Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects.

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.

Disruption of the Atrophy-based Functional Network in Multiple Sclerosis Is Associated with Clinical Disability: Validation of a Meta-Analytic Model in Resting-State Functional MRI.

Background In multiple sclerosis (MS), gray matter (GM) atrophy exhibits a specific pattern, which correlates strongly with clinical disability. However, the mechanism of regional specificity in GM atrophy remains largely unknown. Recently, the network degeneration hypothesis (NDH) was quantitatively defined (using coordinate-based meta-analysis) as the atrophy-based functional network (AFN) model, which posits that localized GM atrophy in MS is mediated by functional networks. Purpose To test the NDH in MS in a data-driven manner using the AFN model to direct analyses in an independent test sample. Materials and Methods Model fit testing was conducted with structural equation modeling, which is based on the computation of semipartial correlations. Model verification was performed in coordinate-based data of healthy control participants from the BrainMap database (https://www.brainmap.org). Model validation was conducted in prospectively acquired resting-state functional MRI in participants with relapsing-remitting MS who were recruited between September 2018 and January 2019. Correlation analyses of model fit indices and volumetric measures with Expanded Disability Status Scale (EDSS) scores and disease duration were performed. Results Model verification of healthy control participants included 80 194 coordinates from 9035 experiments. Model verification in healthy control data resulted in excellent model fit (root mean square error of approximation, 0.037; 90% CI: 0.036, 0.039). Twenty participants (mean age, 36 years ± 9 [standard deviation]; 12 women) with relapsing-remitting MS were evaluated. Model validation in resting-state functional MRI in participants with MS resulted in deviation from optimal model fit (root mean square error of approximation, 0.071; 90% CI: 0.070, 0.072), which correlated with EDSS scores (r = 0.68; P = .002). Conclusion The atrophy-based functional network model predicts functional network disruption in multiple sclerosis (MS), thereby supporting the network degeneration hypothesis. On resting-state functional MRI scans, reduced functional network integrity in participants with MS had a strong positive correlation with clinical disability. © RSNA, 2021 Online supplemental material is available for this article.

Multimodal Abnormalities of Brain Structure and Function in Major Depressive Disorder: A Meta-Analysis of Neuroimaging Studies.

OBJECTIVE: Imaging studies of major depressive disorder have reported structural and functional abnormalities in a variety of spatially diverse brain regions. Quantitative meta-analyses of this literature, however, have failed to find statistically significant between-study spatial convergence, other than transdiagnostic-only effects. In the present study, the authors applied a novel multimodal meta-analytic approach to test the hypothesis that major depression exhibits spatially convergent structural and functional brain abnormalities. METHODS: This coordinate-based meta-analysis included voxel-based morphometry (VBM) studies and resting-state voxel-based pathophysiology (VBP) studies of blood flow, glucose metabolism, regional homogeneity, and amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF). Input data were grouped into three primary meta-analytic classes: gray matter atrophy, increased function, and decreased function in patients with major depression relative to healthy control subjects. In secondary meta-analyses, the data were grouped across primary categories, and in tertiary analyses, by medication status and absence of psychiatric comorbidity. Activation likelihood estimation was used for all analyses. RESULTS: A total of 92 publications reporting 152 experiments were identified, collectively representing 2,928 patients with major depressive disorder. The primary analyses detected no convergence across studies. The secondary analyses identified portions of the subgenual cingulate cortex, hippocampus, amygdala, and putamen as demonstrating convergent abnormalities. The tertiary analyses (clinical subtypes) showed improved convergence relative to the secondary analyses. CONCLUSIONS: Coordinate-based meta-analysis identified spatially convergent structural (VBM) and functional (VBP) abnormalities in major depression. The findings suggest replicable neuroimaging features associated with major depression, beyond the transdiagnostic effects reported in previous meta-analyses, and support a continued research focus on the subgenual cingulate and other selected regions' role in depression.