dataesr/scientific-paragraphs-categorization huggingface dataset:
Taking the loss of degrees of freedom into consideration, we recommend a liberal scrubbing threshold rather than scrubbing with a stringent threshold for datasets with marked motion. For the two anatomical CompCor strategies, compcor performs better than compcor6. The performance of compcor6 is also not consistent across software versions in both datasets. However, compcor introduces large variability into the loss of degrees of freedom. In ds000228, the loss in temporal degrees of freedom is even higher than scrubbing with a stringent threshold. This result is consistent with the observation of Parkes and colleagues (2018) that anatomical CompCor is not sufficient for high motion data. Moreover, this observation puts one of the rationales in the original study, i. e. , to reduce the loss in degrees of freedom, in question  (Behzadi et al.
, 2007). In the absence of physiological recordings, our benchmark is not suitable to examine another property of CompCor, that is the ability to remove unwanted physiology signals  (Behzadi et al. , 2007). The datasets do not include physiology measures to perform alternative strategies such as RETROICOR to mitigate physiology signals explicitly. In our results, aroma shows similar performance with the simple strategy across most metrics, with the exception of DM-FC (where it performs well). This strategy also featured a very low loss of degrees of freedom, making it a "gentle" denoising approach. Previous literature has recommended adding GSR as part of the ICA-AROMA regressors  (Ciric et al. , 2017; Parkes et al. , 2018 ). An early version of this work did include a ICA-AROMA+GSR strategy, which performed very poorly (results not shown in the current version).
Re-executable research object We created a re-executable denoising benchmark with two main outcomes. Firstly, we created a reusable code base that will ensure the robustness of the procedure. The current benchmark includes several parameters, from the choices of atlases, denoising strategies, fMRIPrep versions, to datasets. The code for connectome generation and denoising metric calculation is written as an installable Python library (https://github. com/SIMEXP/fmriprep-denoise-benchmark). Customized scripts to deploy the process for each combination of the parameters are also generated by reusable Python functions. The full workflow can be executed on the two benchmark datasets preprocessed by any release from the fMRIPrep LTS series. Full documentation to re-execute the workflow, from fetching datasets to running the analysis, is available as part of the research object 9.
, 2017) : a connectome generation BIDS-app and a denoising metric generation BIDS-app. BIDS-app is easier for user adoption under the BIDS convention and can expand the scope of the benchmark from the two datasets shown here to any BIDS-compliant dataset. The process of creating this benchmark also provides valuable first hand information about runtime, and the impact of atlas choice on computational costs, which we did not cover here but has big practical implications. High dimensional probabilistic 9 https://preprint. neurolibre. org/10. 55458/neurolibre. 00012/docs/tldr. html 8 https://nilearn. github. io/stable/auto_examples/03_connectivity/plot_signal_extraction. html#the-impactof-global-signal-removal atlases require four times more RAM than discrete segment atlases. For metric generation, high dimensional atlases can have a runtime up to 24 hours compared to 1 hour for low dimensional atlases.
Continual evaluation of software versions Our benchmark results on two versions of the long-term support (LTS) release of fMRIPrep reveals similar trends in the metrics, but some inconsistency. Between the two datasets, ds000228 showed more consistent results than ds000030 across two LTS releases (see Figure  12 ). The marked difference in ds000030 was likely the result of a bug fix implemented in 20. 2. 2LTS 10,11 and that ds000030 had been reported as an affected dataset. The results from the data-driven strategies in both datasets demonstrated inconsistent relative difference when comparing to the baseline strategy. This piece of work is a new addition to the existing literature on the heterogeneity of results found through research software testing  (Bowring et al. , 2019; Gronenschild et al.
2. x series up to September 2024. For fMRIPrep release beyond the LTS version, as long as the API in Nilearn is maintained, the code used to generate all current reports can be applied to the same two datasets. With the high number of tunable parameters (denoise strategies, atlases, software versions), a framework allowing parameter configuration, such as Hydra 12 , would help better manage and expand the benchmark. The current benchmark generates jobs through metadata stored in python dictionaries. By adapting a framework like Hydra, one can deploy the benchmark analysis with a simplified interface. Finally, we note that all of the components necessary to implement our reproducible benchmarks are generic, i. e. software containers, data versioning, open source code with standard APIs, Jupyter Books and the Neurolibre preprint server.
