Summary: A new study has uncovered the brain connectivity patterns that differentiate patients who recover from psychosis from those who do not. Using whole-brain computational models, researchers found that patients in remission show increased neural connectivity, while those with persistent symptoms show reduced connectivity.
Both groups exhibited lower overall neural stability than healthy individuals, but only recovering patients adapted their connectivity in a way that supports recovery. This refined understanding may allow clinicians to predict the course of psychosis and tailor treatments accordingly.
Key Facts:
- Connectivity Patterns: Psychosis remission is associated with increased brain connectivity, while persistence is linked to decreased connectivity.
- Predictive Modeling: Computational models can now help predict individual patient outcomes based on brain scans.
- Precision Medicine: Digital brain twins could test the effects of treatments before application, personalizing care.
Source: UPF Barcelona
A study led by Pompeu Fabra University reveals which brain mechanisms allow psychosis to remit.
The results of this pioneering research could have important clinical implications for exploring new intervention strategies in patients with psychosis.
The study was carried out in collaboration with one of the main psychiatry groups at Lausanne University Hospital (Switzerland).
The study examines differences in the neural connectivity patterns of patients who have recovered from psychosis and subjects who have not. Identifying these differences using computational models has enabled determining which patterns of neural connectivity facilitate the remission of the disease.
The results of the research have recently been published in an article in the journal Nature Mental Health.
Its principal author is Ludovica Mana, a doctor and neuroscientist of the Computational Neuroscience group at the UPF Center for Brain and Cognition (CBC).
The main co-investigators are Gustavo Deco and Manel-Vila Vidal, director and researcher with the same research group, respectively.
This study focuses on psychosis, the serious mental disorder that causes abnormal, disconnected ideas and perceptions of reality, the main symptoms of which are delusions or hallucinations.
In Spain, psychosis affects 1.2% of the population, according to data from the Spanish Ministry of Health (2020).
Around the world, it is estimated that between 1.5 and 3.5% of the population may be diagnosed with psychosis during their lifetime (Calabrese and Al Khalili, 2023).
The research team analysed the MRI brain scans of 88 patients at Lausanne Hospital in the early stages of psychosis and 128 healthy individuals (control group). Thus, the differences between the neural connectivity patterns of people whose psychosis has remitted and subjects with persistent symptoms were explored.
Neural connectivity increases when psychosis remits and decreases when the opposite is true
As a result of this comparative analysis, significant differences have been found in the activity of the brain network of the two patient groups.
In fact, they present opposite connectivity patterns: the neural connectivity of patients with persistent symptoms of psychosis decreases, while it increases among people with remitting psychotic episodes.
Using whole-brain computational models, the study found that both groups have lower overall stability of neuronal connections than healthy patients. The changes undergone by neuronal connections could be due to the brain’s need to adapt to a situation of poor conductivity, caused by psychosis.
However, among recovering patients, changes in the pattern of neural connectivity contribute more widely and effectively to the remission of the disease. This would explain the empirical and clinical differences of the two groups.
Refined computational methods enable predicting a patient’s natural evolution after their first psychotic episodes
Gustavo Deco (UPF) points out that this study “allows us to predict a patient’s natural evolution after their first psychotic episodes”, thanks to the “refinement” of the computational models of the whole brain that allow us mechanistically to analyse its functioning.
Currently, “these whole-brain models are the best and only example of a genuine implementation of precision medicine from digital brain twins”, he states.
Initially, these models were limited to explaining the mechanisms underlying different brain states, that of psychosis, for instance, which was very useful in the first phase. But, now in the second phase, they allow reproducing the individualized brain mechanisms of specific patients, in the line of so-called precision medicine.
“Moreover, they are not only capable of explaining the state of the brain at a specific time, but also of beginning to predict its temporal evolution or even its evolution based on the effect of different pharmacological or electromagnetic treatments that can be tested first using computational models”, Deco explains.
Ludovica Mana (UPF) adds: “This study highlights the need to challenge ourselves and change our outlook: first, going beyond broad diagnostic categories to better understand the diversity of patient experiences, and second, recognizing that computational methods, when carefully combined with clinical knowledge, can really help us further our understanding of mental disorders”.
The findings of this study could have important clinical implications for exploring new intervention strategies in patients with psychosis, hence the need to continue advancing in this line of research.
About this psychosis research news
Author: Irene Peiró
Source: UPF Barcelona
Contact: Irene Peiró – UPF Barcelona
Image: The image is credited to Neuroscience News
Original Research: Closed access.
“Subgroup-specific brain connectivity alterations in early stages of psychosis” by Ludovica Mana et al. Nature Mental Health
Abstract
Subgroup-specific brain connectivity alterations in early stages of psychosis
Functional brain scans have shown that connectivity alterations are strongly associated with the first episode of psychosis, yet it is not well understood whether these alterations vary with the clinical status of patients at the time of scanning.
This cross-sectional study aimed to identify brain connectivity properties that differentiate remitting and non-remitting early psychosis (EP) patients from healthy controls and to explore the mechanisms underlying these differences.
To this end, we analyzed resting-state fMRI and DSI data from 88 EP patients categorized by their remission ability after the first episode of psychosis. We focused on differences between stage III remitting–relapsing (EP3R) and stage III non-remitting (EP3NR) patients.
Opposing functional connectivity (FC) alterations were observed: EP3NR patients exhibited lower FC compared with controls, while EP3R patients showed higher FC, possibly reflecting compensatory mechanisms.
Whole-brain network modeling revealed lower local stability affecting the ability to regulate the flow of stimuli across the network in stage III patients, particularly in EP3R, which may indicate an adaptation to impaired network conductivity.
These findings highlight subgroup-specific brain alterations and underscore the importance of considering this source of heterogeneity in psychosis research.
