TY - JOUR
T1 - Cognitive control neuroimaging measures differentiate between those with and without future recurrence of depression
AU - Langenecker, Scott A.
AU - Jenkins, Lisanne M.
AU - Stange, Jonathan P.
AU - Chang, Yi Shin
AU - DelDonno, Sophie R.
AU - Bessette, Katie L.
AU - Passarotti, Alessandra M.
AU - Bhaumik, Runa
AU - Ajilore, Olusola
AU - Jacobs, Rachel H.
N1 - Funding Information:
Supported by a NIMH Biobehavioral Research Award for Innovative New Scientists (BRAINS MH091811 , SAL). We thank the UM Prechter Bipolar research team (Kelly A. Ryan, Melvin G. McInnis, Gloria Harrington), the Multifaceted Explorations of the Neurobiology of Depressive Disorders laboratory (MEND2, Sara L. Weisenbach, Kelly A Ryan, Laura B. Gabriel, Anne L. Weldon, Michelle T Kassel, Kortni K. Meyers, Erica Hymen, Bethany Pester, Kristy A. Skerrett, and Natania A. Crane) for assistance in data collection, diagnostic interviews, and longitudinal follow-up evaluations. We thank Kristy A. Nielson, Ph.D. and Angela F. Caveney, Ph.D. for comments on earlier versions of this manuscript. Parts of this work were presented at a talk at the annual meeting of the American College of Neuropsychopharmacology in 2015, Hollywood, FL by SAL. The authors have no conflicts of interest to report. All authors had access to the present data and contributed to collection (SAL, LMJ, JPS, SRD, KLB, RHJ), analysis (SAL, LMJ, JPS, YC, AMP, OA, RB, RHJ), and interpretation (all) of the data and writing and editing (all) of the manuscript.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Background: Major Depressive Disorder (MDD) is a prevalent, disruptive illness. A majority of those with MDD are at high risk for recurrence and increased risk for morbidity and mortality. This study examined whether multimodal baseline (and retest) Cognitive Control performance and neuroimaging markers (task activation and neural connectivity between key brain nodes) could differentiate between those with and without future recurrence of a major depressive (MD) episode within one year. We hypothesized that performance and neuroimaging measures of Cognitive Control would identify markers that differ between these two groups. Methods: A prospective cohort study of young adults (ages 18–23) with history (h) of early-onset MDD (N = 60), now remitted, and healthy young adults (N = 49). Baseline Cognitive Control measures of performance, task fMRI and resting state connectivity (and reliability retest 4–12 weeks later) were used to compare those with future recurrence of MDD (N = 21) relative to those without future recurrence of MDD (N = 34 with resilience). The measures tested were (1) Parametric Go/No-Go (PGNG) performance, and task activation for (2) PGNG Correct Rejections, (3) PGNG Commission errors, and (4 & 5), resting state connectivity analyses of Cognitive Control Network to and from subgenual anterior cingulate. Results: Relative to other groups at baseline, the group with MDD Recurrence had less bilateral middle frontal gyrus activation during commission errors. MDD Recurrence exhibited greater connectivity of right middle frontal gyrus to subgenual anterior cingulate (SGAC). SGAC connectivity was also elevated in this group to numerous regions in the Cognitive Control Network. Moderate to strong ICCs were present from test to retest, and highest for rs-fMRI markers. There were modest, significant correlations between task, connectivity and behavioral markers that distinguished between groups. Conclusion: Markers of Cognitive Control function could identify those with early course MD who are at risk for depression recurrence. Those at high risk for recurrence would benefit from maintenance or preventative treatments. Future studies could test and validate these markers as potential predictors, accounting for sample selection and bias in feature detection.
AB - Background: Major Depressive Disorder (MDD) is a prevalent, disruptive illness. A majority of those with MDD are at high risk for recurrence and increased risk for morbidity and mortality. This study examined whether multimodal baseline (and retest) Cognitive Control performance and neuroimaging markers (task activation and neural connectivity between key brain nodes) could differentiate between those with and without future recurrence of a major depressive (MD) episode within one year. We hypothesized that performance and neuroimaging measures of Cognitive Control would identify markers that differ between these two groups. Methods: A prospective cohort study of young adults (ages 18–23) with history (h) of early-onset MDD (N = 60), now remitted, and healthy young adults (N = 49). Baseline Cognitive Control measures of performance, task fMRI and resting state connectivity (and reliability retest 4–12 weeks later) were used to compare those with future recurrence of MDD (N = 21) relative to those without future recurrence of MDD (N = 34 with resilience). The measures tested were (1) Parametric Go/No-Go (PGNG) performance, and task activation for (2) PGNG Correct Rejections, (3) PGNG Commission errors, and (4 & 5), resting state connectivity analyses of Cognitive Control Network to and from subgenual anterior cingulate. Results: Relative to other groups at baseline, the group with MDD Recurrence had less bilateral middle frontal gyrus activation during commission errors. MDD Recurrence exhibited greater connectivity of right middle frontal gyrus to subgenual anterior cingulate (SGAC). SGAC connectivity was also elevated in this group to numerous regions in the Cognitive Control Network. Moderate to strong ICCs were present from test to retest, and highest for rs-fMRI markers. There were modest, significant correlations between task, connectivity and behavioral markers that distinguished between groups. Conclusion: Markers of Cognitive Control function could identify those with early course MD who are at risk for depression recurrence. Those at high risk for recurrence would benefit from maintenance or preventative treatments. Future studies could test and validate these markers as potential predictors, accounting for sample selection and bias in feature detection.
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U2 - 10.1016/j.nicl.2018.10.004
DO - 10.1016/j.nicl.2018.10.004
M3 - Article
C2 - 30321791
AN - SCOPUS:85054738474
VL - 20
SP - 1001
EP - 1009
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
SN - 2213-1582
ER -