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Vulnerability to Depression in Youth: Advances from Affective Neuroscience

Published on Jan 1, 2017in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
· DOI :10.1016/j.bpsc.2016.09.006
Autumn Kujawa19
Estimated H-index: 19
(PSU: Pennsylvania State University),
Katie L. Burkhouse9
Estimated H-index: 9
(UIC: University of Illinois at Chicago)
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Abstract
Abstract Vulnerability models of depression posit that individual differences in trait-like vulnerabilities emerge early in life and increase risk for the later development of depression. In this review, we summarize advances from affective neuroscience using neural measures to assess vulnerabilities in youth at high risk for depression due to parental history of depression or temperament style, as well as prospective designs evaluating the predictive validity of these vulnerabilities for symptoms and diagnoses of depression across development. Evidence from multiple levels of analysis indicates that healthy youth at high risk for depression exhibit abnormalities in components of the Research Domain Criteria positive valence systems, including blunted activation in the striatum during reward anticipation and feedback, and that some of these measures can be used to predict later symptoms. In addition, alterations in components of the Research Domain Criteria negative valence systems, including neural processing of sadness, loss, and threat, have been observed in risk for depression, though effects appear to be more task and method dependent. Within the social processes domain, preliminary evidence indicates that neural processing of social feedback, including heightened reactivity to exclusion and blunted response to social reward, may be related to depression vulnerability. These studies indicate that affective neuroscience can inform understanding of developmental pathways to depression and identify altered emotional processing among youth at high risk. We provide an integrated summary of replicated findings from this literature, along with recommendations for future directions and implications for early intervention.
  • References (101)
  • Citations (8)
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References101
Newest
#1Brandon L. Goldstein (SBU: Stony Brook University)H-Index: 5
#2Stewart A. Shankman (UIC: University of Illinois at Chicago)H-Index: 24
Last.Daniel N. Klein (SBU: Stony Brook University)H-Index: 72
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#1Kristen A. Lindquist (UNC: University of North Carolina at Chapel Hill)H-Index: 20
#2Ajay B. Satpute (Pomona College)H-Index: 18
Last.Lisa Feldman Barrett L F (NU: Northeastern University)H-Index: 131
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#1Katherine R. Luking (SBU: Stony Brook University)H-Index: 11
#2David Pagliaccio (WashU: Washington University in St. Louis)H-Index: 15
Last.Deanna M (WashU: Washington University in St. Louis)H-Index: 75
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#1Katie L. Burkhouse (UIC: University of Illinois at Chicago)H-Index: 9
#2Autumn Kujawa (UIC: University of Illinois at Chicago)H-Index: 19
Last.Heide Klumpp (UIC: University of Illinois at Chicago)H-Index: 19
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#1Anna Weinberg (McGill University)H-Index: 26
#2Alexandria Meyer (SBU: Stony Brook University)H-Index: 13
Last.Greg Hajcak (SBU: Stony Brook University)H-Index: 64
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Cited By8
Newest
#1Diana J. Whalen (WashU: Washington University in St. Louis)H-Index: 14
#2Kirsten Gilbert (WashU: Washington University in St. Louis)H-Index: 7
Last.Deanna M (WashU: Washington University in St. Louis)H-Index: 75
view all 7 authors...
#1Estee M. Hausman (SBU: Stony Brook University)
#2Roman Kotov (SBU: Stony Brook University)H-Index: 36
Last.Daniel N. Klein (SBU: Stony Brook University)H-Index: 72
view all 6 authors...
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