Dataset Information

Using Network Parcels and Resting-State Networks to Estimate Correlates of Mood Disorder and Related Research Domain Criteria Constructs of Reward Responsiveness and Inhibitory Control.

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.

SUBMITTER: Langenecker SA

PROVIDER: S-EPMC8748287 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Using Network Parcels and Resting-State Networks to Estimate Correlates of Mood Disorder and Related Research Domain Criteria Constructs of Reward Responsiveness and Inhibitory Control.

Langenecker Scott A SA Westlund Schreiner Mindy M Thomas Leah R LR Bessette Katie L KL DelDonno Sophia R SR Jenkins Lisanne M LM Easter Rebecca E RE Stange Jonathan P JP Pocius Stephanie L SL Dillahunt Alina A Love Tiffany M TM Phan K Luan KL Koppelmans Vincent V Paulus Martin M Lindquist Martin A MA Caffo Brian B Mickey Brian J BJ Welsh Robert C RC

Biological psychiatry. Cognitive neuroscience and neuroimaging 20210713 1

<h4>Background</h4>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.<h4>Methods</h4>We compared 132 individuals with currently remitted or euthymic mood disorder with 65 healthy comparison participants, ages 18-30 years. Subsets of smaller brain ...[more]

PMID: 34271215

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Dataset Information

Using Network Parcels and Resting-State Networks to Estimate Correlates of Mood Disorder and Related Research Domain Criteria Constructs of Reward Responsiveness and Inhibitory Control.

Background

Methods

Results

Conclusions

Publications

Using Network Parcels and Resting-State Networks to Estimate Correlates of Mood Disorder and Related Research Domain Criteria Constructs of Reward Responsiveness and Inhibitory Control.

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