Cognitive Subtypes In The Clinical High Risk For Psychosis Population

Walid Yassin (1), James Green (2), Matcheri Keshavan (1), Elisabetta Del Re (1), Daniel H Mathalon (3), Tyrone D Cannon (4), Jean Addington (5), Carrie EBearden (6), Kristin S Cadenhead (7), Barbara A Cornblatt (8), Diana O Perkins (9), Elaine F Walker (10), Scott W Woods (4), William S. Stone (1)
1 Harvard Medical School
2 Beth Israel Deaconess Medical Center
3 Department of Psychiatry, UCSF, and SFVA Medical Center, San Francisco CA
4 Department of Psychology, Yale University, New Haven CT
5 Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
6 Departments of Psychiatry and Biobehavioral Sciences and Psychology, UCLA, Los Angeles CA
7 Department of Psychiatry, UCSD, La Jolla CA
8 Department of Psychiatry, Zucker Hillside Hospital, Long Island NY
9 Department of Psychiatry, University of North Carolina, Chapel Hill NC
10 Departments of Psychology and Psychiatry, Emory University, Atlanta GA

Schizophrenia is a chronic mental health condition that severely impacts well-being, with cognitive impairment being a core feature. Identifying cognitive impairment early, particularly in individuals at clinical high risk (CHR) for psychosis, is crucial for maximizing intervention benefits and improving outcomes. Due to the heterogeneity of cognitive impairment in this population, a one-size-fits-all approach to therapeutic interventions is insufficient. Therefore, identifying cognitive subtypes within the CHR population is essential for tailored and effective interventions. This study aims to identify, validate, and characterize cognitive subtypes in large CHR samples and delineate their baseline and longitudinal cognitive and functional trajectories. Utilizing machine learning, we performed cluster analysis on cognitive measures in a large CHR sample from the North American Prodrome Longitudinal Study (NAPLS) 2, and validated our findings with an independent sample from NAPLS 3. We evaluated the resulting clusters on cognition and functioning at baseline and longitudinally, and further assessed the conversion status within these clusters. Our analysis identified two main cognitive clusters: “impaired” and “intact” across all cognitive domains compared to controls. At baseline, differences between the cognitively intact cluster and controls were observed only in verbal abilities and attention and working memory domains. Longitudinally, the cognitively impaired group showed a "catch-up" trajectory in attention and working memory and did not deteriorate further. This group also had higher instances of conversion than the intact group. In the cognitively intact group, those who later converted showed a sharp decline in attention. Global functioning roles and social scales were significantly better in the cognitively intact group at baseline, although global assessment of functioning did not differ. Most cognitive measures showed a meaningful positive relationship with functional measures. Our findings provide evidence for distinct intact and impaired cognitive subtypes in CHR youth, independent of conversion status. Attention and working memory are critical in distinguishing CHR individuals with intact cognition from controls. Early assessment of multiple cognitive domains is vital for identifying trajectories of improvement and deterioration, allowing for tailored interventions to improve outcomes for individuals at high risk for psychosis.