dataesr/scientific-paragraphs-categorization huggingface dataset:
Both functions are taken from the caret R and nnet R packages. This method is able to deal with groups of both qualitative and quantitative variables recorded from the same individuals. The MFA performs a normalization or "weighting" on each group by dividing all the variables belonging to the group by the first eigenvalue from the principal component analysis (PCA) of the group. Then a PCA on all the weighted variables is applied so we can identify the correlation between the grouped or ungrouped qualitative or quantitative variables, the principal component dimensions, and identify the individual coordinates of each observation on the PCA dimensions. The method is implemented using the MFAmix function from the PCAmixdata R package. Moreover, we chose a vectorization visualization approach like that implemented in PCAmixdata where we included the cosine similarity distance to further highlight the parameters that follow the same trajectory as Genotype.
Consequently, these parameters contribute to the separation registered in a deformable way on each mouse image using the ANTs registration toolbox and KEGG databases  (ESLING et al. 2015). Regulatory information was also added to build the final RegPPINet. We used the betweenness centrality analysis to identify hubs, keys for maintaining the network communication flow. To further study the genotype-phenotype relationship in those models we combined the behavioral results and the RNA-Seq data to identify central genes altered in the models and linked to the phenotypes observed using the genotype-phenotype databases GO, KEGG and DisGeNET. First, we downloaded the list of experimentally validated genes known to be involved in hyperactivity or locomotion behavior from the human disease database DIsGeNET (dataset: Hyperactive behavior, C0424295 with 1263 genes) and annotated the genes with a high confidence ortholog in mouse.
We added all the mouse genes involved in GO genesets linked to locomotion or motor behavior (18 GO terms: GO:0007626, GO:0008344, GO:0031987, GO:0033058, GO:0035641, GO:0040011, GO:0040012, GO:0040013, GO:0040017, GO:0043056, GO:0045475, GO:0090325, GO:0090326, GO:0090327, GO:1904059, GO:1904060, GO:0036343, GO:0061744). Then, we queried our RNA-Seq data for these genes to identify those found deregulated in the datasets. A. Mice with trisomy showed deficits in building a nest while a majority of 2n mice were able to build a nest (22 2n with 25 Ts66Yah males and 15 2n with 12 Ts65Dn males). B. Although there was no significant difference in the percentage of alternation between 2n and Ts66Yah males, level was significantly above 50% (chance level) only in 2n and not inTs66Yah mice. FIGURE LEGEND Conversely, the Ts65Dn line showed a strong difference in males, with a significant lower % of alteration for the trisomic group as compared to chance level, as well as a reduced spontaneous alternation compared to 2n mice.
We would like to thank the members of the research group, of the  IGBMC laboratory  and of the  ICS  for their help in brain morphometric analysis. We extend our thanks to the animal caretakers of the ICS who are in charge of mice well-being. This work was supported by the  Jérôme Lejeune foundation. We acknowledge the  Sisley-d'Ornano  and  Jérôme Lejeune Foundations  that awarded MF with a postdoctoral fellowship. This work was supported by the  National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg (Unistra) ,  French government  funds through the " Agence Nationale de la Recherche " in the framework of the  Investissements d'Avenir program  by  IdEx Unistra  ( ANR-10-IDEX-0002 ), a  SFRI-STRAT'US project  ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) and  INBS PHENOMIN  ( ANR-10-IDEX-0002-02 ), and also provided by the  Joint Programming Initiative Neurodegenerative Diseases ( JPND) multinational research  project  HEROES  ( ANR-17-JPCD-0003 ) et  DYRK-DOWN  ( ANR-18-CE16-0020 ).
2021). This phenomenon should depend on genes located in Mmu17 either directly or indirectly, because Ts66Yah was not affected. According to the Mouse Genome Informatics database, mutants from five genes of the region, Arid1b, Cep43, Nox3, Pde10a, and Serac1, are linked to activity, motor and stereotypic behavior and are strong candidates for which overdosage may enhance the activity of the Ts65Dn mouse model. Similarities were also observed in brain and craniofacial morphologies, with convergence found in both DS models. Nonetheless, the severity of the trisomy in these two features were slightly different: The Ts65Dn brain was larger than the Ts66Yah one in many different regions while the shape of the cranium was affected differently, with more severe brachycephaly and increased mid-face shortening in Ts66Yah, compared to Ts65Dn, a phenotype closer to human DS features.
