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Using resting-state intrinsic network connectivity to identify suicide risk in mood disorders

Published online by Cambridge University Press:  10 October 2019

Jonathan P. Stange
University of Illinois at Chicago, Chicago, IL, USA
Lisanne M. Jenkins
Northwestern University, Chicago, IL, USA
Stephanie Pocius
University of Utah, Salt Lake City, UT, USA
Kayla Kreutzer
University of Illinois at Chicago, Chicago, IL, USA
Katie L. Bessette
University of Illinois at Chicago, Chicago, IL, USA
Sophie R. DelDonno
University of Illinois at Chicago, Chicago, IL, USA
Leah R. Kling
University of Illinois at Chicago, Chicago, IL, USA
Runa Bhaumik
University of Illinois at Chicago, Chicago, IL, USA
Robert C. Welsh
University of Utah, Salt Lake City, UT, USA
John G. Keilp
Columbia University, New York, NY, USA
K. Luan Phan
The Ohio State University, Columbus, OH, USA
Scott A. Langenecker*
University of Utah, Salt Lake City, UT, USA
Author for correspondence: Scott A. Langenecker, E-mail:



Little is known about the neural substrates of suicide risk in mood disorders. Improving the identification of biomarkers of suicide risk, as indicated by a history of suicide-related behavior (SB), could lead to more targeted treatments to reduce risk.


Participants were 18 young adults with a mood disorder with a history of SB (as indicated by endorsing a past suicide attempt), 60 with a mood disorder with a history of suicidal ideation (SI) but not SB, 52 with a mood disorder with no history of SI or SB (MD), and 82 healthy comparison participants (HC). Resting-state functional connectivity within and between intrinsic neural networks, including cognitive control network (CCN), salience and emotion network (SEN), and default mode network (DMN), was compared between groups.


Several fronto-parietal regions (k > 57, p < 0.005) were identified in which individuals with SB demonstrated distinct patterns of connectivity within (in the CCN) and across networks (CCN-SEN and CCN-DMN). Connectivity with some of these same regions also distinguished the SB group when participants were re-scanned after 1–4 months. Extracted data defined SB group membership with good accuracy, sensitivity, and specificity (79–88%).


These results suggest that individuals with a history of SB in the context of mood disorders may show reliably distinct patterns of intrinsic network connectivity, even when compared to those with mood disorders without SB. Resting-state fMRI is a promising tool for identifying subtypes of patients with mood disorders who may be at risk for suicidal behavior.

Original Articles
Copyright © Cambridge University Press 2019

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