Various studies have now contributed to a better understanding of the neurobiological mechanisms linked to the onset of psychotic illnesses. The issue is that our ability to recognise those who are at risk and adjust our treatment accordingly is still limited. Many factors, in addition to neurobiological ones, play a role in their development.
That’s why a team at the University of Geneva (UNIGE) has teamed up with a team from EPFL to use an artificial intelligence tool called the network analysis method in a longitudinal study. Over the course of twenty years, this algorithm correlates many variables from various backgrounds — neurobiological, psychological, cognitive, and so on — in order to determine which current symptoms are predictive of a psychotic illness in the child’s future developmental trajectory. These findings, which will be published in the journal eLife, will allow for early treatment of children who are at risk of developing psychological disorders in order to prevent or even avoid them.
One in every 4,000 people has a chromosome 22 microdeletion, which can lead to psychotic illnesses like schizophrenia in adolescence. However, only about a third of them will develop a psychotic disorder at some point. How can we tell which ones are which?
“For the time being, the analyses are looking at the neurobiological mechanisms involved in psychological disorders, as well as the presence of certain symptoms that are assimilated to a psychological illness, without knowing which are the most relevant,” explains Corrado Sandini, first author of the study.
It can be difficult to predict the course of a disease and provide the most appropriate treatment for a patient if you can’t take into account the importance of each symptom.
“This is why we thought of using the network analysis method,” he continues.
This methodology, which is currently being used on adults, allows for the simultaneous consideration of variables from completely different worlds in the same analysis space.
Finding the predictive symptoms
The Geneva team has joined forces with researchers at EPFL to develop this methodology and apply it to a cohort of children and adolescents suffering from a microdeletion of chromosome 22, some of whom have been followed for more than twenty years.
“The aim is to adapt network analysis by tailoring it to young patients in a longitudinal manner, in order to obtain insightful statistics on highly intertwined variables throughout the child’s developmental trajectory,” emphasises Dimitri Van De Ville, a professor in the Department of Radiology and Medical Informatics at UNIGE Faculty of Medicine and at the EPFL Institute of Bioengineering.
The aim is to find the variables in childhood that will foresay the development of psychotic illnesses.
“We will therefore know which battle to fight, thanks to key factors that will enable us to act where and, above all, when it is necessary,” explains Stéphan Eliez.
“If we can identify them, we can try to regulate the symptom to reduce the risk of developing a psychotic illness later on.”
To test the methodology, 40 variables were taken into account for 70 children suffering from a microdeletion of chromosome 22, observed every three years from childhood to adulthood.
“These variables included hallucinations, general mood, feelings of guilt and the management of daily stress,” explains Corrado Sandini.
Questionnaires completed by parents also provided valuable data. Visual representations then shed light/highlighted/determined the most important variables that predict the development of psychological problems three years later.
“We found that an anxious 10-year-old whose anxiety turns into an inability to cope with stress in adolescence is likely to develop a psychological illness. The evolution of anxiety is therefore a significant warning signal,” continues the Geneva researcher. Similarly, sadness, which over time becomes a feeling of guilt, is also a very important symptom.
A personalised method for each child
In order to confirm the results of their algorithm, the researchers applied it to other cohorts vulnerable to psychotic illnesses that have been followed for many years, and were thus able to confirm that the computer tool works. The aim is now to use it as a predictive tool, but also to refine it by integrating other variables, such as weight, to contribute to the clinical assessment. Finally, the interest of this method is obviously the prediction, with the aim of avoiding the disease, but above all its fully personalized quality that studies the developmental trajectory specific to each child.
Source: DOI: 10.7554/eLife.59811
Image Credit: Getty
You were reading: A new way to detect the risk of psychotic illnesses in children early