DOI: 10.5061/dryad.fxpnvx0zq
title: Data from: Gut-resident microorganisms and their genes are associated with cognition and neuroanatomy in children

description: The gastrointestinal tract, its resident microorganisms, and the central
 nervous system are connected by biochemical signaling, also known as the
 ”microbiome-gut-brain-axis.” Both the human brain and the gut microbiome
 have critical developmental windows in the first years of life, raising
 the possibility that their development is co-occurring and likely
 co-dependent. Emerging evidence implicates gut microorganisms and
 microbiota composition in cognitive outcomes and neurodevelopmental
 disorders (e.g., autism and anxiety), but the influence of gut microbial
 metabolism on typical neurodevelopment has not been explored in detail. We
 investigated the relationship of the microbiome with the neuroanatomy and
 cognitive function of 381 healthy children, demonstrating that differences
 in gut microbial taxa and gene functions are associated with overall
 cognitive function and with differences in the size of multiple brain
 regions. Using a combination of multivariate linear and machine learning
 (ML) models, we showed that many species, including Alistipes obesi and
 Blautia wexlerae, were associated with higher cognitive function, while
 some species such as Ruminococcus gnavus were more commonly found in
 children with low cognitive scores after controlling for sociodemographic
 factors. Microbial genes for enzymes involved in the metabolism of
 neuroactive compounds, particularly short-chain fatty acids such as
 acetate and propionate, were also associated with cognitive function. In
 addition, ML models were able to use microbial taxa to predict the volume
 of brain regions, and many taxa that were identified as important in
 predicting cognitive function also dominated the feature importance metric
 for individual brain regions, and for specific subscales of cognitive
 function. For example, B. wexlerae was the most important species in
 models predicting the size of the parahippocampal region in both the left
 and right hemispheres and was among the top predictors of gross motor and
 expressive language performance. Several species from the phylum
 Bacteroidetes, including GABA-producing Bacteroides ovatus, were important
 for predicting the size of the left accumbens area, but not the right.
 These findings provide potential biomarkers of neurocognition and brain
 development and may lead to the future development of targets for early
 detection and early intervention.
description: # Data associated with "Gut-resident microorganisms and their genes
 are associated with cognition and neuroanatomy in children"
 [https://doi.org/10.5061/dryad.fxpnvx0zq](https://doi.org/10.5061/dryad.fxpnvx0zq) Taxonomic and functional profiles from stool metagenomic sequencing from RESONANCE, an accelerated longitudinal cohort of child cognitive development ## Description of the data and file structure These data tables are used as inputs for the analysis in "Gut-resident microorganisms and their genes are associated with cognition and neuroanatomy in children", published in *Science Advances*. Files are comma-separated value (.csv) or [Apache Arrow](https://arrow.apache.org/) (.arrow) formatted. Julia code for loading and analyzing these data can be found in the accompanying Github repository (archived on Zenodo). ### Microbial data  All `*.arrow` files have the following headers: * `sample` (String): Unique identifier for a particular stool sample. Can be joined to subject/timepoint metadata in `complete_filtered_dataset.csv` using the later's `omni` column (see below). * `sidx` (Int): sample (column) index used for populating sparse matrix representation * `feature` (String): microbial feature (eg taxon or UniRef90 ID) * `fidx` (Int): feature (row) index used for populating sparse matrix representation * `value` (Float): relative abundance value #### Files * `taxa.arrow` - Taxonomic profiles generated by [MetaPhlAn](https://github.com/biobakery/MetaPhlAn/), features are taxa. Each taxon level from Kingdom to species, and the abundances of each taxon level for each sample should sum to `100.0`. * `genefamilies.arrow` - Functional profiles generated by [HUMAnN](https://github.com/biobakery/HUMAnN), features are UniRef90s * `ecs.arrow`, `pfams.arrow`, `kos.arrow` - regrouped stratified functional profiles derived from `genefamilies.arrow` into Enzyme Commission (EC), Protein Families (Pfams), or Kegg Orthology (KO) labels respectively using the utility function `humann_regroup_tables` from HUMAnN software. ### Other data CSV files contain additional metadata. Missing values are represented by blank cells, which are read by `CSV.jl` as `missing` values. See below for additional details. #### Subject and biospecimen metadata The file `complete_filtered_dataset.csv` contains subject- and timepoint-associated metadata for all stool samples. Subjects are referred to by unique IDs (Integers) in the `subject` column, and each individual visit is referred to in the `timepoint` column. These two fields can be used to uniquely join to other data tables (eg brain data below). ECHO-coded timepoints, which refer to the children's developmental stage (eg `EC02` for the second "early childhood" stage) are found in the `ECHOTPCoded` column. Columns beginning with `filter_` are boolean columns that can be used to identify rows that are used in each cohort analysis found in the manuscript. Eg, subsetting on rows where `filter_00to120` is true will provide all of the rows used in the cohort of children from birth to 120 months. The `omni` column contains biospecimen IDs (they were collected in OmniGene buffer) which can be used to map metadata rows to microbial data found in the arrow files referred to above. There is a great deal of additional data present in this table - please refer to code repository (below) for descriptions of further headers, and how they are used in the analysis. #### Neuroimaging data The file `brain_normalized.csv` contains neuroimaging data used in the manuscript. Specifically, brain volume segmentation data normalized to total brain volume (TBV). It contains the following headers: Subject / timepoint info: * `subject` * `timepoint` Non-hemisphere-specific brain regions: * `3rd-ventricle` * `4th-ventricle` * `Brain-stem` * `CSF` * `cerebellar-vermal-lobules-I-V` * `cerebellar-vermal-lobules-VI-VII` * `cerebellar-vermal-lobules-VIII-X` * `Gray-matter` * `White-matter` Hemisphere-specific brain regions. Two of each of the following, preceded by `right-` or `left-` * `lateral-ventricle` * `inferior-lateral-ventricle` * `cerebellum-exterior` * `cerebellum-white-matter` * `thalamus-proper` * `caudate` * `putamen` * `pallidum` * `hippocampus` * `amygdala` * `accumbens-area` * `ventral-DC` * `basal-forebrain` * `caudal-anterior-cingulate` * `caudal-middle-frontal` * `cuneus` * `entorhinal` * `fusiform` * `inferior-parietal` * `inferior-temporal` * `isthmus-cingulate` * `lateral-occipital` * `lateral-orbitofrontal` * `lingual` * `medial-orbitofrontal` * `middle-temporal` * `parahippocampal` * `paracentral` * `pars-opercularis` * `pars-orbitalis` * `pars-triangularis` * `pericalcarine` * `postcentral` * `posterior-cingulate` * `precentral` * `precuneus` * `rostral-anterior-cingulate` * `rostral-middle-frontal` * `superior-frontal` * `superior-parietal` * `superior-temporal` * `supramarginal` * `transverse-temporal` * `insula` ## Sharing/Access information Code for loading the data and performing analyses is [available on github](https://github.com/Klepac-Ceraj-Lab/Resonance) and archived on Zenodo: [https://doi.org/10.5281/zenodo.7647509](https://doi.org/10.5281/zenodo.7647509) ## Code/Software See the Readme and setup instructions for using code [on github](https://github.com/Klepac-Ceraj-Lab/Resonance).