dataesr/scientific-paragraphs-categorization huggingface dataset:
Data is presented under the Non-Commercial Common Creative (CC BY-NC) license, free for all scientists to use in a non-commercial setting. A download request can be made at dutchconnectomelab. org for a download link to the data. Data for download includes connectivity matrices with five connectivity weights (NOS, FA, MD, fiber length, SD) at three atlas resolutions, information on the cortical and subcortical nodes, blinded group site and subject demographics (gender, age in 5 year bins, case/control).
CONCLUDING WORDS We performed a few first analyses on the joint dataset, including cross-site consistency, comparison to Human Connectome Project (HCP) data and a first examination of effects of age (see Materials and Methods for more detail). We observed a high average consistency across sites with an average crosssite overlap of 92% (sd:0. 0251) and a cross-site correlation of FA weights r = 0. 88 (sd:0. 0958), as well as a high consistency of the 10K group averaged matrix with data derived from the high-quality HCP, with at least 69% of pathways identified in HCP also observed in the 10K set and with 98% of all nonexisting connections in HCP verified in the 10K set (Figure  1C ). Furthermore, the distribution of weights across reconstructed connections is highly similar across the two datasets (FA weights, r = 0.
93, p < 0. 0001, Figure  1C ). Age analysis shows clear developmental patterns of cortical morphology (Figure  1E ) and white matter microstructure across age. Analysis of inverse MD showed rapid growth of microstructure in early years, with continuing development throughout adolescence, peaking around the beginning of the third decade, followed by a steady pattern of decline throughout aging (Figure  1D ). We acknowledge that there are many shortcomings to the presented MRI connectome dataset. Besides general, inherent limitations of diffusion MRI  (7) , the presented dataset is a collation of data from a wide variety of groups, acquired with different scanners, different scanning protocols, varying data quality etcetera, and includes data from a mixture of different patient and control populations.
While these limitations place constraints on the type of investigations that one can perform with such collated multi-site datasets, we are optimistic that the 10K dataset can be used as a large reference dataset for future studies, enabling many technical and neuroscientific research questions to be addressed (e. g. , Figure  1 ). As such, we hope that the presented data will be of use to the neuroscience community in the examination of the human connectome. Above all, we hope that our report will inspire others to organize exciting 10Kin1day-type of events in the near future, bringing together existing neuroimaging data and further catalyze open neuroimaging research of the healthy and diseased brain.
MATERIALS AND METHODS A total of 42 groups (52 participants) participated in the workshop, some working on multiple datasets. Each dataset included a diffusion MRI scan and T1 MRI scan processed using FreeSurfer  (8). Datasets across groups included data from 1. 5 and 3 Tesla MRI with varying scanner protocols and number of applied DWI gradients. Data included MRI data of healthy participants and patients with a neurological or psychiatric disorder. Twenty-three groups were able to make their data available, making a total of 8,000+ connectome maps publicly available through means of this report. Reconstructed connectome maps are presented anonymously, coded for participation site and disease condition(s). Basic demographics of the datasets are included in the download set.
DWI Preprocessing DWI datasets were corrected for susceptibility and eddy current distortions using the open tools from the FMRIB Software Library (FSL, http://fsl. fmrib. ox. ac. uk). Depending on their DWI dataset, participants preprocessed their data using the FSL eddy_correct or eddy tool. For those DWI sets that included a subset of scans with an opposite k-space read out, an additional field distortion map was formed and applied to the DWI images  (9).
Fiber Reconstruction After preprocessing of the DWI data, in-house developed scripts were used to fit a diffusion tensor to the diffusion signal in each voxel of the white matter mask (selected based on the white matter segmentation map of the FreeSurfer files) using robust tensor fitting  (13). Simple Diffusion Tensor Imaging (DTI) reconstruction was used due to its robustness and relatively low sensitivity to false positive reconstructions compared to more advanced reconstruction methods  (14) , and thus potentially being the least distorting solution for connectome reconstruction and analysis based on MR imaging data  (15). Decomposition of the tensor into eigenvectors and eigenvalues was used to select the main diffusion direction in each voxel, and to compute fractional anisotropy (FA) and mean diffusivity (MD)  (16).
Outliers A total of 15,947 connectome maps were analyzed across the participating groups. Of the datasets that could be shared, 197 were detected as outliers (and were subsequently removed from the dataset). Outliers were detected automatically by testing per dataset and for each connectome map their average connection strength and their distance to the group average prevalence map. The average connection strength of a connectome map was calculated for each of the five connection weights as the mean of the strengths over all existing (nonzero) connections. To measure the presence of odd connections or absence of common connections in a connectome map, we constructed a group prevalence matrix for each dataset, counting per node pair how many times an edge was observed across the group of subjects in the dataset.
For each connectome map the total prevalence of all observed connections and the total prevalence of all non-observed connections was computed. Outliers were identified as connectome maps that displayed on any of the 7 measures (5 weight and 2 prevalence measures) a score below Q1 -2×IQR or above Q3 + 2×IQR, with Q1 and Q3 referring to the first and third quartile, respectively and IQR the interquartile range IQR = Q3 -Q1. This resulted in the detection of 189 outliers in total, which were excluded from the dataset. One complete dataset (set_634413, n=584) showed across all included individual sets an average lower FA / higher MD as compared to the other datasets and this set was excluded from the age curves shown in Figure  1. Due to the high overall sample size, including or excluding this dataset did not change the shape of the final plot.
Comparison to HCP To test the validity of the 10K dataset, we compared the group average matrix of the 10K set to the group average matrix of data from the Human Connectome Project (HCP) (3). First, for the 10K dataset, a group average FA matrix was computed, by including those edges that were observed in at least 33% of the group (i. e. , a group threshold of 33%, >2700 subjects showing a particular network edge). Average weight values of the included edges were taken as the non-zero mean of those edges across the group of subjects. Second, a similar group average FA matrix was derived from previously analyzed HCP data  (22)  (n = 487 datasets). In brief, HCP analysis included the following steps [see  (22)  for more detailed information on the HCP data analysis]. For each of the HCP DWI datasets a connectome was reconstructed based on the minimally preprocessed data of HCP.
Given the high quality of the HCP data, analysis here included reconstruction of multiple diffusion directions, allowing for the reconstruction of more complex fiber configurations (e. g. , crossing fibers)  (22). Similarly as for the 10K data, across the total set of 487 datasets, an average FA group matrix was computed, including those network edges that were observed in at least 33% of the total population (i. e. , >160 datasets) and taking the non-zero mean of FA values across the group of subjects. Comparison between the 10K set and the HCP dataset was computed by means of (1) counting the number of existing connections and non-existing connections in the 10K dataset as observed in the HCP dataset and (2) by correlating the FA weights of the set of edges as observed in both datasets.
Programme,  International Training Programme , r'Birth; DG:  National Science Centre  ( UMO-2011/02/A/NZ5/00329 ); BG:  National Science Centre  ( UMO-2011/02/A/NZ5/00329 ); JH:  Western Sydney University   Postgraduate Research Award ; LH:  Science Foundation Ireland, ERC ; HH:  Research Grant Council (Hong Kong )-GRF 14101714; LJ:  Velux Stiftung , grant  369  &  UZH   University Research Priority Program Dynamics of Healthy Aging ; AJ:  DFG , grant  FOR2107 JA 1890/7-1 ; KJ:  National Science Centre  ( UMO-2013/09/N/HS6/02634 ); VK: The  Russian Foundation for Basic Research  (grant code  15-06-05758  A); TK:  DFG , grant  FOR2107 KI 588/14-1 ,  DFG , grant  FOR2107 KI 588/15-1 ; AK:  DFG , grant  FOR2107 KO 4291/4-1 ,  DFG , grant  FOR2107 KO 4291/3-1 ; IL: The  Russian Foundation for Basic Research  (grant code  15-06-05758  A); EL:  Health and Medical Research Fund  -11121271; SiL:  NHMRC-ARC   Dementia Fellowship   1110414 ,  NHMRC Dementia Research Team  Grant  1095127 ,  NHMRC  Project Grant  1062319 ; CL-J:  537-2011 ,  2014-849 ; AM:  Wellcome Trust   Strategic Award  ( 104036/Z/14/Z ),  MRC  Grant  MC_PC_17209 ; CM:  Heisenberg-Grant ,  German Research Foundation ,  DFG MO 2363/3-2 ; PM:  Foundation for Science and Technology ,  Portugal -PDE/BDE/113601/2015 ; KN:  National Science Centre  ( UMO-2011/02/A/NZ5/00329 ); PN:  National Science Centre  ( UMO-2013/09/N/HS6/02634 ); JiP:  NWO-Veni 451-10-007 ; PaR: PER and US would like to thank the  Schizophrenia Research Institute  and the  Chief-Investigators of the Australian Schizophrenia Research Bank V.
