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Transdiagnostic Dysfunctions in Brain Modules Across Patients with Schizophrenia, Bipolar Disorder, and Major Depressive Disorder: A Connectome-Based Study - PubMed

  • ️Wed Jan 01 2020

Comparative Study

Transdiagnostic Dysfunctions in Brain Modules Across Patients with Schizophrenia, Bipolar Disorder, and Major Depressive Disorder: A Connectome-Based Study

Qing Ma et al. Schizophr Bull. 2020.

Abstract

Psychiatric disorders, including schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD), share clinical and neurobiological features. Because previous investigations of functional dysconnectivity have mainly focused on single disorders, the transdiagnostic alterations in the functional connectome architecture of the brain remain poorly understood. We collected resting-state functional magnetic resonance imaging data from 512 participants, including 121 with SCZ, 100 with BD, 108 with MDD, and 183 healthy controls. Individual functional brain connectomes were constructed in a voxelwise manner, and the modular architectures were examined at different scales, including (1) global modularity, (2) module-specific segregation and intra- and intermodular connections, and (3) nodal participation coefficients. The correlation of these modular measures with clinical scores was also examined. We reliably identify common alterations in modular organization in patients compared to controls, including (1) lower global modularity; (2) lower modular segregation in the frontoparietal, subcortical, visual, and sensorimotor modules driven by more intermodular connections; and (3) higher participation coefficients in several network connectors (the dorsolateral prefrontal cortex and angular gyrus) and the thalamus. Furthermore, the alterations in the SCZ group are more widespread than those of the BD and MDD groups and involve more intermodular connections, lower modular segregation and higher connector integrity. These alterations in modular organization significantly correlate with clinical scores in patients. This study demonstrates common hyper-integrated modular architectures of functional brain networks among patients with SCZ, BD, and MDD. These findings reveal a transdiagnostic mechanism of network dysfunction across psychiatric disorders from a connectomic perspective.

Keywords: brain network; connector; graph theory; modularity; resting-state fMRI.

© The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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Figures

Fig. 1.
Fig. 1.

Differences in measurements of global modular architectures among the 4 groups. Violin plots depict the distributions of measurements in each group, with the dots and lines representing means and standard deviations, respectively. All plots were generated controlling for age and gender. The significance level was set to P < .05 with FDR correction. ***P < .001 and *P < .05. A trend toward significance was observed in the number of connectors (PC > 0.3) between patients with MDD and HCs. BD, bipolar disorder; HC, healthy control; MDD, major depressive disorder; SCZ, schizophrenia.

Fig. 2.
Fig. 2.

Differences in module segregation index and intra- and intermodular connections among the 4 groups. (A) The referenced 8-module parcellation was generated by combining the 7-module parcellation reported by Yeo et al. and the subcortical regions extracted from the Automated Anatomical Labeling atlas. (B) Between-group differences in module segregation. The violin plots depict the distributions of module segregation values in each group, with the dots and lines representing the means and standard deviations, respectively. (C) The matrices on the left present intra- and intermodular connections for each of the 4 groups, and the color bar indicates the number of connections. The matrix on the right illustrates the group effects among the 4 groups. (D) Between-group differences in intra- and intermodular connections for each pair of groups. Red and blue lines indicate significantly more connections and fewer connections, respectively. All of the significance levels were set to P < .05 with the FDR correction. ***P < .001; **P < .01; and *P < .05. BD, bipolar disorder; DAN, dorsal attention network; DMN, default-mode network; FPN, frontoparietal network; HC, healthy control; LIM, limbic network; MDD, major depressive disorder; SCZ, schizophrenia; SMN, sensorimotor network; SUB, subcortical; VAN, ventral attention network; VIS, visual network.

Fig. 3.
Fig. 3.

PCs and connections across psychiatric disorders. (A) Mean PC map for each group. (B) Regions showing significant group effects on PC (P < .05, 10,000 permutations). (C) Pairwise comparisons in regions with significant group effects on PCs. Trends toward significance were observed in the L.dlPFC between patients with MDD and HCs in the R.dlPFC and L. THA between patients with SCZ and BD and in the R. ANG between patients with BD and MDD. (D) Between-group differences in region-to-module connections between each pair of groups. The size of the solid circle represents the significance level. Warmer and cooler colors represent more and fewer connections, respectively. The significance levels for the data shown in C and D were set to P < .05 with the FDR correction. ***P < .001; **P < .01; and *P < .05. ANG, angular gyrus; B, bilateral; BD, bipolar disorder; dlPFC, dorsolateral prefrontal cortex; dmPFC, dorsomedial prefrontal cortex; HC, healthy control; L, left; MDD, major depressive disorder; SCZ, schizophrenia; R, right; THA, thalamus. The surface visualization was generated using BrainNet Viewer (

http://www.nitrc.org/projects/bnv

).

Fig. 4.
Fig. 4.

Correlations between clinical variables and modular architectures in patient groups. Data were fitted by regressing age and gender before Spearman’s correlation analysis was performed. B, bilateral; BPRS, Brief Psychiatric Rating Scale; dlPFC, dorsolateral prefrontal cortex; DMN, default-mode network; FPN, frontoparietal network; HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression scale; L, left; MSI, module segregation index; PC, participation coefficient; R, right; VIS, visual network.

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References

    1. Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3(2):171–178. - PubMed
    1. Cross-Disorder Group of the Psychiatric Genomics C, Lee SH, Ripke S, et al.Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet. 2013;45(9):984–994. - PMC - PubMed
    1. Brainstorm C, Anttila V, Bulik-Sullivan B, et al. . Analysis of shared heritability in common disorders of the brain. Science. 2018;360(6395):eaap8757. - PMC - PubMed
    1. Sharma A, Wolf DH, Ciric R, et al. . Common dimensional reward deficits across mood and psychotic disorders: a connectome-wide association study. Am J Psychiatry. 2017;174(7):657–666. - PMC - PubMed
    1. Zanelli J, Reichenberg A, Morgan K, et al. . Specific and generalized neuropsychological deficits: a comparison of patients with various first-episode psychosis presentations. Am J Psychiatry. 2010;167(1):78–85. - PubMed

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