Effects of Locus Coeruleus Activation on n-Back Performance and Frontoparietal Activity.

Previous studies have linked working memory capacity to restricted hemodynamic responses within critical nodes of the frontoparietal network. Emerging evidence suggests a potential role of the locus coeruleus (LC) in modulating activation of key regions essential for working memory function. This study investigated this hypothesis by examining changes in BOLD signal within the LC and cortex during a parametrically designed verbal working memory task (n-back).

Neural and behavioral markers of inhibitory control predict symptom improvement during internet-delivered cognitive behavioral therapy for depression.

Poor inhibitory control contributes to deficits in emotion regulation, which are often targeted by treatments for major depressive disorder (MDD), including cognitive behavioral therapy (CBT). Brain regions that contribute to inhibitory control and emotion regulation overlap; thus, inhibitory control might relate to response to CBT. In this study, we examined whether baseline inhibitory control and resting state functional connectivity (rsFC) within overlapping emotion regulation-inhibitory control regions predicted treatment response to internet-based CBT (iCBT).

Psychological Resilience and Hardiness as Protective Factors in the Relationship Between Depression/Anxiety and Well-Being: Exploratory and Confirmatory Evidence.

Previous research shows depression and anxiety are negatively correlated with subjective well-being. Additionally, there is evidence psychological resilience positively influences well-being. The present study explored whether the relationship between depression/anxiety and subjective well-being might also be moderated by aspects of psychological resilience - such that depression and anxiety do not reduce well-being to the same extent in individuals high in psychological resilience traits.

Individualised prediction of resilience and vulnerability to sleep loss using EEG features.

It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep-loss phenotype are not scalable or involve effort-dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep.