dataesr/scientific-paragraphs-categorization huggingface dataset:
| Collecting additional data within our working groups Our future work will be dedicated to performing new analyses and including additional data. The ENIGMA-ADHD and -ASD groups are currently working on the analysis of structural connectivity data from diffusion tensor imaging (DTI). With the DTI projects we will perform similar analyses as for brain volume but move beyond testing for isolated brain regions. Here we can, again, make use of processing pipelines provided by ENIGMA, which have already been successfully used  (Favre et al. , 2019; van Velzen et al. , 2019; Villalón-Reina et al. , 2019). Within the ENIGMA-ASD cohort, resting state fMRI data are also being analyzed, pooled together with existing datasets such as the EU-AIMS cohort, parcellated into standard functional regions of interest, and used for a graph-theory analysis of the functional brain (dis)connectivity.
The addition of DTI and resting state fMRI data to the existing structural brain data in the ENIGMA-ADHD and ASD working groups is an important step toward true multimodal imaging data integration, one of the most important long-term goals of these ENIGMA working groups. All our current findings, as well as the literature on ADHD and ASD, overwhelmingly indicate that neural alterations are visible across all available imaging modalities. There currently exist no large-scale dataset where structural, functional, and connectivity data are combined, so it is largely unclear how findings among these different modalities are interrelated. To move toward a more complete neurobiological model of ADHD and ASD, multimodal data integration will be key. To learn more about the overlap and differences between ADHD and ASD, we want to focus on samples that have allowed dual diagnosis of both disorders.
A major next step in the ENIGMA consortium is the aim to unite multimodal imaging comparisons across the neurodevelopmental disorder working groups, not only for ADHD and ASD, but also including ENIGMA-OCD and Tourette's syndrome. Additionally, for both ADHD and ASD it would be of great interest to combine brain data from longitudinal samples. The previously reported delay of maturation in ADHD, the absence of case-control differences in the adult sub-analysis in ENIGMA-ADHD, or the changes restricted to adolescence in ASD and the changes in the presentation of the disorders all support looking more closely and with better data at the life-span perspective of brain changes related to ADHD. Early biomarkers associated with ASD's development and treatment outcome would additionally be of tremendous value to the clinical community.
Currently, and to this end, medium scale multicenter longitudinal data are being collected as part of the EU-AIMS project  (Murphy & Spooren, 2012) , which may offer a potential collaboration partner for ENIGMA-ASD to investigate both longitudinal structural brain analysis, but also includes extensive behavioral phenotyping as well as EEG and eye-tracking data, which offers new opportunities to link the ENIGMA imaging findings to a wider set of behavioral and biological metrics. The behaviors which are associated with both ADHD and ASD are not unique to just a patient population, but exist as continuous traits within the general population  (Asherson & Trzaskowski, 2015; Bralten et al. , 2018). This means that both the genetic and neuroimaging features which are linked to ADHD and ASD may also be found as distributed traits in population samples.
Combining the results of the ENIGMA analysis and the analysis of population-based brain data have been successful in the case of ADHD cortical analyses  (Hoogman et al. , 2019). We want to expand these types of analyses because it gives us a better picture of brain characteristics across the whole spectrum of these psychiatric traits. Lastly, to combine genetic and neuroimaging data within ENIGMA-ADHD and ASD, ideally one would need genetic and imaging data from the same subjects to investigate which genetic factors contribute to the brain characteristics that have been found. Unfortunately, the samples in ENIGMA-ADHD and ASD are still too small to conduct such analyses. However, combining data from multiple large-scale databases of other collaborations has shown that this also delivers new information, for example the project about the genetic overlap of ADHD risk and genetic factors involved in ADHD related brain volumes  (Klein et al.
