dataesr/scientific-paragraphs-categorization huggingface dataset:
Such differences might affect the estimation of classical measures such as BOLD signal, cortical thickness, and voxel-based morphometry metrics ( fig. 1  ). When conjointly analysing MRI datasets from different sites, researchers might therefore be interested in controlling this source of variability. Another source of variability in MRI datasets is the potential impact of fluid control on primate psychological well-being. This impact is likely to depend on the details of the fluid control protocols. Researchers usually ensure that this impact is not too severe by ensuring that NHP subjects maintain a healthy body weight. However, more subtle consequences cannot be excluded without a systematic assessment of subjects' well-being using more sensitive measures (e. g. Hage et al. , 2014 ; Gray et al.
2016 ; Pfefferle et al. , 2018 ). Such assessments at the behavioural level are difficult (see section 4 ) and suffer from methodological limitations (e. g. Poirier and Bateson, 2017 ; Poirier et al. , 2019a ). Large MRI datasets where variation in fluid restriction is not confounded with scanning sites offer the unique opportunity to investigate the consequences of fluid control on subjects' well-being at the neural level  ( Poirier et al. , 2019b ) , and, if metadata related to spontaneous home cage behaviour are also shared, at the behavioural level as well. Such data would allow researchers to scientifically assess and if necessary refine fluid control protocols, in line with the 3R's  ( Russell and Burch, 1959 ).
Blood pulsation and respiration also result in motion of large brain regions such as the thalamus, the diencephalon or the brain stem. In the anesthetized monkey, this information can be recorded using pulse oximetry (measuring changes in the infrared light absorbed by blood infused tissue, due to global changes in oxygenation levels) or electrocardiograms (ECG). Respiration rate can be recorded directly from the ventilator that is maintaining anaesthesia. Alternatively, if respiration signals are unavailable, they can also be extracted from the ECG signals  ( Labate et al. , 2013 ). Heart and respiratory rate are highly dependent on anaesthesia and medication kinetics and may vary during data acquisition if care is not taken to prevent this. It is typically easier to stabilize these parameters using gas or perfusion anaesthesia.
The size of the image will determine the precision of the calibration. In highly trained animals, eye tracking calibration involves fixating stimuli of only a few pixels in size and discriminating a colour change for a reward. Eye tracking information is very rich and multiple distinct signals can be extracted. Epochs of eyes open vs. eyes closed, that have been associated with changes in global neuronal signal amplitude, can for example be estimated  ( Wong et al. , 2015 ). More specific information on eye position and saccades, blinks and pupil size can also be extracted, all of which have been associated with specific cortical activations. As a result, not considering them as regressors of non-interest can lead to important confounds both in resting state experiments (reporting temporal correlations biased by these eye signals) as well as during task-based experiments, when eye signals correlate with task timings (and are thus not averaged out during data analysis).
This approach offers the opportunity to assess quantitative differences between species, taking into account shared ancestry, and to infer the evolutionary history of cognitive traits. However, it requires extensive datasets covering a large variety of species. The field of primate cognition has recently seen the emergence of an international consortium to support large-scale collaborations in primate behavioural research  ( D. Altschul and Beran, 2019a; D Altschul and Beran, 2019b ). This initiative has produced one of the largest and most diverse primate datasets describing behaviour, enabling researchers to address novel research questions regarding the evolution of primate cognition (in this case, short-term memory). The recent expansion of neuroimaging to several NHP species, combined with large-scale data sharing initiatives  ( Milham et al.
