In everyday life, we are immersed in a multitude of sounds. Sound waves convey language, emotion, and other vital information on events in the environment. To recognize sounds, we effortlessly analyze and combine their elementary features. For example, we recognize a high and fast fluctuating sound as birdsong. Alternatively, a low and more slowly fluctuating sound is recognized as the voice of a colleague. Previous research in animals suggested several neuronal mechanisms for the analysis and combination of sound features. However, insufficient spatial resolution of non-invasive methods precluded investigating if these mechanisms are present and relevant for human listening in natural environments. In this project, I will use 7 Tesla functional magnetic resonance imaging (fMRI) and a novel analysis method to study the neural mechanisms underlying feature processing of natural sounds in the human brain. My results will provide a detailed view on the neural basis of human audition, bridging the gap with findings from animal research. Furthermore, they may provide the methodological basis for similar investigations throughout the brain. I will perform this research at the Center for Magnetic Resonance Research (CMRR) in Minneapolis (USA), which provides unique facilities and immense technical expertise in MRI at ultra-high magnetic fields.

There is a lot of misunderstanding about ADHD, also among young people. Recognizing and managing ADHD is important at this stage of life for their personal, social and cognitive development. Within the ADHDplaza project 10 VMBO-schools were visited for a lesson about ADHD and 10 PhD-students were trained in sciencecommunication. The effect measurements showed that knowledge about ADHD among the students had increased. The way students described ADHD was more diverse and positive after the lesson. The PhD-students felt strengthened in their sciencecommunication skills after participating in ADHDplaza. ADHDplaza will keep connecting the target group and ADHD-science in the future.

Children’s reading development shows large variability. Reading problems (dyslexia) are currently diagnosed and treated after a child has failed to respond adequately to reading instruction, i.e., around 8-9 years. This is problematic because early intervention is crucial for optimal (reading) development and social opportunities. To enable earlier prediction of reading problems, the researchers design tailored learning tasks and characterize individual differences in children’s learning trajectories by modelling their performance and brain activity. With this knowledge, they develop a digital learning test that predicts who will learn to read fluently and who will need extra support to prevent reading problems.

The aim of this proposal is to study the interplay of attention and entrainment during the processing of sounds in complex environments. We will use psychophysics and layer-specific fMRI at 7 Tesla to measure behavior and auditory cortical responses in human participants exposed to (multi-) sensory entrainment in combination with attention manipulations. Anticipated results will provide important insights on fundamental neural processing mechanisms at the basis of efficient speech and music analysis.

Novel computer vision algorithms bear great promise for measuring movements with high spatial detail and very precise timing in-the-wild. This is unprecedented as it may allow researchers to track what was thusfar impossible to track: human motor development. Infants acquire a vast array of fundamental motor skills within their first years of life. This development happens at home, whereas current-day tools for measuring motor development are lab-bound. I will put this novel methodology to a set of stringent tests to uncover the viability of measuring infants’ motor skills at home, exactly where and when they develop.