ACKNOWLEDGMENTS Martine Hoogman  is supported by a personal Veni grant of the  Netherlands Organization for Scientific Research (NWO , grant number  91619115 ). Odile A. van den Heuvel is supported by a personal VIDI grant of the  Netherlands Organization for Scientific Research (NWO/ ZonMw , number  91717306 ). This work has further benefited from grants for the NeuroIMAGE study which was supported by  NIH  Grant  R01MH62873  (to  Stephen V. Faraone ),  NWO   Large Investment Grant   1750102007010  (to  Jan Buitelaar ),  ZonMW  grant  60-60600-97-193 ,  NWO  grants  056-13-015  and  433-09-242 , and matching grants from  Radboud University Nijmegen Medical Center, University Medical Center Groningen and Accare , and  Vrije Universiteit Amsterdam. This also included support from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  278948  (TACTICS), 602805 (Aggressotype), 603016 (MATRICS), and  602450  (Imagemend), and the  Innovation Medicine Initiative  grants  115300  ( EU-AIMS)  and  777394  ( AIMS-2-TRIALS ).
Support was also received from the  Dutch National Science Agenda for the NWA   NeurolabNL  project (grant  400 17 602 ). The first and senior authors would like to acknowledge the  Cognomics Initiative , a joint initiative by researchers of the  Donders Centre for Cognitive Neuroimaging , the  Human Genetics and Cognitive Neuroscience departments of the Radboud University Medical Center , and the  Max Planck Institute for Psycholinguistics in Nijmegen. The Cognomics Initiative  has received support from the participating departments and centres and from external grants, that is, the  Biobanking and Biomolecular Resources Research Infrastructure (Netherlands)  ( BBMRI-NL ), the  Hersenstichting Nederland , the  Netherlands Organization for Scientific Research (NWO) , and the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements n 602450 (IMAGEMEND).
In addition, the work was supported by a grant for the  ENIGMA Consortium  (grant number  U54 EB020403 ) from the  BD2K Initiative  of a cross-NIH partnership. The  POND (Province of Ontario Neurodevelopmental Disorders ) data collection was supported by the grant  IDS-I l-02  (to  Evdokia Anagnostou ,  Jason Lerch ) from the  Ontario Brain Institute , as well as CIHR support from grants  CIHR-106582 ,  CIHR-142379  (to Margot Taylor). Initial support for the ENIGMA ADHD and ASD working groups was provided by the  U. S. National Institutes of Health Big Data to Knowledge Program  ( BD2K ), under grant  U54 EB020403. Joel T. Nigg  is supported by an  NIH  grant  R01 MH115357. We would like to thank all consortium authors that are not named co-authors on this article:  Sara Ambrosino ,  Anatoly Anikin ,  Philip Asherson ,  Cibele Bandeira ,  Alexandr Baranov ,  Sarah Baumeister ,  Ramona Baur-Streubel ,  Joseph Biederman ,  Janita Bralten ,  Ivanei Bramati ,  Anna Calvo ,  Mara Cercignani ,  Tiffany Chaim-Avancini ,  Kaylita Chantiluke ,  Anastasia Christakou ,  Ana Cubillo ,  Renata Cupertino ,  Anders Dale ,  Patrick de Zeeuw ,  Alysa Doyle ,  Eric Earl ,  Thomas Ethofer ,  Andreas Fallgatter ,  Matt Gabel ,  Tinatin Gogberashvili ,  Neil Harrison ,  Catharina Hartman ,  Dirk Heslenfeld ,  Sarah Hohmann ,  Marie Høvik ,  Terry Jernigan ,  Dmitry Kapilushniy ,  Bernd Kardatzki ,  Clare Kelly ,  Gregor Kohls ,  Sara Lera-Miguel ,  Astri Lundervold ,  Charles Malpas ,  Hazel McCarthy ,  Mitul Mehta ,  Leyla Namazova-Baranova ,  Rosa Nicolau ,  Stephanie Novotny ,  Bob Oranje ,  Yannis Paloyelis ,  Felipe Picon ,  Anouk Schrantee ,  Lena Schwarz ,  Lizanne Schweren ,  Jochen Seitz ,  Norbert Skokauskas ,  Juan Carlos Soliva Vila ,  Anastasia Solovieva ,  Michael Stevens ,  Gustavo Sudre ,  Fernanda Tovar-Moll , Theo van Erp,  Alasdair Vance ,  Yolanda Vives-Gilabert ,  Georg von Polier ,  Yuliya Yoncheva ,  Marcus Zanetti ,  Georg Ziegler , and  Kathrin Zierhut.
