{"doi":"10.1016/j.nicl.2018.06.006","title":"Acute trajectories of neural activation predict remission to pharmacotherapy in late-life depression","abstract":null,"journal":"NeuroImage: Clinical","year":2018,"id":618149,"datarank":0.5333022092234121,"base_score":3.5553480614894135,"endowment":3.5553480614894135,"self_citation_contribution":0.5333022092234121,"citation_network_contribution":0.0,"self_endowment_contribution":0.5333022092234121,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":34,"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":506161,"name":"Maxwell Wang","orcid":"0000-0002-9217-6593","position":1,"is_corresponding":false},{"id":295793,"name":"Carmen Andreescu","orcid":"0000-0003-3767-5127","position":2,"is_corresponding":false},{"id":270464,"name":"Dana Tudorascu","orcid":"0000-0003-4675-3692","position":3,"is_corresponding":false},{"id":301455,"name":"Meryl A. Butters","orcid":"0000-0002-2563-817X","position":4,"is_corresponding":false},{"id":700918,"name":"Jordan F. Karp","orcid":"0000-0002-5171-5028","position":5,"is_corresponding":false},{"id":303746,"name":"Charles F. Reynolds","orcid":"0000-0002-2605-7887","position":6,"is_corresponding":false},{"id":1282076,"name":"Howard J. Aizenstein","orcid":null,"position":7,"is_corresponding":false},{"id":652063,"name":"Helmet T. Karim","orcid":"0000-0002-9286-0694","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Acute trajectories of neural activation predict remission to pharmacotherapy in late-life depression","abstract":"Pharmacological treatment of major depressive disorder (MDD) typically involves a lengthy trial and error process to identify an effective intervention. This lengthy period prolongs suffering and worsens all-cause mortality, including from suicide, and is typically longer in late-life depression (LLD). Our group has recently demonstrated that during an open-label venlafaxine (serotonin-norepinephrine reuptake inhibitor) trial, significant changes in functional resting state connectivity occurred following a single dose of treatment, which persisted until the end of the trial. In this work, we propose an analysis framework to translate these perturbations in functional networks into predictors of clinical remission. Participants with LLD (N = 49) completed 12-weeks of treatment with venlafaxine and underwent functional magnetic resonance imaging (fMRI) at baseline and a day following a single dose of venlafaxine. Data was collected at rest as well as during an emotion reactivity task and an emotion regulation task. Remission was defined as a Montgomery-Asberg Depression Rating Scale (MADRS) ≤10 for two weeks. We computed eigenvector centrality (whole brain connectivity) and activation during the emotion regulation and emotion reactivity tasks. We employed principal components analysis, Tikhonov-regularized logistic classification, and least angle regression feature selection to predict remission by the end of the 12-week trial. We utilized ten-fold cross-validation and Receiver Operator Curves (ROC) curve analysis. To determine task-region pairs that significantly contributed to the algorithm's ability to predict remission, we used permutation testing. Using the fMRI data at both baseline and after the first dose of treatment yielded a sensitivity of 72% and a specificity of 68% (AUC = 0.77), a 15% increase in accuracy over baseline MADRS. In general, the accuracy at baseline was further improved by using the change in activation following a single dose. Activation of the frontal cortex, hippocampus, parahippocampus, caudate, thalamus, medial temporal cortex, middle cingulate, and visual cortex predicted treatment remission. Acute, dynamic trajectories of functional imaging metrics in response to a pharmacological intervention are a valuable tool for predicting treatment response in late-life depression and elucidating the mechanism of pharmacological therapies in the context of the brain's functional architecture.","is_dataset_classified":null,"base_score":3.5553480614894135,"endowment":3.5553480614894135,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"30013927","pmcid":"PMC6024196","openalex_id":"https://openalex.org/W2807094104","authors":[],"funders":[{"funder_name":"National Institute of Mental Health","grant_id":"5R01 MH083660","title":null},{"funder_name":"National Institute of Mental Health","grant_id":"5R01 AG033575","title":null},{"funder_name":"National Institute of Mental Health","grant_id":"P30 MH090333","title":null},{"funder_name":"National