dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013). A different approach is the one adopted in  (Phillips et al. , 2011) , where the authors analyzed the diffusion values by using meshes of the WM. The meshes of all subjects are aligned in terms of their sulcal lines and the FA values are obtained from the DTI image using a 10 mm sphere to average the values around each mesh vertex. The FA values were previously masked and thresholded in order to keep only those corresponding to the SWM. The method was applied to the data of 150 subjects in total, including: schizophrenia patients and their relatives, and community comparison subjects and their relatives. Results from this analysis showed a reduced SWM FA in patients specially in the left temporal and bilateral occipital regions. The same approach described in  (Phillips et al. , 2011)  was used in later analyses in order to study the relation of SWM in a variety of topics.
In  (Phillips et al. , 2013)  a study about the age, sex and hemispheres is performed over the data of 65 subjects. They found a decrease of FA related to age, as well as an increase of AD and RD, and also a leftward asymmetry. Another study using the same approach as a base  (Phillips et al. , 2011)  was focused in studying the relation of SWM with Huntington's disease  (Phillips et al. , 2016a). The study was performed over the data of 25 pre-symptomatic subjects, 24 patients and 49 healthy controls. An increase of the AD and RD disperse in the brain was found for pre-symptomatic subjects compared to controls. For patients this increase was found in all the brain. Another work covered the relation of SWM with Alzheimer's disease  (Phillips et al. , 2016b). Forty-four patients and forty-seven healthy controls were analyzed.
A study performed by  Catani et al. showed that damage in this bundle has been related to progressive aphasia  (Catani et al. , 2013). The analysis was performed using the DTI data of 35 patients and 29 controls. Using manually drawn ROIs, the authors segmented the fiber bundles by setting them as seed regions. Another pathology of interest in clinical studies is the bipolar disorder. In  (Zhang et al. , 2018c)  the authors utilized DTI data from 37 bipolar patients and 42 healthy controls and probabilistic tractography to calculate population-based SWM masks. TBSS analysis was employed to measure the FA of the SWM, as well as the MD, AD and RD. The clusters found after this analysis were reported according to the atlas presented in  (Guevara et al. , 2017)  in order to identify the cortical regions connected by them.
One way or another, it is certain that most stable bundles can be segmented no matter the input data resolution of the technique employed. In an attempt to prove that, we compared three publicly available atlases, which were conceived automatically by different approaches. Even though the methods used to create the atlases differ in spatial normalization, tractography and the segmentation technique, and are based on two different databases, there is a large number of similar bundles among the three resulting atlases. We provide a compilation of 213 distinctive bundles from the three atlases, with useful information such as reproducibility and variability across subjects for three different configurations of diffusion local model and tractography methods. Results prove that the development of more sophisticated methods is leading to a better description of SWM bundles.
Furthermore, while algorithmic progress now allow the manipulation of huge tractograms including a million streamlines, exploring further the short range connectivity may require to scale-up even further the amount of streamlines of interest. The different clinical studies included in this review reveal that SWM is specially affected in different pathologies. These studies expose changes in different diffusion indices which are driven most likely by phenomena related to the myelination of the fibers. The SWM is specially exposed during the brain maturation as its myelination occurs mostly during the third and fourth decade of life, making the SWM more prone to lesions. Their study is very sensitive to the dMRI data quality. The advancement of dMRI equipment and methods makes it now possible to acquire datasets that allow the study of a considerable set of short fibers, ideally based on HARDI or multi-shell data.
Acknowledgements This work was partially funded by the  Human Brain Project , funded from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the Specific Grant Agreements No. 785907  ( SGA2 ) and No:  604102  ( SGA1 ). This work has also received funding by  Chilean  grants  CONICYT FONDECYT 1190701 ,  CONICYT PIA/Anillo de Investigaci on en Ciencia y Tecnología   ACT172121 , and the Basal Center FB0008. HCP Data were 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.
