1
|
Winter NR, Blanke J, Leenings R, Ernsting J, Fisch L, Sarink K, Barkhau C, Emden D, Thiel K, Flinkenflügel K, Winter A, Goltermann J, Meinert S, Dohm K, Repple J, Gruber M, Leehr EJ, Opel N, Grotegerd D, Redlich R, Nitsch R, Bauer J, Heindel W, Gross J, Risse B, Andlauer TFM, Forstner AJ, Nöthen MM, Rietschel M, Hofmann SG, Pfarr JK, Teutenberg L, Usemann P, Thomas-Odenthal F, Wroblewski A, Brosch K, Stein F, Jansen A, Jamalabadi H, Alexander N, Straube B, Nenadić I, Kircher T, Dannlowski U, Hahn T. A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder. JAMA Psychiatry 2024; 81:386-395. [PMID: 38198165 PMCID: PMC10782379 DOI: 10.1001/jamapsychiatry.2023.5083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 11/05/2023] [Indexed: 01/11/2024]
Abstract
Importance 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), no informative biomarkers have been identified. Objective To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. Design, Setting, and Participants This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Exposure Patients with MDD and healthy controls. Main Outcome and Measure Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Results Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Conclusion and Relevance Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.
Collapse
Affiliation(s)
- Nils R. Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Julian Blanke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | - Lukas Fisch
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Kelvin Sarink
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Carlotta Barkhau
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Daniel Emden
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Elisabeth J. Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health, Jena, Germany
- German Center for Mental Health (DZPG), Jena, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ronny Redlich
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health, Jena, Germany
- Department of Psychology, University of Halle, Halle, Germany
- German Center for Mental Health (DZPG), Halle, Germany
| | - Robert Nitsch
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Jochen Bauer
- Clinic for Radiology, University of Münster, University Hospital Münster, Münster, Germany
| | - Walter Heindel
- Clinic for Radiology, University of Münster, University Hospital Münster, Münster, Germany
| | - Joachim Gross
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Benjamin Risse
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | - Till F. M. Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Andreas J. Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Markus M. Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stefan G. Hofmann
- Department of Clinical Psychology, Philipps-University Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
- Core Facility Brain Imaging, Faculty of Medicine, Philipps-University Marburg, Marburg, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| |
Collapse
|
2
|
Richter M, Mota S, Hater L, Bratek R, Goltermann J, Barkhau C, Gruber M, Repple J, Storck M, Blitz R, Grotegerd D, Masuhr O, Jaeger U, Baune BT, Dugas M, Walter M, Dannlowski U, Buhlmann U, Back M, Opel N. Narcissistic dimensions and depressive symptoms in patients across mental disorders in cognitive behavioural therapy and in psychoanalytic interactional therapy in Germany: a prospective cohort study. Lancet Psychiatry 2023; 10:955-965. [PMID: 37844592 DOI: 10.1016/s2215-0366(23)00293-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND Narcissistic personality traits have been theorised to negatively affect depressive symptoms, therapeutic alliance, and treatment outcome, even in the absence of narcissistic personality disorder. We aimed to examine how the dimensional narcissistic facets of admiration and rivalry affect depressive symptoms across treatment modalities in two transdiagnostic samples. METHODS We did a naturalistic, observational prospective cohort study in two independent adult samples in Germany: one sample pooled from an inpatient psychiatric clinic and an outpatient treatment service offering cognitive behavioural treatment (CBT), and one sample from an inpatient clinic providing psychoanalytic interactional therapy (PIT). Inpatients treated with CBT had an affective or psychotic disorder. For the other two sites, data from all service users were collected. We examined the effect of core narcissism and its facets admiration and rivalry, measured by Narcissistic Admiration and Rivalry Questionnaire-short version, on depressive symptoms, measured by Beck's Depression Inventory and Patient Health