Conclusions This work introduces new software libraries to systematically evaluate the impact of a wide range of denoising strategies on resting state functional connectivity across datasets, and versions of the fMRIPrep preprocessing pipeline. We used this software infrastructure to implement a fully reproducible benchmark of denoising strategies on two datasets with varied characteristics, including age, motion level and the presence of clinical diagnoses. We would like to provide two strategy recommendations based on this benchmark, depending on a key consideration: whether preserving continuous sampling time series is needed (e. g. to train auto-regressive models) or not (e. g. to generate correlation coefficients across brain parcels). To preserve the continuous sampling property of time series, simple+gsr is the recommended strategy, especially for datasets with low motion, and appears to be robust across software versions.
If continuous temporal sampling is not a priority, scrubbing. 5 was the best strategy for datasets with marked motion where denoising quality can be favored over loss of temporal degrees of freedom. The performance of aroma departed from the conclusions of previous denoising benchmark works and only performed well in one metric. The denoising benchmark also demonstrated differences in the performance of specific denoising strategies across multiple fMRIPrep versions. We hope that our benchmark provides useful guidelines for the community and demonstrates the importance of continuous evaluation of denoising methods. Our software infrastructure is broadly applicable and may be beneficial to implement reproducible benchmarks across different tools and research fields.
Datasets Dataset ds000228 (N = 155) contains fMRI scans of participants watching a silent version of a Pixar animated movie "Partly Cloudy". The dataset includes 33 adult subjects (Age Mean(s. d. ) = 24. 8(5. 3), range = 18 -39; 20 female) and 122 child subjects (Age Mean(s. d. ) = 6. 7(2. 3), range = 3. 5 -12. 3; 64 female). T1w images were collected with the following parameters: TR = 2530 ms, TE = 1. 64 ms, Flip Angle = 7°, 1 mm isotropic voxels. BOLD images were collected with the following parameters: TR = 2000 ms, TE = 30 ms, Flip Angle = 90°, 3 x 3 x 3. 3 mm voxels. All images were acquired on a 3T Siemens Trio Tim Scanner. For more information on the dataset please refer to  (Richardson et al. , 2019). Dataset ds000030 includes multiple tasks collected from subjects with a variety of neuropsychiatric diagnosis, including ADHD, bipolar disorder, schizophrenia, and healthy controls.
Evaluation of the outcome of denoising strategies We first performed Pearson's correlations to understand the overall numerical similarities of the denoised connectomes across different strategies. For each parcellation scheme, we computed a correlation matrix across the thirteen strategies. These correlation matrices were then averaged across the parcellation schemes within each dataset. The averaged correlation matrices were reordered into blocks of clusters with the function scipy. cluster. hierarchy. linkage. The aim was to provide an overview of the similarity of connectomes generated with the strategies. We then used selected metrics described in the previous literature to evaluate the denoising results  (Ciric et al. , 2017; Parkes et al. , 2018). After investigating the metrics with fMRIPrep version 20.
For example, CompCor may only be maximally effective in low-motion data  (Behzadi et al. , 2007). Volume-censoring-based strategies are unsuitable for time series analysis where a uniform sampling of signals is required, such as most time-frequency analysis implementations. Researchers thus need to evaluate the best denoising strategy based on the available benchmarks, the profile of their data and their choice of analytical techniques. The existing benchmarks have a few pitfalls that limit the integration to each researcher's unique needs. The benchmark research and denoising methods development are conducted on in-house preprocessing solutions with different datasets. From the research standpoint, this is not necessarily a pitfall as it showcases that workflows built upon different software following the general shared principle for preprocessing have converging conclusions.
However, this is a problem for user adoption of the recommended strategy to their choice of preprocessing workflow. To correctly implement the best approach for their study, researchers need to understand the extensive literature and then construct the workflow. Another pitfall is that results in benchmarks are subject to the scope of datasets evaluated. As researchers use in-house tools and analysis code, there is no direct way to apply the suggested strategy or generate the benchmark statistics for evaluation on a new dataset. The denoising benchmark literature provides a good overview of the progress of methods in the field of resting state functional connectivity, but falls short in providing a reproducible path for the general community to adapt the results to modern solutions of preprocessing, such as fMRIPrep.