birncommunity. org)and the  NIH Big Data  to Knowledge ( BD2K ) award ( U54 EB020403  to  Paul Thompson ). NvH:  NWO-VIDI (452-11-014 ); MW:  National Science Centre  ( UMO-2011/02/A/NZ5/00329 );  Veronica O'Keane :  Meath Foundation ; AV and AW:  CRC Obesity Mechanism  ( SFB 1052 ) Project  A1  funded by  DFG. The funding sources had no role in the study design, data collection, analysis, and interpretation of the data. ACKNOWLEDGMENTS We further like to thank  Joanna Goc ,  Veronica O'Keane ,  Devon Shook , and  Leonardo Tozzi  for their participation and/or support of the  10K  project. HCP data was provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
FUNDING The 10Kin1day workshop was generously sponsored by the  Neuroscience and Cognition program Utrecht (NCU)  of the  Utrecht University  (https://www. uu. nl/en/research/ neuroscience-and-cognition-utrecht), the ENIGMA consortium (http://enigma. ini. usc. edu), and personal grants: MvdH:  NWO-VIDI (452-16-015 ), MQ Fellowship; SB-C: the  Wellcome Trust; Medical Research Council UK ;  NIHR CLAHRC for Cambridgeshire and Peterborough Foundation National Health Services Trust ; Autism Research Trust; LB: New Investigator Award,  Canadian Institutes of Health Research ;  Dara Cannon: Health Research Board (HRB) ,  Ireland  (grant code  HRA-POR-2013-324 ); SC:  Research Grant Council (Hong Kong )-GRF 14101714; Eveline Crone: ERC-2010-StG-263234; UD:  DFG , grant  FOR2107 DA1151/5-1 ,  DA1151/5-2 ,  SFB-TRR58 , Project  C09 ,  IZKF , grant  Dan3/012/17; SD: MRC-RFA-UFSP-01-2013  ( Shared Roots MRC Flagship grant ); TF:  Marie Curie.
Laboratory Data All blood draws were performed following a 12-h fasting period in a resting state in the morning and timing was consistent between pre-and post-testing sessions. Blood samples were collected in EDTA K2-coated tubes and centrifuged to collect plasma. Samples were kept in -80 • C freezers for 2-3 years before BDNF analyses. Plasma BDNF concentrations were measured in duplicates, and samples from the same donor (prepost) were run on the same plate with the Human BDNF enzyme-linked immunosorbent assay (Quantikine ELISA Kit; R&D Systems, Minneapolis, MN, United States) according to the manufacturer's instructions. These assays have intraassay coefficients of variation between 3. 8 and 6. 2%. The technician was blinded to participants' group assignment when completing ELISA assay.
Due to the skewed distribution of BDNF concentrations, log10 transformed values were used in the analyses. Effect sizes (Hedges' g) were also calculated when significant differences between groups were observed. The nonparametric Spearman's rank-order correlation was performed to measure the strength and direction of the association between changes in plasma BDNF concentrations and RNG scores. Statistical analyses were performed using SPSS 24 (Chicago, IL). Results were considered statistically significant when p ≤ 0. 05.
6 309 CSA patients under ASV Rates of hospitalisations, emergency 2 years pre- ASV did not change et al. , 2019 study, using the ±26. 5 events•h -1 ), with ASV versus healthcare utilisation department visits, outpatient visits and post-ASV healthcare utilisation [55] Rochester therapy (65% ⩾4 h per night and medications prescribed per year initiation Epidemiology on ⩾70% nights in their first (mean±SD) Project database month), and had ⩾1 month of clinical data before and after ASV initiation Continued.
Methods Forty FA patients were enrolled at the Piti e-Salpêtri ere University Hospital in Paris into a prospective, longitudinal evaluation of hearing, speech, and voice (UE FP7-HEALTH-2009/contract n°E10015DD) within the EFACTS framework (www. e-facts. eu, NCT02069509). The study was accepted by the regional health institutional review board IDF-6 on February 20, 2015. Informed consent was obtained from all individual participants included in the study. Neurological evaluation included SARA, a semi-quantitative scale developed to assess ataxia, with values from 0 (no ataxia) to 40 (most severe ataxia). Eight items assess stance, sitting, speech disturbances, finger chase, dysmetria, nose-finger test, tremor, fast alternating hand movements, and heel-shin slide. 2, 11 In addition, we used the CCFS (composite cerebellar functional score), a quantitative assessment including a nine-hole pegboard test and the click test (time needed to perform 10 fingerpointing cycles).
The total MBLF score is derived by summing each category and ranges from 0 to 111 (worst to best). A profile of the scores is thus obtained by domain (face: /6, eye: /9, cheeks: /30, lips: /27, and tongue: /39, Fig. S2 ). Speech intelligibility was evaluated by the French TPI test from the BECD battery (Ortho  Editions, 2006), an identification task consisting of a multiple choice for 13 sets of four words representative of 13 phonetic contrasts, with a maximum possible score of 52. Acoustic analysis was based on MonPaGe, a computerized protocol for the evaluation of pathological speech in French,  13  and Praat software (http://www. fon. hum. uva. nl/praat/, version 5. 3. 40). The oral diadochokinetic (DDK) was assessed by the repetition of the syllables [badego] as fast and accurately as possible in a single breath.
The harmonics-to-noise ratio (HNR) is a measure in dB that quantifies the amount of noise in the voice. The fundamental frequency (F0, in Hertz) was measured. Jitter and shimmer (in %) were used to determine the perturbation index of the laryngeal vibrator. The maximum phonation time (MPT) was measured as the longest period during which the patient can sustain the phonation of the vowel [a] in a single breath. "Voice Handicap index" (VHI) was used to evaluate the handicap related to speech and voice disabilities,  14, 15  with a total score from zero to 120 (highest perceived handicap) and from 0 to 40 for each of its three subscales Emotional, Functional, and Physical. All statistical analyses were performed using Statview 5. 0. 1 (SAS Institute Inc. ). Audiological, and correlations were done, at baseline, using the Pearson test.
Acknowledgments The authors wish to extend their gratitude to the study participants and the  European Friedreich Ataxia Consortium for Translational Studies (EFACTS). We are indebted to the  French Friedreich Ataxia Association (AFAF)  and their support for this study. We would like to thank  Lynda Bennamar  and  Marie Biet  for assistance with the logistics,  Dominique Clero ,  Genevi eve Touretz-Hermer ,  Sandrine Goutaland-Perrin ,  Lisa Prost, Anne-Claire Vilar del Peso, Laurie du Verdier de Genouillac, Oph elie Demarne , and  Mathilde Sibade  for collecting data, and the  Unadr eo-Lurco  for support. We also thank  Elodie Lannad  ere and  Diane Picard  for the translation of the MBLF.
Contributions: C. E. K. wrote original draft, designed and drew figures. F. R. -A. participated to the revision of the manuscript. N. H. -C. and L. B. participated to the preparation of the table. E. R. reviewed the manuscript. S. M. -L. wrote, reviewed and edited the manuscript. Funding: C. E. K. holds a PhD fellowship from the Région Centre Val de Loire. F. R. -A. holds a postdoctoral fellowship from the Fondation pour la Recherche Médicale (FRM SPF201809006912). L. B. is funded by the.
Funding: C. E. K. holds a PhD fellowship from the  Région Centre Val de Loire. F. R. -A. holds a postdoctoral fellowship from the  Fondation pour la Recherche Médicale  ( FRM SPF201809006912 ). L. B. is funded by the  Région Centre Val de Loire (APR IR LINGO). E. R. is funded by the  Grant Agreement LabEx MabImprove  and the  Région Centre Val de Loire ARD2020 Biomedicaments program. S. M. -L. is funded by  CNRS , the  Région Centre Val de Loire (APR IR PAIN) , the Ligue contre le cancer and the  Association pour la Recherche sur la Sclérose Latérale Amyotrophique (ARSLA).
Author Contributions: C. E. K. wrote original draft, designed and drew figures. F. R. -A. participated to the revision of the manuscript. N. H. -C. and L. B. participated to the preparation of the table. E. R. reviewed the manuscript. S. M. -L. wrote, reviewed and edited the manuscript. Funding: C. E. K. holds a PhD fellowship from the Région Centre Val de Loire. F. R. -A. holds a postdoctoral fellowship from the Fondation pour la Recherche Médicale (FRM SPF201809006912). L. B. is funded by the.