Conversely, this region may change penetrance or expressivity of features in individual with DS. Indeed, the SCAF8-PDE10A on human chromosome 6 is also subjected to copy number variation according to the DECIPHER database (FIRTH et al. 2009). In human, the region is rearranged in the 3' part (Fig S1B) and contains the ARID1B, SERAC1, GTF2H5, RPSH3 and PDE10A disease genes. Thus, the additional trisomy of Scaf8-Pde10a has a strong effect on the expression of several DS related features in models. This phenomenon of additive effect due to genetic interaction has been observed in other copy number variation diseases (PIZZO et al. 2019). Overall it highlights the need of complete evaluation of the genetic background in individual with DS, to define potential interaction with other candidate disease-associated variants and to get a better understanding of the DS complexity.
The parameters for this sequence were: TR 325 ms, TE 32 ms, Rare factor = 6, interecho spacing 10. 667 ms, 92 kHz bandwidth. The second imaging protocol consisted of a 3D T2*-weighted Fast Low Angle (FLASH) sequence with the following parameters: TR 50 ms, TE 25 ms, FA 50 degrees, 28 kHz bandwidth. The output image matrixes for both sequences were 195 x 140 x 90 over a field of view 19. 5 x 14. 0 x 9. 0 mm3 yielding an isotropic resolution of 100 μm and were reconstructed using ParaVision 6. 0. 1. Each MRI image was segmented into twenty anatomical structures according to a multi-atlas label propagation framework. To this end, the ten manually segmented in-vitro MR images from the MRM NeAt Mouse Brain Database (http://brainatlas. mbi. ufl. edu/) were considered(MA et al. 2005). The image processing pipeline consisted of the following steps: (i) a skull-stripping step was first performed using the tissue brain segmentation method provided in SPMMouse (http://www.
No differences in longitudinal change in glucose metabolism were observed compared to neighboring or contralateral regions or to the change observed in the same region in ADNI participants. No signi cant effect on cognition evolution was observed in comparison to the ADNI participants as expected due to the small sample size and duration of the trial. Conclusions: These results demonstrate the safety of ultrasound-based BBB disruption and potential of this technology to be used as a therapy for AD patients. They support further research of this technique in a larger clinical trial with a device designed to sonicate larger volumes of tissue and in combination with disease modifying drugs to further enhance the effects observed. Trial registration: NCT03119961 (clinicaltrials. gov).
Study Design This investigator-sponsored phase I/II trial was a single center, exploratory clinical trial (NCT03119961) initiated at Hôpital de la Pitié Salpêtrière (Paris, France). The primary objective was to evaluate changes on PET imaging on β-amyloid and glucose in the region of interest (ROI) targeted by the ultrasound device. Secondary objectives were to assess the radiographic and clinical tolerance of repeated BBB opening by ultrasound and to examine the opening of the BBB on T1-weighted (T1w) MRI, and to study the evolution of cognitive decline. The study was approved by the Paris VI Ethical Committee. Informed consent was obtained from all participants. Patients between the ages of 50-85, with early-stage AD (mini-mental state examination [MMSE] 20-26) were eligible. Inclusion was based on cognitive assessment  (24)  and an MRI showing one of the three most frequent phenotypic presentations of the disease (hippocampal amnesia, logopenic aphasia, or posterior cortical atrophy syndrome).
Diagnosis was con rmed by the presence of cerebrospinal uid levels of ptau/Αβ1-42 > 0. 11  (25). No control subjects were included in the study, but 45 controls were sampled from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu) through a matching procedure taking into account age, gender, MMSE score and diagnosis (mild cognitive impairment/AD).