, 2018 ( Milham et al. , , 2020 ) ) , offer the opportunity to follow a similar approach. It is worth noting that current European (as of 2010) and US (as of 2015) regulations do not authorize experimental research in great apes (e. g. gorillas, orang-utans and chimpanzees). Thus, for these species, neuroimaging data are either collected post-mortem or during veterinary investigations related to the individual's health. Combining primate neuroimaging data from multiple species with primate phylogenetic trees (for a digital version, see  Arnold et al. , 2010 )  will allow investigating  ( Freckleton et al. 2002 , MacLean and Hare, 2012 )  or controlling for  ( Grafen 1989 ; Pagel 1999 )  the variability in species differences due to shared ancestry. Examples of potential applications include quantifying how much of the between-species variability of any neuroimaging trait (e.
g. cortical thickness, brain gyrification, connectivity profile of a specific brain region) is due to shared ancestry (phylogenetic signal) and how much is due to other factors (e. g. a shared physical environment or shared level of complexity in social relationships), and inferring this trait in common ancestors  ( Heuer et al. , 2019 ). Ultimately, comparing and merging the evolutionary history of cognitive traits from a behavioural, neural, and genetic point of view promise to transform our understanding of primate cognition. Here we show the organization of an example BIDS dataset, with file descriptions (connected to files with coloured lines). Briefly, each study has a top-level directory (e. g. PrimateBIDS ), which contains folders for raw data ( raw ) and phenotype information ( pheno ), along with descriptor files where researchers can share descriptions of the subjects, dataset, and accompanying genetic information.
The pheno folder houses across-subject data (. tsv ) and descriptions of those data (. json ). The raw folder contains a folder for each subject, e. g. subj-XX. Each subject-folder, in turn, contains anat, func , and beh folders, which contain anatomical, functional, and behavioural data, respectively. Similar to the pheno folder, each of these folders contains data (e. g. nii. gz or. tsv ) with an accompanying descriptor file (. json ), which describes relevant acquisition/quantification parameters. Individual subject folders can be used to share any data that cannot be easily summarized into an across-subject tabular. tsv file in the pheno folder.
Organization of metadata sharing: Introduction to BIDS Neuroimaging experiments are complicated and often require relevant scan parameters and associated metadata for interpretation. Data can be organized in many different ways, and a lack of standard organization can lead to misunderstandings when sharing data, even for similar experiments from the same lab. Recent efforts to develop consensus on how to organize neuroimaging data have led to the development of the BIDS  ( Gorgolewski, et al. , 2016 ). BIDS is a consensus framework for how to organize neuroimaging data and associated metadata. The BIDS format makes it easy for other investigators to understand what data was collected to facilitate collaboration. In addition, BIDS-Apps ( http://bids-apps. neuroimaging. io/ ) and other software tools can interpret BIDS formatted data, greatly simplifying the analysis process and providing an opportunity to standardize analysis pipelines.
The open-science neuroimaging data-sharing resource PRIMatE Data Exchange (PRIME-DE) accepts and encourages contributors to upload BIDS formatted data. By sharing data in BIDS format, neuroimaging methods research can use the large-scale neuroimaging datasets, such as PRIME-DE, to further test, develop, and optimize NHP neuroimaging tools. The full BIDS specification can be found at https://bidsspecification. readthedocs. io/en/stable. In brief, it specifies a standard naming structure for reconstructed images and folders, which include multiple key-value pairs separated by a "_ " with keys and values separated by a "-". For example, each functional scan is stored as a 4-D niftii named sub-XX_task-XX_bold. nii. gz file in dataset/subj-XX/func , and each T1-anatomical data is stored as a 3-D niftii file named sub-XX_T1w.
nii. gz in dataset/subj-XX/anat. There are a number of tools that can help researchers get their data into BIDS format (e. g. HeuDiConv https://neuroimagingcore-docs. readthedocs. io/en/latest/pages/heudiconv. html ) and validate that a BIDS dataset (e. g. BIDS-validator https://bidsstandard. github. io/bids-validator/ ). These tools are rapidly improving, and evolving, to further simplify the process of converting DICOM data into this standardized BIDS format to facilitate data sharing and homogenized analyses. We realize that many existing, ongoing, and future projects will identify study-specific reasons to use their own data structure. To address this problem, researchers can write scripts to convert between various standardized data formats. In the context of the Human Connectome Project (HCP), for example, researchers have created heuristics for converting from HCP to BIDS, as well as BIDS-Apps that are designed to run HCP processing pipelines on BIDS datasets ( https://github.