Study Participants The studies and the LRRK2 mutation carriers were grouped into 3 cohorts. The first cohort was primarily identified from The Michael J. Fox Foundation's LRRK2 Consortium and consisted of research sites worldwide (referred to as the MJFF consortium cohort). We searched PubMed and identified study groups that reported LRRK2 mutation carriers and then asked them to participate in this study (PubMed IDs: 16240353, 16333314, 18986508, and 16960813). [36] [37] [38] [39]  We also made announcements at international conferences to recruit more study mutation carriers. Details can be found in their publications. [36] [37] [38] [39]  To maximize participation and facilitate uniform data preparation across sites, a minimal dataset was submitted for all subjects that included LRRK2 mutation status, sex, age-at-onset (for PD cases), age at last evaluation (for non-PD participants), and pedigree information, along with the availability of a minimal amount of DNA (approximately 2 ug).
The minimal phenotypic data were sent to Indiana University and the subjects were assigned a unique identifier. The second cohort was from Tel Aviv University, Israel (referred to as the Israel cohort). Participants were of Ashkenazi origin and recruited from the Movement Disorders Unit at Tel Aviv Medical Center. PD diagnosis was confirmed by a movement disorders specialist and clinical disease status (PD or not diagnosed as PD) was evaluated at the time of blood draw for genetic testing. The third cohort (referred to as the 23andMe cohort) consisted of research participants of the personal genetics company 23andMe, Inc. who were LRRK2 G2019S carriers and whose PD status was known. Individuals who reported via an online survey that they had been diagnosed with PD by a medical professional, were asked to provide their age at diagnosis.
Family relationships were adjusted by using a kinship matrix calculated using R package COXME (https://cran. r-project. org/ web/packages/coxme/index. html). For the age-at-onset model, a linear mixed model was fit with the same covariates as the penetrance model and a kinship matrix to adjust family relationships. Although adjusting 10 PCs and family relationships could minimize the effects of population stratifications and shared genetic and environment factors among family members, the use of this mixed samples was designed to search for common genetic variants that have the same effects in participants from all populations and could miss population-specific or family specific findings. Variants with MAF > 1% were tested for association in these 2 models. In addition to the single variant analyses, we performed gene-based association analyses for both the.
In Silico Functional Studies To evaluate whether the genomewide significant findings had immediate biological consequences on gene expression (expression quantitative trait locus [eQTL]) of nearby genes, we searched Open Targets Genetics (https:// genetics. opentargets. org/) and GTEx (https://www. gtexportal. org/). In addition, protein-protein interaction (PPI) data were assessed to identify whether the protein product of the nominated gene either interacts directly with LRRK2 or has common interactors that are shared with LRRK2, using Protein Interaction Network Online Tool (PINOT) version 1. 0 46 queried on June 16, 2020 (http://www. reading. ac. uk/bioinf/PINOT/PINOT_form. html). We also performed chromatin interaction mapping to check whether our top findings interact with LRRK2 distantly.
Polygenic Risk Score Analyses In the largest GWAS analysis of PD susceptibility to date, Nalls et al meta-analyzed 17 datasets with 56,306 PD cases or proxy-cases and 1. 4 million controls. 4  Based on their results, they developed a PRS using summary statistics of 1,805 variants that can explain 26% of PD heritability. 4  In this study, we performed PRS analysis using these 1,805 variants. Detailed information about how to select these 1,805 variants was described in Nalls et al. 4  Because we were searching for LRRK2 modifiers, variants in the LRRK2 region (chr12: 40,118,913-41,263,086) were excluded. The PRS was calculated as a weighted summation of effective alleles with the logarithm of odds ratios as the weights. This derived PRS was used to fit the same models with the same set of covariates as described for the genomewide association analyses using the R package COXME.