Questionnaire-Depression Scale, at baseline and after treatment in patients treated with CBT and PIT. Primary analyses were regression models, predicting baseline and post-treatment depression severity from core narcissism and its facets. Mediation analysis was done in the outpatient CBT group for the effect of the therapeutic alliance on the association between narcissism and depression severity after treatment. FINDINGS The sample included 2371 patients (1423 [60·0%] female and 948 [40·0%] male; mean age 33·13 years [SD 13·19; range 18-81), with 517 inpatients and 1052 outpatients in the CBT group, and 802 inpatients in the PIT group. Ethnicity data were not collected. Mean treatment duration was 300 days (SD 319) for CBT and 67 days (SD 26) for PIT. Core narcissism did not predict depression severity before treatment in either group, but narcissistic rivalry was associated with higher depressive symptom load at baseline (β 2·47 [95% CI 1·78 to 3·12] for CBT and 1·05 [0·54 to 1·55] for PIT) and narcissistic admiration showed the opposite effect (-2·02 [-2·62 to -1·41] for CBT and -0·64 [-1·11 to -0·17] for PIT). Poorer treatment response was predicted by core narcissism (β 0·79 [0·10 to 1·47]) and narcissistic rivalry (0·89 [0·19 to 1·58]) in CBT, whereas admiration showed no effect. No effect of narcissism on treatment outcome was discernible in PIT. Therapeutic alliance mediated the effect of narcissism on post-treatment depression severity in the outpatient CBT sample. INTERPRETATION As narcissism affects depression severity before and after treatment with CBT across psychiatric disorders, even in the absence of narcissistic personality disorder, the inclusion of dimensional assessments of narcissism should be considered in future research and clinical routines. The relevance of the therapeutic alliance and therapeutic strategy could be used to guide treatment approaches. FUNDING IZKF Münster. TRANSLATION For the German translation of the abstract see Supplementary Materials section.
Collapse
Affiliation(s)
- Maike Richter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
| | - Simon Mota
- Department of Psychology, University of Münster, Münster, Germany
| | - Leonie Hater
- Department of Psychology, University of Münster, Münster, Germany
| | - Rebecca Bratek
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Carlotta Barkhau
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, Goethe University Frankfurt, University Hospital, Frankfurt, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, Goethe University Frankfurt, University Hospital, Frankfurt, Germany
| | - Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Rogério Blitz
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | | | | | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne Parkville, VIC, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne Parkville, VIC, Australia; Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Münster, Germany
| | - Martin Dugas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany; German Center for Mental Health (DZPG), Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Münster, Germany
| | - Ulrike Buhlmann
- Department of Psychology, University of Münster, Münster, Germany
| | - Mitja Back
- Department of Psychology, University of Münster, Münster, Germany; Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany; German Center for Mental Health (DZPG), Germany
| |
Collapse
|
3
|
Malihi L, Hübner U, Richter ML, Moelleken M, Przysucha M, Busch D, Heggemann J, Hafer G, Wiemeyer S, Heidemann G, Dissemond J, Erfurt-Berge C, Barkhau C, Hendriks A, Hüsers J. Can Synthetic Images Improve CNN Performance in Wound Image Classification? Stud Health Technol Inform 2023; 302:927-931. [PMID: 37203538 DOI: 10.3233/shti230311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large amounts, Generative Adversarial Networks (GAN) are a standard tool for synthesising artificial training images that can be used to augment the data set. We investigated the quality of synthetic wound images regarding two aspects: (i) improvement of wound-type classification by a Convolutional Neural Network (CNN) and (ii) how realistic such images look to clinical experts (n = 217). Concerning (i), results show a slight classification improvement. However, the connection between classification performance and the size of the artificial data set is still unclear. Regarding (ii), although the GAN could produce highly realistic images, the clinical experts took them for real in only 31% of the cases. It can be concluded that image quality may play a more significant role than data size in improving the CNN-based classification result.
