Scott A Langenecker1, Mindy Westlund Schreiner2, Leah R Thomas3, Katie L Bessette4, Sophia R DelDonno5, Lisanne M Jenkins6, Rebecca E Easter5, Jonathan P Stange7, Stephanie L Pocius2, Alina Dillahunt2, Tiffany M Love2, K Luan Phan8, Vincent Koppelmans2, Martin Paulus9, Martin A Lindquist10, Brian Caffo10, Brian J Mickey2, Robert C Welsh2. 1. Department of Psychiatry, University of Utah, Salt Lake City, Utah. Electronic address: s.langenecker@hsc.utah.edu. 2. Department of Psychiatry, University of Utah, Salt Lake City, Utah. 3. Department of Psychiatry, University of Utah, Salt Lake City, Utah; Department of Psychology, University of Utah, Salt Lake City, Utah. 4. Department of Psychiatry, University of Utah, Salt Lake City, Utah; Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois. 5. Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois. 6. Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Evanston, Illinois. 7. Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois; Department of Psychology, University of Southern California, Los Angeles, California. 8. Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio. 9. Laureate Institute for Brain Research, Tulsa, Oklahoma. 10. Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Abstract
BACKGROUND: Resting-state graph-based network edges can be powerful tools for identification of mood disorders. We address whether these edges can be integrated with Research Domain Criteria (RDoC) constructs for accurate identification of mood disorder-related markers, while minimizing active symptoms of disease. METHODS: We compared 132 individuals with currently remitted or euthymic mood disorder with 65 healthy comparison participants, ages 18-30 years. Subsets of smaller brain parcels, combined into three prominent networks and one network of parcels overlapping across these networks, were used to compare edge differences between groups. Consistent with the RDoC framework, we evaluated individual differences with performance measure regressors of inhibitory control and reward responsivity. Within an omnibus regression model, we predicted edges related to diagnostic group membership, performance within both RDoC domains, and relevant interactions. RESULTS: There were several edges of mood disorder group, predominantly of greater connectivity across networks, different than those related to individual differences in inhibitory control and reward responsivity. Edges related to diagnosis and inhibitory control did not align well with prior literature, whereas edges in relation to reward responsivity constructs showed greater alignment with prior literature. Those edges in interaction between RDoC constructs and diagnosis showed a divergence for inhibitory control (negative interactions in default mode) relative to reward (positive interactions with salience and emotion network). CONCLUSIONS: In conclusion, there is evidence that prior simple network models of mood disorders are currently of insufficient biological or diagnostic clarity or that parcel-based edges may be insufficiently sensitive for these purposes.
BACKGROUND: Resting-state graph-based network edges can be powerful tools for identification of mood disorders. We address whether these edges can be integrated with Research Domain Criteria (RDoC) constructs for accurate identification of mood disorder-related markers, while minimizing active symptoms of disease. METHODS: We compared 132 individuals with currently remitted or euthymic mood disorder with 65 healthy comparison participants, ages 18-30 years. Subsets of smaller brain parcels, combined into three prominent networks and one network of parcels overlapping across these networks, were used to compare edge differences between groups. Consistent with the RDoC framework, we evaluated individual differences with performance measure regressors of inhibitory control and reward responsivity. Within an omnibus regression model, we predicted edges related to diagnostic group membership, performance within both RDoC domains, and relevant interactions. RESULTS: There were several edges of mood disorder group, predominantly of greater connectivity across networks, different than those related to individual differences in inhibitory control and reward responsivity. Edges related to diagnosis and inhibitory control did not align well with prior literature, whereas edges in relation to reward responsivity constructs showed greater alignment with prior literature. Those edges in interaction between RDoC constructs and diagnosis showed a divergence for inhibitory control (negative interactions in default mode) relative to reward (positive interactions with salience and emotion network). CONCLUSIONS: In conclusion, there is evidence that prior simple network models of mood disorders are currently of insufficient biological or diagnostic clarity or that parcel-based edges may be insufficiently sensitive for these purposes.
Authors: R H Jacobs; A Barba; J R Gowins; H Klumpp; L M Jenkins; B J Mickey; O Ajilore; M Peciña; M Sikora; K A Ryan; D T Hsu; R C Welsh; J-K Zubieta; K L Phan; S A Langenecker Journal: Psychol Med Date: 2016-01-20 Impact factor: 7.723
Authors: Theodore D Satterthwaite; Joseph W Kable; Lillie Vandekar; Natalie Katchmar; Danielle S Bassett; Claudia F Baldassano; Kosha Ruparel; Mark A Elliott; Yvette I Sheline; Ruben C Gur; Raquel E Gur; Christos Davatzikos; Ellen Leibenluft; Michael E Thase; Daniel H Wolf Journal: Neuropsychopharmacology Date: 2015-03-13 Impact factor: 7.853
Authors: Scott A Langenecker; Angela F Caveney; Bruno Giordani; Elizabeth A Young; Kristy A Nielson; Lisa J Rapport; Linas A Bieliauskas; Matthew J Mordhorst; Sheila Marcus; Naomi Yodkovik; Kevin Kerber; Stanley Berent; Jon-Kar Zubieta Journal: Psychiatry Res Date: 2007-04-19 Impact factor: 3.222