The minor allele (C allele) is associated with higher expression of CORO1C and MYO1H. We did not find any previous report that LRRK2 interacts with CORO1C or MYO1H directly, however, there are several proteins that are the common interactors of both LRRK2 and CORO1C: ABCE1, ACTR2, CDC42, DAPK1, MYO1C, RAC1, and TP53, as identified by using PINOT,  46  and it remains to be determined whether the interaction of LRRK2 with CORO1C is within a single complex or dependent upon these common interacting proteins. Chromatin interaction mapping did not find any variant that interacts with LRRK2 distantly. Among those 1,805 variants that were obtained from the study of Nalls et al 2019,  4  20 variants were not present in our datasets. An additional 27 variants were located in the LRRK2 region and were excluded, and 1,758 variants were included in the PRS calculation.
Nevertheless, we detected a genomewide significant variant. We provide experimental data to show CORO1C and LRRK2 interact, and support that observation by proteomics literature. The PRS analysis suggested that there is unlikely to be one or several single LRRK2 modifiers, but similar to overall PD risk, penetrance of LRRK2 mutations is affected by multiple genetic variants. Given the significant therapeutic efforts underway to develop targets for patients with PD carrying LRRK2 mutations, further replication of these results is essential. Furthermore, the genetic variants identified in this study and the PRS evaluated in the LRRK2 mutation carriers, may be used in the future to make personalized prevention and treatment possible.
Acknowledgments The authors thank the research participants from all sites who made this study possible and  Tara Candido  for collecting the data. Members of the 23andMe Research Team are:  Michelle Agee ,  Adam Auton ,  Robert K. Bell ,  Katarzyna Bryc ,  Sarah L. Elson ,  Nicholas A. Furlotte ,  David A. Hinds ,  Karen E. Huber ,  Aaron Kleinman ,  Nadia K. Litterman ,  Matthew H. McIntyre ,  Joanna L. Mountain ,  Elizabeth S. Noblin ,  Carrie A. M. Northover ,  Steven J. Pitts ,  J. Fah Sathirapongsasuti ,  Olga V. Sazonova ,  Janie F. Shelton ,  Suyash Shringarpure ,  Chao Tian ,  Joyce Y. Tung ,  Vladimir Vacic , and  Catherine H. Wilson. Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the  National Institutes of Health  to The  Johns Hopkins University , contract number  HHSN268201200008I.
The authors acknowledge the  Indiana University Pervasive Technology Institute  for providing [HPC (Big Red II, Karst, Carbonate)] visualization, database, storage, or consulting resources that have contributed to the research results reported within this paper. pEGFP-C1  and  pEGFP-CORO1C 53  were kind gifts from  Prof. Dr. Brunhilde Wirth  ( University of Cologne; Germany ). The 3xFlag-LRRK2 is as described. 64 This work is supported by  The Michael J. Fox Foundation for Parkinson's Research  Grants  7984 ,  7984. 01 ,  7984. 02 , and  8981 ; The  Albertson Parkinson's Research Foundation ; The  Brookdale Foundation ; The  Else Kroener Fresenius Foundation ; The  Haworth Family Professorship in Neurodegenerative Diseases  fund; The  Little Family Foundation ; The  Parkinson's Foundation ; The  Sol Goldman Charitable Trust ;  NIH   AG010124 ,  AG062418 ,  K02NS080915 ,  NS036630 ,  NS053488 ,  NS071674 ,  P50NS039764 ,  P50NS062684 ,  P50NS072187 ,  R01NS 065070 ,  R01NS078086 ,  R01NS096740 ,  U54NS100693 ,  U54NS110435 ,  UL1TR000040 , and  UL1TR001873 ; The  Department of Veterans Affairs   5I01CX001702 ;  DOD W81XWH-17-1-0249 ; The  DFG FOR2488 ; The  Canadian Consortium on Neurodegeneration in Aging ; The  Canadian Institutes of Health Research ; The  2019 Biomarkers Across Neurodegenerative Diseases Grant Program ;  BAND3 18063.
ACCEPTED MANUSCRIPT We thank  Mathilde Guerville  and  Annaëlle Sinquin  as well as  Helen Raybould. We thank  Isabelle Nogret  for her technical help. Caecal Metabolomic analysis were performed within the metaboHUB  French infrastructure  ( ANR-INBS-0010 ). This work was partly funded by a grant from  INRAE AlimH division.
