most citedPredicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks

9 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY20214 cited

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…

q-bio.NC20211 cited

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…

cs.LG20211 cited

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…

eess.IV20219 cited

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…

cs.SE2020

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…