Three brain research projects receive Academy of Finland funding

Professor Synnöve Carlson studies long-term effects of extremely premature birth on the white matter microstructure, and on brain activation during tasks that require attention, memory, and social cognition. The participants are school-aged children and adults born prematurely, and age-matched controls born full-term. They undergo structural and functional magnetic resonance imaging of the brain, neuropsychological examination, and tests measuring motor control.

The study investigates whether abnormalities in brain activation and white matter microstructure that her group observed earlier in prematurely born children have persisted, or whether changes occur as children develop. The study aims at identifying neural correlates related to learning difficulties and behavioural problems that children and adults born extremely preterm often experience. This information can be utilised when developing rehabilitation and support measures for children born prematurely.

A research consortium led by Professor Iiro Jääskeläinen examines the ingroup-outgroup emotional responses by combining cognitive neuroimaging, social psychology and virtual reality. In the past,  ingroup-outgroup emotional responses have been studied with surveys, but surveys may create pressure to adapt to norms, or respondents may find it challenging to correctly name their emotional reactions. The consortium includes professors Inga Jasinskaja-Lahti and Niklas Rajava from the University of Helsinki.

Data will be also collected with tools such as webcams and wearable sensors which will record the interaction between the brain and the body. These data are combined with the group's emotional reactions and people's behaviour in different everyday situations.  In addition, the study looks at the impacts of different interactive situations on the emotional reactions of the group.

Postdoctoral Researcher Anni Nora studies the brain activity of children during language processing. Developmental language disorder (DLD) is a common developmental difficulty, but its origin on the neural level remains unknown. The study uses magnetoencephalography (MEG) to measure the cerebral cortex responses of patients and the healthy controls.

Using a new machine-learning model developed by the research group, the aim is to find out whether DLD is related to a possible lack of timing-related speech monitoring in the activity of the cerebral cortex. In addition, the longitudinal study examines how the processing and learning of speech sounds changes in the cerebral cortex of the subjects with DLD and control subjects during the monitoring period. The aim is to find the underlying causal mechanisms to support the diagnosis and rehabilitation of the general developmental disorder.