Dataset Information

Decreased Functional Connectivity in Insular Subregions in Depressive Episodes of Bipolar Disorder and Major Depressive Disorder.

ABSTRACT: Objective: Clinically, it is very difficult to distinguish between major depressive disorder (MDD) and bipolar disorder (BD) in the period of depression. Increasing evidence shows that the insula plays an important role in depression. We aimed to compare the resting-state functional connectivity (rsFC) of insular subregions in patients with MDD and BD in depressive episodes (BDD), who had never experienced manic or hypomanic episodes when they were scanned to identify biomarkers for the identification of two diseases. Methods: We recruited 21 BDD patients, 40 MDD patients and 70 healthy controls (HC). Resting-state functional magnetic resonance imaging (rs-fMRI) was performed. BDD patients had never had manic or hypomanic episodes when they were scanned, and the diagnoses were determined by follow-up. We divided the insula into three parts including the ventral anterior insular cortex (v-AIN), dorsal anterior insular cortex (d-AIN), and posterior insula (PI). The insular-based rsFC was compared among the three groups, and an analysis of the correlation between the rsFC value and Hamilton depression and anxiety scales was carried out. Results: BDD and MDD patients demonstrated decreased rsFC from the v-AIN to the left superior/middle frontal gyrus compared with the HC group. Versus MDD and HC groups, BDD patients exhibited decreased rsFC from the v-AIN to the area in the left orbital frontal gyrus and left superior temporal gyrus (included temporal pole), from the PI to the right lateral postcentral gyrus and from all three insular subregions to the somatosensory and motor cortex. Meanwhile, a correlation between the rsFC value of the PI-right lateral postcentral gyrus and anxiety score was observed in patients. Conclusion: Our findings show BDD and MDD patients have similar decreases in insular connectivity in the dorsal lateral frontal regions, and BDD patients have specific decreased insular connectivity, especially in the somatosensory and motor cortex, which may be used as imaging evidence for clinical identification.

SUBMITTER: Yin Z

PROVIDER: S-EPMC6246657 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Decreased Functional Connectivity in Insular Subregions in Depressive Episodes of Bipolar Disorder and Major Depressive Disorder.

Yin Zhiyang Z Chang Miao M Wei Shengnan S Jiang Xiaowei X Zhou Yifang Y Cui Lingling L Lv Jing J Wang Fei F Tang Yanqing Y

Frontiers in neuroscience 20181114

<b>Objective:</b> Clinically, it is very difficult to distinguish between major depressive disorder (MDD) and bipolar disorder (BD) in the period of depression. Increasing evidence shows that the insula plays an important role in depression. We aimed to compare the resting-state functional connectivity (rsFC) of insular subregions in patients with MDD and BD in depressive episodes (BDD), who had never experienced manic or hypomanic episodes when they were scanned to identify biomarkers for the i ...[more]

PMID: 30487732

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Project description:Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.

Dataset Information

Decreased Functional Connectivity in Insular Subregions in Depressive Episodes of Bipolar Disorder and Major Depressive Disorder.

Publications

Decreased Functional Connectivity in Insular Subregions in Depressive Episodes of Bipolar Disorder and Major Depressive Disorder.

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