{"doi":"10.3389/fnagi.2020.00107","title":"Differences in Cerebral Structure Associated With Depressive Symptoms in the Elderly With Alzheimer’s Disease","abstract":"Background: Alzheimer’s Disease (AD) is characterized by global deterioration in multiple cognitive domains. In addition to cognitive impairment, depressive symptoms are common issues that trouble AD patients. The neuroanatomical basis of depressive symptoms in AD patients has yet to be elucidated. Method: Twenty AD patients and twenty-two healthy controls (HCs) were recruited for the present study. Depressive symptoms in AD patients and HCs were assessed according to the Hamilton Depression Rating Scale (HDRS). Anatomical structural differences were assessed between AD patients and HCs using voxel-based morphometry (VBM) and surface-based morphometry (SBM). Correlation analyses were conducted to investigate relationships between depressive symptoms and structural altered regions. Multiple pattern analysis using linear support vector machine (SVM) was performed in another independent cohort, which were collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) data and contained twenty AD patients and twenty HCs, to distinguish AD patients from HCs. Results: Compared with HCs, AD patients exhibited global cerebral atrophy in gray matter volume and cortical thickness, including frontal, parietal, temporal, occipital and insular lobes. In addition, insular gray matter volume was negatively correlated with depressive symptoms. Moreover, SVM-based classification achieved an accuracy of 77.5%, a sensitivity of 70% and a specificity of 85% by leave-one-out cross-validation. Conclusion: Gray matter volume of the insula displayed atrophy among AD patients, which is associated with depressive symptoms. Our observations provide a potential neural substrate for analysis to examine the co-occurrence of AD with depressive symptoms.","journal":"Frontiers in Aging Neuroscience","year":2020,"id":77341,"datarank":0.4493598410330987,"base_score":2.995732273553991,"endowment":2.995732273553991,"self_citation_contribution":0.4493598410330987,"citation_network_contribution":0.0,"self_endowment_contribution":0.4493598410330987,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":19,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8261,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":402775,"name":"Xingqi Wu","orcid":"0000-0003-0646-1353","position":1,"is_corresponding":false},{"id":402776,"name":"Qiang Wei","orcid":"0000-0003-3750-3042","position":2,"is_corresponding":false},{"id":294401,"name":"Kai Wang","orcid":"0000-0002-6170-4744","position":3,"is_corresponding":false},{"id":402777,"name":"Yanghua Tian","orcid":"0000-0001-5038-0599","position":4,"is_corresponding":false},{"id":403645,"name":"and the Alzheimer’s Disease Neuroimaging Initiative","orcid":null,"position":5,"is_corresponding":false},{"id":402774,"name":"Yue Wu","orcid":"0000-0002-7365-0931","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T21:48:50.085103Z","pmid":"32477094","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":[]}