Resting-state functional connectome predicts individual differences in depression during COVID-19 pandemic

Area of Science:

• Neuroscience
• Psychiatry
• Computational Biology

Background:

• Stressful life events are established risk factors for depression.
• The COVID-19 pandemic presented a unique, widespread stressful event.
• Increased depressive symptoms were noted globally during the pandemic.

Purpose of the Study:

• To identify neural markers for depression during the COVID-19 pandemic.
• To utilize connectome-based predictive modeling (CPM) for this identification.
• To assess if these markers predict depression risk longitudinally.

Main Methods:

• Connectome-based predictive modeling (CPM) was employed.
• A longitudinal dataset was used to assess risk prediction.
• Support vector machine (SVM) algorithms classified major depression disorder (MDD) patients and healthy controls.

Main Results:

• High-risk groups showed greater depression levels and increases during the pandemic.
• CPM successfully captured depression-related patterns using resting-state functional connectivity.
• Neural markers implicated emotion-regulation and interoception circuits in depression.

Conclusions:

• Resting-state functional connectivity offers potential neural markers for identifying susceptible populations.
• This study enhances understanding of depression mechanisms during acute, life-threatening events.
• CPM is a viable method for detecting depression-related neural signatures.