Psychological Predictors of Postconcussive Symptoms Following Traumatic Injury.

OBJECTIVE: To determine the contribution of demographics, injury type, pain, and psychological factors on postconcussive symptoms. SETTING AND PARTICIPANTS: Recently injured (n = 54) and noninjured (n = 184) adults were recruited from a hospital emergency department or the community. Thirty-eight individuals met the diagnostic criteria for a mild traumatic brain injury and 16 individuals received treatment for a minor traumatic non-brain injury. MAIN MEASURES: Standardized tests were administered to assess 4 postconcussion symptom types and theorized predictors including a "physiogenic" variable (injury type) and "psychogenic" variables (symptoms of anxiety, depression, and stress) within 1 month of the injury. RESULTS: In the injured sample, after controlling for injury type, demographics, and pain (chronic and current), a hierarchical regression analysis revealed that the combination of psychological symptoms predicted affective (F10,42 = 2.80, P = .009, Rchange = 0.27) but not other postconcussion symptoms types. Anxiety (ß = .48), stress (ß = .18), and depression (ß = -.07) were not statistically significant individual predictors (P > .05). Cognitive and vestibular postconcussion symptoms were not predicted by the modeled factors, somatic sensory postconcussion symptoms were predicted by demographic factors only, and the pattern of predictors for the symptom types differed for the samples. CONCLUSIONS: Traditional explanatory models do not account for these findings. The predictors are multifactorial, different for injured versus noninjured samples, and symptom specific.

Interaction of multiple gene variants and their effects on schizophrenia phenotypes.

BACKGROUND: Schizophrenia is a clinically heterogeneous disorder and may be explained by its complex genetic architecture. Many schizophrenia susceptibility genes were identified but the picture remains unclear due to inconsistent or contradictory genetic association studies. This confusion may, in part, be because symptoms result from the combined interaction of many genes and these interacting genes are associated with specific sub-phenotypes of schizophrenia rather than schizophrenia as a whole. This study investigates the relationship between schizophrenia susceptibility genes and schizophrenia sub-phenotypes by identifying multiple gene variant interactions. MATERIALS AND METHODS: Fifty SNPs from 21 genes were genotyped in 235 Australian participants with schizophrenia screened for various phenotypes. Schizophrenia participants were grouped into relevant phenotype clusters using cluster analysis and normalized phenotype cluster scores were calculated for each patient. The relationship between genotypes and normalized phenotype cluster scores were analyzed by linear regression analysis. RESULTS: Three phenotype clusters were identified. There was some overlap in symptoms between phenotype clusters, particularly for depression. However, cluster 1 appears to be characterized by speech disorder and affective behavior symptoms, cluster 2 has predominantly hallucination symptoms and cluster 3 has mainly delusion symptoms. Interaction of five SNPs was found to have an effect on cluster 1 symptoms; ten SNPs on cluster 2 symptoms; and eight SNPs on cluster 3 symptoms. CONCLUSION: The interaction of specific susceptibility genes is likely to lead to specific clinical sub-phenotypes of schizophrenia. Larger patient cohorts with more extensive clinical data will improve the detection of gene interactions and the resultant schizophrenia clinical phenotypes.

Principal components analysis of the Neurobehavioral Symptom Inventory in a nonclinical civilian sample.

The study examined the component structure of the Neurobehavioral Symptom Inventory (NSI) under five different models. The evaluated models comprised the full NSI (NSI-22) and the NSI-20 (NSI minus two orphan items). A civilian nonclinical sample was used. The 575 volunteers were predominantly university students who screened negative for mild TBI. The study design was cross-sectional, with questionnaires administered online. The main measure was the Neurobehavioral Symptom Inventory. Subscale, total and embedded validity scores were derived (the Validity-10, the LOW6, and the NIM5). In both models, the principal components analysis yielded two intercorrelated components (psychological and somatic/sensory) with acceptable internal consistency (alphas > 0.80). In this civilian nonclinical sample, the NSI had two underlying components. These components represent psychological and somatic/sensory neurobehavioral symptoms.