PREDICTING DEPRESSED AND ELEVATED MOOD SYMPTOMATOLOGY IN BIPOLAR DISORDER USING BRAIN FUNCTIONAL CONNECTOMES Article Swipe
Anjali Sankar
,
Xilin Shen
,
Lejla Čolić
,
Danielle A. Goldman
,
Luca M. Villa
,
Jihoon Kim
,
Brian Pittman
,
Dustin Scheinost
,
R. Todd Constable
,
Hilary P. Blumberg
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/ijnp/pyae059.027
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/ijnp/pyae059.027
Background Depressed and elevated mood symptom severity underlie the suffering associated with bipolar disorder and are among the strongest clinical predictors of functional impairment and disability in the disorder. Furthermore, mood symptoms contribute to the high risk for suicide in the disorder. Elucidation of the brain functional disturbances that contribute to mood symptoms in individuals with bipolar disorder could advance our understanding of the pathophysiology of the disorder and enable the development of more targeted therapeutic approaches. Aims and Objectives The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder using the machine learning approach Connectome-based Predictive Modeling (CPM). Methods Functional magnetic resonance imaging data were obtained from 81 adults with bipolar disorder while they performed an emotion processing task. CPM with 5000 permutations of leave-one- out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales. The predictive ability of the identified connectomes was tested in an independent sample of 43 adults with bipolar disorder. Results CPM predicted the severity of depressed [concordance between actual and predicted values (r = 0.23, pperm (permutation test) = 0.031) and elevated (r = 0.27, pperm = 0.01) mood. Functional connectivity of left dorsolateral prefrontal cortex and supplementary motor area nodes, with inter- and intra-hemispheric connections to other anterior and posterior cortical, limbic, motor, and cerebellar regions, predicted depressed mood severity. Connectivity of left fusiform and right visual association area nodes with inter- and intra-hemispheric connections to the motor, insular, limbic, and posterior cortices predicted elevated mood severity. These networks were predictive of mood symptomatology in the independent sample (r = 0.45, p = 0.002). Discussion and Conclusion This study identified distributed functional connectomes predictive of depressed and elevated mood severity in bipolar disorder. Connectomes subserving emotional, cognitive, and psychomotor control predicted depressed mood severity, while those subserving emotional and social perceptual functions predicted elevated mood severity. Identification of these connectome networks may help inform the development of targeted treatments for mood symptoms.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1093/ijnp/pyae059.027
- OA Status
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- Related Works
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- OpenAlex ID
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https://openalex.org/W4407387473Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/ijnp/pyae059.027Digital Object Identifier
- Title
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PREDICTING DEPRESSED AND ELEVATED MOOD SYMPTOMATOLOGY IN BIPOLAR DISORDER USING BRAIN FUNCTIONAL CONNECTOMESWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-01Full publication date if available
- Authors
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Anjali Sankar, Xilin Shen, Lejla Čolić, Danielle A. Goldman, Luca M. Villa, Jihoon Kim, Brian Pittman, Dustin Scheinost, R. Todd Constable, Hilary P. BlumbergList of authors in order
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https://doi.org/10.1093/ijnp/pyae059.027Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1093/ijnp/pyae059.027Direct OA link when available
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Connectome, Bipolar disorder, Mood, Psychology, Functional connectivity, Clinical psychology, Neuroscience, PsychiatryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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