AI Tool Identifies Brain Network Linked to Psychosis in Alzheimer’s, Paving Way for Early Diagnosis and Treatment

Artificial Intelligence Identifies Brain Network Predictive of Psychosis in Alzheimer’s Disease Manhasset, NY—Researchers at Northwell Health’s Feinstein Institutes for Medical Research have developed an artificial intelligence (AI) tool that can identify a specific brain metabolic network linked to the prediction of psychosis in Alzheimer's disease (AD). Published this week in Brain Communications, this groundbreaking study, led by Dr. Jeremy L. Koppel, marks a significant step toward earlier diagnosis, the advancement of new treatments, and the implementation of personalized medicine for AD patients. Psychosis, characterized by delusions and hallucinations, affects nearly half of all individuals with Alzheimer's disease, significantly worsening their quality of life and complicating their care. Early detection of this condition could enable more effective interventions and improve patient outcomes. However, traditional methods of identifying individuals at risk of developing psychosis have been limited, often relying on symptoms that appear only after the condition has progressed. The research team employed machine learning algorithms to analyze complex brain imaging data from positron emission tomography (PET) scans. These scans measure the metabolic activity of different brain regions. By comparing the scans of AD patients who developed psychosis with those who did not, the AI tool was able to pinpoint a distinct pattern of metabolic activity associated with psychotic episodes. Dr. Koppel explained, "Our AI-based approach allowed us to detect subtle changes in brain metabolism that are not visible through conventional means. This network provides a biological marker that could help us predict and potentially prevent the onset of psychosis in Alzheimer's patients." The study involved over 200 participants with Alzheimer's disease, making it one of the largest investigations of its kind. The AI tool showed a high degree of accuracy in predicting which patients would develop psychosis within two years, achieving a sensitivity and specificity of about 85%. This level of precision is particularly noteworthy given the complexity and variability of AD. One of the key strengths of the study is its ability to integrate multiple types of data. In addition to PET scans, the researchers also considered clinical and cognitive assessments, allowing a more comprehensive understanding of the factors contributing to psychosis in AD. Dr. Koppel emphasized, "By combining diverse data sources, we can better understand the interplay between brain function and clinical symptoms, leading to more targeted and effective treatment strategies." The implications of this research are far-reaching. Early identification of psychosis risk could facilitate the monitoring of high-risk individuals and the development of preventive therapies. Additionally, it opens up new avenues for research into the mechanisms underlying AD-related psychosis, potentially leading to novel therapeutic targets. Dr. Kevin D. Young, Chair of Psychiatry at Northwell Health, highlighted the importance of this work: "This study represents a critical advance in our ability to predict and manage psychosis in Alzheimer's disease. It brings us one step closer to personalized and precision medicine, where treatments can be tailored to individual patients based on their unique biological profiles." The next steps for the research include validating the findings in larger, more diverse populations and exploring how this metabolic network might change over time in response to different interventions. Dr. Koppel noted, "We are excited about the potential of our AI tool and plan to continue refining it to make it even more accurate and applicable to a broader range of patients." This innovative use of AI in medical research underscores the growing importance of technology in advancing our understanding of complex neurological conditions. As the field continues to evolve, it holds promise for transforming the way we diagnose and treat Alzheimer's disease, ultimately improving the lives of millions affected by this debilitating condition.