JuAML

We contributed to a project investigating how working memory is affected by age and which brain networks might be involved. [Read More]

We have developed a high-level user-oriented machine-learning library julearn in Python (>=3.6). This library provides users with the possibility of testing ML models directly from pandas dataframes, while keeping the flexibiliy of using scikit-learn’s models. You can create pipelines with preprocessing steps. In addition, we also provide the functionality to... [Read More]

Interpretability is an important consideration for machine learning systems, especially in the clinical domain. We took first steps to develop a methods that creates complex representations of base features while still keeping them interpretable. [Read More]

We developed a new method and applied to to create multimodal convergent parcels of the human striatum. [Read More]

Intrinsic connectivity patterns of task-defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to molecular architecture [Read More]