{"doi":"10.3390/bs15111552","title":"Predictors of Transition from Mild Cognitive Impairment to Normal Cognition and Dementia","abstract":"Mild cognitive impairment (MCI) represents a heterogeneous state between normal aging and dementia, with varied transition pathways. While factors influencing MCI progression are known, their role in cognitive reversal is unclear. This study analyzed 756 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants, classified as progressive MCI (pMCI, N = 272, mean age = 75.10 ± 7.34 years), reversible MCI (rMCI, N = 52, mean age = 69.94 ± 7.98 years) and stable MCI (sMCI, N = 432, mean age = 73.34 ± 7.44 years) based on 36-month follow-up. We compared demographic, lifestyle, clinical, cognitive, neuroimaging, and biomarker data across groups and developed a prediction model. Patients in the rMCI group were significantly younger and had a higher level of education compared with those in the pMCI group. Memory, general cognition, daily functional activities, and hippocampal volume effectively distinguished all three groups. In contrast, Aβ, tau, and other brain regions were able to distinguish only between progressive and non-progressive cases. Informant-reported Everyday Cognition (Ecog) scales outperformed self-reported Ecog scales in differentiating subtypes and predicting progression. Multinomial regression revealed that higher education, larger hippocampal volume, and lower daily functional impairment were associated with reversion, whereas APOE ε4, poorer memory, and greater brain atrophy predicted progression (model accuracy: 78%). The results confirm the significant utility of hippocampal volume, education level, and daily functional activities for assessing baseline disparities and predicting reversion. This study highlights the differential contributions of cognitive abilities and brain regions on MCI reversal, advancing understanding of MCI heterogeneity and providing evidence for precise diagnosis and treatment in early MCI.","journal":"Behavioral Sciences","year":2025,"id":527510,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8671,"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":1382490,"name":"Lin Liu","orcid":"0009-0008-6346-7472","position":1,"is_corresponding":false},{"id":242117,"name":"Zifeng Yang","orcid":"0000-0002-2681-4171","position":2,"is_corresponding":false},{"id":1404258,"name":"Jialing Fan","orcid":"0000-0002-7562-4841","position":3,"is_corresponding":false},{"id":243718,"name":"for the Alzheimer’s Disease Neuroimaging Initiative","orcid":null,"position":4,"is_corresponding":false},{"id":1086497,"name":"Jia Gao","orcid":"0000-0001-6203-209X","position":0,"is_corresponding":true}],"reference_count":62,"raw_metadata":null,"created_at":"2026-07-19T02:50:39.280101Z","pmid":"41301354","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":[]}