Acknowledgements The project is funded by  IVADO PRF3, CCNA  and  Courtois Foundation , the neuromind collaboration. HTW and NC funded by  Institut de valorisation des données (IVADO)  postdoctoral research funding. SLM was funded by the  National Institute on Deafness and Other Communication Disorders (NIDCD ; Grant  5T32DC000038 ). CJM funded by  NIMH   5R24MH117179. FP funded by  Courtois Neuromod. PB funded by  Fonds de Recherche du Québec -Santé (FRQ-S).
Figure. 3 3 Figure. 3 Mean framewise displacement of each dataset. To evaluate the metrics in a practical analytic scenario, we excluded subjects with high motion while preserving 1 minute of data for functional connectivity calculation: gross mean framewise displacement > 0. 25 mm, above 80. 0% of volumes removed while scrubbing with a 0. 2 mm threshold. In ds000228, the child group still had higher motion compared to the adult groups. In ds000030, where all subjects were adults, the control group only showed significant differences in motion with the schizophrenia group. In both datasets, the sample sizes from each group were highly imbalanced (see Table3), hence no between group differences were assessed in quality metrics analysis.
The two datasets we analyzed contain different numbers of discrete cosine-basis regressors (ds000228: 4; ds000030: 3) due to difference in time series length (ds000228: 168; ds000030: 152). The simple and simple+gsr strategies include the same amount of head motion and tissue signal regressors between the two datasets (simple: 26, simple+gsr: 27). For volume censoring strategies, we observed a higher loss in volumes in ds000228, compared to ds000030. (number of excised volumes at 0. 5 mm: ds000030: 2. 5(4. 4) range=[0 21], ds000228: 9. 3(8. 8) range=[0 30]; number of excised volumes at 0. 2 mm: ds000030: 29. 4(30. 1) range=[0 110], ds000228: 53. 0(34. 1) range=[1 130]. compcor also showed variability in numbers of regressors when using all components that explain 50% of signal variance (number of CompCor regressors: ds000030: 47.
The following section describes the logic behind the design of the API. For documentation of the actual function, please see the latest version of Nilearn documentation (https://nilearn. github. io/stable/). Confound variables were retrieved using (i) a basic API that retrieves different classes of confound regressors, nilearn. interfaces. fmriprep. load_confounds (simplified as load_confounds); and (ii) a higher level wrapper to implement common strategies from the denoising literature, nilearn. interfaces. fmriprep. load_confounds_strategy (simplified as load_confounds_strategy).
Nilearn documentation for load_confounds_strategy: https://nilearn. github. io/stable/modules/generated/nilearn. interfaces. fmriprep. load_confounds _strategy. html#nilearn. interfaces. fmriprep. load_confounds_strategy 1 Nilearn documentation for load_confounds: https://nilearn. github. io/stable/modules/generated/nilearn. interfaces. fmriprep. load_confounds _strategy. html#nilearn. interfaces. fmriprep. load_confounds.
EEG analysis EEG data was converted from native Micromed TRC-file format to mat-and dat-files using SPM toolbox ImaGIN (https://github. com/manikbh/ImaGIN2). Data were filtered with a 50 Hz notch and 1-90 Hz Butterworth band pass filters using SPM and ImaGIN. Absence seizures were manually inspected from a filtered signal and spectrogram. Seizures were confirmed as SWDs if they had a typical regular pattern, had at least double the amplitude compared to baseline signal and were within the 7-12 Hz frequency range. Two successive seizures were counted as one if there was less than 1 s of baseline signal between them. Seizure was counted as absence seizure only if it lasted at least 2 s, since behavioral deficits are not obvious in shorter seizures  (Blumenfeld 2012 ). Seizure initiation and ending time points as well as stimulation onsets were marked in seconds and converted to MRI time in volumes.
fMRI analysis To prepare the fMRI data for analysis, data were motion corrected, co-registered with anatomical MRI, and spatially smoothed to reduce noise level. The preprocessing steps were performed using advanced normalizing tools (ANTs, http://stnava. github. io/ANTs/) and python based graphical user interphase mri_works (https://montigno. github. io/mri_works/Home/index. html). The first 5 volumes from each fMRI data were removed to allow signal to reach steady state. For motion correction, all volumes were transformed to an average image taken from first 10 volumes by using antsMotionCorr. Normalization was performed indirectly and using the T1-FLASH images, as ZTE images lack anatomical contrast. First, the T1-FLASH images of each animal were corrected from intensity non-uniformity using N4BiasFieldCorrection from ANTs.
