Nils R. Winter
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    • Machine learning-based prediction of illness course in major depression: The relevance of risk factors
    • deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks
    • GateNet: A novel neural network architecture for automated flow cytometry gating
    • Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders
    • The impact of depression and childhood maltreatment experiences on psychological adaptation from lockdown to reopening period during the COVID-19 pandemic
    • Association between resting-state connectivity patterns in the defensive system network and treatment response in spider phobia—a replication approach
    • A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder
    • Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity
    • Interrelated effects of age and parenthood on whole-brain controllability: protective effects of parenthood in mothers
    • Altered brain dynamic in major depressive disorder: state and trait features
    • Cognitive performance and brain structural connectome alterations in major depressive disorder
    • Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies
    • From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling
    • Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder
    • PHOTONAI-Graph - A Python Toolbox for Graph Machine Learning
    • Shared and distinct structural brain networks related to childhood maltreatment and social support: connectome-based predictive modeling
    • Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders
    • Temporal Stability and State-Dependence of Retrospective Self-Reports of Childhood Maltreatment in Healthy and Depressed Adults
    • Towards a network control theory of electroconvulsive therapy response
    • An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling
    • Association Between Genetic Risk for Type 2 Diabetes and Structural Brain Connectivity in Major Depressive Disorder
    • Brain structural correlates of recurrence following the first episode in patients with major depressive disorder
    • Canonical Correlation Analysis and Partial Least Squares for identifying brain-behaviour associations: a tutorial and a comparative study
    • Childhood trauma moderates schizotypy-related brain morphology: Analyses of 1,182 healthy individuals from the ENIGMA Schizotypy working group
    • Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities
    • Recommendations for machine learning benchmarks in neuroimaging
    • Resting-state functional connectivity patterns associated with childhood maltreatment in a large bicentric cohort of adults with and without major depression
    • Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders
    • The Impact of Cognitive Reserve on Cognitive Impairment, Brain Network Pathology and Disease Trajectory in Major Depressive Disorder
    • White matter fiber microstructure is associated with prior hospitalizations rather than acute symptomatology in major depressive disorder
    • Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group
    • Editorial: Predicting Chronological Age From Structural Neuroimaging: The Predictive Analytics Competition 2019
    • From ‘loose fitting’ to high-performance, uncertainty-aware brain-age modelling
    • From multivariate methods to an AI ecosystem
    • Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning
    • Interpreting weights of multimodal machine learning models—problems and pitfalls
    • Neural processing of emotional facial stimuli in specific phobia: An fMRI study
    • PHOTONAI—A Python API for rapid machine learning model development
    • Systematic misestimation of machine learning performance in neuroimaging studies of depression
    • Big Data‚ KI und Maschinenlernen auf dem Weg zur Precision-Psychiatry – wie verändern sie den therapeutischen Alltag?
    • Cortical surface area alterations shaped by genetic load for neuroticism
    • Influence of electroconvulsive therapy on white matter structure in a diffusion tensor imaging study
    • Predicting intelligence from brain gray matter volume
    • Replication of a hippocampus specific effect of the tescalcin regulating variant rs7294919 on gray matter structure
    • Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder
    • The PHOTON Wizard - Towards Educational Machine Learning Code Generators
    • An example preprint / working paper
    • Childhood maltreatment moderates the influence of genetic load for obesity on reward related brain structure and function in major depression
    • Mediation of the influence of childhood maltreatment on depression relapse by cortical structure: a 2-year longitudinal observational study
    • No Alterations of Brain Structural Asymmetry in Major Depressive Disorder: An ENIGMA Consortium Analysis
    • Social anhedonia in major depressive disorder: a symptom-specific neuroimaging approach
    • Time heals all wounds? A 2-year longitudinal diffusion tensor imaging study in major depressive disorder
    • Elevated body-mass index is associated with reduced white matter integrity in two large independent cohorts
    • Facial width-to-height ratio differs by social rank across organizations, countries, and value systems
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    • An example conference paper
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    • scikit-learn
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scikit-learn

Oct 26, 2023 · 1 min read
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scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Last updated on Oct 26, 2023
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Dr. rer. nat. Nils R. Winter, M.Sc.
Authors
Dr. rer. nat. Nils R. Winter, M.Sc.
Postdoctoral Researcher in Translational Psychiatry & Machine Learning
← PyTorch Oct 26, 2023

© 2025 Me. This work is licensed under CC BY NC ND 4.0

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