The Clinical High Risk (CHR) paradigm is a pivotal framework in psychiatry aimed at early detection and prevention of psychotic disorders. This approach identifies individuals at CHR for psychosis if they exhibit signs of attenuated positive symptom syndrome (APSS), brief intermittent psychotic syndrome (BLIPS), and/or genetic risk and deterioration syndrome (GRDS). These criteria are assessed through semistructured interviews, encapsulating a proactive stance towards psychosis management.
TABLE 1- Clinical High Risk (CHR)
The Clinical High Risk (CHR) paradigm is a foundational framework within psychiatry that plays a crucial role in the early detection and prevention of psychotic disorders. This approach focuses on identifying individuals who are at a high risk for developing psychosis before the onset of full-blown symptoms. By detecting and intervening at this early stage, clinicians aim to mitigate the progression of psychosis and improve long-term outcomes for patients.
The CHR paradigm categorizes individuals as being at risk for psychosis if they exhibit certain criteria. These criteria typically include signs of attenuated positive symptom syndrome (APSS), brief intermittent psychotic syndrome (BLIPS), and/or genetic risk and deterioration syndrome (GRDS). Let’s break down each of these components:
- Attenuated Positive Symptom Syndrome (APSS): This refers to the presence of subtle psychotic-like experiences or symptoms that are less severe than those typically seen in full-blown psychosis. Examples of attenuated positive symptoms include mild hallucinations, unusual beliefs, or perceptual disturbances.
- Brief Intermittent Psychotic Syndrome (BLIPS): BLIPS involves the occurrence of brief episodes of psychosis that are intermittent in nature. These episodes may last for a short duration (e.g., hours to days) and may not occur with regular frequency. Individuals experiencing BLIPS may exhibit symptoms such as hallucinations, delusions, or disorganized thinking during these episodes.
- Genetic Risk and Deterioration Syndrome (GRDS): This component of the CHR paradigm focuses on individuals who have a genetic predisposition to psychosis and are experiencing a decline in functioning or cognitive abilities. GRDS takes into account both genetic risk factors for psychosis, such as family history, as well as observable deterioration in social, occupational, or cognitive functioning.
To identify individuals who meet the criteria for CHR, clinicians typically employ semistructured interviews. These interviews involve a systematic approach to gathering information from the individual about their experiences, symptoms, and functioning. By utilizing a semistructured format, clinicians can ensure that relevant areas are covered while still allowing flexibility to explore individual nuances and context.
The CHR paradigm represents a proactive stance towards the management of psychosis. Rather than waiting for individuals to develop full-blown symptoms, clinicians using this approach aim to intervene early to prevent or delay the onset of psychosis. Early intervention strategies may include psychotherapy, medication management, family support, and psychosocial interventions tailored to the individual’s needs.
In summary, the CHR paradigm in psychiatry is a comprehensive framework designed to identify individuals at high risk for developing psychosis before the onset of full-blown symptoms. By targeting individuals exhibiting APSS, BLIPS, and/or GRDS through semistructured interviews, clinicians can take proactive steps towards early intervention and prevention of psychotic disorders.
Epidemiological Insights and Risk Assessment
Recent meta-analyses and systematic reviews have shed light on the predictive validity and risk factors associated with the transition from CHR to full-blown psychosis. Notably, a significant proportion of individuals at CHR develop psychosis within a few years of identification, underscoring the critical need for early intervention and sustained monitoring. A recent meta-analysis highlighted that approximately 25% of individuals at CHR for psychosis (CHR-P) transition to psychosis within three years, emphasizing the importance of extended clinical monitoring and preventive care for this population.
Sociodemographic and Environmental Predictors
Research into predictors of psychosis transition has explored various sociodemographic and environmental factors. Age, sex, and ethnicity, for example, have been scrutinized, with mixed findings reported across studies. While some analyses suggest these factors play a role in transition risk, the overall evidence remains inconclusive. Environmental predictors such as trauma, living status, and employment have also been investigated, with some evidence suggesting these factors may influence the risk of transition to psychosis, albeit with generally weak associations.
Advances in Neuroimaging and Brain Structure Analysis
The CHR state has been associated with significant alterations in brain structure, notably in gray matter volume, cortical surface area (SA), and cortical thickness (CT). These changes are not just confined to the period leading up to psychosis onset but can continue to evolve after disease manifestation. Studies employing structural magnetic resonance imaging (MRI) have documented progressive decreases in gray matter volume in key brain regions among CHR individuals. Furthermore, large-scale analyses, such as those conducted by the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) CHR Working Group, have identified widespread lower CT in CHR individuals, especially in frontal and temporal regions, which are also implicated in established schizophrenia.
- Clinical High Risk (CHR) State: This refers to a state in which individuals are considered to be at an increased risk of developing a psychotic disorder, such as schizophrenia, based on certain criteria, as outlined in the previous explanation. CHR individuals may experience attenuated psychotic symptoms or other risk factors that indicate a heightened vulnerability to psychosis.
- Alterations in Brain Structure: Research has consistently shown that individuals at CHR for psychosis exhibit significant changes in the structure of their brains compared to those without such risk. These alterations typically involve differences in gray matter volume, cortical surface area (SA), and cortical thickness (CT). Gray matter refers to the regions of the brain that contain nerve cell bodies, while cortical SA and CT represent different aspects of the outer layer of the brain (cortex).
