Glutamate imaging (GluCEST) reveals lower brain GluCEST contrast in patients on the psychosis spectrum.

Psychosis commonly develops in adolescence or early adulthood. Youths at clinical high risk (CHR) for psychosis exhibit similar, subtle symptoms to those with schizophrenia (SZ). Malfunctioning neurotransmitter systems, such as glutamate, are implicated in the disease progression of psychosis. Yet, in vivo imaging techniques for measuring glutamate across the cortex are limited. Here, we use a novel 7 Tesla MRI glutamate imaging technique (GluCEST) to estimate changes in glutamate levels across cortical and subcortical regions in young healthy individuals and ones on the psychosis spectrum. Individuals on the psychosis spectrum (PS; n=19) and healthy young individuals (HC; n=17) underwent MRI imaging at 3 and 7 T. At 7 T, a single slice GluCEST technique was used to estimate in vivo glutamate. GluCEST contrast was compared within and across the subcortex, frontal, parietal and occipital lobes. Subcortical (χ2 (1)=4.65, P=0.031) and lobular (χ2 (1)=5.17, P=0.023) GluCEST contrast levels were lower in PS compared with HC. Abnormal GluCEST contrast levels were evident in both CHR (n=14) and SZ (n=5) subjects, and correlated differentially, across regions, with clinical symptoms. Our findings describe a pattern of abnormal brain neurochemistry early in the course of psychosis. Specifically, CHR and young SZ exhibit diffuse abnormalities in GluCEST contrast attributable to a major contribution from glutamate. We suggest that neurochemical profiles of GluCEST contrast across cortex and subcortex may be considered markers of early psychosis. GluCEST methodology thus shows promise to further elucidate the progression of the psychosis disease state.

Nonverbal behavioral patterns predict social rejection elicited aggression.

Aggression elicited by social rejection is costly, prevalent, and often lethal. Attempts to predict rejection-elicited aggression using trait-based data have had little success. This may be because in-the-moment aggression is a complex process influenced by current states of attention, arousal, and affect which are poorly predicted by trait-level characteristics. In a study of young adults (N = 89; 18-25 years), machine learning tested the extent to which nonverbal behavioral indices of attention (eye gaze), arousal (pupillary reactivity), and affect (facial expressions) during a novel social interaction paradigm predicted subsequent aggression towards rejecting and accepting peers. Eye gaze and pupillary reactivity predicted aggressive behavior; predictions were more successful than measures of trait-based aggression and harsh parenting. These preliminary results suggest that nonverbal behavior may elucidate underlying mechanisms of in-the-moment aggression.