(2018) of cerebellar development in jcpp. 12920 attention deficit hyperactivity disorder. Hoogman Brain imaging of the cortex in ADHD Peer reviewed and published https://doi. org/10. 1176/ et al. (2019) ADHD: A coordinated analysis appi. ajp. 2019. 18091033 of large-scale clinical and population-based samples. Postema, van Altered structural brain ASD Peer reviewed and published https://doi. org/10. 1038/ Rooij, asymmetry in autism spectrum s41467-019-13005-8 et al. (2019) disorder in a study of 54 datasets. Zhang-James Machine learning classification of ADHD Under review, published on https://doi. org/10. 1101/ et al. (2019) attention-deficit/hyperactivity bioRxiv 546671 disorder using structural MRI data. Boedhoe Subcortical brain volume, regional ADHD & ASD (and Accepted for publication at AM.
https://doi. org/10. 1101/ et al. (2019) cortical thickness and cortical OCD) J. Psy, published on bioRxiv 673012 surface area across attention- deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive- compulsive disorder (OCD). Li et al. (2019) Characterizing neuroanatomic ADHD Under review, published on https://doi. org/10. 1101/ heterogeneity in people with bioRxiv 868414 and without ADHD based on subcortical brain volumes. Postema et al. An ENIGMA consortium analysis ADHD Under review, published on https://doi. org/10. 1101/ (2020) of structural brain asymmetries bioRxiv 2020. 03. 03. 974758 in attention-deficit/ hyperactivity disorder in 39 datasets. Patel et al. Virtual histology of cortical ADHD & ASD (and Not peer reviewed, submitted NA (2020) thickness reveals shared other working neurobiology underlying six groups) psychiatric disorders: A meta- analysis of 148 cohorts from the ENIGMA Consortium.
2002) , improved TB-dependent memory in aged mice (Treatment effect on freezing ratio F 2,27 = 9. 375; p < 0. 0008; post hoc PLSD: p = 0. 004 and p = 0. 0005 for TAT-9C vs TAT-AA and vehicle, respectively; N = 10 by group). *p < 0. 05, **p ≤ 0. 01; ***p ≤ 0. 001; open circles for PLA comparison with the naive condition. Data presented as mean ± SEM 20 s, 40 s, or 60 s-trace). One part of each trained group was killed immediately after learning for PLA analysis, while the other part performed a retention test 24 h later in order to assess TB-dependent memory. Retention of the tone-shock association was measured by the fear response (freezing) elicited by re-exposure to the tone alone in a neutral context (i. e. , conditioned fear response). Behavioral results confirmed our previous findings  (Sellami et al.
| Dendritic spine density analysis Golgi-Cox staining was performed using the FD Rapid GolgiStain kit according to manufacturer's instructions (FD NeuroTechnologies). Mice were sacrificed by cervical dislocation and brains rapidly removed, rinsed briefly in water and processed according to the manufacturer's protocol. Sections of 100 μm were obtained with a Leica VT1200S vibratome (Leica Microsystems). Sections of 150 μm were obtained with a Leica VT1200S vibratome (Leica Microsystems). Images of CA1 stratum radiatum were taken using a Zeiss microscope at ×63 magnification and then spines were measured using ImageJ software. Protuberance with a height <1. 8 µm were classified as spines. Spines morphology was classified according to spine's head diameter: above 0. 6 µm spines were classified as mushrooms, and below 0.