Global Scale Analyses & Comparison With An External Control Cohort Potential effects upon amyloid and FDG PET were additionally evaluated by comparing SUVRs for whole cortex and a temporoparietal ROI (comprising the angular, supramarginal and superior temporal gyri) in the hemisphere of the implant versus the opposite hemisphere and a slightly different set of processing steps described in supplementary materials. This provided a comparator to the standard ROI analyses that had been performed using somewhat different technical approaches and reference regions. As no control group was recruited in the study, we selected patients from the ADNI database  (41). The selection was made from ADNI subjects who had at least two sessions (on average 30 months apart) with T1w MRI, FDG and amyloid PET data, and with an MCI or AD diagnosis at the considered sessions  (42, 43).
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more timeconsuming, Cartesian-grid scheme.
, 2015 ; Dauguet et al. , 2007 ; Gao et al. , 2013 ; Schilling et al. , 2019a ; Schilling et al. , 2019b ; Thomas et al. , 2014 ; van den Heuvel et al. , 2015 ). Furthermore, the majority of such studies only considered the end points of the fiber bundles, and not their complete trajectory  ( Ambrosen et al. , 2020 ; Azadbakht et al. , 2015 ; Donahue et al. , 2016 ; Girard et al. , 2020 ; Hagmann et al. , 2008 ; van den Heuvel et al. , 2015 ). That is because they did not have dMRI and tracer data from the same brains, hence they relied on connectivity matrices from existing tracer databases. The IronTract Challenge is the first open tractography challenge to be conducted on high-resolution, densely sampled brain dMRI data. This allowed us to evaluate tractography accuracy for two widely adopted sampling schemes: multi-shell and Cartesian-grid.
Outline of the challenge Both rounds of the IronTract challenge followed the outline shown in Fig. 1. In vivo tracer injections and ex vivo dMRI scanning were performed on two macaque brains (see 2. 2 Data description ). The dMRI data, acquired on a Cartesian grid, were resampled onto the two-shell of the HCP acquisition protocol  ( Harms et al. , 2018 ). We will refer to these datasets as diffusion spectrum imaging ( DSI ) and HCP respectively. The organizing team uploaded the data to the QMENTA platform ( https://qmenta. com/irontract-challenge/ ) and the challenge teams could download them along with the tracer injection sites in the dMRI space. Each team analyzed the data with methods of their choice (see 2. 3 Analysis of dMRI data by challenge participants ). In round 1, this included image pre-processing, orientation reconstruction, tractography, and tractogram post-processing.
In round 2, the pre-and postprocessing steps were standardized across all teams. Each team produced tractograms with a range of thresholds and uploaded them to the QMENTA platform. A score was computed on the fly by comparing the tractograms to the tracer data (see 2. 4 ROC analysis ). For the training case, participants were shown their score and were allowed to repeat data analysis and upload of results. Thus, participants tuned their analysis pipelines to maximize their score on the training case. Finally, they applied the optimized pipeline to the data from the validation case. The organizing team computed AUC scores on the validation case and used them for the final ranking of the challenge teams.
Tracer injections The training and validation datasets came from two different male rhesus macaques. The former received an injection of the anterograde/bidirectional tracer Lucifer Yellow in the anterior frontal cortex (frontal pole). The latter received an injection of the anterograde/bidirectional tracer Fluorescein in the ventrolateral prefrontal cortex (vlPFC). Surgery and tissue preparation were performed at the University of Rochester Medical Center. Details of these procedures were described previously  ( Haber, 1988 ; Lehman et al. , 2011 ; Safadi et al. , 2018 ). Briefly, each monkey received an injection of a bidirectional tracer conjugated with dextran amine (40-50 nl, 10% in 0. 1 M phosphate buffer, pH 7. 4; Invitrogen). Twelve days after the injection, animals were perfused and their brains were postfixed overnight and cryoprotected in increasing gradients of sucrose  (10, 20, and 30%).
We followed this procedure to generate data on the two qshells of the lifespan and disease HCP acquisition protocol  ( Harms et al. , 2018 ). This in vivo protocol includes 93 directions with  = 1 , 500  ∕   2 and 92 directions with 3 , 000  ∕   2. We multiplied these b-values by the 4x factor required to achieve comparable diffusion contrast ex vivo as in vivo  ( Dyrby et al. , 2011 ) , i. e. , we used  = 6000 and 12000  ∕   2. We refer to this q-space sampling scheme as HCP. To assess the SNR, we first fit the tensor model to the data and then delineated a mask encompassing the CC by selecting the highly red voxels in the color-coded fractional anisotropy (FA) map. We extracted the mean signal from this mask and from a mask outside the brain to capture the noise. We computed the SNR as the mean (signal)/standard deviation (noise)  ( Jones et al.
