Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life

Implications of sea ice roughness variability for SAR ice type classification

Arctic sea ice thickness and surface morphology obtained by means of helicopter-borne electromagnetic induction sounding and laser altimetry have been investigated in order to improve radar ice type classification. Simultaneously acquired Synthetic Aperture Radar (SAR) images are available for many of the flight tracks. Since ice thickness measurements are considerably more difficult to accomplish than surface measurements, it is important to improve techniques for estimating thickness from surface characteristics by means of remote sensing. Radar signatures are dependent on ice surface topography and ice volume properties, but ice thickness cannot be measured directly by means of radar. The surface and thickness profiles were analysed in order to improve understanding of the relation between surface roughness and ice thickness. The stochastic properties of the surface profiles have been analysed and parameters have been extracted to characterize the roughness. Based on the available thickness information, profiles have been grouped into thickness classes, and the roughness parameters for the different groups have been analysed. In addition, normalized backscatter coefficients obtained from SAR images have been classified into groups and compared to the roughness parameters. Independently, a clustering algorithm has been applied to the roughness parameters, and the resulting roughness classes have been compared to the ice thickness classes previously obtained

Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints?

In schizophrenia, neurocognitive subtypes can be distinguished based on cognitive performance and they are associated with neuroanatomical alterations. We investigated the existence of cognitive subtypes in shortly medicated recent onset psychosis patients, their underlying gray matter volume patterns and clinical characteristics. We used a K-means algorithm to cluster 108 psychosis patients from the multi-site EU PRONIA (Prognostic tools for early psychosis management) study based on cognitive performance and validated the solution independently (N = 53). Cognitive subgroups and healthy controls (HC; n = 195) were classified based on gray matter volume (GMV) using Support Vector Machine classification. A cognitively spared (N = 67) and impaired (N = 41) subgroup were revealed and partially independently validated (N-spared = 40, N-impaired = 13). Impaired patients showed significantly increased negative symptomatology (p(fdr) = 0.003), reduced cognitive performance (p(fdr) < 0.001) and general functioning (p(fdr) < 0.035) in comparison to spared patients. Neurocognitive deficits of the impaired subgroup persist in both discovery and validation sample across several domains, including verbal memory and processing speed. A GMV pattern (balanced accuracy = 60.1%, p = 0.01) separating impaired patients from HC revealed increases and decreases across several fronto-temporal-parietal brain areas, including basal ganglia and cerebellum. Cognitive and functional disturbances alongside brain morphological changes in the impaired subgroup are consistent with a neurodevelopmental origin of psychosis. Our findings emphasize the relevance of tailored intervention early in the course of psychosis for patients suffering from the likely stronger neurodevelopmental character of the disease