com/BIDS-Apps/HCPPipelines ;  Glasser et al. 2013 , Smith et al. 2013 ). In addition to neuroimaging data, the BIDS format has been expanded to allow for standardized sharing of related metadata when available, including in-scanner task/manipulation data (including different "resting " conditions, such as levels of anaesthesia), in-scanner physiological data, as well as behavioural data collected outside of the scanner, rearing information (e. g. early-life rearing/housing information), and genetic descriptors (including single-nucleotide polymorphisms and whole-genome sequences). Fig. 2  outlines how to incorporate the data described above into the BIDS format. Some types of data, including genetic information, can be shared in multiple ways. Individual SNP data can be shared in the tabular dataset/participants.
tsv file, with columns for individual SNP locations and rows for subjects. If whole-genome sequencing data is available, information about, and links to, this data can be shared in the dataset/genetic_info. json file. Importantly, the sharing of genetic information is not limited to DNA, but can include epigenetic, transcriptomic, metabolomic, and proteomic data (see Fig  2  ). As with genomic data, these data can be shared for individual sites in the dataset/participants. tsv or link to larger datasets in the dataset/genetic_info. json file. Additional details can be found in the Genetic Descriptor BIDS specifica-tion ( https://bids-specification. readthedocs. io/en/latest/04-modalityspecific-files/08-genetic-descriptor. html ). Behavioural data acquired outside the MRI scanner can also be shared in different places, at the study level, in the dataset/pheno/ folder, and at the subject level, in the dataset/subj-XX/beh/ folder.
The pheno folder contains. tsv files with tabular measure by subject data, i. e. one subject per row, and one column per measure. Each. tsv file is accompanied by a. json file, which describes the measures for which there are data. The pheno folder is intended for storing phenotypic data that is easily summarized at the subject level. For instance, summary statistics from standardized cognitive tests should be shared here. For more complex data, BIDS also specifies a dataset/subj-XXX/beh/ folder, within each subject directory. The Beh folder contains. tsv files where rows denote time/trial rather than subject, and accompanying. json files that clearly describe the contents of the. tsv files. Depending on whether behavioural data are time-locked to events or not, they can be stored in. tsv files named subj-XXX_task-XXX-events.
tsv. gz or subj-XXX_task-XXXbeh. tsv. gz , respectively ( Fig. 2  ). The Beh folder is designed to contain experimental or observational data that cannot be easily summarized into a tabular format across-subjects, or that would benefit from being shared in their raw format to allow researchers to derive multiple phenotypic measures. Examples of such data include non-standardised cognitive tests and observations of home cage spontaneous behaviour. As this can be confusing, it is worth reiterating that BIDS is designed to store behavioural data in two locations, the dataset/pheno/ folder, for tabular across-subjects data, and the dataset/subj-XX/beh/ folder, for individual subject data that cannot be easily coerced into a across-subjects tabular format.
Conclusion Combined with open sharing of neuroimaging data, metadata has the power to transform primate neuroimaging. Metadata will help removing noise from the datasets, increasing the statistical power of studies that combine datasets from different facilities. Perhaps more importantly, they will also allow the integration of neuroimaging data into a multi-disciplinary framework, providing an integrative view of the primate brain within a wider context of primate biology, to understand its mechanisms and evolution. Here, we provide suggestions for what metadata to share, along with an introduction to the BIDS framework which facilitates sharing these data in a standard form. BIDS allows researchers to share behavioural data (e. g. task performance, home cage spontaneous behaviours), welfare data (e.
g. rearing history, housing), genetic data (e. g whole-genome sequencing), manual lateralization data (i. e. estimated handedness), fluid information (i. e. fluid intake and control), physiological data (e. g. ECG), acquisition conditions (e. g. awake-task, anaesthesia levels), as well as neuroimaging data (e. g. BOLD, diffusion-imaging) and relevant descriptors (e. g. acquisition parameters). As primate researchers move toward open sharing of neuroimaging data, we urge them to share as much metadata as possible and to use the BIDS format, which is already accepted in PRIME-DE. Here, we have provided suggestions about what metadata are relevant, and where it can be shared in the BIDS format. These suggestions are not meant to be definitive and should be seen as a starting point for further discussion within the whole primate neuroimaging community.
