The inconsistent expressions related to schizophrenia are newly structured in a recent study by researchers at the Universitas Pompeau Fabra (Barcelona), and Oxford University. Marco Loh, Edmund Rolls and Gustavo Deco have created a dynamical system framework to discuss the disorder, publishing on November 9, 2007 in the journal PLoS Computational Biology.
People with schizophrenia are known to have difficulty in maintaining attention, unstable thoughts, and reduced emotions. Creating a unifying and statistical model to understand these symptoms has always posed a challenge to researchers and clinicians. For this study Loh et al. developed a top-down analytical approach based on the different types of symptoms and related them to instabilities in attractor neural networks in a statistical dynamical framework.
The researchers found that a decrease in the excitatory NMDA-mediated synaptically activated receptor conductances reduces the depth of the attractor basins, therefore reducing the stability of attention in the presence of noise caused by the statistically variable firing of neurons, thus increasing distractibility. This reduced depth in the attractor basins destabilizes the activity at the network level. The cognitive symptoms of schizophrenia (like distractibility) could be caused by this attractor instability in the prefrontal cortex
Loh et al. also found that lower firing rates are produced by reducing the excitatory (NMDA) synaptic conductances, which could account in the orbitofrontal cortex for the negative symptoms associated with schizophrenia, such as a reduction of emotions.
Decreasing both the NMDA and the inhibitory conductances results in switches between attractor states and jumps from spontaneous activity into one of the attractors. This action may cause symptoms related to temporal lobe dysfunction such as delusions and paranoia.
The dynamical framework put forth in this study may better the understanding of the symptoms of schizophrenia, therefore culminating in better treatment for those with the disorder.
CITATION: Loh M, Rolls ET, Deco G (2007) A dynamical systems hypothesis of schizophrenia. PLoS Comput Biol 3(11): e227. doi:10.1371/journal.pcbi.0030227
Oxford University Press Office
PLEASE MENTION THE OPEN ACCESS JOURNAL PLoS COMPUTATIONAL BIOLOGY (www.ploscompbiol.org) AS THE SOURCE FOR THIS ARTICLE AND PROVIDE A LINK TO THE FREELY AVAILABLE TEXT. THANK YOU.
PLoS Computational Biology is an open-access, peer-reviewed journal published weekly by the Public Library of Science (PLoS) as the official journal of the International Society for Computational Biology (ISCB).
This press release refers to an upcoming article in PLoS Computational Biology. The release is provided by the article authors. Any opinions expressed in this release or article are the personal views of the journal staff and/or article contributors, and do not necessarily represent the views or policies of PLoS. PLoS expressly disclaims any and all warranties and liability in connection with the information found in the releases and articles and your use of such information.
About PLoS Computational Biology
PLoS Computational Biology (www.ploscompbiol.org) features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLoS Computational Biology are open access. Everything is immediately available subject only to the condition that the original authorship and source are properly attributed. Copyright is retained by the authors. The Public Library of Science uses the Creative Commons Attribution License.
About the Public Library of Science
The Public Library of Science (PLoS) is a non-profit organization of scientists and physicians committed to making the world's scientific and medical literature a freely available public resource. For more information, visit http://www.plos.org.
AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert! system.