Resting-state functional connectivity graph-properties correlate with bipolar disorder-risk in young medication-free depressed subjects

•Network-based statistics using a resting state functional connectivity.•Performance of graph theory analysis with whole-brain graph properties.•Investigation of brain network associated with a risk for bipolar disorder.•Classification for young depressed subjects at risk for developing bipolar diso...

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Veröffentlicht in:Journal of affective disorders Jg. 301; S. 52 - 59
Hauptverfasser: Cha, Jungwon, Spielberg, Jeffrey M., Hu, Bo, Altinay, Murat, Anand, Amit
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 15.03.2022
Schlagworte:
Bipolar depression
Graph theory
Machine learning classification
Network-based statistics
Resting state FMRI
Unipolar depression
Unipolar depression
Graph theory
Network-based statistics
Machine learning classification
Bipolar depression
Resting state FMRI
ISSN:0165-0327, 1573-2517
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Zusammenfassung:•Network-based statistics using a resting state functional connectivity.•Performance of graph theory analysis with whole-brain graph properties.•Investigation of brain network associated with a risk for bipolar disorder.•Classification for young depressed subjects at risk for developing bipolar disorder. Major Depressive Disorder (MDD) is frequently associated with risk factors for the development of Bipolar Disorder (BD). Using graph theory, we investigated brain network properties associated with BD risk factors in young MDD subjects. Resting-state fMRI was acquired from a large cohort (N= 104) of medication-free currently depressed participants (25 BD depression (BDD), 79 MDD). Lifetime mania symptom count (LMSC), current Young Mania Rating Scale (YMRS) score, and family history of mood disorders (FHMD) were examined as BD risk factors. Functional connectivity matrices from 280 regions of interests (ROIs) were first entered into the Network Based Statistic (NBS) toolbox to identify connections that varied with each risk factor. Next, within the correlated network for each risk factor, global and nodal graph properties for the top five linked nodes were calculated. Last, using identified graph properties, machine learning classification (MLC) between BDD, MDD with BD risk factors (MDD+), and without BD risk factors (MDD-) was conducted. LMSC positively correlated with left lateral orbitofrontal cortex (LOFC) Communication Efficiency and with left middle temporal Eigenvector Centrality. Current YMRS score positively correlated with right amygdala Communication Efficiency and Closeness Centrality. FHMD positively correlated with right insula Eigenvector Centrality. Acceptable MLC accuracy was seen between BDD and MDD- using middle temporal Eigenvector Centrality, whereas moderate accuracy was seen between MDD+ and MDD- using OFC Communication Efficiency. Although participants were medication-free, they were not medication-naïve. Functional connectome graph properties may serve as BD vulnerability biomarkers in young individuals with MDD.
ISSN:0165-0327
1573-2517
DOI:10.1016/j.jad.2022.01.033