AUTHOR=Li Leiting , Sun Meirong , Qi Mengdi , Li Yiwen , Li Dongwei 

TITLE=Neural correlates of emotional working memory predict depression and anxiety

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1574901

DOI=10.3389/fnins.2025.1574901

ISSN=1662-453X

ABSTRACT=IntroductionEmotional working memory (WM) plays a critical role in cognitive functions such as emotion regulation, decision-making, and learning. Understanding how emotional stimuli, particularly negative ones, affect WM performance is crucial for identifying cognitive markers of mental health issues like anxiety and depression. Our objective is to determine whether trait anxiety and depression levels are associated with specific performance outcomes in emotional WM and whether behavioral and neural indicators demonstrate statistically significant correlations with individual anxiety and depression levels in university students.MethodsIn our research: Experiment 1 (n = 25) tested WM performance with both positive and negative emotional stimuli under different cognitive loads (2 vs. 4 items), while Experiment 2 (n = 34) combined EEG recording to investigate the neural index of anxiety and depression during negative emotional WM.ResultsResults showed that negative emotional stimuli impaired WM performance, especially under higher cognitive loads, with anxiety level being linked to increased theta activity during encoding and depression level associated with decreased alpha activity during retrieval. Additionally, individuals with higher anxiety exhibited reduced sensitivity to cognitive load differences in WM tasks involving negative emotions.DiscussionThese results demonstrated that specific EEG patterns during negative emotional WM were significantly associated with individual anxiety and depression levels, suggesting the potential utility of EEG measures for identifying at-risk individuals of anxiety and depression in university student populations. By linking cognitive and neural indicators, the study contributes to the development of personalized interventions for mental health monitoring and treatment.