Studying brain function in people with psychosis has revealed important differences in connectivity between various brain networks, leading to the discovery of a promising biomarker for diagnosing and better understanding this disorder.
Psychosis is a complex condition that affects people’s thoughts, emotions, and perceptions. Previous research has shown that individuals with psychosis have differences in the way different parts of the brain “communicate.”
These differences include increased communication between the thalamus (a structure that acts as a sensory “hub”) and the cerebral cortex, and reduced communication between areas of the cortex that regulate the senses and movement.
These changes have been documented before, but the question remains: Are these changes seen across all sensory networks in the brain? Are they related to other factors of the illness, such as medications or comorbidities? Can these differences be used as a solid basis for a biomarker, an objective indicator of the presence of psychosis?
Researchers at the University of Rochester Medical Center investigated these questions using data from the Human Connectome Early Psychosis Project, a study that examines brain connections in individuals with psychosis. They examined 105 patients with psychosis (including affective and non-affective psychosis) and 54 healthy controls.
They analyzed connectivity. Resting-State Functional Connectivity (RSFC) was used.
Resting-state functional connectivity (RSFC) is an approach used in neuroscience to analyze how different regions of the brain connect and interact when a person is in a resting state, that is, not performing specific activities.
The researchers use functional magnetic resonance imaging (fMRI) to measure brain activity. During the scan, participants lie down, usually with their eyes closed, but are awake and not performing active tasks.
fMRI detects changes in blood oxygen levels, which are related to neural activity. This is called the blood oxygen level-dependent (BOLD) response. RSFC analyzes BOLD signals over time to identify patterns of synchronization between different brain regions
The BOLD signals from each brain region are correlated with those from other regions, creating a matrix of functional connectivity.
They analyzed the activity of 718 brain regions to measure how different parts were connected, considering both the connection between the thalamus and the cortex and the connections within the cortex.
They focused on specific sensory networks (primary visual, secondary visual, auditory, and somatomotor) based on a recent partition of the brain network. This ensured that they were studying well-defined and relevant connections.
To calculate the functional connectivity matrices, they used a technique called regularized partial correlation. This method reduces noise and confusion by measuring the direct relationships between two brain regions, excluding the influence of other areas.
For example, by measuring the connection between the visual and somatomotor networks, the method eliminates the impact of other networks that may be indirectly influencing this relationship.
They then analyzed connectivity. Resting-State Functional Connectivity (RSFC) was used. RSFC measures how different areas of the brain are connected while a person is at rest.
The researchers found specific differences in connectivity between patients with psychosis and healthy individuals. They first identified thalamocortical hyperconnectivity and corticocortical hypoconnectivity. These changes were evident in the secondary somatomotor and visual networks (visual2).
The primary auditory and visual networks, however, did not show these changes.
This led to a simple and effective biomarker. By combining and normalizing data from the secondary somatomotor and visual networks, the researchers created a reliable indicator of the presence of psychosis.
This biomarker was based on the difference between thalamocortical and corticocortical connectivity. It demonstrated robust statistical power.
The identified biomarker has very promising characteristics. It is independent of external factors.
It was not influenced by the use of antipsychotic medications, other psychiatric comorbidities, substances, anxiety, or demographic data such as age and gender. It also has moderate reliability. With an ICC (intraclass correlation coefficient) of 0.62, the marker is relatively consistent in repeated measurements.
It can be detected in scans lasting only 5 minutes. It also showed a good ability to distinguish groups in rigorous cross-validation tests (AUC = 0.79). Finally, the biomarker was effective in differentiating patients with advanced psychosis from healthy controls or with ADHD in two independent databases.
This study represents a significant advance in the understanding and diagnosis of psychosis. It provides a simple, robust, and objective method for identifying the disease, even in its early stages.
It can be integrated with existing neurocognitive assessments to improve diagnostic accuracy. Furthermore, highlighting specific alterations in secondary somatomotor and visual networks opens up new possibilities for targeted and personalized interventions.
The results introduce a biomarker of brain connectivity that could revolutionize the way we detect and treat psychosis. It is fast, reliable, independent of external confounds, and applicable in a variety of contexts. This discovery represents a step forward in the use of neuroscience and technology to combat mental illness.
READ MORE:
Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.
Keane BP, Abrham YT, Cole MW. et al.
Mol Psychiatry (2024).
Abstract:
People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual (“visual2”), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients—both affective and non-affective—exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged and normalized the visual2 and somatomotor network connections, and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p = 2e-10, Hedges’ g = 1.05). This “somato-visual” biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, anxiety, or demographics. It had moderate test-retest reliability (ICC = 0.62) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC = 0.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.
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