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Classification of depressed and non-depressed MCI and non-depressed cognitively normal individuals using resting-state metrics: A multi-group study with machine learning and graph reinforcement learning

Journal of Affective Disorders(2026)10.1016/j.jad.2026.121719Source: DataRank Database

Classification of depressed and non-depressed MCI and non-depressed cognitively normal individuals using resting-state metrics: A multi-group study with machine learning and graph reinforcement learning is a research paper published in Journal of Affective Disorders (2026). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (5 grants)Topics (18)IDs (PubMed)

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Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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Authors (11)

Jiaying Song,Xiaomeng Xue,Jinying Wu,Hang Zhang ORCID,Bingqi Fu

Live enrichment

PubMed:41933620

National Natural Science Foundation of China

Grant: 82374578

U.S. Department of Defense

Grant: W81XWH-12-2-0012

National Institutes of Health

Grant: U01 AG024904

National Natural Science Foundation of China

Grant: 82001896

Alzheimer's Disease Neuroimaging Initiative

MeSH Terms

BrainCerebral CortexHumansMagnetic Resonance ImagingBrain MappingDepressionAgedFemaleMaleMachine LearningCognitive DysfunctionReinforcement Machine LearningGraph Neural Networks

Keywords

Graph theoryFunctional ConnectivityDepressed Mild Cognitive Impairment (Dmci)Graph Reinforcement Learning (Grl)Non-depressed Mild Cognitive Impairment (Ndmci)