MEG resting state functional connectivity in Parkinson's disease related dementia

Parkinson's disease (PD) related dementia (PDD) develops in up to 60% of patients, but the pathophysiology is far from being elucidated. Abnormalities of resting state functional connectivity have been reported in Alzheimer's disease (AD). The present study was performed to determine whether PDD is likewise characterized by changes in resting state functional connectivity. MEG recordings were obtained in 13 demented and 13 non-demented PD patients. The synchronization likelihood (SL) was calculated within and between cortical areas in six frequency bands. Compared to non-demented PD, PDD was characterized by lower fronto-temporal SL in the alpha range, lower intertemporal SL in delta, theta and alpha1 bands as well as decreased centro-parietal gamma band synchronization. In addition, higher parieto-occipital synchronization in the alpha2 and beta bands was found in PDD. The observed changes in functional connectivity are reminiscent of changes in AD, and may reflect reduced cholinergic activity and/or loss of cortico-cortical anatomical connections in PDD. © 2008 The Author(s)

EEG-Based Functional Brain Networks: Does the Network Size Matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks – whose nodes can vary from tens to hundreds – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies

New algorithm improves fine structure of the barley consensus SNP map

<p>Abstract</p> <p>Background</p> <p>The need to integrate information from multiple linkage maps is a long-standing problem in genetics. One way to visualize the complex ordinal relationships is with a directed graph, where each vertex in the graph is a bin of markers. When there are no ordering conflicts between the linkage maps, the result is a directed acyclic graph, or DAG, which can then be linearized to produce a consensus map.</p> <p>Results</p> <p>New algorithms for the simplification and linearization of consensus graphs have been implemented as a package for the R computing environment called DAGGER. The simplified consensus graphs produced by DAGGER exactly capture the ordinal relationships present in a series of linkage maps. Using either linear or quadratic programming, DAGGER generates a consensus map with minimum error relative to the linkage maps while remaining ordinally consistent with them. Both linearization methods produce consensus maps that are compressed relative to the mean of the linkage maps. After rescaling, however, the consensus maps had higher accuracy (and higher marker density) than the individual linkage maps in genetic simulations. When applied to four barley linkage maps genotyped at nearly 3000 SNP markers, DAGGER produced a consensus map with improved fine structure compared to the existing barley consensus SNP map. The root-mean-squared error between the linkage maps and the DAGGER map was 0.82 cM per marker interval compared to 2.28 cM for the existing consensus map. Examination of the barley hardness locus at the 5HS telomere, for which there is a physical map, confirmed that the DAGGER output was more accurate for fine structure analysis.</p> <p>Conclusions</p> <p>The R package DAGGER is an effective, freely available resource for integrating the information from a set of consistent linkage maps.</p

Mobile Phones and Multiple Sclerosis – A Nationwide Cohort Study in Denmark

We investigated the risk of, prognosis of and symptoms of multiple sclerosis (MS) among all Danish residents who owned a mobile phone subscription before 1996. Using the Danish Multiple Sclerosis Registry and Civil Registration System, study subjects were followed up for MS through 2004. Poisson models were used to calculate incidence rate ratios (IRR, age range: 18–64 years) and mortality rate ratios (MRR, age range: 18+) and to compare presenting symptoms among subscribers and all non-subscribers. A total of 405 971 subscription holders accrued four million years of follow up, with men accounting for 86% of the observation time. Among subscription holding men, the IRR of MS was close to unity, overall as well as 13+ years after first subscription (IRR 1.02, 95% CI: 0.48–2.16). Among women, the IRR was 3.43 (95% CI: 0.86–13.72) 13+ years after first subscription, however, based on only two cases. Presenting symptoms of MS differed between subscribers and non-subscribers (p = 0.03), with slightly increased risk of diplopia in both genders (IRR: 1.38, 95% CI: 1.02–1.86), an increased risk of fatigue among women (IRR: 3.02, 95% CI: 1.45–6.28), and of optic neuritis among men (IRR: 1.38, 95% CI: 1.03–1.86). Overall the MRR was close to one (MRR: 0.91, 95%CI 0.70–1.19) among MS-patients with a subscription and although we observed some increased MRR estimates among women, these were based on small numbers. In conclusion, we found little evidence for a pronounced association between mobile phone use and risk of MS or mortality rate among MS patients. Symptoms of MS differed between subscribers and nonsubscribers for symptoms previously suggested to be associated with mobile phone use. This deserves further attention, as does the increased long-term risk of MS among female subscribers, although small numbers and lack of consistency between genders prevent causal interpretation

Automated Discrimination of Brain Pathological State Attending to Complex Structural Brain Network Properties: The Shiverer Mutant Mouse Case

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert's clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbpshi/Mbpshi, n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractography algorithms and a graph framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6–100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers

A Field Guide to Pandemic, Epidemic and Sporadic Clones of Methicillin-Resistant Staphylococcus aureus

In recent years, methicillin-resistant Staphylococcus aureus (MRSA) have become a truly global challenge. In addition to the long-known healthcare-associated clones, novel strains have also emerged outside of the hospital settings, in the community as well as in livestock. The emergence and spread of virulent clones expressing Panton-Valentine leukocidin (PVL) is an additional cause for concern. In order to provide an overview of pandemic, epidemic and sporadic strains, more than 3,000 clinical and veterinary isolates of MRSA mainly from Germany, the United Kingdom, Ireland, France, Malta, Abu Dhabi, Hong Kong, Australia, Trinidad & Tobago as well as some reference strains from the United States have been genotyped by DNA microarray analysis. This technique allowed the assignment of the MRSA isolates to 34 distinct lineages which can be clearly defined based on non-mobile genes. The results were in accordance with data from multilocus sequence typing. More than 100 different strains were distinguished based on affiliation to these lineages, SCCmec type and the presence or absence of PVL. These strains are described here mainly with regard to clinically relevant antimicrobial resistance- and virulence-associated markers, but also in relation to epidemiology and geographic distribution. The findings of the study show a high level of biodiversity among MRSA, especially among strains harbouring SCCmec IV and V elements. The data also indicate a high rate of genetic recombination in MRSA involving SCC elements, bacteriophages or other mobile genetic elements and large-scale chromosomal replacements

A Field Guide to Pandemic, Epidemic and Sporadic Clones of Methicillin-Resistant Staphylococcus aureus

In recent years, methicillin-resistant Staphylococcus aureus (MRSA) have become a truly global challenge. In addition to the long-known healthcare-associated clones, novel strains have also emerged outside of the hospital settings, in the community as well as in livestock. The emergence and spread of virulent clones expressing Panton-Valentine leukocidin (PVL) is an additional cause for concern. In order to provide an overview of pandemic, epidemic and sporadic strains, more than 3,000 clinical and veterinary isolates of MRSA mainly from Germany, the United Kingdom, Ireland, France, Malta, Abu Dhabi, Hong Kong, Australia, Trinidad & Tobago as well as some reference strains from the United States have been genotyped by DNA microarray analysis. This technique allowed the assignment of the MRSA isolates to 34 distinct lineages which can be clearly defined based on non-mobile genes. The results were in accordance with data from multilocus sequence typing. More than 100 different strains were distinguished based on affiliation to these lineages, SCCmec type and the presence or absence of PVL. These strains are described here mainly with regard to clinically relevant antimicrobial resistance- and virulence-associated markers, but also in relation to epidemiology and geographic distribution. The findings of the study show a high level of biodiversity among MRSA, especially among strains harbouring SCCmec IV and V elements. The data also indicate a high rate of genetic recombination in MRSA involving SCC elements, bacteriophages or other mobile genetic elements and large-scale chromosomal replacements