ABSTRACT
A number of different gene polymorphisms have been found to dispose for the development of schizophrenia. However, no single gene polymorphism is sufficient for the precipitation of schizophrenia. Swedish psychosis patients (n=103) and control subjects (n=89) were analyzed for 36 single nucleotide polymorphisms in 30 candidate genes for schizophrenia. Evidence of association was analyzed with Bayesian statistical methods. Variants in the genes coding for dopamine-D2 receptor, brain-derived neurotrophic factor (BDNF), neuropeptide Y (NPY), neuregulin 1, reelin and synapsin 3 showed association with schizophrenia, although few subjects were found in the minority allele for the two latter variants. The six gene variants, all with suspected connection to schizophrenia, were found to be risk factors when considered in combination, but not separately. The results indicate that the Bayesian statistical method gives additional possibilities in the search for risk factors for schizophrenia or other complex disorders.
Subject(s)
Bayes Theorem , Genetic Variation/genetics , Polymorphism, Single Nucleotide/genetics , Psychotic Disorders/genetics , Schizophrenia/genetics , Adult , Alleles , Brain-Derived Neurotrophic Factor/genetics , Cell Adhesion Molecules, Neuronal/genetics , Chromosome Mapping , Extracellular Matrix Proteins/genetics , Female , Genetic Predisposition to Disease/genetics , Humans , Male , Middle Aged , Nerve Tissue Proteins/genetics , Neuregulin-1 , Neuropeptide Y/genetics , Neuropeptides/genetics , Phosphoproteins/genetics , Receptors, Dopamine D2/genetics , Reelin Protein , Risk Factors , Serine Endopeptidases/genetics , SynapsinsABSTRACT
BACKGROUND: Relationships between cognitive deficits and brain morphological changes observed in schizophrenia are alternately explained by less gray matter in the brain cerebral cortex, by alterations in neural circuitry involving the basal ganglia, and by alteration in cerebellar structures and related neural circuitry. This work explored a model encompassing all of these possibilities to identify the strongest morphological relationships to cognitive skill in schizophrenia. METHODS: Seventy-one patients with schizophrenia and sixty-five healthy control subjects were characterized by neuropsychological tests covering six functional domains. Measures of sixteen brain morphological structures were taken using semi-automatic and fully manual tracing of MRI images, with the full set of measures completed on thirty of the patients and twenty controls. Group differences were calculated. A Bayesian decision-theoretic method identified those morphological features, which best explained neuropsychological test scores in the context of a multivariate response linear model with interactions. RESULTS: Patients performed significantly worse on all neuropsychological tests except some regarding executive function. The most prominent morphological observations were enlarged ventricles, reduced posterior superior vermis gray matter volumes, and increased putamen gray matter volumes in the patients. The Bayesian method associated putamen volumes with verbal learning, vigilance, and (to a lesser extent) executive function, while caudate volumes were associated with working memory. Vermis regions were associated with vigilance, executive function, and, less strongly, visuo-motor speed. Ventricular volume was strongly associated with visuo-motor speed, vocabulary, and executive function. Those neuropsychological tests, which were strongly associated to ventricular volume, showed only weak association to diagnosis, possibly because ventricular volume was regarded a proxy for diagnosis. Diagnosis was strongly associated with the other neuropsychological tests, implying that the morphological associations for these tasks reflected morphological effects and not merely group volumetric differences. Interaction effects were rarely associated, indicating that volumetric relationships to neuropsychological performance were similar for both patients and controls. CONCLUSION: The association of subcortical and cerebellar structures to verbal learning, vigilance, and working memory supports the importance of neural connectivity to these functions. The finding that a morphological indicator of diagnosis (ventricular volume) provided more explanatory power than diagnosis itself for visuo-motor speed, vocabulary, and executive function suggests that volumetric abnormalities in the disease are more important for cognition than non-morphological features.
Subject(s)
Cognition Disorders/psychology , Neuropsychological Tests , Schizophrenic Psychology , Adult , Awareness , Bayes Theorem , Brain/pathology , Cohort Studies , Female , Humans , Learning , Magnetic Resonance Imaging , Male , Middle Aged , Psychotic Disorders/psychology , Reference Values , Regression Analysis , Schizophrenia/pathologyABSTRACT
A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations.
