Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Jan Ernsting
Counteracting the overestimation of effect size in mass-univariate brain-wide association studies using cross-validation
Abstract Brain-wide association effects are commonly overestimated in psychiatric neuroimaging research, leading to low replicability and generalizability of findings, and ultimately to poor translation to clinical utility. Cross-validation is currently not standardly used in univariate neuroimaging analyses but could pose a potential counteracting measure for this problem.
MRI-derived estimation of biological aging in patients with affective disorders in a 9-year follow-up - a prospective marker of future recurrence - Molecular Psychiatry
Abstract We investigated whether the brain age gap (BAG)—the difference between chronological age and age estimated from structural MRI scans—is associated with long-term disease course in affective disorders, using a prospective nine-year follow-up design. T1-weighted MRI data were collected at two time points (mean interval = 8.98 ± 2.20 years) from patients with Major Depressive Disorder (MDD; N = 32), Bipolar Disorder (BD; N = 6), and healthy controls (HC; N = 37) across two sites.
Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model - Molecular Psychiatry
|
Abstract Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD.
By Roberto Goya-Maldonado, Alyssa R. Amod, Zeynep Başgöze, Francesco Benedetti, Bianca Besteher, Katharina Brosch, Robin Bülow, Romain Colle, Baptiste Couvy-Duchesne, Kathryn Cullen, Udo Dannlowski, Christopher G Davey, Jan Ernsting, Jennifer Evans, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Nynke A. Groenewold, Tim Hahn Verified, Alec J. Jamieson, Andriana Karuk, Tilo Kircher, Bonnie Klimes-Dougan, Thomas Lancaster, Susanne Meinert, Bryon A. Mueller, Igor Nenadic, Amar Ojha, Sara Poletti, Maria J Portella, Joaquim Radua, Jair C. Soares, Aleix Solanes, Dan Stein, Frederike Stein, Aleks Stolicyn, Sophia I. Thomopoulos, Yara J. Toenders, Aslihan Uyar-Demir, Eduard Vieta, Martin Walter, Heather C. Whalley, Sarah Whittle, Nils Winter, Katharina Wittfeld, Margaret Wright, Mon-Ju Wu, Lianne Schmaal, Paul Thompson
|
Nature
Verified
A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder
Key Points Question Can multivariate machine learning approaches identify the neural signature of major depressive disorder in individual patients? Findings In this case-control neuroimaging study that included 1801 patients with depression and healthy controls, the best machine learning algorithm only achieved a diagnostic classification accuracy of 62% across major neuroimaging modalities.
A Systematic Evaluation of Machine Learning-based Biomarkers for Major Depressive Disorder across Modalities
Abstract 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.
Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder - Molecular Psychiatry
Abstract Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain’s large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability—i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive.
Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder
Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities | Depressive Disorders | JAMA Psychiatry | JAMA Network [Skip to Navigation] Figure 1. Research Design and Analytical Procedure Schematic representation of the research design and analytical procedure.
An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling
Tim Hahn https://orcid.org/0000-0001-6541-3795 , Jan Ernsting https://orcid.org/0000-0003-4649-2111, Nils R.
ODM Data Analysis-A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data
Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Jan
Contact Jan, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack