A brain computer interface based on neural network with efficient pre-processing

金沢大学理工研究域　電子情報学系Brain Computer Interface (BCI) is one of hopeful interface technologies between human and machine. However, brain waves are very weak and there exist many kinds of noises. Therefore, what kinds of features are useful, how to extract the useful features, how to suppress noises, and so on are very important. On the other hand, neural networks are very useful technology for pattern classification. Especially, multilayer neural networks trained through the error back-propagation algorithm have been widely used in a wide variety of field. In this paper, the neural network is applied to the BCI. Amplitude of the FFT of the brain waves are used for the input data. Several kinds of techniques are introduced in this paper. Segmentation along the time axis for fast response, nonlinear normalization for emphasizing important information with small magnitude, averaging samples of the brain waves for suppressing noise effects and reduction in the number of the samples for achieving a small size network, and so on are newly introduced. Simulation was carried out by using the brain waves, which are available from the web site of Colorado state university. The number of mental tasks is five. Ten data sets for each mental task are prepared. Among them, 9 data sets are used for training, and the rest one data set is used for testing. Selection of the one data set for testing is changed and accuracy of the correct classifications are averaged over the possible selections. Approximately, 80% of correct classification of the brain waves is obtained, which is higher than the conventional. © 2006 IEEE

CNVs in Three Psychiatric Disorders

BACKGROUND: We aimed to determine the similarities and differences in the roles of genic and regulatory copy number variations (CNVs) in bipolar disorder (BD), schizophrenia (SCZ), and autism spectrum disorder (ASD). METHODS: Based on high-resolution CNV data from 8708 Japanese samples, we performed to our knowledge the largest cross-disorder analysis of genic and regulatory CNVs in BD, SCZ, and ASD. RESULTS: In genic CNVs, we found an increased burden of smaller (500 kb) exonic CNVs in SCZ/ASD. Pathogenic CNVs linked to neurodevelopmental disorders were significantly associated with the risk for each disorder, but BD and SCZ/ASD differed in terms of the effect size (smaller in BD) and subtype distribution of CNVs linked to neurodevelopmental disorders. We identified 3 synaptic genes (DLG2, PCDH15, and ASTN2) as risk factors for BD. Whereas gene set analysis showed that BD-associated pathways were restricted to chromatin biology, SCZ and ASD involved more extensive and similar pathways. Nevertheless, a correlation analysis of gene set results indicated weak but significant pathway similarities between BD and SCZ or ASD (r = 0.25–0.31). In SCZ and ASD, but not BD, CNVs were significantly enriched in enhancers and promoters in brain tissue. CONCLUSIONS: BD and SCZ/ASD differ in terms of CNV burden, characteristics of CNVs linked to neurodevelopmental disorders, and regulatory CNVs. On the other hand, they have shared molecular mechanisms, including chromatin biology. The BD risk genes identified here could provide insight into the pathogenesis of BD

Comparative Analyses of Copy-Number Variation in Autism Spectrum Disorder and Schizophrenia Reveal Etiological Overlap and Biological Insights

Summary: Compelling evidence in Caucasian populations suggests a role for copy-number variations (CNVs) in autism spectrum disorder (ASD) and schizophrenia (SCZ). We analyzed 1,108 ASD cases, 2,458 SCZ cases, and 2,095 controls in a Japanese population and confirmed an increased burden of rare exonic CNVs in both disorders. Clinically significant (or pathogenic) CNVs, including those at 29 loci common to both disorders, were found in about 8% of ASD and SCZ cases, which was significantly higher than in controls. Phenotypic analysis revealed an association between clinically significant CNVs and intellectual disability. Gene set analysis showed significant overlap of biological pathways in both disorders including oxidative stress response, lipid metabolism/modification, and genomic integrity. Finally, based on bioinformatics analysis, we identified multiple disease-relevant genes in eight well-known ASD/SCZ-associated CNV loci (e.g., 22q11.2, 3q29). Our findings suggest an etiological overlap of ASD and SCZ and provide biological insights into these disorders. : Kushima et al. perform comparative analyses of CNVs in ASD and SCZ in a Japanese population. They identify pathogenic CNVs and biological pathways in each disorder with significant overlap. Patients with pathogenic CNVs have a higher prevalence of intellectual disability. Disease-relevant genes are detected in eight well-known ASD/SCZ-associated CNV loci. Keywords: autism spectrum disorder, schizophrenia, copy-number variation, array comparative genomic hybridization, genetic overlap, Japanese population, oxidative stress response, genome integrity, lipid metabolism, gene ontolog