602-Whole-Brain Connectivity Predicts Psychosis Risk
This study investigates how brain connectivity patterns can identify individuals at ultra-high risk (UHR) for psychosis and predict their everyday social and occupational functioning. Researchers utilized resting-state fMRI and machine learning to demonstrate that both increased and decreased connectivity—particularly involving the thalamus as a central hub—accurately distinguish UHR individuals from healthy peers. Notably, the same neural networks that signal a risk for psychosis also correlate with a person’s level of functioning, regardless of whether they eventually develop a full…
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