All these achievements are widely documented in a number of review articles covering different aspects of the non-contact monitoring of cardiac signals (see  [13] ,  [17] -  [21] ), and some of them have even brought to commercial solutions. This compulsive development of rPPG techniques emphasizes the importance of fair comparison of competing algorithms while promoting reproducible research. This is usually conducted on empirical basis since theoretical evaluations are almost infeasible due to the complex operations or transformations each algorithm performs. Therefore, empirical comparisons focused on publicly available benchmark datasets have become, under many respects, a cogent issue to face in establishing ranking among methods. However, existing comparisons suffer under several aspects that deserve to be thorough.
These will be widely discussed in Section II, but can be broadly recapped in the following points: 1) lack of a standardized pre/post processing procedure, 2) non reproducible evaluation, 3) absence of comparison over multiple datasets, 4) unsound statistical evaluation. To overcome these problems and promote the development of new methods and their experimental analysis, we propose a framework supporting the main steps of the rPPG-based pulse rate recovery, together with a sound statistical assessment of methods' performance. The framework is conceived to cope with the analysis on multiple datasets and to support each development stage of the overall Virtual Heart Rate (VHR) recovery process. This should allow researchers and practitioners to make principled choices about the best analysis tools, to fine tune process parameters or method metaparameters, and to inquire what are the steps that mainly influence the quality of the estimations carried out.
To concretely support experimental work within the field, the framework is instantiated into a fully open-source Python platform, namely pyVHR 1. It allows to easily handle rPPG methods and data, while simplifying the statistical assessment. Precisely, its main features lie in the following. Analysis-oriented. It constitutes a platform for experiment design, involving an arbitrary number of methods applied to multiple video datasets. It provides a systemic endto-end pipeline, allowing to assess different rPPG algorithms, by easily setting parameters and meta-parameters. Openness. It comprises both method and dataset factory, so to easily extend the pool of elements to be evaluated with newly developed rPPG methods and any kind of video datasets. Robust assessment. The outcomes are arranged into structured data ready for in-depth analyses.
Performance comparison is carried out based on robust non-parametric statistical tests. To the best of our knowledge, this proposal represents a novelty within the rPPG research field. In order to substantiate our framework, we analyse eight well-known rPPG methods, namely ICA  [22] , PCA  [10] , GREEN  [5] , CHROM  [23] , POS  [13] , SSR  [14] , LGI  [15] , PBV  [16]  (cfr. Table  1 , Section III). Such methods have been selected in order to provide a substantial (although not exhaustive) set of well-known, widely adopted and methodologically representative techniques. It is worth remarking that an extensive review of all the rPPG methods proposed so far is out of the scope of the present work, whose primary concerns have been cogently remarked above. Experiments are performed on five publicly available datasets, namely PURE  [24] , LGI  [15] , UBFC  [25] , MAH-NOB  [26]  and COHFACE  [27]  The experimental results, some rather surprising, suggest that the four best performing methods, namely POS, CHROM, PCA and SSR, behave in the same way, leading to the conclusion that the "small" differences among these four are at chance level.
The detailed results achieved by extensive tests conducted on the declared methods/datasets are reported in Section IV. The paper is organized as follows. Section II summarizes the background and rationale about the rPPG approaches and their assessment. Section III presents the framework features and functionalities in the form of a pipeline to process the information at the various stages. Section IV reports a comprehensive statistical comparison of popular algorithms over multiple datasets using non-parametric significance hypothesis testing. Section V provides a discussion and draws some conclusions.
One striking example is the face detection module; while not strictly being part of the rPPG computation from the RGB signal, it is an extremely sensible link in the chain, whose failure would lead to poor quality predictions. Other examples entail the skin detection/ROI extraction module, the filtering of the predicted rPPG signal or the spectral estimation method employed. Standardizing such procedures would allow to set up a fair comparison for all the rPPG methods involved in the analysis. A glance at the related literature reveals how there is a lack of a benchmark commonly recognised as suitable for testing rPPG methods. Indeed, experiments are generally conducted either on private datasets (e. g. [13] ,  [14] ,  [16] ,  [23] ), or on public ones that are not conceived for rPPG assessment (e.
g. [19] ,  [20] ), preventing in both cases fair comparisons. Moreover, the different experimental conditions (e. g, illumination, subject movements, in the wild/controlled environment), or different ground truth reference signals (e. g. , electrocardiogram (ECG) or BVP), are likely to prejudice comparisons, too. For instance, the public dataset Mahnob HCI-Tagging  [26]  was not designed for rPPG benchmarking, but rather for studying human emotions. Yet, it has been adopted to evaluate rPPG techniques  [19]  due to the fact that is freely available and provides recordings of the ECG signal (from which BVP can be recovered) and face videos. The same observations hold for the DEAP dataset  [28]  which has been used for rPPG algorithm evaluation  [20]  despite being collected for the analysis of human affective states.
Heusch and Marcel  [19]  proposed a novel dataset for the reproducible assessment of rPPG algorithms. In this work, authors compare three rPPG methods on the newly collected dataset (COHFACE) and on the Mahnob HCI-Tagging dataset. Despite being a remarkable effort towards the principles advocated in the present research, it presents some pitfalls. Besides the absence of proper statistical evaluation, the most important is surely represented by the fact the all the analyses where carried out solely on compressed video datasets. Indeed, recent research has shown that a sound video acquisition pipeline of rPPG pulse-signal should require uncompressed coding  [29]. Clearly, such recordings are often too large to be easily published online. As a consequence, many suitable datasets are often kept private.
3) Comparison over multiple datasets A long debated issue in the pattern recognition field is represented by the bias of the dataset used when performing an analysis. As a matter of fact, running the same algorithm on different datasets may produce markedly different results. In other words, every dataset has its own bias, consequently the performances reported on a single dataset reflect such biases  [30]. rPPG methods make no exception, being de facto very sensitive to different conditions  [15] ,  [24]  (video compression, different lighting conditions, different setups). Hence, a sound statistical procedure for the comparison on multiple dataset is needed. To the best of our knowledge, no such analyses were proposed earlier in literature for the assessment of rPPG methods.
4) Rigorous statistical evaluation Typically, the performance assessment mostly relies on basic statistical and common-sense techniques, such as roughly rank a new method with respect to the state-of-the-art. These crude methods of analysis often make the assessment unfair and statistically unsound, showing at the same time that there is no established procedure for comparing multi classifiers over multiple datasets. Here we claim that a good research practice in the field should not limit to barely report performance numbers. A partial remedy for this manifold situation probably lies in a correct experimental analysis. Many works set the focus on establishing the "winner" of a given dataset competition; as a consequence, the very question of whether the improvement over other methods is statistically significant is by and large neglected.
g. , classifiers) are compared on multiple datasets  [31]. When the FT rejects the null hypothesis because the rank sums are different, generally multiple comparisons are carried out to establish which are the significant differences among algorithms. The latter comprises the Nemenyi post-hoc test  [35]. This allows for determining whether the performance of two algorithms is significantly different when the corresponding average of rankings is at least as great as its critical difference. Of interest for the work presented here, the Nemenyi test takes into account and properly control the multiplicity effect while doing multiple comparisons  [31]. It assumes that the value of the significance level α is adjusted in a single step by dividing it merely by the number of comparisons performed.
6) BVP spectrum. The BPM estimate ĥ(t) is obtained from the spectral analysis of the BVP signal y(t), either by power spectral density (PSD) or by short-time Fourier transform (STFT). The final stage proceeds with the error prediction analysis and the statistical assessment. The latter is normally extended to multiple methods across several datasets. Error computation is essentially based on standard metrics (for details cfr. Section IV-B) such as Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), or Pearson Correlation Coefficient (PCC), and aims at comparing the ground truth BPM h(t) with the estimate ĥ(t) obtained via the above pipeline. Some of the most important processing stages, involving relevant choices within the framework, are further detailed in the following subsections.