each dataset is provided in Supplementary Section 8. 2. Here we describe the main sources of variation observed between the pipelines' results across the three datasets. Our analyses of the ds000001 dataset suggest that di↵erences in each software's first-level signal model were the largest contributor to variation across the three software packages' final statistical results. This is highlighted in Figure  2  and (Supplementary) Figure  S1  (All supplementary figures described in this section can be found in Supplementary Section 8. 3), where in both figures we compare the results from the two analysis workflows which di↵ered only by the choice of first-level signal model: In Fig.
2 and S1, correlation plots, bottom left), indicating that dissimilarities between the signal model's applied by the two pipelines ultimately led to radically di↵erent activation profiles in the unthresholded group-level t-statistic images. In terms of preprocessing, the results from our analyses across all studies indicate that AFNI's preprocessing pipeline was the most similar of the three software packages to fMRIPrep, while FSL's was the least similar. Evidence for this is seen in Figures  S2  and S3 , where we compare results obtained for ds000001 and ds000109, respectively, for two pairs of workflows: pipelines 1A and 2AF, which di↵er only as to whether fMRIPrep or AFNI's preprocessing procedure was carried out, and pipelines 6AF and 7F, which di↵er only as to whether fMRIPrep or FSL's preprocessing was applied.
However, it is notable that all correlations and Dice values were greater than 0. 8 here, and the overall variation between results for ds000109 was much less than that observed for ds000001. For ds000120, the group-level model and inference procedure was the largest source of variability between software. This is seen in Figure  4 , where the two analysis workflows which di↵ered only by the choice of group-level inference model are compared: pipeline 2AS ap-plying AFNI's group-level modelling and inference, and pipeline 3AS applying SPM's grouplevel modelling and inference. Similar to ds000109, while the main e↵ects were captured in the thresholded t-statistic maps by both packages (for ds000120, both 2AS and 3AS identified large clusters in the visual cortex), there was more disagreement over the presence of weaker e↵ects.
In both figures, it can be seen that the thresholded results obtained for these two pipelines (Figs. S5 and S6, middle plots) were qualitatively very similar, that the unthresholded maps obtained with these two workflows were almost perfectly correlated (Figs. S5 and S6, bottom-left plots), and that Dice comparisons for the thresholded maps were consistently around 90%. Dice coe cients obtained from reanalyses of the ds000109 dataset, focusing on the collection of results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL. The two sets of results given by pipelines 3AF and 4AF are displayed, which di↵ered only as to whether AFNI's or FSL's first-level noise model was implemented. Preprocessing aside, the interchange of the first-level noise model impacted the final group-level results more than any other modelling decision.
This is highlighted in the correlation and Dice plots at the bottom of the figure: the blue windows on the o↵-diagonals show that the pairwise correlation and Dice values for pipelines 3AF and 4AF are smaller than the corresponding values obtained for all other pairs of adjacent pipelines. In the thresholded t-statistic maps (blue window, middle), it can be seen that while both of these workflows captured the main e↵ects in the precuneus and frontal brain areas, pipeline 4AF (that used FSL's first-level noise model) also determined numerous smaller activation clusters which were not captured by pipeline 3AF (that used AFNI's noise model). Dice coe cients obtained from reanalyses of the ds000120 dataset, focusing on the collection of results obtained from hybrid pipelines that implemented procedures from both AFNI and SPM.
Consensus Analyses Slice views of the thresholded z-statistic maps from the consensus analyses performed on the ds000001 and ds000109 datasets are presented in Figures  5  and 6 , respectively. For each dataset, the consensus analysis took the form of an image-based meta-analysis conducted on the unthresholded group-level z-statistic maps obtained from all 26 pipelines through which the data had been analyzed. The image-based meta-analysis computed a third-level z-statistic map, where each statistic value in the image reflected the level of evidence to which all pipelines had agreed activation was present at a given voxel. This map was then thresholded to determine voxels for which the consensus z-statistic was significantly greater than zero after a voxelwise FDR correction (p < 0. 05). The equivalent one-sided correction was also performed to determine voxels whose consensus statistic was significantly less than zero.
The main e↵ects seen here were also captured in all of the thresholded group-level t-statistic maps obtained from each individual analysis workflow, displayed 440 in Supplementary Figs. S11-S14. Once again, the thresholded z-statistic for a consensus on negative activations failed to determine any brain areas that were statistically significant after the voxelwise correction. R L R L R L R L R L R L R L Figure  5 : Results of the ds000001 image-based meta-analysis. A consensus analysis was performed on the unthresholded z-statistic maps obtained from all 26 pipelines used to analyze the ds000001 dataset, accounting for the correlation between pipelines owing to the same underlying data and identical procedures implemented across parts of the analysis workflow. The thresholded z-statistic map displayed shows voxels for which the group-level consensus statistic was significantly greater than zero after a voxelwise FDR correction (p < 0.
05). Activation was found in the anterior cingulate, the insular cortex (bilateral) and the inferior frontal gyrus (right side only) after accounting for between-pipeline variation. No (negative) activation was obtained when the equivalent inference was performed to determine voxels where the consensus statistic was significantly less than zero. L R L R L R L R L R L R L Figure  6 : Results of the ds000109 image-based meta-analysis. A consensus analysis was performed on the unthresholded z-statistic statistical maps obtained from all 26 pipelines used to analyze the ds000109 dataset, accounting for the correlation between pipelines owing to the same underlying data and identical procedures implemented across parts of the analysis workflow. The thresholded z-statistic map displayed shows voxels for which the group-level consensus statistic was significantly greater than zero after a voxelwise FDR correction (p < 0.