0 or 6. 4x objective, using Neurolucida software (MBF Bioscience). Fibers traveling together were outlined as a group or bundle. Axons were charted as they left the tracer injection site and followed through the right hemisphere, until the anterior commissure. The 2D outlines were combined across slices using IMOD software (Boulder Laboratory  ( Kremer et al. , 1996 ) ) to create 3D renderings of the structures and pathways as they traveled through them. These 2D outlines were used to further refine bundle contours and ensure spatial consistency across sections.
Round 2 In the second round, analysis and scoring of the training and validation cases were performed as described above. The difference was that the pre-and post-processing steps were standardized across teams. Participants downloaded pre-processed dMRI data from the QMENTA platform and were provided two scripts for the post-processing steps. The orientation reconstruction and tractography methods were not standardized. Pre-processing: This followed the dMRI pre-processing procedures that had been used in round 1 by Team 1, the team that achieved the best performance (see 3. 1 Round 1 Results ). They included denoising  ( Veraart et al. , 2016 )  and correction for Gibbs ringing  ( Kellner et al. , 2016 )  in MRtrix3  ( Tournier et al. , 2019 ) , and correction for motion and eddy-current distortions in FSL  ( Andersson et al.
(ii) Anatomical ROIs. This strategy had been implemented by Team 2 in round 1. ROIs from the PennCHOP macaque atlas  ( Feng et al. , 2017 )  were transformed to the space of each dMRI dataset. Only streamlines intersecting at least one of these ROIs were retained. The ROIs were selected on the base of general knowledge of projections of the prefrontal cortex  ( Lehman et al. , 2011 )  and were located in: the cingulum bundle, the genu of the corpus callosum, the external capsule, the anterior limb of the internal capsule, and the uncinate fasciculus (Supplementary Fig. 1 ). For round 2, after applying the anatomical ROIs, the same smoothing (  = 0. 5 ) and iterative thresholding (200 steps on the log of the streamline count) as in the Gaussian filtering strategy were performed. It is important to differentiate between the anatomical ROIs used by Team 2, which were based on prior knowledge of the brain regions that are connected to the specific injection sites, and other masks.
AUC score We adopted the area under the receiver operating characteristic (ROC) curve (AUC) as our main performance score. The ROC analy-sis was performed as follows. For each of the submitted tractograms, we obtained the numbers of voxels that were true positive (TP; voxels included both in the tractogram and in the tracer mask), true negative (TN; voxels included neither in the tractogram nor in the tracer mask), false positive (FP; voxels included in the tractogram but not in the tracer mask), and false negative (FN; voxels included in the tracer mask but not in the tractogram). The computation of TN and FP was performed for only for voxels included in a brain mask. The mask excluded brain regions were not labeled in the tracing data ( e. g. , because they were too caudal to contain projections of these injection sites).
Localization of challenging areas Having tracer and dMRI data from the same brain allows us to identify the exact locations where tractography goes wrong, and thus the fiber geometries that are consistently challenging across tractography methods. To this end, we extracted a map of TP voxels at FPR = 0. 1, for each of the submissions that participated in both rounds of the challenge. We binarized these maps and summed them across all submissions. This yielded a histogram that showed the number of teams that achieved a TP in each voxel of the tracer mask. This allowed us to identify the locations where errors occurred consistently across tractography methods in round 1, and to examine whether the pre-and postprocessing steps that were applied in round 2 mitigated these common errors.
Round 1 results (variable pre-and post-processing) Out of 30 registered teams, 12 completed the challenge (total submissions: 227; training: 186; validation: 38) and 16 final submissions were ranked. A detailed list is reported in Supplementary Table  1. Overall, results from round 1 showed that, in both training and validation cases, no submission could achieve high TPR without also generating a large number of false positives ( Fig. 2 A ). Most submissions achieved TPRs higher than 0. 8 only at FPRs higher than 0. 2. Almost all submissions achieved higher accuracy in the training case (mean AUC = 0. 20) than in the validation case (mean AUC = 0. 16). Three teams only (Teams 1, 2, 6) obtained similar accuracy across datasets, with even higher accuracy for the validation case ( Fig. 2 B ).
The effect of acquisition scheme and propagation method We compared an HCP-style, two-shell acquisition scheme with a much more densely sampled DSI scheme. Overall, higher accuracy was achieved by methods that used the full DSI data (515 diffusion volumes) ( Fig. 2  ). However, a few of the methods that used the HCP data approached the accuracy of the DSI methods. For methods that could be applied to both schemes, the loss in accuracy when using HCP versus DSI data was lower than 10% (Supplementary Table  1 , Fig. 2  ). This illustrates that when analysis methods are carefully optimized, the twoshell HCP scheme represents an advantageous trade-off between accuracy and acquisition time, given that DSI acquisition involves 2. 8 times more directions and 3. 3 times higher maximum b -value. Previous validation studies showed that DSI produces more accurate fiber orientation estimates both in simulations  ( Daducci et al.
This was the case in almost all selected ROIs and especially in those that included branching, fanning, or turning fibers ( Fig. 10  ). Less sharp ODFs, when combined with probabilistic tractography, allow a broader range of orientations to be sampled from the same ODF peak. This can be beneficial in areas of branching or fanning. Areas where fibers take sharp turns remain a challenge for all methods. They can only be resolved by relaxing bending angle thresholds to a degree where the FPR becomes prohibitively high. In a previous study, we evaluated a different set of tractography methods on the dataset that we refer to as the training set here  ( Grisot et al. , 2021 ). We observed the highest accuracy from the combination of probabilistic tractography with GQI, a reconstruction method that does not produce particularly sharp ODFs.
Acknowledgements Data acquisition was supported by the  National Institute of Mental Health  ( R01-MH045573 ,  P50-MH106435 ). Additional research support was provided by the  National Institute of Biomedical Imaging and Bioengineering  ( R01-EB021265 ) and the  National Institute of Neurological Disorders and Stroke  ( R01-NS119911 ). Imaging was carried out at the  Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital , using resources provided by the  Center for Functional Neuroimaging Technologies ,  P41-EB015896 , a  P41 Biotechnology Resource Grant , and instrumentation supported by the  NIH   Shared Instrumentation Grant Program  ( S10RR016811 ,  S10RR023401 ,  S10RR019307 , and  S10RR023043 ). Andrey Zhylka  is supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant  ( 765148 ).
Ye Wu and Pew-Thian Yap were supported in part by the  National Institute of Mental Health  ( R01-MH125479 ), and the  National Institute of Biomedical Imaging and Bioengineering  ( R01-EB008374 ). The team at  Boston Children's Hospital  was supported in part by the  National Institutes of Health (NIH)  grants  R01-NS106030 ,  R01-EB031849 , and  R01-EB019483. Team from  UW-Madison  would like to acknowledge the  NIH  grants  R01NS123378 ,  U54HD090256 ,  R01NS092870 ,  R01EB022883 ,  R01AI117924 ,  R01AG027161 ,  RF1AG059312 ,  P50AG033514 ,  R01NS105646 ,  UF1AG051216 ,  R01NS111022 ,  R01NS117568 ,  P01AI132132 ,  R01AI138647 ,  R34DA050258 , and  R01AG037639. Erick J. Canales-Rodríguez  was supported by the  Swiss National Science Foundation ,  Ambizione  grant  PZ00P2_185814. Matteo Mancini  was funded by the  Wellcome Trust  through a  Sir Henry Wellcome Postdoctoral Fellowship  [ 213722/Z/18/Z ].
Data availability The authors declare that the data supporting the findings of this study are available on the QMENTA platform ( https://qmenta. com/ irontract-challenge/ ). The post-processing scripts used in Round2 are available at https://github. com/chiaramff/IronTract. Detailed information on how to reproduce the tractograms generated by the challenge teams, and links to code repositories are provided in supplementary note 1 and supplementary note 2 in the supplementary information.