- Gray Matter Volume: Studies using structural magnetic resonance imaging (MRI) have observed progressive decreases in gray matter volume among individuals at CHR for psychosis. This means that certain areas of the brain show a reduction in the amount of gray matter present. These structural changes are believed to reflect underlying neurobiological abnormalities associated with the risk of developing psychosis.
- Cortical Surface Area (SA): The cortical surface area refers to the total area of the outer layer of the brain’s cortex. Research, including large-scale analyses conducted by groups like ENIGMA, has demonstrated alterations in cortical SA among CHR individuals. Specifically, these studies have found widespread reductions in cortical SA, particularly in regions of the frontal and temporal lobes of the brain.
- Cortical Thickness (CT): Cortical thickness refers to the thickness of the outer layer of the brain’s cortex. Studies have shown that CHR individuals exhibit lower cortical thickness compared to healthy controls, particularly in frontal and temporal regions of the brain. These regions are also implicated in established schizophrenia, suggesting a continuity of structural abnormalities across different stages of the disorder.
- ENIGMA CHR Working Group: ENIGMA is a collaborative research consortium that aims to conduct large-scale analyses of brain imaging data to better understand neuropsychiatric disorders. The ENIGMA CHR Working Group specifically focuses on studying brain structure and function in individuals at CHR for psychosis. By pooling data from multiple research sites, this group can perform meta-analyses to identify consistent patterns of structural alterations in CHR individuals across different studies.
The Role of Machine Learning in Predicting Psychosis Conversion
The integration of machine learning with structural MRI (sMRI) data presents a promising avenue for enhancing the predictive accuracy of psychosis conversion. By leveraging cross-sectional sMRI data and advanced analytical techniques, researchers aim to differentiate between CHR individuals who will transition to psychosis and those who will not. This approach underscores the potential of neuroanatomical developmental patterns as biomarkers for psychosis conversion, highlighting the intersection between altered developmental processes and psychosis risk.
Harnessing Machine Learning for Predicting Psychosis: A Multisite Study Analysis
In a groundbreaking study, researchers have applied machine learning techniques to distinguish between healthy controls (HC) and individuals at clinical high risk for psychosis who later developed the condition (CHR-PS+). This study represents a significant advancement in the utilization of machine learning within psychiatric research, specifically in the context of psychosis risk assessment. By employing a two-step evaluation process, including an independent confirmatory dataset from a distinct site with a different protocol, the study aimed to validate the efficacy of the classifier developed.
Achieving an 85% accuracy rate in the training dataset for 2-class classification through non-linear adjustments for age and sex, this study marks a notable improvement over previous efforts which reported a 94% accuracy. The study demonstrates the classifier’s ability to effectively identify CHR-PS- individuals, with the CHR-UNK group—the most uncertain in terms of future psychosis development—being most likely classified as HC, indicating no significant difference in prediction probability from HC.
In terms of performance, the classifier differentiated HC from CHR-PS+ groups with 85% accuracy in the training set and 68% in the test set. Notably, the classifier achieved a 73% accuracy on the independent confirmatory dataset. This surpasses previous studies, showcasing the model’s robust predictive performance across new data sets. The use of ComBat to harmonize multisite data was pivotal, not only in increasing statistical power but also in enhancing the accuracy of the machine learning model.
The neuroanatomical patterns, particularly the superior temporal, insula, superior frontal, superior parietal, fusiform, isthmus of cingulate, and parahippocampal gyri, were instrumental in distinguishing CHR-PS+ from HCs. These findings align with existing literature on cortical alterations in CHR individuals and underscore the significance of structural brain changes in the risk assessment for psychosis.
Interestingly, no significant associations were found between the prediction probability and variables such as sex, IQ, or antipsychotic use across CHR groups. This suggests that the machine learning classifier’s ability to identify at-risk individuals does not rely on these demographic or clinical factors but rather on the distinct neuroanatomical features.
The study also highlighted the importance of considering the CHR subgroup-specific changes in structural MRI (sMRI) metrics. Although no differences in prediction probability were observed among APSS, BLIPS, or GRDS status, previous research has shown subgroup-specific brain changes. The findings suggest the potential for more accurate predictive models through comprehensive sampling of CHR participants across subgroups and clinical stages.
Moreover, the classifier’s prediction probability varied significantly among the HC, CHR-UNK, CHR-PS-, and CHR-PS+ groups at baseline, offering insights into the neuroanatomical deviance associated with psychosis conversion. This underscores the classifier’s utility in identifying individuals at baseline who are at increased risk for psychosis, based on neuroanatomical alterations.
Despite its promising results, the study acknowledges several limitations, including potential information leakage due to the harmonization process and the inability to test the effect of psychosis-by-age interaction on prediction probability due to the lack of longitudinal MRI data. Furthermore, the absence of data on substance use, which is associated with psychosis risk, and the focus on distinguishing between CHR-PS+ and HC groups rather than between CHR-PS+ and CHR-PS- highlight areas for future research.
In conclusion, this study’s successful application of a machine learning classifier to multisite dataset analysis represents a significant step forward in the early identification of psychosis risk. By focusing on neuroanatomical deviations and employing advanced statistical techniques for data harmonization, the research provides a promising framework for future studies aimed at refining predictive models for psychosis and enhancing clinical outcomes through early intervention.
reference link : https://www.nature.com/articles/s41380-024-02426-7#Sec17