{"doi":"10.64898/2026.01.30.26345227","title":"Distinct intrinsic neural connectivity of an emotion regulation network across the menopausal transition","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Menopause is a major psychoneuroendocrine transition which can impact emotional functioning and mental health. Although emotion regulation (ER) is fundamental for mental health, intrinsic neural connectivity supporting ER during the menopausal transition remains unexplored. Addressing this gap, this study provides the first examination of intrinsic effective connectivity within an ER-related network across menopausal stages. Resting-state fMRI data were acquired from 76 healthy premenopausal (n = 32), perimenopausal (n = 19), and postmenopausal (n = 25) women. Effective connectivity within a predefined ER network was examined using spectral dynamic causal modeling. Further, we assessed how intrinsic connectivity predicts self-reported ER ability within each group. While self-reported ER ability did not differ across groups, resting-state effective connectivity within the ER network varied in a stage-dependent manner, with most heterogeneous effects observed between pre- and perimenopause, suggesting a non-monotonic trajectory. Perimenopause was characterized by distinct changes in frontal interactions, reflecting a redistribution rather than a gradual shift toward postmenopausal connectivity. Differences regarding postmenopause were restricted to greater weighting of temporo-parietal network components. Connectivity–ER ability associations revealed stage-specific predictive profiles, with distributed fronto-temporal connectivity predicting ER ability in premenopause, frontal-restricted connectivity in perimenopause, and a single frontal connection with reversed predictive direction in postmenopause. Our findings demonstrate that comparable levels of trait-based ER ability are associated with divergent intrinsic network configurations rather than a uniform architecture. Identifying perimenopause as distinct transition window of intrinsic network organization advances hormone-sensitive models of intrinsic connectivity and provides a framework for understanding how baseline network organization may adapt during psychoneuroendocrine transitions in women.</jats:p>","journal":null,"year":null,"id":639435,"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":1661313,"name":"Ann-Christin S. Kimmig","orcid":"0000-0002-8353-6593","position":1,"is_corresponding":false},{"id":1661314,"name":"Sofia Amaoui","orcid":"0000-0003-3603-7879","position":2,"is_corresponding":false},{"id":1661315,"name":"Laura Gervais","orcid":"0009-0006-2854-3172","position":3,"is_corresponding":false},{"id":6035,"name":"Alkistis Skalkidou","orcid":"0000-0002-4935-7532","position":4,"is_corresponding":false},{"id":1322460,"name":"Carmen Morawetz","orcid":"0000-0003-3607-7627","position":5,"is_corresponding":false},{"id":972801,"name":"Birgit Derntl","orcid":"0000-0003-0133-4486","position":6,"is_corresponding":false},{"id":1661312,"name":"Franziska Weinmar","orcid":"0000-0002-0222-9402","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Distinct intrinsic neural connectivity of an emotion regulation network across the menopausal transition","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Menopause is a major psychoneuroendocrine transition which can impact emotional functioning and mental health. Although emotion regulation (ER) is fundamental for mental health, intrinsic neural connectivity supporting ER during the menopausal transition remains unexplored. Addressing this gap, this study provides the first examination of intrinsic effective connectivity within an ER-related network across menopausal stages. Resting-state fMRI data were acquired from 76 healthy premenopausal (n = 32), perimenopausal (n = 19), and postmenopausal (n = 25) women. Effective connectivity within a predefined ER network was examined using spectral dynamic causal modeling. Further, we assessed how intrinsic connectivity predicts self-reported ER ability within each group. While self-reported ER ability did not differ across groups, resting-state effective connectivity within the ER network varied in a stage-dependent manner, with most heterogeneous effects observed between pre- and perimenopause, suggesting a non-monotonic trajectory. Perimenopause was characterized by distinct changes in frontal interactions, reflecting a redistribution rather than a gradual shift toward postmenopausal connectivity. Differences regarding postmenopause were restricted to greater weighting of temporo-parietal network components. Connectivity–ER ability associations revealed stage-specific predictive profiles, with distributed fronto-temporal connectivity predicting ER ability in premenopause, frontal-restricted connectivity in perimenopause, and a single frontal connection with reversed predictive direction in postmenopause. Our findings demonstrate that comparable levels of trait-based ER ability are associated with divergent intrinsic network configurations rather than a uniform architecture. Identifying perimenopause as distinct transition window of intrinsic network organization advances hormone-sensitive models of intrinsic connectivity and provides a framework for understanding how baseline network organization may adapt during psychoneuroendocrine transitions in women.</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":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W7127051300","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2026/02/01/2026.01.30.26345227.full.pdf","host_type":"repository"},{"url":"https://www.medrxiv.org/content/medrxiv/early/2026/02/01/2026.01.30.26345227.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.64898/2026.01.30.26345227","host_type":"publisher"},{"url":"https://doi.org/10.64898/2026.01.30.26345227","host_type":"repository"}],"fields_of_study":["Functional Brain Connectivity Studies","Mental Health Research Topics","Stress Responses and Cortisol"],"mesh_terms":[],"keywords":["Transition (genetics)","Weighting","Artificial neural network","Default mode network","Biological neural network","Heterogeneous network"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-07T00:21:22.948263Z","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":[]}