TY - JOUR
T1 - Childhood trauma history is linked to abnormal brain connectivity in major depression
AU - Yu, Meichen
AU - Linn, Kristin A.
AU - Shinohara, Russell T.
AU - Oathes, Desmond J.
AU - Cook, Philip A.
AU - Duprat, Romain
AU - Moore, Tyler M.
AU - Oquendo, Maria A.
AU - Phillips, Mary L.
AU - McInnis, Melvin
AU - Fava, Maurizio
AU - Trivedi, Madhukar H.
AU - McGrath, Patrick
AU - Parsey, Ramin
AU - Weissman, Myrna M.
AU - Sheline, Yvette I.
N1 - Funding Information:
We thank Dr. Danielle Bassett, Dr. Richard Betzel, and Dr. Theodore Satterthwaite for their helpful suggestions about the estimation of functional connectivity and motion correction. We thank Jared Zimmerman for many helpful discussions and suggestions about presentation of the results. We also thank Rastko Ciric and Irem Aselcioglu for helping with data processing and providing valuable support and discussion. We thank Maria Prociuk for her assistance with the preparation and submission of the manuscript. We also thank all participants for their participation. We acknowledge the following support: Grant U01 MH109991 (to Y.I.S.), Grants R01 NS085211 and RG-1707-28586 (to R.T.S.), Grant R01-MH111886 (to D.J.O.), Grant U01 MH092221 (to M.H.T.), and Grant U01 MH092250 (to P.M., R.P., and M.M.W.). The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.
Funding Information:
ACKNOWLEDGMENTS. We thank Dr. Danielle Bassett, Dr. Richard Betzel, and Dr. Theodore Satterthwaite for their helpful suggestions about the estimation of functional connectivity and motion correction. We thank Jared Zimmerman for many helpful discussions and suggestions about presentation of the results. We also thank Rastko Ciric and Irem Aselcioglu for helping with data processing and providing valuable support and discussion. We thank Maria Prociuk for her assistance with the preparation and submission of the manuscript. We also thank all participants for their participation. We acknowledge the following support: Grant U01 MH109991 (to Y.I.S.), Grants R01 NS085211 and RG-1707-28586 (to R.T.S.), Grant R01-MH111886 (to D.J.O.), Grant U01 MH092221 (to M.H.T.), and Grant U01 MH092250 (to P.M., R.P., and M.M.W.). The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.
Publisher Copyright:
© 2019 National Academy of Sciences. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.
AB - Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.
KW - Childhood trauma
KW - Dimensional symptoms
KW - Major depressive disorder
KW - Network connectivity
KW - Resting-state networks
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UR - http://www.scopus.com/inward/citedby.url?scp=85065190730&partnerID=8YFLogxK
U2 - 10.1073/pnas.1900801116
DO - 10.1073/pnas.1900801116
M3 - Article
C2 - 30962366
AN - SCOPUS:85065190730
SN - 0027-8424
VL - 116
SP - 8582
EP - 8590
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 17
ER -