An algorithm to predict psychotic illnesses

Date:
September 29, 2021
Source:
Universite' de Gene`ve
Summary:
Researchers have used the method of longitudinal network analysis
applied to children, in order to detect the symptoms that herald
the development of psychotic illness in the future.



FULL STORY ==========================================================================
One third of children with a microdeletion of chromosome 22 will later
develop a psychotic illness such as schizophrenia. But how do we know
which of these children might be affected? Today, various studies have contributed to the understanding of the neurobiological mechanisms
that are associated with the development of psychotic illnesses. The
problem is that the ability to identify those at risk and adapt their
treatment accordingly remains limited. Indeed, many variables -- other
than neurobiological -- contribute to their development.


==========================================================================
This is why a team from the University of Geneva (UNIGE) has joined
forces with a team from the EPFL to use in a longitudinal manner an
artificial intelligence tool: the network analysis method. This algorithm correlates many variables from different backgrounds -- neurobiological, psychological, cognitive, etc. - - over a period of twenty years, in
order to determine which current symptoms are predictive of a psychotic
illness in the child's future developmental trajectory. These results,
to be read in the journal eLife, will enable early treatment of children
deemed to be at risk of developing psychological disorders, with the
aim of preventing or even avoiding them.

One in 4,000 people have a microdeletion of chromosome 22, which can
lead to the development of psychotic illnesses, such as schizophrenia,
in adolescence.

However, only one third of them will eventually be affected by a psychotic disorder. How can we determine which ones? "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, a researcher at the Department of Psychiatry of UNIGE Faculty of Medicine, to the Fondation
Po^le Autisme and first author of the study.

Not being able to take into account the degree of importance of each
symptom can be problematic in predicting the course of the disease and providing the most appropriate treatment for the patient. "This is why
we thought of using the network analysis method," he continues. This methodology, which is currently used on adults, makes it possible to
combine variables from completely different worlds in the same analysis
space, while considering them individually. "Since the development
of psychotic illnesses depends on many variables other than purely neurobiological ones, this algorithm would make it possible to highlight
the most important symptoms to alert about the potential risks of a child becoming schizophrenic, for example," says Ste'phan Eliez, professor
in the Department of Psychiatry at the UNIGE Faculty of Medicine and to
the Fondation Po^le Autisme.

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 bof 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
Ste'phan 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 personalised
quality that studies the developmental trajectory specific to each child.

========================================================================== Story Source: Materials provided by Universite'_de_Gene`ve. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Corrado Sandini, Daniela Zo"ller, Maude Schneider, Anjali Tarun,
Marco
Armondo, Barnaby Nelson, Paul G Amminger, Hok Pan Yuen,
Connie Markulev, Monica R Scha"ffer, Nilufar Mossaheb, Monika
Schlo"gelhofer, Stefan Smesny, Ian B Hickie, Gregor Emanuel Berger,
Eric YH Chen, Lieuwe de Haan, Dorien H Nieman, Merete Nordentoft,
Anita Riecher-Ro"ssler, Swapna Verma, Andrew Thompson, Alison
Ruth Yung, Patrick D McGorry, Dimitri Van De Ville, Stephan
Eliez. Characterization and prediction of clinical pathways of
vulnerability to psychosis through graph signal processing.

eLife, 2021; 10 DOI: 10.7554/eLife.59811 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210929092643.htm

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