Neural markers of depression risk predict the onset of depression
Name
nihms-1520564.pdf
Description
Accepted version
Size
511.02 KB
Format
Adobe PDF
Checksum (MD5)
b77084735740cd784b42011ba0a47dcb
Author(s)
Whitfield-Gabriel, Susan
Gabrieli, John D. E.
Date Issued
March 2019
Journal
Psychiatry research. Neuroimaging
Publisher
Elsevier BV
Citation
Shapero, Benjamin G., et al., "Neural markers of depression risk predict the onset of depression." Psychiatry research. Neuroimaging 285 (2019): p. 31-39 doi 10.1016/J.PSCYCHRESNS.2019.01.006 ©2019 Author(s)
Version
Author's final manuscript
Abstract
Although research highlights neural correlates of Major Depressive Disorder (MDD), it is unclear whether these correlates reflect the state of depression or a pre-existing risk factor. The current study examined whether baseline differences in brain activations, resting-state connectivity, and brain structural differences between non-symptomatic children at high- and low-risk for MDD based on familial depression prospectively predict the onset of a depressive episode or increases in depressive symptomatology. We re-assessed 44 participants (28 high-risk; 16 low-risk) who had undergone neuroimaging in a previous study 3–4 years earlier (Mean age at follow-up = 14.3 years, SD = 1.9 years; 45% females; 70% Caucasian). We investigated whether baseline brain imaging data (including an emotional face match task fMRI, resting-state fMRI and structural MRI) that differentiated the risk groups also predicted the onset of depression. Resting-state functional connectivity abnormalities in the default mode and cognitive control network that differentiated high-risk from low-risk youth at baseline predicted the onset of MDD during adolescence, after taking risk status into account. Increased functional activation to both happy and fearful faces was associated with greater decreases in self-reported depression symptoms at follow-up. This preliminary evidence could be used to identify youth at-risk for depression and inform early intervention strategies. ©2019
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.PSCYCHRESNS.2019.01.006
