Differences in frontal cortical activation by a working memory task after substitution of risperidone for typical antipsychotic drugs in patients with schizophrenia

Antipsychotic drug treatment of schizophrenia may be complicated by side effects of widespread dopaminergic antagonism, including exacerbation of negative and cognitive symptoms due to frontal cortical hypodopaminergia. Atypical antipsychotics have been shown to enhance frontal dopaminergic activity in animal models. We predicted that substitution of risperidone for typical antipsychotic drugs in the treatment of schizophrenia would be associated with enhanced functional activation of frontal cortex. We measured cerebral blood oxygenation changes during periodic performance of a verbal working memory task, using functional MRI, on two occasions (baseline and 6 weeks later) in two cohorts of schizophrenic patients. One cohort (n = 10) was treated with typical antipsychotic drugs throughout the study. Risperidone was substituted for typical antipsychotics after baseline assessment in the second cohort (n = 10). A matched group of healthy volunteers (n = 10) was also studied on a single occasion. A network comprising bilateral dorsolateral prefrontal and lateral premotor cortex, the supplementary motor area, and posterior parietal cortex was activated by working memory task performance in both the patients and comparison subjects. A two-way analysis of covariance was used to estimate the effect of substituting risperidone for typical antipsychotics on power of functional response in the patient group. Substitution of risperidone increased functional activation in right prefrontal cortex, supplementary motor area, and posterior parietal cortex at both voxel and regional levels of analysis. This study provides direct evidence for significantly enhanced frontal function in schizophrenic patients after substitution of risperidone for typical antipsychotic drugs, and it indicates the potential value of functional MRI as a tool for longitudinal assessment of psychopharmacological effects on cerebral physiology.

Evolution of RNA editing in trypanosome mitochondria

Two different RNA editing systems have been described in the kinetoplast-mitochondrion of trypanosomatid protists. The first involves the precise insertion and deletion of U residues mostly within the coding regions of maxicircle-encoded mRNAs to produce open reading frames. This editing is mediated by short overlapping complementary guide RNAs encoded in both the maxicircle and the minicircle molecules and involves a series of enzymatic cleavage-ligation steps. The second editing system is a C34 to U34 modification in the anticodon of the imported tRNATrp, thereby permitting the decoding of the UGA stop codon as tryptophan. U-insertion editing probably originated in an ancestor of the kinetoplastid lineage and appears to have evolved in some cases by the replacement of the original pan-edited cryptogene with a partially edited cDNA. The driving force for the evolutionary fixation of these retroposition events was postulated to be the stochastic loss of entire minicircle sequence classes and their encoded guide RNAs upon segregation of the single kinetoplast DNA network into daughter cells at cell division. A large plasticity in the relative abundance of minicircle sequence classes has been observed during cell culture in the laboratory. Computer simulations provide theoretical evidence for this plasticity if a random distribution and segregation model of minicircles is assumed. The possible evolutionary relationship of the C to U and U-insertion editing systems is discussed.

Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond

Single-stranded regions in RNA secondary structure are important for RNA–RNA and RNA–protein interactions. We present a probability profile approach for the prediction of these regions based on a statistical algorithm for sampling RNA secondary structures. For the prediction of phylogenetically-determined single-stranded regions in secondary structures of representative RNA sequences, the probability profile offers substantial improvement over the minimum free energy structure. In designing antisense oligonucleotides, a practical problem is how to select a secondary structure for the target mRNA from the optimal structure(s) and many suboptimal structures with similar free energies. By summarizing the information from a statistical sample of probable secondary structures in a single plot, the probability profile not only presents a solution to this dilemma, but also reveals ‘well-determined’ single-stranded regions through the assignment of probabilities as measures of confidence in predictions. In antisense application to the rabbit β-globin mRNA, a significant correlation between hybridization potential predicted by the probability profile and the degree of inhibition of in vitro translation suggests that the probability profile approach is valuable for the identification of effective antisense target sites. Coupling computational design with DNA–RNA array technique provides a rational, efficient framework for antisense oligonucleotide screening. This framework has the potential for high-throughput applications to functional genomics and drug target validation.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.