Image Acquisition and Analysis Image acquisition and analysis were performed as previously described in  Ceredig et al. (2018). Briefly, images were acquired with the Leica TCS SP5 confocal microscope using a 20× dry objective (Numerical Aperture: 0. 7), the 40× resolution was achieved with a digital zoom factor. Confocal acquisitions in the sequential mode (single excitation beams: 405, 488 and 568 nm) were used for marker co-localization to avoid potential crosstalk between the different fluorescence emissions. Images were acquired with the LCS (Leica) software using randomly selected sections. The ImageJ software cell counter (approximately 15 non-adjacent sections per condition and per animal) was used to count on-screen neurons expressing a given fluorescent marker manually and blindly.
Threshold was applied to fluorescence detection. Only neurons from L4-L6 DRGs with a visible nucleus were considered. Cells expressing a given marker and eGFP fluorescence were analyzed separately. During the analysis, we recorded all cross-sectional areas of cell profiles for each marker. No difference was observed in the distribution of the neuronal populations between male and female mice and data were pooled for subsequent analysis. DOPeGFP subcellular distribution was expressed as a ratio of membrane-associated vs. cytoplasmic fluorescence densities determined as previously described  (Erbs et al. , 2016). Acquisitions using 63× (NA: 1. 4) oil objective were performed to determine DOPeGFP subcellular distribution. Briefly, quantification of internalization was performed using the ImageJ software on 8-bit raw confocal images from neurons randomly sampled.
Nuclear fluorescence was used to define the background level (no threshold was applied). Cytosolic fluorescence intensity was subtracted from whole-cell fluorescence intensity to obtain surface fluorescence intensity. Fluorescence intensity values were divided per surface unit (pixel) to obtain densities. Ratio of membrane-associated (Df memb) vs. cytoplasmic (Df cyto) fluorescence densities was calculated to normalize data across neurons examined. A value of 1. 0 results from equal densities of DOPeGFP at the cell surface and in the cytoplasm. Free nerve endings in the glabrous part of the skin were visualized using a 20× dry objective (Nikon Eclipse 80i). Counting on blinded samples was performed manually on screen using the Neurolucida software (V. 10 MBF Bioscience) on at least four randomly chosen sections per animal.
Statistical Analysis Behavioral analysis of von Frey testing was performed using Statistica v12 (StatSoft, France) and Graph-Pad Prism v7 (GraphPad, San Diego, CA, USA). Changes in the PWT, as a function of post-operative time (within factor) and experimental treatment (between factor), were analyzed using two-way ANOVA with repeated measures (two-way rANOVA) analysis followed by Tukey HSD post hoc test. Baseline PWT in males and females were compared using a two-sample student's t-test. Exact p-values below 0. 0001 were not provided for behavioral data (Graph-Pad Prism v7, GraphPad, San Diego, CA, USA). For cross-sectional area measurements, data were pooled per treatment group for each marker (CGRP or eGFP). In all groups, cross-sectional areas were found not normally distributed (p-value always < 10 -8 , Shapiro-Wilk test) using R (R Core Team, 2017).
ACKNOWLEDGMENTS We would like to thank the  Chronobiotron animal facility  ( UMS 3415 CNRS ), and the imaging Platform of the  Institut des Neurosciences Cellulaires et Intégratives (UPS 3156 CNRS)  for their assistance. We are also thankful to the  Institut de la Clinique de la Souris (Illkirch, France ) animal facility,  Anne Robé  and  David Reiss  at  IGBMC  for breeding and genotyping of Nav1. 8 + DOP cKO mouse line. They would like to acknowledge  Dr. Jean-Luc Rodeau  for his instrumental help with the statistical analysis,  Prof. Nelly Boehm ,  Prof. Remy Schlichter  and  Dr. Sylvain Hugel  for valuable scientific discussions, and  Elisabeth Waltisperger  for excellent technical contribution.
FUNDING This work was supported by  Centre National de la Recherche Scientifique (CNRS) ,  University of Strasbourg (Université de Strasbourg ), and  Institut National de la Santé et de la Recherche Médicale  (INSERM; contracts  UPR3212 ,  UMS3415 ,  UMR7104  and  U964 ) and by the  European Union Seventh Framework Programme   FP7-Health-2013-Innovation  (grant  1602919 ). RC was funded by the  Ministère de l'Enseignement Supérieur et de la Recherche et de l'Innovation  and the  Fondation pour la Recherche Médicale  ( DPA20140629804 ).
Subjects Forty-two male subjects, 26-74 years of age, participating to the HumanFATaste program were included in this study. All subjects received detailed information about the study and gave written consent (ANSM n° B90706-40). HumanFA-Taste was approved by the local ethics committee (Comité de Protection des Personnes-CPP Est1, n°2009/18) and registered at ClinicalTrials. gov (#NCT02028975). T2D patients (D, n = 13) displayed a BMI ≥ 30 kg/m 2 , a fasted glycemia > 6. 10 mmol. L -1 and a glycated HbA1c > 6. 0% were included in this study (Fig. 1a ). Weight-matched normoglycemic controls (C, n = 29) were selected according to the following inclusion criteria: fasted glycemia < 6. 10 mmol. L -1 , glycated hemoglobin A1c < 6. 0%, no hypoglycemic drugs intake or surgical treatment of obesity.
There was a statistically significant difference in alpha diversity between DLLT and DHLT groups, but only at the genus level (p = 0. 0265), with a higher diversity in the DLLT group (Fig. 3c ). The LEfSe analysis comparison of DLLT and DHLT groups identified two genus level differential features, with a high proportion of this difference due to unclassified bacteria of the phylum Bacteroidetes (Fig. 3d ). A significant positive correlation with LA detection threshold (i. e. , a lower sensitivity) was observed for the phylum Proteobacteria and genus Clostridium_XIV while a negative correlation (i. e. , a higher sensitivity) was found for the family Porphyromodaceae (Fig. 3e ), suggesting that the composition of the CVP microbiota might affect the fatty taste sensitivity. Predicted metagenomics was performed using PICRUSt and the relative abundance of KEGG modules for metabolic pathways and structural complex features were determined for DLLT versus DHLT.
1 modulates STS phenotypes. To further understand the functional role of this region, we first used databases of information on gene tissue expression and chromatin states in various brain tissues at different stages of development. Second, we investigated the effect of this region on brain phenotypes by taking advantage of the multimodal sequences acquired in the same UK Biobank subjects (T1w, diffusion tensor imaging (DTI) and resting state). We observed that this gene has a localized effect on the superior temporal regions, particularly on the left STS, which suggests a role in the establishment of the linguistic network.
Methods Subjects. In this study, we used the UK Biobank data released in October 2018, including 22,392 subjects with T1-weighted MRI who were aged 44 to 81 years at the time of the imaging visit. The genetic data have already been subject to rigorous quality control  (Bycroft et al. 2018). We limited our analysis to individuals identified by the UK Biobank as belonging to the main subset of white British ancestry. Subjects with high genotyped missingness, high heterozygosity or sex mismatches were excluded from our analysis  (Bycroft et al. 2018). Briefly, the UK Biobank contains genotypes of 488,377 participants who were genotyped on the following arrays: UK BiLEVE Axiom Array (807,411 markers; 49,950 participants) and UK Biobank Axiom Array (825,927 markers; 438,427 participants). The two arrays are very similar, sharing approximately 95% of the marker content.
Genotypes were called from the array intensity data in 106 batches of approximately 4700 samples each using a custom genotype-calling pipeline. Among individuals with the STAP phenotype, 13,956 individuals of British ancestry were scanned at Cheadle and 2,559 at Nottingham, while, 1,935 individuals of non-British ancestry were scanned at Cheadle and 226 at Nottingham. In addition, we described previous results obtained with the HCP cohort (Le Guen,  Leroy, et al. 2018) , and details are included in Supplementary Methods. Ethical approval. UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC), which covers the UK. The present study was approved by the UK Biobank ethics committee under application number 25251. Imputation. Phasing and imputation were carried out by the UK Biobank and are described in detail elsewhere  (Bycroft et al.
2018). Briefly, phasing on the autosomes was performed using SHAPEIT3  (O'Connell et al. 2016)  with the 1000 Genomes Phase 3 dataset as a reference panel. For imputation, both the Haplotype Reference Consortium (HRC) reference panel  (McCarthy et al. 2016 ) and a merged UK10K/1000 Genomes Phase 3 panel were used. This imputation resulted in a dataset with 92,693,895 autosomal SNPs, short indels and large structural variants. This customized imputation represents the most reliable high-resolution imputation to date and enables fine-mapping to identify causal genetic variants  (Schaid et al. 2018). Quality control and filtering. QC was performed using PLINK v1. 9 18,19. We removed all SNPs with a call rate < 90% accounting for the two genotyping platforms used to genotype individuals (784,256 genotyped variants remained).
(2015). From this set of vertices, we obtained individual profiles of the geodesic depth and depth potential function (PF), which considers both sulcal depth and convexity. We averaged these values along the STAP for each subject to obtain our depth phenotypes. We also applied the PP detection method described in Le Guen,  Leroy, et al. (2018)  and benchmarked on manually labeled data from  Leroy et al. (2015)  to identify subjects with at least one sulcal interruption in their depth profile. This process led to the binary phenotype "presence or absence of PP". Among our British discovery sample that completed the pipeline (16,515 subjects), 9,501 subjects had a left PP (57. 53%), and 2,587 had a right PP (15. 66%). We defined the asymmetry index as 2(L-R)/(L+R). Phenome-wide analysis. We processed the T1w and T2w images with the Freesurfer v6 pipeline  (Fischl 2012) , and extracted individual measures from the following output files: ?h.
Genome-wide associations in the UK Biobank. The analyses of genotype-phenotype associations were performed with PLINKv1. 9  (Purcell et al. 2007 ) using the imputed genotype data. The same thresholds as above were applied (missing genotype: 5%, hwe: 10 -6 , and maf: 0. 01). We included the same covariates as those in the quantitative genetics section. For association with the lead SNP, we controlled a posteriori for the MRI scanner center and brain volumes (Tables  S3-S4 ). These two covariates do not affect the main result presented here. GWAs replication in the IMAGEN cohort. For the replication of GWAS, we included 1726 subjects with T1 MR Images (ADNI-MPRAGE whose parameters are available in  (Schumann et al. 2010) ) and genotyping data. The SNPs common to the Illumina 610-Quad and 660-Quad arrays were considered and we used the imputed data available from the IMAGEN project (https://imagen2.
cea. fr). Briefly, imputation was performed using MaCH using the 1000 genome Phase 3 dataset as a reference (v3. 20101123). Functional annotation. The functional annotations of loci were provided by FUMA  (Watanabe et al. 2017) , which notably annotates SNP location, expression quantitative trait loci (eQTL) querying the Braineac  (Ramasamy et al. 2014) , GTEx (GTEx Consortium 2017), Common Mind (CMC)  (Fromer et al. 2016) , and eQTLGen  (Võsa et al. 2018 ) databases, and splicing QTL (sQTL) in GTEx. GTEx, Braineac and eQTLGen were further manually queried. Additionally, we queried the Brain-eMeta eQTL summary data  (Qi et al. 2018 ), which is a set of eQTL data from a meta-analysis of GTEx brain(GTEx Consortium 2017), CMC  (Fromer et al. 2016) , and ROSMAP  (Ng et al. 2017). The estimated effective is n = 1,194 samples.