{"doi":"10.1101/2025.11.03.683805","title":"CeDNe: A multi-scale computational framework for modeling structure-function relationships in the\n                  <i>C. elegans</i>\n                  nervous system","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Understanding how neural circuits generate behavior requires integrating structural and functional data across scales.\n                  <jats:italic>C. elegans</jats:italic>\n                  with its complete connectome, genetically identifiable neurons, single-cell transcriptome, neuropeptide-receptor distribution, and an amenability to simultaneous measurement of brain-wide neural activity and behavior presents a unique opportunity for such a multiscale circuit analysis. However, the absence of a unifying framework to connect these diverse datasets limits our ability to connect network structure and attributes with function. Here we introduce CeDNe (\n                  <jats:italic>C. elegans</jats:italic>\n                  Dynamical Network), an open-source computational framework that integrates anatomical, molecular, and imaging datasets into a unified graph-based representation that enables multimodal data analysis by cross-referencing different omics layers in a single computational environment. Specifically, CeDNe provides modular tools for visualizing and analyzing network connectivity, motif distribution, and circuit paths. Further, it incorporates a computational framework that simulates neural dynamics and optimizes network models to bridge structural connectivity with neural activity. Thus, CeDNe establishes a scalable foundation for data-driven modeling of the nervous system. This open-source tool not only facilitates computational connectomics and multimodal analyses in\n                  <jats:italic>C. elegans</jats:italic>\n                  but also serves as a generalizable framework for investigating structure-function relationships in neural networks of other organisms.\n                </jats:p>","journal":null,"year":null,"id":608883,"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":297565,"name":"Yun Zhang","orcid":"0000-0002-7631-858X","position":1,"is_corresponding":false},{"id":1195580,"name":"Sahil Moza","orcid":"0000-0002-2225-8841","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"CeDNe: A multi-scale computational framework for modeling structure-function relationships in the\n                  <i>C. elegans</i>\n                  nervous system","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Understanding how neural circuits generate behavior requires integrating structural and functional data across scales.\n                  <jats:italic>C. elegans</jats:italic>\n                  with its complete connectome, genetically identifiable neurons, single-cell transcriptome, neuropeptide-receptor distribution, and an amenability to simultaneous measurement of brain-wide neural activity and behavior presents a unique opportunity for such a multiscale circuit analysis. However, the absence of a unifying framework to connect these diverse datasets limits our ability to connect network structure and attributes with function. Here we introduce CeDNe (\n                  <jats:italic>C. elegans</jats:italic>\n                  Dynamical Network), an open-source computational framework that integrates anatomical, molecular, and imaging datasets into a unified graph-based representation that enables multimodal data analysis by cross-referencing different omics layers in a single computational environment. Specifically, CeDNe provides modular tools for visualizing and analyzing network connectivity, motif distribution, and circuit paths. Further, it incorporates a computational framework that simulates neural dynamics and optimizes network models to bridge structural connectivity with neural activity. Thus, CeDNe establishes a scalable foundation for data-driven modeling of the nervous system. This open-source tool not only facilitates computational connectomics and multimodal analyses in\n                  <jats:italic>C. elegans</jats:italic>\n                  but also serves as a generalizable framework for investigating structure-function relationships in neural networks of other organisms.\n                </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":"21097893","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"","grant_id":"NS115484","title":null},{"funder_name":"","grant_id":"MH130064","title":null}],"total_grants":2,"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/2025/11/04/2025.11.03.683805.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2025.11.03.683805","host_type":"publisher"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12637529","host_type":"repository"}],"fields_of_study":[],"mesh_terms":[],"keywords":[],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-30T23:44:58.075419Z","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":[]}