Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

Repple, Jonathan; Gruber, Marius; Mauritz, Marco; Lange, Siemon C. de; Winter, Nils R.; Goltermann, Janik; Meinert, Susanne; Grotegerd, Dominik; Leehr, Elisabeth J.; Enneking, Verena; Borgers, Tiana; Klug, Melissa; Lemke, Hannah; Waltemate, Lena; Thiel, Katharina; Winter, Alexandra; Breuer, Fabian; Grumbach, Pascal; Hofmann, Hannes; Stein, Frederike; Ringwald, Kai G.; Pfarr, Julia; Thomas-Odenthal, Florian; Meller, Tina; Jansen, Andreas; Nenadić, Igor; Redlich, Ronny; Bauer, Jochen; Kircher, Tilo; Hahn, Tim; Heuvel, Martijn van den; Dannlowski, Udo

doi:10.1016/j.biopsych.2022.05.031

Cited by 20 publications

(18 citation statements)

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“…Second, we used epicenter models to relate nodal connectivity profiles to the spatial distribution of cortical thickness alterations. Finally, given that psychiatric disorders are increasingly conceptualized as dimensional rather than strictly categorical disorders (27, 28), we examined whether our network models would reflect SCZ-specific features or rather represent a shared signature across other major psychiatric conditions (29) such as has been shown for subcortical volume, cortical thickness and surface area measures (30, 31), as well as for structural connectome properties (32). Therefore, we extended our analysis to meta-analytic case-control alterations in BD and MDD.…”

Section: Introductionmentioning

confidence: 99%

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Georgiadis

Larivière

Glahn

et al. 2023

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Objective: Schizophrenia is associated with widespread brain-morphological alterations, believed to be shaped by the underlying connectome architecture. This study tests whether large-scale structural reorganization in schizophrenia relates to normative network architecture, in particular regional centrality/hubness and connectivity patterns. We examine network effects in schizophrenia across different disease stages, and transdiagnostically explore consistency of such relationships in patients with bipolar and major depressive disorder. Methods: We studied anatomical MRI scans from 2,439 adults with schizophrenia and 2,867 healthy controls from 26 ENIGMA sites. Case-control patterns of structural alterations were evaluated against two network susceptibility models: 1) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; 2) epicenter models, which identify regions whose typical connectivity profile most closely resembles the disease-related morphological alteration patterns. Both susceptibility models were tested across schizophrenia disease stages and compared to meta-analytic bipolar and major depressive disorder case-control maps. Results: In schizophrenia, regional gray matter reductions co-localized with interconnected hubs, in both the functional (r=0.58, pspin<0.0001) and structural connectome (r=0.32, pspin=0.01). Epicenters were identified in temporo-paralimbic regions, extending to frontal areas. We found unique epicenters for first-episode and early stages, and a shift from occipital to temporal-frontal epicenters in chronic stages. Transdiagnostic comparisons revealed shared epicenters in schizophrenia and bipolar, but not major depressive disorders. Conclusions: Cortical reorganization over the course of schizophrenia closely reflects brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters. The observed overlapping epicenters for schizophrenia and bipolar disorder furthermore suggest shared pathophysiological processes within the schizophrenia-bipolar-spectrum.

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Section: Introductionmentioning

confidence: 99%

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Georgiadis

Larivière

Glahn

et al. 2023

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“…Further, studies suggest that effect sizes are larger in other diagnostic groups such as psychotic disorders (Hettwer et al, 2022). Similarly, Repple and Gruber et al (2022) present transdiagnostic structural connectome alterations, with largest effect sizes found in patients with schizophrenia, possibly rendering smaller samples sufficient to detect effects.…”

Section: Discussionmentioning

confidence: 99%

“…One possible solution that has been suggested repeatedly is to validate brain effects in independent data via cross-validation (Klapwijk et al, 2021;Kriegeskorte et al, 2010;Rosenberg & Finn, 2022). While cross-validation methods are standardly used in multivariate brain analyses to identify and counteract overfitting and assure generalizability of models (e.g., Redlich et al, 2014Redlich et al, , 2016Repple et al, 2023;Schaffer, 1993), they are rarely ever used on univariate brain effects, likely also because common neuroimaging analysis software packages do not offer options to conduct cross-validation. However, specifically in the domain of neuroimaging research, cross-validation methods may be useful even for univariate models as the outlined overestimation of effects can be seen as an overfitting of models in the face of high analytic flexibility in combination with a high number of tests in this domain.…”

