Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Muscle in the caterpillar Manduca sexta responds to an immune challenge, but at a cost, suggesting a physiological trade-off.

Although skeletal muscle is a specialized tissue that provides the motor for movement, it also participates in other functions, including the immune response. However, little is known about the effects of this multitasking on muscle. We show that muscle loses some of its capacity while it is participating in the immune response. Caterpillars (Manduca sexta) were exposed to an immune challenge, predator stress or a combination of immune challenge and predator stress. The expression of immune genes (toll-1, domeless, cactus, tube and attacin) increased in body wall muscle after exposure to an immune challenge. Muscle also showed a reduction in the amount of the energy storage molecule glycogen. During an immune challenge, the force of the defensive strike, an important anti-predator behaviour in M. sexta, was reduced. Caterpillars were also less able to defend themselves against a common enemy, the wasp Cotesia congregata, suggesting that the effect on muscle is biologically significant. Our results support the concept of an integrated defence system in which life-threatening events activate organism-wide responses. We suggest that increased mortality from predation is a non-immunological cost of infection in M. sexta. Our study also suggests that one reason non-immunological costs of infection exist is because of the participation of diverse organs, such as muscle, in immunity.