Browsing by Author "Rao, Hengyi"

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    Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation
    (National Academy of Science, 2023) Chai, Ya; Gehrman, Philip; Yu, Meichen; Mao, Tianxin; Deng, Yao; Rao, Joy; Shi, Hui; Quan, Peng; Xu, Jing; Zhang, Xiaocui; Lei, Hui; Fang, Zhuo; Xu, Sihua; Boland, Elaine; Goldschmied, Jennifer R.; Barilla, Holly; Goel, Namni; Basner, Mathias; Thase, Michael E.; Sheline, Yvette I.; Dinges, David F.; Detre, John A.; Zhang, Xiaochu; Rao, Hengyi; Radiology and Imaging Sciences, School of Medicine
    Sleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation. Here, we used functional MRI to examine associations between amygdala- and DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients. Imaging data showed that TSD enhanced both amygdala- and DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients. These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.
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    Functional connectomics in depression: insights into therapies
    (Elsevier, 2023) Chai, Ya; Sheline, Yvette I.; Oathes, Desmond J.; Balderston, Nicholas L.; Rao, Hengyi; Yu, Meichen; Radiology and Imaging Sciences, School of Medicine
    Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.