Section: Introductionmentioning

confidence: 99%

Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies

Goltermann

Winter

Gruber

et al. 2023

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Introduction:Statistical effect sizes are systematically overestimated in small samples, leading to poor generalizability and replicability of findings in all areas of research. Due to the large number of variables, this is particularly problematic in neuroimaging research. While cross-validation is frequently used in multivariate machine learning approaches to assess model generalizability and replicability, the benefits for mass-univariate brain analysis are yet unclear. We investigated the impact of cross-validation on effect size estimation in univariate voxel-based brain-wide associations, using body mass index (BMI) as an exemplary predictor.Methods:A total of n=3401 adults were pooled from three independent cohorts. Brain-wide associations between BMI and gray matter structure were tested using a standard linear mass-univariate voxel-based approach. First, a traditional non-cross-validated analysis was conducted to identify brain-wide effect sizes in the total sample (as an estimate of a realistic reference effect size). The impact of sample size (bootstrapped samples ranging from n=25 to n=3401) and cross-validation on effect size estimates was investigated across selected voxels with differing underlying effect sizes (including the brain-wide lowest effect size). Linear effects were estimated within training sets and then applied to unseen test set data, using 5-fold cross-validation. Resulting effect sizes (explained variance) were investigated.Results:Analysis in the total sample (n=3401) without cross-validation yielded mainly negative correlations between BMI and gray matter density with a maximum effect size of R2p=.036 (peak voxel in the cerebellum). Effects were overestimated exponentially with decreasing sample size, with effect sizes up to R2p=.535 in samples of n=25 for the voxel with the brain-wide largest effect and up to R2p=.429 for the voxel with the brain-wide smallest effect. When applying cross-validation, linear effects estimated in small samples did not generalize to an independent test set. For the largest brain-wide effect a minimum sample size of n=100 was required to start generalizing (explained variance >0 in unseen data), while n=400 were needed for smaller effects of R2p=.005 to generalize. For a voxel with an underlying null effect, linear effects found in non-cross-validated samples did not generalize to test sets even with the maximum sample size of n=3401. Effect size estimates obtained with and without cross-validation approached convergence in large samples.Discussion:Cross-validation is a useful method to counteract the overestimation of effect size particularly in small samples and to assess the generalizability of effects. Train and test set effect sizes converge in large samples which likely reflects a good generalizability for models in such samples. While linear effects start generalizing to unseen data in samples of n>100 for large effect sizes, the generalization of smaller effects requires larger samples (n>400). Cross-validation should be applied in voxel-based mass-univariate analysis to foster accurate effect size estimation and improve replicability of neuroimaging findings. We provide open-source python code for this purpose (https://osf.io/cy7fp/?view_only=a10fd0ee7b914f50820b5265f65f0cdb).

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“…associating neuroimaging markers with long-term disease trajectories. [8,[56][57][58] Likewise, investigating symptoms rather than syndromes has been promoted lately, with network theory of psychopathology providing one conceptual framework possibly able to model symptom dynamics independent of psychiatric category. [59] Indeed, our results regarding correlations of misclassification frequency provide support for associations between symptom severity and neurobiological markers, suggesting that patients with higher levels of current symptoms, lower global functioning and more unfavourable disease courses in the past are easier to detect and correctly classify.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Evaluation of Machine Learning-based Biomarkers for Major Depressive Disorder across Modalities

Winter

Blanke

Leenings

et al. 2023

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Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over- as well as underestimation of true model accuracy. Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression. Findings: Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification. Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification — even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments — does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research. Funding: The German Research Foundation, and the Interdisciplinary Centre for Clinical Research of the University of Münster.

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Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

Cited by 20 publications

References 43 publications

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies

A Systematic Evaluation of Machine Learning-based Biomarkers for Major Depressive Disorder across Modalities

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