{"doi":"10.1101/2022.12.05.519094","title":"Predicting distributed working memory activity in a large-scale mouse brain: the importance of the cell type-specific connectome","abstract":"Abstract Recent advances in connectome and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the mouse multiregional brain for a cardinal cognitive function called working memory, the brain’s ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for inter-areal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":297817,"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":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7787,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":268771,"name":"Seán Froudist‐Walsh","orcid":"0000-0003-4070-067X","position":1,"is_corresponding":false},{"id":986365,"name":"Jorge Jaramillo","orcid":"0000-0002-6666-899X","position":2,"is_corresponding":false},{"id":986366,"name":"Junjie Jiang","orcid":"0000-0003-2930-7770","position":3,"is_corresponding":false},{"id":263061,"name":"Xiao‐Jing Wang","orcid":"0000-0003-3124-8474","position":4,"is_corresponding":false},{"id":555485,"name":"Xingyu Ding","orcid":"0000-0002-6614-5195","position":0,"is_corresponding":true}],"reference_count":105,"raw_metadata":null,"created_at":"2026-07-19T00:31:31.270564Z","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":[]}