{"doi":"10.1017/dep.2024.6.pr2","title":"Review: Neural signatures of emotional biases predict clinical outcomes in difficult-to-treat depression — R0/PR2","abstract":"Background:Neural predictors underlying variability in depression outcomes are poorly understood. Functional MRI measures of subgenual cortex connectivity, self-blaming and negative perceptual biases have shown prognostic potential in treatment-naïve, medication-free and fully remitting forms of major depressive disorder (MDD). However, their role in more chronic, difficult-to-treat forms of MDD is unknown.Methods:Forty-five participants (n = 38 meeting minimum data quality thresholds) fulfilled criteria for difficult-to-treat MDD. Clinical outcome was determined by computing percentage change at follow-up from baseline (four months) on the self-reported Quick Inventory of Depressive Symptomatology (16-item). Baseline measures included self-blame-selective connectivity of the right superior anterior temporal lobe with an a priori Brodmann Area 25 region-of-interest, blood-oxygen-level-dependent a priori bilateral amygdala activation for subliminal sad vs happy faces, and resting-state connectivity of the subgenual cortex with an a priori defined ventrolateral prefrontal cortex/insula region-of-interest.Findings:A linear regression model showed that baseline severity of depressive symptoms explained 3% of the variance in outcomes at follow-up (F[3,34] = .33, p = .81). In contrast, our three pre-registered neural measures combined, explained 32% of the variance in clinical outcomes (F[4,33] = 3.86, p = .01).Conclusion:These findings corroborate the pathophysiological relevance of neural signatures of emotional biases and their potential as predictors of outcomes in difficult-to-treat depression.","journal":null,"year":2024,"id":504077,"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":0.9546,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":285450,"name":"Gareth J. Barker","orcid":"0000-0002-5214-7421","position":1,"is_corresponding":false},{"id":846532,"name":"Owen O’Daly","orcid":"0000-0001-5690-1252","position":2,"is_corresponding":false},{"id":235034,"name":"Beata R. Godlewska","orcid":"0000-0002-5973-3765","position":3,"is_corresponding":false},{"id":1109754,"name":"Ewan Carr","orcid":"0000-0002-1146-4922","position":4,"is_corresponding":false},{"id":653841,"name":"Kimberley Goldsmith","orcid":"0000-0002-0620-7868","position":5,"is_corresponding":false},{"id":254355,"name":"Allan H. Young","orcid":"0000-0003-2291-6952","position":6,"is_corresponding":false},{"id":630868,"name":"Jorge Moll","orcid":"0000-0002-4297-591X","position":7,"is_corresponding":false},{"id":623463,"name":"Roland Zahn","orcid":"0000-0002-8447-1453","position":8,"is_corresponding":false},{"id":1109756,"name":"Diede Fennema","orcid":"0000-0002-1470-4063","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:10:35.597333Z","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":[]}