{"doi":"10.1101/2023.07.24.550218","title":"Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling","abstract":"<jats:title>ABSTRACT</jats:title>\n                <jats:p>Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.</jats:p>","journal":null,"year":null,"id":623312,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":804339,"name":"Damon Pham","orcid":"0000-0001-7563-4727","position":1,"is_corresponding":false},{"id":288577,"name":"Amanda F. Mejia","orcid":"0000-0002-4312-8974","position":2,"is_corresponding":false},{"id":302194,"name":"Silvina L. Ferradal","orcid":"0000-0002-4129-1823","position":3,"is_corresponding":false},{"id":1610804,"name":"Diego Derman","orcid":"0000-0002-6895-9248","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling","abstract":"<jats:title>ABSTRACT</jats:title>\n                <jats:p>Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39149306","pmcid":null,"openalex_id":"https://openalex.org/W4385263626","authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"1R01EB027119-01","title":"Bayesian methods for cortical surface neuroimaging data"},{"funder_name":"European Commission","grant_id":"319456","title":"The Developing Human Connectome Project"},{"funder_name":"National Institutes of Health","grant_id":"5UL1TR002529-02","title":"Indiana Clinical and Translational Sciences Institute"},{"funder_name":"National Institutes of Health","grant_id":"5R01AG083919-02","title":"Individual functional brain mapping for biomarker discovery in Alzheimer's"}],"total_grants":4,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2023/07/26/2023.07.24.550218.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2023/07/26/2023.07.24.550218.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2023.07.24.550218","host_type":"publisher"},{"url":"https://doi.org/10.1101/2023.07.24.550218","host_type":"repository"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39149306","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11326129","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11326129/pdf/nihpp-2023.07.24.550218v2.pdf","host_type":"repository"},{"url":"https://europepmc.org/article/PPR/PPR697617","host_type":"Europe_PMC"},{"url":"https://europepmc.org/api/fulltextRepo?pprId=PPR697617&type=FILE&fileName=EMS182014-pdf.pdf&mimeType=application/pdf","host_type":"Europe_PMC"},{"url":"https://doi.org/10.1162/imag_a_00504","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/40800976","host_type":""},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12319736/","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/40800976/","host_type":""},{"url":"http://dx.doi.org/10.1101/2023.07.24.550218","host_type":""}],"fields_of_study":["Functional Brain Connectivity Studies","Neural dynamics and brain function","Advanced MRI Techniques and Applications","05 social sciences","0501 psychology and cognitive sciences"],"mesh_terms":[],"keywords":["Human Connectome Project","Connectome","Resting state fMRI","Functional magnetic resonance imaging","Bayesian probability","Computer science","Functional connectivity","Neuroscience","Artificial intelligence","Pattern recognition (psychology)","Machine learning","Psychology","Article","Research Article"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Partnerships for the goals"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T23:14:34.058791Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}