| Title: | Studying mental health via research domain criteria, neuroimaging and convolutional neural networks |
| Lead Institution: | Charite - Universitatsmedizin Berlin |
| Principal investigator: | Dr Kerstin Ritter |
Application 33073
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
We want to employ state-of-the-art convolutional neural networks (CNNs) for characterizing mental health based on structural and functional MRI data. Main research questions: How well do CNN-derived MRI representations characterize mental health? And how can we visualize and interpret these CNN-derived MRI representations? - Proposed research supports characterization of individual subjects with respect to mental health - Link of imaging data and behavioral measures - Improvement of diagnostics for derived mental illnesses Based on UK Biobank data, we aim to establish a relationship between mental health measures on the one hand and MRI data on the other hand. To do so, we will employ state-of-the-art machine learning algorithms, in particular convolutional neural networks, which are very powerful in detecting hidden connections in data. Novel visualization techniques will then be used to understand what image features have been used and to what extent they fit into respective models of mental health. Subset of all currently available patients with MRI data and additional data as it becomes available (especially GP data)1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 15034 | Do Transformers and CNNs Learn Different Concepts of Brain Age? | Nys Tjade Siegel (+4) | 2025 | Human Brain Mapping |
Enabling scientific discoveries that improve human health