Average signal change due to stimulation was between 1-1. 5% with gradient-echo-EPI sequence while it was ~0. 3% with ZTE. However, activation maps (conducted with Aedes, https://github. com/mjnissi/aedes) demonstrated less susceptibility-induced image distortions in ZTE sequence compared to EPI. Moreover, when studying the effect of fMRI artifacts on EEG signal, more pronounced and higher amplitude gradient switching artifacts were detected when using EPI compared to ZTE sequence. When comparing peak acoustic noise levels inside the 9. 4T Bruker magnet, ZTE (78. 7 dB) was 35. 8 dB quieter than EPI (114. 5 dB) equaling ~62 times weaker sound pressure  (Schomer 1998). Motion during fMRI imaging using ZTE was low (Figure  2b ). Mean framewise head translation was only 0. 69 ± 0. 32 µm and head rotation 0.
Acknowledgements We would like to thank  Jan Warnking  for giving valuable technical and data analysis assistance. We also thank  Argheesh Bhanot  for giving us feedback in HRF analysis. This work was supported by the  Human Brain Project Third Specific Grant Agreement , project nr. 945539. Grenoble MRI  facility IRMaGe is partly funded by the  French program Investissement d'avenir  run by the  Agence Nationale de la Recherche : grant  Infrastructure d'avenir en Biologie Santé   ANR-11-INBS-0006.
eLife assessment This useful study compares gene expression patterns among different autonomic ganglia and will be of interest to developmental neuroscientists and neurophysiologists. The study expands the database of genes expressed by subpopulations of autonomic neurons in ganglia, a key step in decoding their developmental origins and physiological functions. The alternative view that the pelvic ganglionic neurons are actually modified sympathetic neurons is not convincingly demonstrated given the large number of molecular and functional differences known to be present between cranial and sacral neurons.
Bioinformatic analysis For each of the 6 samples (pelvic_1, stellate, sphenopalatine, pelvic_2, coeliac and lumbar) we performed demultiplexing, barcode processing, and gene counting using the Cell Ranger software (v. 6. 0. 1). The 'filtered_feature_bc_matrix' files (uploaded at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE232789 ) were used as our starting points for defining cells. For each dataset, only droplets that expressed more than 1500 genes, less than 11000 genes and a percentage of mitochondrial genes below 15% were retained. This resulted in the selection of 4404 pelvic_1 cells, 7225 stellate cells, 4630 sphenopalatine cells, 1643 pelvic_2 cells, 1428 coeliac and 2120 lumbar cells. We used Seurat version 3 (21 ) to read, manipulate, assemble, and normalize (  22 ) the datasets.
Specifically, we concatenated cells from all 6 datasets and normalized the data using the sctransform (SCT) method for which we fitted a Gamma-Poisson Generalized Linear Model ("glmGamPoi" option). Using the Seurat framework, we then performed a PCA on the normalized dataset. Next, we integrated all 6 datasets using Batchelor (23 ) (a strategy for batch correction based on the detection of mutual nearest neighbors), and we visualized all cells, including neurons, in 2 dimensions using the Uniform Manifold Approximation and Projection method (24 ) on the first 50 components from the PCA. We selected neurons based on their mean expression of a set of neuronal marker genes (Stmn2, Stmn3, Gap43 and Tubb3). Specifically, cells were classified as neurons if their mean marker SCTnormalized gene expression exceeded a threshold of 3, that best separated the bimodal distribution of the higher and lower values of the mean expression of neuronal markers.
S1A  ), and the comparable expression level of the top 100 genes across both pelvic batches in the violin plots (Table  S2 ). To search in an unbiased manner for gene expression similarities between pelvic neurons and either sympathetic or parasympathetic ones, we systematically compared the expression of every gene of the dataset across all possible splits among ganglia, i. e. between every subset of ganglia ("subset_1"), and its complementary subset ("subset_2"). Because we treated each of the 4 pelvic clusters as a ganglion, there were 2^8=256 such splits (2 of them, defined by "all_8_groups vs none" and "none vs all_8_groups", being meaningless). For every split and every gene, we devised a score that would best reflect how higher the proportion is of cells expressing the gene (i. e. one read of the gene or more) in subset_1 than in subset_2.