, 2018). We begin by leveraging only the genomic depth and single-cell resolution of this dataset. Next, we introduce the transcriptomic neurotaxonomy developed in the same resource publication and explore the additional analytical power of a neurotaxonomic framework. Then, we distill these findings into specific and testable predictions concerning intracortical peptidergic modulation networks. Finally, we discuss the potential of a neurotaxonomically integrated view of neuromodulatory and synaptic networks to reveal previously obscure principles of cortical sensory, mnemonic and motor function.
Results The present study is based on analysis of a resource single-cell RNA-seq dataset acquired at the Allen Institute  (Tasic et al. , 2018)  and available for download at http://celltypes. brain-map. org/rnaseq/. These RNA-seq data were acquired from a total of 22,439 isolated neurons, with detection of transcripts from a median of 9462 genes per cell  (min = 1445; max = 15,338)  and an overall total of 21,931 protein-coding genes detected. Neurons were sampled from two distant and very different neocortical areas: 13,491 neurons from primary visual cortex (VISp), and 8948 neurons from anterior lateral motor cortex (ALM). Single neuron harvesting methods were designed to mildly enrich samples for GABAergic neurons, such that the sampled neuron population is roughly half GABAergic (47%) and half glutamatergic (53%).
The resource publication  (Tasic et al. , 2018 ) should be consulted for full details of neuron harvesting, sample preparation, sequencing and data processing. Since we refer very frequently here to this resource publication and dataset, we'll refer to both now simply as  'Tasic 2018', and  all further references here to neuron 'class', 'subclass' or 'type' should be understood to refer specifically to the particular mouse neocortex neurotaxonomy described in the Tasic 2018 publication. The Tasic 2018 single-cell RNA-seq data tables report the abundance of transcripts from individual neurons in both 'counts per million reads' (CPM) and 'fragments per kilobase of exon per million reads mapped' (FPKM) units. Our analysis of this data compares gene expression levels quantitatively, with two distinct use cases: (1) comparisons across large sets of different genes, and (2) comparisons of the same gene across different individual cells, cell types and brain areas.
We have relied upon FPKM data  (Mortazavi et al. , 2008; Pimentel, 2014) , for use case 1 (i. e. , the Tables 1 comparisons across genes). For use case 2 (as in all figures below), we have preferred the CPM units, because these units were used to generate the Tasic 2018 neurotaxonomy. In any case, choice of CPM vs. FPKM units would have very little impact on the present outcomes.
Conservation of NP-GPCR gene expression statistics between VISp and ALM Figure  2B  provides evidence for strong conservation of differential NP-GPCR expression profiles between distant cortical areas VISp and ALM. The paired bars represent fractions of cells expressing a given gene in each of the two areas, again revealing strong similarities of differential expression profiles in the two very different neocortical areas (r = 0. 959, p<2. 2E-16). Figure  2C  represents frequencies of NP-GPCR gene co-expression multiples detected in individual neurons. These data establish that multiple NP-GPCR genes are co-expressed in almost all cortical neurons and that numbers of genes co-expressed are even higher than those noted above for co-expression of NPP genes. Modal numbers of co-expressed NP-GPCR genes detected is six in both VISp and ALM with  broad plateaus extending out to 12 co-expressed NP-GPCR genes per cell.
To explore this possibility and compare NP transcriptomes to other transcriptome subsets quantitatively, we developed the analysis presented in Figure  4. We began by asking whether there exists a low dimensional representation of gene expression that naturally separates neurons of different types into distinct parts of that low-dimensional space. The extent to which a neuron's location in such a space can be inferred from the expression of a limited subset of genes (such as the 47 NP genes) would then provide a measure of sufficiency of that subset to determine the type of that neuron accurately. Hierarchical clustering methods to define neuron types based upon gene expression are well established  (Hastie et al. , 2001; Oyelade et al. , 2016)  but have difficulty when comparing and making inferences between datasets.
We therefore devised a machine learning approach based on linking deep neural networks called autoencoders  (Hinton and Salakhutdinov, 2006)  to address this question explicitly and quantitatively. We trained a single autoencoder network to represent cells in a low dimensional space based on CPM values of the 6083 most highly expressed genes (HE genes) in the Tasic 2018 dataset. Figure  4A  shows the result of one such two dimensional encoding, where each of the 22,439 individual neurons appear as a distinct dot colored by its type assignment. The tight grouping of type-coding colors evident in Figure  4A  implicitly conveys that position within this latent space corresponds well to neuron types, despite the fact that the autoencoder did not have prior information about the Tasic 2018 classification.