Back

Impaired experience-based attentional suppression in major depression: a neurophysiological and cognitive vulnerability marker

Qiu, N.; Li, P.; Li, S.; Li, K.; Yan, H.; Gan, X.; Li, Y.; Hu, L.; Becker, B.; Yao, D.

2025-07-28 psychiatry and clinical psychology
10.1101/2025.07.28.25332298 medRxiv
Show abstract

Major Depressive Disorder (MDD) is a leading cause of disability worldwide. Core cognitive deficits, such as impaired selective attention, may contribute to its pathogenesis, yet their neurophysiological basis remains unclear. We here employed an additional-singleton paradigm and examined lateralized ERPs and EEG-based effective connectivity in individuals with MDD (n = 33), as compared to healthy controls (HCs; n = 31) to examine impaired statistical learning (SL) of distractor locations, a mechanism supporting proactive attention suppression. While HCs demonstrated typical SL effects including faster reaction times and reduced suppression-related Pd amplitudes at high-probability distractor locations, MDD patients failed to benefit behaviorally from SL and instead exhibited enhanced N2pc amplitudes reflecting attention-related selection to high-probability distractor locations, and increased fronto-parietal-occipital connectivity in alpha and theta bands, suggesting maladaptive attentional engagement and compensatory network hyperactivation. Crucially, more negative N2pc amplitudes and earlier latencies were significantly associated with slower cognitive flexibility and greater suicidal symptomology in MDD, independent of medication status. These findings identify a behavioral and neurophysiological candidate mechanism of impaired SL-guided proactive selective attention in MDD. Dysfunctions in SL-guided attentional suppression may represent a neurophysiological phenotype of cognitive vulnerability in depression with potential utility for biomarker and treatment development.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.