Discussion Comparisons of the statistical maps obtained from the collection of pipelines applied to the three datasets have shown both the robustness and fragility of group-level task-fMRI results in response to variation of the software package at di↵erent stages of the analysis workflow. While results were found to be highly stable across all datasets when the analysis package used to model the low-frequency fMRI drifts was interchanged, other analytic manipulations produced more appreciable changes in the group-level results. For instance, the final regions of activation obtained for the ds000001 dataset were found to be highly contingent on the software package used to model the fMRI signal; switching between AFNI/FSL's signal model (pipelines 5AF and 6AF in Fig. 2 ) and SPM/FSL's signal model (pipelines 5SF and 6SF in Fig.
However, these findings do suggest that the main sources of software-variability across the analysis pipeline can be heterogeneous and dependent on external factors such as the analysis design or task paradigm under investigation. One reason that the quantitative comparisons for the ds000001 dataset were generally worse than the corresponding ds000109 and ds000120 comparisons is likely to be due to the smaller sample size for this study (15 for ds000001 vs 21 and 17 for ds000109 and ds000120 respectively). As well as this, the larger impact of the signal model for ds000001 may be attributed to varying aspects between the three studies' analysis designs. In particular, the event-related design used for ds000001's balloon analog risk task could have been more sensitive to di↵erences between each package's hemodyanmic response model compared to the block design used for ds000109.
In addition to this, while ds000109 and ds000120 did not apply any modulated regressor orthogonalization methods, for each of the three ds000001 task events represented in the GLM (pumps, cash-outs, and explosions) the response time regressors were orthogonalized with respect to the average activity regressor (e. g. pumps response time condition orthogonalized with respect to pumps average condition). It has been previously observed in the fMRI literature that the three software packages handle orthogonalization di↵erently  (Mumford et al. , 2015) : while in FSL each regressor can be orthogonalized with respect to any other individual regressor the user has specified, in SPM orthogonalization is applied automatically, and each regressor is orthogonalized with respect to all other conditions preceding it in the model.
Figure2: Comparisons of the group-level thresholded t-statistic maps, correlation values, and Dice coe cients obtained from reanalyses of the ds000001 dataset, focusing on the collection of results obtained from hybrid pipelines that implemented procedures from both AFNI and FSL. Blue windows highlight the disagreement between the two sets of results given by pipelines 5AF and 6AF, which di↵ered only as to whether AFNI's or FSL's first-level signal model was used. The interchange of the signal model between packages led to more expansive di↵erences in the final results than any other individual processing step: in the thresholded group-level t-statistic maps (middle), the expansive clusters of positive activation in the anterior cingulate (among other brain regions) identified by the pipeline using AFNI's signal model (workflow 5AF) were lost when interchanged with FSL's signal model (workflow 6AF).
Figure4: Comparisons of the group-level thresholded t-statistic maps, correlation values, and Dice coe cients obtained from reanalyses of the ds000120 dataset, focusing on the collection of results obtained from hybrid pipelines that implemented procedures from both AFNI and SPM. The two sets of results given by pipelines 2AS and 3AS are displayed, which di↵ered only as to whether AFNI's or SPM's group-level inference model was implemented. The interchange of the second-level model impacted the final results more than any other modelling decision. This is highlighted in the correlation and Dice plots at the bottom of the figure: the blue windows on the o↵-diagonals show that the pairwise correlation and Dice values for pipelines 2AS and 3AS are smaller than the corresponding values obtained for all other pairs of adjacent pipelines.
In addition, this Work Group was ably assisted by colleagues from the independent Evidence Review Team (ERT) led by Jonathan C. Craig, MBChB, DipCH, FRACP, M Med (Clin Epi), PhD, Martin Howell, PhD, and David J. Tunnicliffe, PhD, who made this guideline possible. KDIGO recently appointed Marcello A. Tonelli, MD, SM, MSc, FRCPC as its first Guideline Methods Chair. He was tasked with improving KDIGO guideline methodology by reinforcing the linkage between the recommendations and the corresponding evidence, standardizing the guideline format, reducing unnecessary length, and strengthening the utility of the guideline for its users. To meet these goals, Dr. Tonelli suggested KDIGO work with MAGICapp, a web-based publishing platform for evidence-based guidelines. The program uses a predefined format and allows for direct linkage of the evidence to the recommendation statement, and the generation of patient decision aids directly from the evidence syntheses used to support the guideline.
There are no RCTs comparing the efficacy and/or safety of low-molecular-weight heparin to warfarin in NS. There are many drug-drug interactions with warfarin, especially with immunosuppressive agents, such as CNIs, so the physician should be mindful when the patient is on multiple drugs. Direct oral anticoagulant (DOACs) have not been systematically studied in nephrotic patients for prophylaxis or treatment of thrombosis. In August 2018, the literature consisted only of 4 case reports and 3 conference proceedings. 41, 42 An open-label pharmacokinetic study of apixaban is underway in nephrotic patients without diabetes, with a primary outcome for dosing information, not clinical outcomes (NCT02599532). DOACs may have fewer drug interactions than warfarin, but their safety and efficacy for both treatment and prophylaxis of venous thromboembolism (VTE), arterial thromboembolism (ATE), and PE in NS require additional study.
Key information Balance of benefits and harms. This is a weak recommendation due to the significant risk of toxicity with the therapy. Consideration of glucocorticoid therapy must include a discussion regarding the risk of treatment-emergent toxicity associated with this medication and individualized risk assessment. The efficacy and toxicity of lower doses of glucocorticoids in similar populations are not known and are the subject of an ongoing investigation (NCT01560052). Quality of evidence. This recommendation is based upon moderate-quality evidence. The quality of the evidence from 4 RCTs that have compared glucocorticoid therapy with supportive therapy was moderate for critical and important outcomes (all-cause mortality, kidney failure, infection, doubling of SCr, and annual GFR loss) because of study limitations or imprecision (few events).