Multiple Pathways Analysis of Brain Functional Networks from EEG Signals: An Application to Real Data

In the present study, we propose a theoretical graph procedure to investigate multiple pathways in brain functional networks. By taking into account all the possible paths consisting of h links between the nodes pairs of the network, we measured the global network redundancy R (h) as the number of parallel paths and the global network permeability P (h) as the probability to get connected. We used this procedure to investigate the structural and dynamical changes in the cortical networks estimated from a dataset of high-resolution EEG signals in a group of spinal cord injured (SCI) patients during the attempt of foot movement. In the light of a statistical contrast with a healthy population, the permeability index P (h) of the SCI networks increased significantly (P < 0.01) in the Theta frequency band (3-6 Hz) for distances h ranging from 2 to 4. On the contrary, no significant differences were found between the two populations for the redundancy index R (h) . The most significant changes in the brain functional network of SCI patients occurred mainly in the lower spectral contents. These changes were related to an improved propagation of communication between the closest cortical areas rather than to a different level of redundancy. This evidence strengthens the hypothesis of the need for a higher functional interaction among the closest ROIs as a mechanism to compensate the lack of feedback from the peripheral nerves to the sensomotor areas.

Increased Expression of GluR2-flip in the Hippocampus of the Wistar Audiogenic Rat Strain After Acute and Kindled Seizures

The Wistar Audiogenic Rat (WAR) is an epileptic-prone strain developed by genetic selection from a Wistar progenitor based on the pattern of behavioral response to sound stimulation. Chronic acoustic stimulation protocols of WARs (audiogenic kindling) generate limbic epileptogenesis, confirmed by ictal semiology, amygdale, and hippocampal EEG, accompanied by hippocampal and amygdala cell loss, as well as neurogenesis in the dentate gyrus (DG). In an effort to identify genes involved in molecular mechanisms underlying epileptic process, we used suppression-subtractive hybridization to construct normalized cDNA library enriched for transcripts expressed in the hippocampus of WARs. The most represented gene among the 133 clones sequenced was the ionotropic glutamate receptor subunit II (GluR2), a member of the a-amino-3-hydroxy-5-methyl-4-isoxazoleopropionic acid (AMPA) receptor. Although semiquantitative RT-PCR analysis shows that the hippocampal levels of the GluR2 subunits do not differ between naive WARs and their Wistar counterparts, we observed that the expression of the transcript encoding the splice-variant GluR2-flip is increased in the hippocampus of WARs submitted to both acute and kindled audiogenic seizures. Moreover, using in situ hybridization, we verified upregulation of GluR2-flip mainly in the CA1 region, among the hippocampal subfields of audiogenic kindled WARs. Our findings on differential upregulation of GluR2-flip isoform in the hippocampus of WARs displaying audiogenic seizures is original and agree with and extend previous immunohistochemical for GluR2 data obtained in the Chinese P77PMC audiogenic rat strain, reinforcing the association of limbic AMPA alterations with epileptic seizures. (C) 2009 Wiley-Liss, Inc.

Estimating complex cortical networks via surface recordings-A critical note

We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites Effects of this spatial sampling in relation to structural network measures like centrality and assortativity were analyzed using multivariate classifiers A simplified model of 3D brain connectivity incorporating both short- and long-range connections served for testing. To mimic M/EEG recordings we sampled this model via non-overlapping regions and weighted nodes and connections according to their proximity to the recording sites We used various complex network models for reference and tried to classify sampled versions of the ""brain-like"" network as one of these archetypes It was found that sampled networks may substantially deviate in topology from the respective original networks for small sample sizes For experimental studies this may imply that surface recordings can yield network structures that might not agree with its generating 3D network. (C) 2010 Elsevier Inc All rights reserved

Measuring time series predictability using support vector regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.