Summary: A new study has revealed brain connectivity patterns that distinguish patients who recover from psychosis from those who do not. Recent investigations employing computational whole-brain modeling techniques have revealed distinct patterns of neural connectivity associated with clinical outcomes in psychiatric populations. Specifically, individuals who have achieved remission exhibit significantly enhanced global and regional neural connectivity, suggesting a restoration of integrative brain network function.
In contrast, patients experiencing persistent symptomatology demonstrate marked reductions in connectivity, indicative of ongoing network-level dysregulation. These findings underscore the potential utility of whole-brain models in characterizing neurobiological correlates of treatment response and may inform future interventions aimed at modulating connectivity to improve clinical outcomes.
Both groups showed lower overall neurocognitive stability than healthy individuals, but only recovered patients adjusted their communication in a way that promoted recovery. This deeper understanding could allow clinicians to predict the course of psychosis and adapt treatment accordingly.
Important facts:
- Brain Connectivity: Remission in psychosis is linked to increased brain connectivity, suggesting improved neural integration and recovery. In contrast, persistent psychosis shows reduced connectivity, reflecting ongoing network dysfunction. This connectivity model highlights the brain’s dynamic role in symptom progression and resolution.
- Forecasting models: Computer models can now help predict the individual development of individual patients based on brain scans.
- Health-related medicine: Digital brain twins can test the effectiveness of treatments before they are given, thus personalizing care.
Source: UPF Barcelona
A study from Pompeu Fabra University found that increased brain connectivity helps psychosis subside. Reduced connectivity, on the other hand, is linked to persistent symptoms.
The results of this important study may have important clinical implications for the development of new intervention strategies for psychiatric patients.
A groundbreaking study by Pompeu Fabra University, in collaboration with the University Hospital of Lausanne, reveals how brain connectivity influences psychosis remission. Increased neural integration supports recovery by enhancing communication across brain networks. In contrast, reduced connectivity is associated with the persistence of psychotic symptoms. This international effort sheds light on the brain’s role in mental health outcomes.
The study examined differences in neural connectivity between patients who recovered from psychiatric illness and those who did not. Using computer models to identify these differences made it possible to determine which neural connectivity patterns promote remission of the disease.
The research findings were recently published in an article in the journal Nature Mental Health.
The lead author is Ludovica Manna, a physician and neuroscientist in the Computational Neuroscience Group of the Center for Brain and Cognition (CBC) at UPF.
The main co-researchers are Gustavo Deco and Manuel Villa Vidal, director and researcher of the same research group, respectively.
The focus of this study is on psychosis, a serious mental disorder that results in unusual and contradictory thoughts and perceptions, the main symptoms of which are delusions and hallucinations.
According to a study, 1.2% of the population in Spain suffers from mental illness. Data from the Spanish Ministry of Health (2020).
According to Calabrese and Al-Khalili (2023), an estimated 1.5–3.5% of the global population will experience psychosis at some point in their lives. This highlights the significant public health impact of psychotic disorders worldwide. Early detection and intervention remain crucial for improving outcomes.
The research team analyzed brain MRI scans of 88 patients in the early stages of psychosis and 128 healthy people (control group) from Lausanne Hospital. The study examined differences in neural connectivity patterns between those in remission and those with persistent symptoms.
Neural connectivity increases when psychosis decreases and decreases when the opposite occurs
This comparative analysis showed that there were significant differences in brain network activity between the two patient groups.
In fact, they show an inverse connectivity pattern: patients with persistent psychotic symptoms have reduced neural connectivity, while it increases in people with psychotic episodes in remission.
Using computational whole-brain models, the study found that both groups exhibited overall stabilization of neural connectivity compared to healthy patients. These changes in neural connectivity may be due to the brain’s need to adopt undesirable behaviors due to psychosis.
In recovered patients, shifts in neuronal connectivity play a more decisive role in driving remission from psychosis. These adaptive changes enhance communication across brain networks, supporting cognitive and emotional stability. Such findings underscore the brain’s capacity for functional reorganization during recovery.
This would explain the experimental and clinical differences between the two groups.
Modern computer methods can predict a patient’s natural course after the first psychotic episodes
Gustavo Deco (UPF) explains that this study allows us to predict the natural evolution of a patient after his first psychotic episodes, thanks to the “refinement” of computer models of the whole brain, which allows us to analyze its functioning mechanistically.
“These whole-brain models are currently the best and only example of real implementation of precision medicine with digital brain twins,” he says.
Initially, these models were limited to describing the mechanisms underlying various brain conditions, such as psychosis, which initially proved very useful. However, in a second phase, they enabled us to reproduce the individual mechanisms of the brain of specific patients, in line with so-called precision medicine.
“According to researcher Gustavo Deco, advanced brain models can do more than capture a snapshot of neural activity—they can forecast how the brain evolves over time. These models simulate responses to pharmacological and electromagnetic treatments, offering a virtual testing ground before clinical application. This predictive capability enhances personalized medicine and reduces trial-and-error in psychiatric care. By integrating computational neuroscience with clinical insights, researchers are paving the way for more effective interventions. Deco’s work exemplifies the fusion of theory and therapy in modern brain science”.
Ludovica Mana (UPF) added: “This study highlights the need to challenge ourselves and change our perspective: first, by looking beyond broad diagnostic categories to better understand the diversity of patients’ experiences, and second, by recognizing that computer-based methods, when carefully combined with clinical knowledge, can help us understand mental disorder.”
The results of this study may have important clinical implications for the development of new intervention strategies for psychiatric patients.
Abstract
Subgroup-specific changes in brain connectivity in the early stages of psychosis
Functional brain scans have shown a strong correlation between changes in connectivity and a first psychotic episode. However, it is unclear whether these changes vary depending on the patient’s medical condition at the time of the scan.
The aim of this cross-sectional study was to identify brain connectivity features that distinguish sending and non-sending early psychosis (EP) patients from healthy controls and to investigate the mechanisms underlying these differences.
To this end, we analyzed resting-state fMRI and differential diagnostic imaging (DSI) data from 88 patients with epileptic psychosis, classified according to their ability to achieve remission after their first psychotic episode. We focused on the differences between patients with relapsing-remitting stage III psychosis (RR3) and non-remitting stage III psychosis (NR3).
Opposite changes in functional connectivity (FC) were observed: EP3NR patients had lower FC than controls, whereas EP3R patients had higher FC, possibly due to compensatory mechanisms.
Whole-brain network modeling revealed a reduction in spatial stability, which affected the ability to regulate the flow of impulses through the network in stage III patients, particularly in EP3R. This may indicate an adaptation to reduce network conductance.
These findings shed light on brain changes specific to certain subgroups and highlight the importance of considering this source of heterogeneity in psychosis research.
