The Lab for Neuroimaging and Neuroinformatics (LNN) @ UCPH focuses on leveraging advanced imaging technologies and computational methods to study the human brain. We integrate neuroimaging data with neuroinformatics approaches to understand brain structure, function, and connectivity. Our research areas typically include the development and application of machine learning algorithms, image analysis techniques, and data integration methods to advance the field of neuroscience and improve clinical outcomes in neurology and psychiatry. Our work often involves interdisciplinary collaboration, bridging gaps between computational sciences and neuroscience.
Interests
- Medical Image Analysis
- Fairness and Bias in Medicine
- Explainable AI
- Neuroinformatics
Meet the Group
Research Scientists
Postdoctoral Fellows and PhD students
Research Assistant
Ongoing projects
BrainDrugs - WP5: Mining structural MRI data in Epilepsy
Epilepsy is the most common serious chronic neurological disorder, affecting individuals of all ages. Structural magnetic resonance imaging (MRI) plays a critical role as a first-line radiological procedure in assessing abnormalities associated with epilepsy. Drug-resistant epilepsy patients face an increased risk of injury and mortality. Subtle epileptic features can sometimes be missed by the initial reporting radiologist who may have less specialized experience in epilepsy. Thus, relying solely on radiology reports may be insufficient, as anomalies might be overlooked due to poor image quality or a lack of expertise in epilepsy neuroimaging. Therefore, improving MR image assessment in epilepsy is essential.
This project is part of the Lundbeck Foundation-funded BrainDrugs Center, a research alliance based at the Neurobiology Research Unit (NRU) - Rigshospitalet, led by Prof. Gitte M. Knudsen (NRU) which aims to improve precision medicine in the treatment of depressive disorder and epilepsy.
In this specific work package, led by Assoc. Prof. Melanie Ganz and Prof. Gitte M. Knudsen (NRU) with Llucia Coll Benejam as postdoctoral researcher, we aim to develop transparent predictive computer-based models to aid in identifying structural MRI abnormalities in epileptic patients. This includes establishing a text-mining model to automatically label radiology reports, as well as comparing MRI features identified by our deep learning image-based model proposal with the outcomes of existing task-specific methods in epilepsy research.
Development and Validation of Biomarkers for biological brain aging
Aging is associated with a wide range of molecular and structural changes throughout an individual’s lifespan, resulting in the deterioration of physical abilities and increased risk of disease. In the brain, aging results in cognitive decline and predisposes to neurodegenerative disorders like Alzheimer’s disease and Parkinson’s disease. Therefore, developing biomarkers capable of robustly capturing age-related biological changes is necessary to understand the pathophysiology of various disorders and potentially assess the effects of interventions that target aging.
This project is a collaboration between the Data Analysis group at the Neurobiology Research Unit (NRU), and lead by the Group of Pontus Plavén-Sigray at the Karolinska Institutet. In particular, we aim to:
- Validate existing biomarkers of brain aging on large datasets, focusing on test-retest reliability and construct validity. Both are important and need to be established before application in clinical trials
- Develop new biomarkers of aging using machine learning and neuroimaging. Mainly, we are interested in whether brain imaging using positron emission tomography yields important information regarding aging.
Trustmind
The mental health system in Europe faces challenges, with extensive waiting times for diagnostic psychiatric appointments, and in Denmark, a national average waiting time of 92 weeks to see a specialist.
We aim to develop AI algorithms for mental health screening, specifically targeting Major Depressive Disorder (MDD), which affects up to 6.9% of the European population. The algorithms will help to reduce the waiting time for diagnosis and improve the overall efficiency of the mental health system.
The research project is based on a collaboration between three senior scientists from different disciplines—neuroscience, AI as well as medicine—as well as two collaborators from the field of ethics.
The TRUSTMIND project is anchored at the Image section of the Department of Computer Science and led by Assc. Prof. Melanie Ganz-Benjaminsen.
Co-applicants:
- Professor Merete Osler - Center for Clinical Research and Prevention at Bispebjerg and Frederiksberg Hospitals (CCRP-RH) and Institute of Public Health at the University of Copenhagen
- Professor Aasa Feragen - DTU Compute at Denmark's Technical University (DTU)
Partners:
- Associate Professor Sune Hannibal Holm, Department of Food and Resource Economics, University of Copenhagen
- Associate Professor Katharina O'Cathair, LAW, University of Copenhagen