The community will need to come together to agree what, where, and how metadata should be shared precisely. Metadata sharing protocols could then be published on the PRIMatE-Resource Exchange website ( https://prime-re. github. io/ ). In particular, a major challenge for primate open-science moving forward will be to develop standardized description of recommended metadata which can be shared in the form of common. json files (e. g. nomenclature for animal housing, common ethogram for spontaneous behaviour, formal naming convention for primate cognitive/behavioural tasks (though see  Poldrak et al. , 2011 ). We encourage NHP researchers to discuss and agree collectively how to in-corporate metadata in the BIDS format, to ensure optimal progress in the field of NHP neuroimaging.
[56]. Data analysis. We pursued a pathway analysis of summary data from the meta-analysis described above. Specifically, we performed competitive gene-set analyses using the Multi-marker Analysis of GenoMic Annotation (MAGMA) software (Version 1. 06)  [57]. MAGMA uses a nested approach to first summarize single-nucleotide polymorphism (SNP)-level associations into gene-level associations and then gene set-level associations, allowing for the detection of aggregated signals at even modest levels. In the current analysis, SNP-level estimates were obtained from the meta-analysis of depression described above  [56]. For the gene-level analyses, we annotated each of the SNPs reported in the summary data to genes using Fig. 1  Gene sets regulating three aspects of sensitive period functioning in the sensory systems of rodents.
Research question 2: investigating the developmental regulation of depression-implicated sensitive period gene sets Gene expression data. We investigated the temporal expression patterns of sensitive period genes within the left hemisphere of three brain regions involved in the pathophysiology of depression (amygdala, hippocampus, and medial prefrontal cortex (mPFC))  [60]. Data came from BrainSpan  [61, 62]  (http://www. brainspan. org), a transcriptional atlas of healthy, postmortem brain donors (ages 5. Quality-controlled, quantile-normalized exon microarray data  [63]  were downloaded from Gene Expression Omnibus (GSE25219). For each brain region, expression levels for all probes within an exon were averaged to obtain an expression value for each exon. Probes were annotated to genes using the UCSC Human Genome (hg19) reference sequence.
Similar to prior studies  [64, 65] , we used the median of all exons within each gene as the estimate of gene expression. Expression values are presented in log(2) values; thus, each one-unit difference represents a doubling of expression. Although RNA-sequencing data were available in BrainSpan, that dataset contained only one-third of the sample size, which was too small for our analysis (see Supplemental Materials). Data analysis. To investigate whether the expression patterns of sensitive period genes were developmentally regulated, we tested the hypothesis that developmental stage at the time of death explained a significant amount of variation in gene expression. Specifically, we performed multiple regression analysis using an omnibus F-test to compare the full model (with developmental stage included as a categorical variable) to a baseline model (only adjusting for two principal components capturing genetic ancestry).
An F-test was then performed to compare the genotype-by-developmental timing model (with the interaction terms) to the baseline model (without the interaction terms). Of note, we restricted these exploratory analyses to the mPFC, as we saw the most evidence for developmental regulation in this brain region. More details are provided in Supplemental Materials. Research question 3: investigating interactions between genome-wide and gene set-level genetic liability to depression, timing of exposure to adversity, and depressive symptoms in development (i. e. , dG × E) Dataset and measures. To examine potential dG × E, we analyzed data from 6254 child participants in the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK-based prospective, longitudinal birth cohort of children followed for more than two decades  [66] [67] [68].
Approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Consent for biological samples was collected in accordance with the Human Tissue Act 2004  [69]. More details are available on the ALSPAC website, including a fully searchable data dictionary: http://www. bristol. ac. uk/alspac/researchers/our-data/. The analytic sample included all participants who were genotyped and had data on the outcome, average depressive symptoms from 10. 5 to 23 years of age  [70, 71]. Details about the genotype data collection and quality-control procedure are provided in Supplemental Materials. Depressive symptoms across adolescence and into young adulthood were measured by averaging clinically administered and child self-reports of depressive symptoms using the Short Mood and Feelings Questionnaire  [72]  (Supplemental Materials).
Similarly, caregiver physical or emotional abuse was measured through repeated assessments completed by the mother and her partner, which reported whether either of them had been physically or emotionally cruel to the child. Exposure was defined as a three-level variable: no exposure before age 10. 5 years, exposure during ages 1-5 years, and exposure at other time points outside the sensitive period (grouped into before or after the sensitive period). Data analysis. Using summary statistics provided by Howard et al. [56]  as weights, we generated a polygenic risk score (PRS) representing risk for depression conferred by common variants in the gene set(s) associated with depression in analyses of the first research question (3617 SNPs before clumping). As a comparison, we additionally generated a genomewide PRS including all SNPs associated with depression in the summary statistics at the threshold of p < 0.
05. Prior to computing the PRS, clumping was performed to eliminate SNPs that were in high LD (r 2 > 0. 25). PRS calculations were conducted in PLINK 1. 90  [79]. All analyses controlled for the following covariates (measured at childbirth): sex, maternal age, number of previous pregnancies, home ownership, highest level of maternal education, and maternal marital status. We also adjusted for the top four genetic principal components to control for population stratification. To reduce potential bias and maximize statistical power, missing exposure and covariate data were multiplyimputed using the MICE package  [80]  in R among participants with data on average depressive symptoms. All subsequent multiple regression analyses were performed using 20 imputed datasets. Estimates were combined to account for variation between-and within-imputed datasets.
Gene set (sensitive period functioning) Number These results came from the most recent genome-wide meta-analysis of depression  [53]. Using the Multi-marker Analysis of GenoMic Annotation (MAGMA) software, we first annotated SNPs to genes, ran a gene-level analysis, and then ran a competitive gene-set analysis using the gene sets as described, testing the null hypothesis that genes in the tested gene set were jointly no more associated with the phenotype than genes not in the gene set. The corrected p-value refers to p-values based on 10,000 random permutations, which empirically accounted for multiple testing. Bolded values indicate a corrected p-value < 0. 05. upregulated early in life and downregulated later in life for both major and minor allele homozygotes, whereas the expression level was more stable over time for heterozygotes (Supplemental Fig.
Further, the limited sample size in BrainSpan reduced the generalizability and power of our analyses, Fig. 5  Associations between gene set-level PRS of the opening genes and depressive symptoms at different levels of exposure to childhood adversity, predicted by multiple regression modeling. Depressive symptoms were measured using the Short Mood and Feelings Questionnaire, which ranged on a scale of 0-26, as represented on the Y-axis. The opening genetic score summarizes genetic variations associated with depression in a set of genes involved in regulating the opening of sensitive periods, based on primarily preclinical studies of cortical regions of the brain. The intercepts and slopes plotted above were based on fitted regression models examining potential gene-bydevelopment interplay using data from the ALSPAC (Fig.
4 ,  Model 7). A Lines representing a linear relationship between the opening genetic score and depressive symptoms in different exposure groups did not show any obvious patterns of cross-over. B We observed evidence for a statistical interaction, where a positive association between the genetic score and depressive symptoms was only observed for individuals exposed to caregiver physical or emotional abuse between ages 1 and 5 years in B. which should be addressed when larger databases of brain gene expression across the life span become available. Third, measures of depression varied across research questions: whereas depression examined in Question 1 (from the GWAS of depression) was a minimally phenotyped binary construct, depressive symptoms were measured through research-clinic administered and selfreported questionnaires in Question 3 (ALSPAC).