Skip to main content

Training the Next Generation of European Visual Neuroscientists for the benefit of innovation in health care and high-tech industry

Article Category

Article available in the folowing languages:

Novel technologies help unravel the complexity of vision and the brain

The human visual brain can learn and adapt to an ever-changing visual environment. Increasing our knowledge about the stability and plasticity of the visual brain can boost innovation in healthcare and technology such as artificial intelligence.


The rapid development of visual aids and restorative technologies such as retinal implants necessitates an understanding of the underlying neural mechanisms. With the support of the Marie Curie programme, the NextGenVis project set out to understand the adaptive capacity of the visual system and how its neural machinery can reorganise. Training the next generation of visual neuroscientists To achieve these goals, the NextGenVis research training programme funded 15 PhD students across Europe to work on important issues related to human vision. The project combined unique expertise and resources in brain imaging, psychology, neurology, ophthalmology, and computer science. As project coordinator Prof. Frans Cornelissen explains “our goal was to train the next generation of visual neuroscientists and lay the foundation for future discoveries and innovations in neuroscience, neurology and ophthalmology.″ The students investigated topics ranging from basic clinical questions about eye and brain disease through to computational models of vision that inform the rapidly expanding field of machine learning. Robust deep-learning algorithms that more closely resemble how the human brain works can further help understand the visual cortex. The goal was to understand the visual system as a whole, including the eyes, the visual pathways and the brain. Pioneering work provides very detailed information on the visual brain The network produced promising results, some with direct marketable consequences. A rapid assessment procedure based on eye-tracking has the potential to revolutionise some aspects of early visual screening and aid in the diagnosis of glaucoma and many other eye- and neurological diseases. Moreover, a functional MRI (fMRI)-based technique was developed for mapping the neuronal population receptive field. This helped increase the precision with which the brain can be assessed. Researchers also created a state-of-the-art pipeline that allows for the accurate analysis of brain processing and helps unravel the information flow between different layers of the brain. The fMRI technique was used to assess brain activity aiming to support the rehabilitation of patients with posterior cortical atrophy, a brain disease associated with vision and attention deficits that may be a precursor to Alzheimer’s disease. To help diagnose vision loss in patients with albinism – a congenital disease characterised by reduced pigmentation in the eyes and skin – researchers successfully performed MRI measurements of the optic nerves. MRI and fMRI were also used to map both structure and visual function in the brains of patients with colour blindness. At the same time, researchers demonstrated that monitoring of vision could be employed for the diagnosis of neurodegenerative disorders such as Parkinson’s disease. To further understand how the visual brain functions, NextGenVis researchers examined the visual processing of faces, an important parameter in social interactions. Through a mathematical description, they were able to determine how the visual properties of faces are detected and recognised by the brain. According to Prof. Cornelissen: “Understanding cortical stability and plasticity in the human visual system is thus of significant scientific and clinical relevance.″ NextGenVis will continue to provide fundamental insights into the visual cortex and its potential reorganisation in health and disease.


NextGenVis, visual, brain, MRI, visual cortex, albinism, Parkinson’s disease, glaucoma, Alzheimer’s disease, neuronal population receptive field mapping, machine learning

Discover other articles in the same domain of application

Scientific advances

9 October 2020