Abstract
Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.
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Abbreviations
- MDD:
-
Major depressive disorder
- NDH:
-
Network degeneration hypothesis
- CBMA:
-
Coordinate-based meta-analysis
- ALE:
-
Activation Likelihood Estimation
- CBMA-ALE:
-
Coordinate-based meta-analysis Activation Likelihood Estimation
- MRI:
-
Magnetic resonance imaging
- rs-fMRI:
-
Resting-state functional magnetic resonance imaging
- T1:
-
Structural weighted magnetic resonance imaging
- VBM:
-
Voxel-based morphometry
- VBP:
-
Voxel-based pathophysiology
- ALFF:
-
Amplitude of low frequency fluctuations
- ReHo:
-
Regional homogeneity
- ASL:
-
Arterial spin labeling
- PET:
-
Positron emission tomography
- SPECT:
-
Single photon emission tomography
- ROI:
-
Region of interest
- GOBS:
-
Genetics of Brain Structure Study
- MDDall:
-
All GOBS patients in heterogenous clinical grouping
- MDD + comorbid:
-
MDD patients with psychiatric comorbidity
- MDDonly:
-
MDD patients without psychiatric comorbidity
- MDDrecurrent:
-
MDD patients with recurrent episodes of depression
- MDDfirst:
-
MDD patients with only one reported episode of depression
- FSL:
-
Software name for functional, structural, and diffusion image processing tools
- FSL-VBM:
-
FSL’s voxel-based morphometry processing tool
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This work was supported by grants from National Institute of Mental Health (R01-MH074457-14) and the U.S. Department of Defense (ISG/W81XWH1320065).
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JPG carried out all data analysis and drafted the manuscript. JM and AAB participated in study design and statistical analysis. CL and CF participated in analysis of primary data cohorts and statistical analysis. FC and TC participated in statistical analysis. KSC, JB, DCG, HSM, provided and participated in analysis of primary data cohorts. PTF participated in study design. All authors participated in revision and approval of the final manuscript.
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Gray, J.P., Manuello, J., Alexander-Bloch, A.F. et al. Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects. Neuroinform 21, 443–455 (2023). https://doi.org/10.1007/s12021-022-09614-2
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DOI: https://doi.org/10.1007/s12021-022-09614-2