Institute of Mental Health","grant_id":"R01 MH076079","title":null},{"funder_name":"National Institute of Mental Health","grant_id":"K23 MH086686","title":null},{"funder_name":"National Institute of Mental Health","grant_id":"T32 MH019986","title":null},{"funder_name":"National Institute of Mental Health","grant_id":"P50 AG05133","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG033575","title":null},{"funder_name":"NIA NIH HHS","grant_id":"P50 AG005133","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"R01 MH108509","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"R01 MH083660","title":null},{"funder_name":"National Institutes of Health","grant_id":"5T32MH019986-08","title":"Clinical Research Training in Late-Life Mood Disorders"},{"funder_name":"National Institutes of Health","grant_id":"5K23MH086686-05","title":"Functional and Structural Neuroanatomy in Late-Life Generalized Anxiety Disorder"},{"funder_name":"National Institutes of Health","grant_id":"5P30MH090333-02","title":"RESEARCH METHODS CORE"},{"funder_name":"National Institutes of Health","grant_id":"5P50AG005133-31","title":"AMYLOID DEPOSTION, VASCULAR DISEASE AND CLINICAL PROGRESSION OF AD"},{"funder_name":"National Institutes of Health","grant_id":"5R01MH083660-05","title":"1/3-Incomplete Response in Late-Life Depression: Getting to Remission"},{"funder_name":"National Institutes of Health","grant_id":"2R01MH076079-06","title":"Pharmacologic MRI Predictors of Treatment Response in Late-Life Depression"},{"funder_name":"National Institutes of Health","grant_id":"5R01AG033575-03","title":"Optimizing Care for Older Adults with Back Pain and Depression"}],"total_grants":18,"fwci":1.7461,"citation_percentile":0.83633443,"influential_citations":0,"citation_trend":[{"year":2019,"count":6},{"year":2020,"count":6},{"year":2021,"count":4},{"year":2022,"count":6},{"year":2023,"count":5},{"year":2024,"count":2},{"year":2025,"count":1},{"year":2026,"count":4}],"oa_status":"gold","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.sciencedirect.com/science/article/pii/S221315821830189X/pdf","host_type":"journal"},{"url":"https://www.sciencedirect.com/science/article/pii/S221315821830189X/pdf","host_type":"GOLD"},{"url":"https://www.sciencedirect.com/science/article/pii/S221315821830189X/pdf","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S221315821830189X?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S221315821830189X?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.nicl.2018.06.006","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/30013927","host_type":"repository"},{"url":"https://doaj.org/article/60ce2e57a11f428d88694571512f3e1f","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6024196","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC6024196","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC6024196?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1016/j.nicl.2018.06.006","host_type":""},{"url":"https://dx.doi.org/10.1016/j.nicl.2018.06.006","host_type":""}],"fields_of_study":["Functional Brain Connectivity Studies","Treatment of Major Depression","Mental Health Research Topics","Medicine","Psychology","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":["Venlafaxine Hydrochloride","Aged","Antidepressive Agents","Brain","Major Depressive Disorder","Depressive Disorder, Major","Emotions","Female","Humans","Magnetic Resonance Imaging","Male","Middle Aged","Predictive Value of Tests","Psychiatric Status Rating Scales","Treatment Failure","Selective Serotonin Reuptake Inhibitors"],"keywords":["Late life depression","Major depressive disorder","Venlafaxine","Psychology","Major depressive episode","Functional magnetic resonance imaging","Hamilton Rating Scale for Depression","Depression (economics)","Psychiatry","Antidepressant","Internal medicine","Medicine","Mood","Neuroscience","Cognition","Prediction","fMRI","Lld","Male","Psychiatric Status Rating Scales","Computer applications to medicine. Medical informatics","Emotions","R858-859.7","Venlafaxine Hydrochloride","Brain","Regular Article","Middle Aged","Magnetic Resonance Imaging","Antidepressive Agents","Predictive Value of Tests","Humans","Female","Neurology. Diseases of the nervous system","Treatment Failure","RC346-429","Selective Serotonin Reuptake Inhibitors","Aged"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. Good health"},{"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":"2026-08-03T03:37:21.935175Z","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":[]}