Collapse
Affiliation(s)
- Leila Malihi
- Institute of Cognitive Science, Osnabrück University, Germany
| | - Ursula Hübner
- Health Informatics Research Group, Osnabrück University of AS, Germany
| | - Mats L Richter
- Institute of Cognitive Science, Osnabrück University, Germany
| | - Maurice Moelleken
- Department of Dermatology, Venerology and Allergology, University Hospital of Essen, Germany
| | - Mareike Przysucha
- Health Informatics Research Group, Osnabrück University of AS, Germany
| | - Dorothee Busch
- Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Germany
| | - Jan Heggemann
- Christian Hospital Melle, Niels Stensen Hospitals, Germany
| | - Guido Hafer
- Christian Hospital Melle, Niels Stensen Hospitals, Germany
| | | | | | - Joachim Dissemond
- Department of Dermatology, Venerology and Allergology, University Hospital of Essen, Germany
| | - Cornelia Erfurt-Berge
- Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Germany
| | | | | | - Jens Hüsers
- Health Informatics Research Group, Osnabrück University of AS, Germany
| |
Collapse
|
4
|
Herpertz J, Richter MF, Barkhau C, Storck M, Blitz R, Steinmann LA, Goltermann J, Dannlowski U, Baune BT, Varghese J, Dugas M, Lencer R, Opel N. Symptom monitoring based on digital data collection during inpatient treatment of schizophrenia spectrum disorders - A feasibility study. Psychiatry Res 2022; 316:114773. [PMID: 35994863 DOI: 10.1016/j.psychres.2022.114773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 10/15/2022]
Abstract
Digital acquisition of patients' self-reports on individual risk factors and symptom severity represents a promising, cost-efficient, and increasingly prevalent approach for standardized data collection in psychiatric clinical routine. Yet, studies investigating digital data collection in patients with a schizophrenia spectrum disorder (PSSDs) are scarce. The objective of this study was to explore the feasibility of digitally acquired self-report assessments of risk and symptom profiles at the time of admission into inpatient treatment in an age-representative sample of hospitalized PSSDs. We investigated the required support, the data entry pace, and the subjective user experience. Findings were compared with those of patients with an affective disorder (PADs). Of 82 PSSDs who were eligible for inclusion, 59.8% (n=49) agreed to participate in the study, of whom 54.2% (n=26) could enter data without any assistance. Inclusion rates, drop-out rates, and subjective experience ratings did not differ between PSSDs and PADs. Patients reported high satisfaction with the assessment. PSSDs required more support and time for the data entry than PADs. Our results indicate that digital data collection is a feasible and well-received method in PSSDs. Future clinical and research efforts on digitized assessments in psychiatry should include PSSDs and offer support to reduce digital exclusion.
Collapse
Affiliation(s)
- Julian Herpertz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Maike Frederike Richter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Carlotta Barkhau
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Rogério Blitz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Institute of Medical Informatics, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Lavinia A Steinmann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Bernhard T Baune
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne Parkville, Melbourne, Australia; Department of Psychiatry, University of Münster, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, Münster, Germany; Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Interdisciplinary Centre for Clinical Research Münster, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany.
| |
Collapse
|
5
|
Winter NR, 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 EJ, Grotegerd D, Redlich R, Jansen A, Nenadic I, Nöthen MM, Forstner A, Rietschel M, Groß J, Bauer J, Heindel W, Andlauer T, Eickhoff SB, Kircher T, Dannlowski U, Hahn T. Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities. JAMA Psychiatry 2022; 79:879-888. [PMID: 35895072 PMCID: PMC9330277 DOI: 10.1001/jamapsychiatry.2022.1780] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
IMPORTANCE 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. OBJECTIVE 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. DESIGN, SETTING, AND PARTICIPANTS 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. MAIN OUTCOMES AND MEASURES 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. RESULTS 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. CONCLUSIONS AND RELEVANCE 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.
Collapse
Affiliation(s)
- Nils R. Winter
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Ramona Leenings
- University of Münster, Institute for Translational Psychiatry, Münster, Germany,University of Münster, Department of Mathematics and Computer Science, Münster, Germany
| | - Jan Ernsting
- University of Münster, Institute for Translational Psychiatry, Münster, Germany,University of Münster, Department of Mathematics and Computer Science, Münster, Germany
| | - Kelvin Sarink
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Lukas Fisch
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Daniel Emden
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Julian Blanke
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Janik Goltermann
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Nils Opel
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Carlotta Barkhau
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Susanne Meinert
- University of Münster, Institute for Translational Psychiatry, Münster, Germany,University of Münster, Institute for Translational Neuroscience, Münster, Germany
| | - Katharina Dohm
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Jonathan Repple
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Marco Mauritz
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Marius Gruber
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Elisabeth J. Leehr
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Dominik Grotegerd
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Ronny Redlich
- University of Münster, Institute for Translational Psychiatry, Münster, Germany,Institute of Psychology, University of Halle, Halle, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany
| | - Igor Nenadic
- Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany
| | - Markus M. Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Andreas Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany,Centre for Human Genetics, University of Marburg, Marburg, Germany,Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Joachim Groß
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Clinical Radiology, University of Münster, Münster, Germany
| | - Walter Heindel
- Department of Clinical Radiology, University of Münster, Münster, Germany
| | - Till Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany,Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany
| | - Udo Dannlowski
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Tim Hahn
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| |
Collapse
|