NImRLS

Neuroimaging Biomarkers for Restless Leg Syndrome

“You have to see the data from large-scale health studies as a gold mine”, says Dr. Federico Raimondo: “We know there is a lot of gold in there, but we have to find it first!” Through this HIP project, the computer specialist and colleagues aim to tap more effectively into the existing troves of data. “The biggest challenge is in making these gigantic amounts of data manageable.”

The team aims to blaze a trail through the data in a concrete case study. Their subject is restless leg syndrome (RLS), which is behind many severe sleep disorders and forms of depression. Research has already identified many of the genes implicated in this widespread disorder, but there is not enough linking the physical manifestations in the brain to the genetic basis so that those manifestations can be used as biomarkers in diagnosing the condition. Large-scale health studies have databases that hold both types of information – brain scans and genomic data – on each individual patient.

The task is now to connect the two. “We are searching though patient brain scans for structures that are typical for this syndrome,” Federico Raimondo says. The team can draw on data from the UK Biobank, for example, which currently stores data and images from more than 40,000 participants. The aim of the project is to organise these volumes of data so that machine learning systems can sift through them for relevant connections. The information delivered is expected to be highly useful in health research overall, since the highly complex data analysis software should be portable to other fields of medical research.

Publications

Hamdan, S., More, S., Sasse, L., Komeyer, V., Patil, K. R., & Raimondo, F. (2023). Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models (arXiv:2310.12568). arXiv. https://doi.org/10.48550/arXiv.2310.12568

Other projects


 

SATOMI

Tackling the segmentation and tracking challenges of growing colonies and microbialdiversity

An artificial intelligence will observe the growth of bacteria: from microscope images of bacterial cultures taken at regular intervals, it will precisely track the development and division of individual cells – even when multiple bacterial species are cultivated together.
Decorative image with blue, green, pink and yellow colors
 

BRLEMMM

Breaking resolution limit of electron microscopy for magnetic materials

A new method will make it possible to take images of the magnetic properties of materials under the electron microscope and to correlate these properties with their atomic structure. In order to achieve high resolution, a special algorithm must be developed to compute the magnetic properties from the microscope data.
Image: DZNE

JIMM

Geophysical Joint Inversion for Accurate Brain Myelin Mapping

The aim of this project is to develop a method for clinically diagnosing neurodegenerative diseases. The content of myelin in the brain – a substance that becomes degraded in diseases – will be quantified using methods from geophysics in order to facilitate early detection and treatment.