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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
15.03.2022
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| ISSN: | 0165-0327, 1573-2517 |
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| Abstract | •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. |
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| AbstractList | •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. |
| Author | Anand, Amit Spielberg, Jeffrey M. Cha, Jungwon Hu, Bo Altinay, Murat |
| Author_xml | – sequence: 1 givenname: Jungwon surname: Cha fullname: Cha, Jungwon organization: Department of Psychiatry, Mass General Brigham, Harvard Medical School, United States – sequence: 2 givenname: Jeffrey M. surname: Spielberg fullname: Spielberg, Jeffrey M. organization: Department of Psychological and Brain Sciences, University of Delaware, United States – sequence: 3 givenname: Bo surname: Hu fullname: Hu, Bo organization: Center for Quantitative Health Sciences, Cleveland Clinic, United States – sequence: 4 givenname: Murat surname: Altinay fullname: Altinay, Murat organization: Center for Behavioral Health, Cleveland Clinic, United States – sequence: 5 givenname: Amit surname: Anand fullname: Anand, Amit email: anand7@bwh.harvard.edu organization: Department of Psychiatry, Mass General Brigham, Harvard Medical School, United States |
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| CitedBy_id | crossref_primary_10_1016_j_jaac_2025_07_421 crossref_primary_10_3390_brainsci13050801 crossref_primary_10_1016_j_neubiorev_2025_106201 |
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| Keywords | Unipolar depression Graph theory Network-based statistics Machine learning classification Bipolar depression Resting state FMRI |
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