📣 Back with the fifth post featuring one of the NIAA projects sponsored by the Network Institute.

🔍 Today’s spotlight is on “Depression Detection from Multimodal Neuroimaging Data – Towards Explainability and Generalization”, led by Jakub Frac and Alexander Schmatz, under the supervision of Shujian Yu and Guido van Wingen.

Mental health illnesses have long been among the most serious and prevalent public health issues. According to Statistics Netherlands’ review of 2023, young people’s mental health has not fully recovered since the pandemic. Numerous surveys have shown a rise in mental health issues compared to two years earlier, especially among young women. Specifically, 18 percent of young people in the Netherlands (aged 12 to 24) were considered mentally unhealthy in 2023, up from 11 percent in 2019 and 2020.

🌎 This project aims to advance existing AI approaches from two perspectives. First, they aim to develop a new, explainable GNN to enhance both explainability and generalization. Second, they aim to further improve the detection accuracy by incorporating other modalities of neuroimaging data, such as structural magnetic resonance imaging (sMRI). Thus, creating a multi-modal depression detection system is another key objective.

We gladly share insight from this impactful research through the poster below. Congratulations to everyone involved! 🎉

Collaborators: