{"doi":"10.1016/j.celrep.2025.115565","title":"Neural correlates of device-based sleep characteristics in adolescents","abstract":"Understanding the brain mechanisms underlying adolescent sleep patterns and their impact on psychophysiological development is complex. We applied sparse canonical correlation analysis (sCCA) to data from 3,222 adolescents in the Adolescent Brain Cognitive Development (ABCD) study, integrating sleep characteristics with multimodal imaging. This reveals two key sleep-brain dimensions: one linking later sleep onset and shorter duration to decreased subcortical-cortical connectivity and another associating a higher heart rate and shorter light sleep with lower brain volumes and connectivity. Hierarchical clustering identifies three biotypes: biotype 1 has delayed, shorter sleep with a higher heart rate; biotype 3 has earlier, longer sleep with a lower heart rate; and biotype 2 is intermediate. These biotypes also differ in cognitive performance and brain structure and function. Longitudinal analysis confirms these differences from ages 9 to 14, with biotype 3 showing consistent cognitive advantages. Our findings offer insights into optimizing sleep routines for better cognitive development. • Device-based sleep characteristics are linked to brain measures in two high-dimensional spaces • Sleep-informed brain dimensions reveal three adolescent biotypes • Three adolescent biotypes show a trend toward graded differences in cognitive performance • Biotypic differences in cognitive and brain development persist through early adolescence Ma et al. conducted a wearable device study of 3,222 adolescents, mapping multidimensional sleep characteristics to brain structures and functions. They identified two key sleep-brain associations in high-dimensional spaces and three replicable biotypes with different cognitive development patterns, providing a theoretical basis for optimizing sleep strategies.","journal":"Cell Reports","year":2025,"id":517308,"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":0.9164,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":307513,"name":"Barbara J. Sahakian","orcid":"0000-0001-7352-1745","position":1,"is_corresponding":false},{"id":1020868,"name":"Bei Zhang","orcid":"0000-0003-0845-7534","position":2,"is_corresponding":false},{"id":1383549,"name":"Zeyu Li","orcid":"0000-0002-8275-8637","position":3,"is_corresponding":false},{"id":590342,"name":"Jin‐Tai Yu","orcid":"0000-0002-2532-383X","position":4,"is_corresponding":false},{"id":1383550,"name":"Fei Li","orcid":"0000-0003-4748-9301","position":5,"is_corresponding":false},{"id":334370,"name":"Jianfeng Feng","orcid":"0000-0001-5987-2258","position":6,"is_corresponding":false},{"id":316788,"name":"Wei Cheng","orcid":"0000-0003-1118-1743","position":7,"is_corresponding":false},{"id":506683,"name":"Qing Ma","orcid":"0000-0002-5625-1330","position":0,"is_corresponding":true}],"reference_count":67,"raw_metadata":null,"created_at":"2026-07-19T02:48:59.410472Z","pmid":"40244849","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":[]}