Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities. Winter, N. R., Leenings, R., Ernsting, J., Sarink, K., Fisch, L., Emden, D., Blanke, J., Goltermann, J., Opel, N., Barkhau, C., Meinert, S., Dohm, K., Repple, J., Mauritz, M., Gruber, M., Leehr, E. J., Grotegerd, D., Redlich, R., Jansen, A., Nenadic, I., Nöthen, M. M., Forstner, A., Rietschel, M., Groß, J., Bauer, J., Heindel, W., Andlauer, T., Eickhoff, S. B., Kircher, T., Dannlowski, U., & Hahn, T. JAMA Psychiatry, July, 2022.
Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities [link]Paper  doi  abstract   bibtex   
Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression.To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables.This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022.Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status.A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables.Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.
@article{winter_quantifying_2022,
	title = {Quantifying {Deviations} of {Brain} {Structure} and {Function} in {Major} {Depressive} {Disorder} {Across} {Neuroimaging} {Modalities}},
	issn = {2168-622X},
	url = {https://doi.org/10.1001/jamapsychiatry.2022.1780},
	doi = {10.1001/jamapsychiatry.2022.1780},
	abstract = {Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression.To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables.This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022.Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status.A total of 1809 individuals (861 patients [47.6\%] and 948 controls [52.4\%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4\%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87\% and 95\%, with classification accuracies ranging between 54\% and 56\% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables.Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.},
	urldate = {2022-08-19},
	journal = {JAMA Psychiatry},
	author = {Winter, Nils R. and Leenings, Ramona and Ernsting, Jan and Sarink, Kelvin and Fisch, Lukas and Emden, Daniel and Blanke, Julian and Goltermann, Janik and Opel, Nils and Barkhau, Carlotta and Meinert, Susanne and Dohm, Katharina and Repple, Jonathan and Mauritz, Marco and Gruber, Marius and Leehr, Elisabeth J. and Grotegerd, Dominik and Redlich, Ronny and Jansen, Andreas and Nenadic, Igor and Nöthen, Markus M. and Forstner, Andreas and Rietschel, Marcella and Groß, Joachim and Bauer, Jochen and Heindel, Walter and Andlauer, Till and Eickhoff, Simon B. and Kircher, Tilo and Dannlowski, Udo and Hahn, Tim},
	month = jul,
	year = {2022},
}
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