Acknowledgements We thank  Nicolas Narboux-Nême  for help with the analysis of Dlx5 expression,  Sonia Garel  for the gift of the Ebf3 probe,  Amandine Delecourt  and  Gwendoline Firmin  for help with the mouse colonies. The Brunet laboratory is supported by  INSERM ,  CNRS ,  ANR-19-CE16-0029-01 ,  ANR-17-CE16-0006-01 ,  FRM EQU202003010297. M. A. is a recipient of a doctoral fellowship from  Labex MemoLife. B. D. was funded by grant  APP2001128  from the  National Health & Medical Research Council, Australia. H. R. has been supported by a grant from the  Wilhelm Sander Foundation. Highthroughput sequencing was performed by i) the  GenomiqueENS core facility  supported by the  France Génomique national infrastructure , funded as part of the " Investissements d'Avenir" program  managed by the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-0009 ); ii) the  ICGex NGS  platform of the  Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan   Cancer III ) and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOS-Inserm_12554 ).
Acknowledgments We are grateful to the children, parents, and caregivers associated with the  FamilieSCN2A Foundation  for participating in this study. We thank  Drs. B. Barbour ,  M. Casado ,  J. Christie ,  F. Dunn ,  A. Nelson ,  P. Reeson ,  M. Scanziani , and  M. Schonewille  for feedback on this work, and to  Dr. J. Christie  for discussions that motivated this study. This work was supported by an  Action Potential Award  from the  FamilieSCN2A Foundation  (CW), a research grant from  BioMarin Pharmaceutical Inc. (KJB), the  Weill Neurohub Investigator Program  (KJB, NA),  SFARI 629287  (KJB, NA),  Howard Hughes Medical Institute  funds awarded to  M. Scanziani  (GB), and  National Institutes of Health  grants  F32 MH125536  (ADN) and  R01 MH125978  (KJB).
For representative images and videos of astrocytic calcium activity an event-based analysis tool (Automatic Quantification and Analysis (AQuA): https://github. com/yu-lab-vt/AQuA) was used. Basal astrocyte calcium activity was quantified from the astrocyte calcium event frequency, which was calculated from the number of events per min within 3 min of recording. Evoked astrocyte calcium activity was quantified from the calcium event probability, which was calculated from the number of calcium elevations grouped in 15 s bins recorded from 8-30 astrocytes per field of view. The time of occurrence was considered at the onset of the calcium event. For each astrocyte analyzed, values of 0 and 1 were assigned for bins showing either no response or a calcium event, respectively, and the calcium event probability was obtained by dividing the number of astrocytes showing an event at each time bin by the total number of monitored astrocytes  (Navarrete & Araque, 2010).
Statistical analyses Summary results are presented as mean of independent data points ± SEM that represent the number of animals, slices, neurons or cultures tested. Datasets were initially tested for normal distribution and statistical analysis of the differences between groups were determined by two-tailed unpaired or Student paired t test, Mann-Whitney test, one-way ANOVA or two-way ANOVA followed by Šídák's test for multiple comparisons. Differences were considered to be significantly different when P < 0. 05. Correlation analysis was performed by Pearson's or Spearman´s test.
Acknowledgements We would like to thank the personnel of the  Animal Facilities of the University of the Basque Country  and  Neurocentre Magendie  for mouse care. We also thank  G. Perea  for providing IP 3 R 2 -/-mice,  S. Calovi  and  F. N. Soria  and for in the analysis of confocal images. This work was funded by  FEDER  and  Instituto de Salud Carlos III  ( PI21/00629 , to S. M. and A. R. -A;  CB06/05/00 , to C. M. ),  Basque Government  ( PIBA19-0059  and  IT1473-22 , to S. M. ;  IT1203-19 , to C. M. ),  ARSEP Foundation  (to S. M. and G. M. ),  INSERM  (to G. M. ), the  European Research Council  ( MiCaBra ,  ERC-2017-AdG-786467 , to G. M. ),  Fondation pour la Recherche Medicale  (FRM,  DRM20101220445  to G. M. ), the  Human Frontiers Science Program  (to G. M. ),  Region Aquitaine  ( CanBrain ,  AAP2022A-2021-16763610  and  -17219710  to G.