118 citations · 210 across the 16 of their papers we have counts for
17 papers
Energy, Information, and the Limits of Neuropsychiatric Classification
Farbod Khodadadi, Mina Kheirkhah, Bita Shariatpanahi +15
Importance: The search for reliable neuroimaging biomarkers of mental disorders has remained elusive despite decades of research. Why clinically consequential disorders yield weak…
Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires
Doan Nam Long Vu, Rui Tan, Lena Moench +10
Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced previous work to rely mainly on g…
deepmriprep: Voxel-based Morphometry (VBM) Preprocessing via Deep Neural Networks
Lukas Fisch, Nils R. Winter, Janik Goltermann +25
Voxel-based Morphometry (VBM) has emerged as a powerful approach in neuroimaging research, utilized in over 7,000 studies since the year 2000. Using Magnetic Resonance Imaging (MRI…
GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating
Lukas Fisch, Michael O. Heming, Andreas Schulte-Mecklenbeck +12
Flow cytometry is widely used to identify cell populations in patient-derived fluids such as peripheral blood (PB) or cerebrospinal fluid (CSF). While ubiquitous in research and cl…
Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model
Roberto Goya-Maldonado, Tracy Erwin-Grabner, Ling-Li Zeng +84
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…
Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks
Lukas Fisch, Stefan Zumdick, Carlotta Barkhau +8
Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research…
From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling
Jan Ernsting, Nils R. Winter, Ramona Leenings +13
The brain-age gap is one of the most investigated risk markers for brain changes across disorders. While the field is progressing towards large-scale models, recently incorporating…
More Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities
Nils R. Winter, Ramona Leenings, Jan Ernsting +28
Introduction: Identifying neurobiological differences between patients suffering from Major Depressive Disorder (MDD) and healthy individuals has been a mainstay of clinical neuros…
Towards a Network Control Theory of Electroconvulsive Therapy Response
Tim Hahn, Hamidreza Jamalabadi, Erfan Nozari +24
Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of…
A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder
Tim Hahn, Hamidreza Jamalabadi, Daniel Emden +31
Background: The evolution of symptoms over time is at the heart of understanding and treating mental disorders. However, a principled, quantitative framework explaining symptom dyn…
Genetic, Individual, and Familial Risk Correlates of Brain Network Controllability in Major Depressive Disorder
Tim Hahn, Nils R. Winter, Jan Ernsting +27
Background: A therapeutic intervention in psychiatry can be viewed as an attempt to influence the brain's large-scale, dynamic network state transitions underlying cognition and be…
An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
Tim Hahn, Jan Ernsting, Nils R. Winter +31
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a corn…
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks
Lukas Fisch, Jan Ernsting, Nils R. Winter +33
Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing…
The PHOTON Wizard -- Towards Educational Machine Learning Code Generators
Ramona Leenings, Nils Ralf Winter, Kelvin Sarink +4
Despite the tremendous efforts to democratize machine learning, especially in applied-science, the application is still often hampered by the lack of coding skills. As we consider…
PHOTONAI -- A Python API for Rapid Machine Learning Model Development
Ramona Leenings, Nils Ralf Winter, Lucas Plagwitz +12
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access…
Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression
Claas Flint, Micah Cearns, Nils Opel +15
We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of m…
Biological sex classification with structural MRI data shows increased misclassification in transgender women
Claas Flint, Katharina Förster, Sophie A. Koser +16
Transgender individuals (TIs) show brain structural alterations that differ from their biological sex as well as their perceived gender. To substantiate evidence that the brain str…