Inverse Modeling for MEG/EEG data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur

Electrophysiological modeling in generalized epilepsy using surface EEG and anatomical brain structures

Deep brain structures involve significantly in the pathology of brain diseases such as epilepsy, Alzheimer, and Parkinson. Physiological brain modeling has become an emerging approach to investigate the coupling dynamics of the brain activity ofthese diseases. We propose a method using the surface EEG signals integrated with the anatomical individual brain to build the electrophysiological model of the epileptic patient’s brain. The EEG-driven model is used to investigate the deep brain activities of 23 patients diagnosed with generalized epilepsy from CHB-MIT Scalp EEG Database. Significant changes in the electrical activities in hippocampus, accumbens, amygdala, provide us insights into the dynamics ofactive brain regions during epilepsy. All of these brain regions show the significant energy variation defined by 5 features (Mean, Max, Min, Standard deviation, Power spectral density) with the p-value < 0.05 in both pre-ictal vs ictal and ictal vs post-ictal. Such result shows the potential of using EEG as a tool to capture the deep brain activity of epilepsy and other diseases that alter deep brain structures. The proposed model may be used to enhance the sensitivity of detecting and predicting epilepsy, detect the progression of the brain lesion, and support the decision-making for a brain medical intervention

Sensitivity of MEG and EEG to Source Orientation

An important difference between magnetoencephalography (MEG) and electroencephalography (EEG) is that MEG is insensitive to radially oriented sources. We quantified computationally the dependency of MEG and EEG on the source orientation using a forward model with realistic tissue boundaries. Similar to the simpler case of a spherical head model, in which MEG cannot see radial sources at all, for most cortical locations there was a source orientation to which MEG was insensitive. The median value for the ratio of the signal magnitude for the source orientation of the lowest and the highest sensitivity was 0.06 for MEG and 0.63 for EEG. The difference in the sensitivity to the source orientation is expected to contribute to systematic differences in the signal-to-noise ratio between MEG and EEG.National Institutes of Health (U.S.) (Grant NS057500)National Institutes of Health (U.S.) (Grant NS037462)National Institutes of Health (U.S.) (Grant HD040712)National Center for Research Resources (U.S.) (P41RR14075)Mind Research Networ

Factors associated with unintended pregnancy in Brazil: cross-sectional results from the Birth in Brazil National Survey, 2011/2012

Background Unintended pregnancy, a pregnancy that have been either unwanted or mistimed, is a serious public health issue in Brazil. It is reported for more than half of women who gave birth in the country, but the characteristics of women who conceive unintentionally are rarely documented. The aim of this study is to analyse the prevalence and the association between unintended pregnancy and a set of sociodemographic characteristics, individual-level variables and history of obstetric outcomes. Methods Birth in Brazil is a cross-sectional study with countrywide representation that interviewed 23,894 women after birth. The information about intendedness of pregnancy was obtained after birth at the hospital and classified into three categories: intended, mistimed or unwanted. Multinomial regression analysis was used to estimate the associations between intendedness of a pregnancy, and sociodemographic and obstetric variables, calculating odds ratios and 95 % confidence intervals. All significant variables in the bivariate analysis were included in the multinomial multivariate model and the final model retaining variables that remained significant at the 5 % level. Results Unintended pregnancy was reported by 55.4 % of postpartum women. The following variables maintained positive and significant statistical associations with mistimed pregnancy: maternal age < 20 years (OR = 1.89, 95 % CI: 1.68–2.14); brown (OR = 1.15, 95 % CI: 1.04–1.27) or yellow skin color (OR = 1.56, 95 % CI: 1.05–2.32); having no partner (OR = 2.32, 95 % CI: 1.99–2.71); having no paid job (OR = 1.15, 95 % CI: 1.04–1.27); alcohol abuse with risk of alcoholism (OR = 1.25, 95 % CI: 1.04–1.50) and having had three or more births (OR = 2.01, 95 % CI: 1.63–2.47). The same factors were associated with unwanted pregnancy, though the strength of the associations was generally stronger. Women with three or more births were 14 times more likely to have an unwanted pregnancy, and complication in the previous pregnancies and preterm birth were 40 % and 19 % higher, respectively. Previous neonatal death was a protective factor for both mistimed (OR = 0.61, 95 % CI: 0.44–0.85) and unwanted pregnancy (OR = 0.44, 95 % CI: 0.34–0.57). Conclusions This study confirms findings from previous research about the influence of socioeconomic and individual risk factors on unintended pregnancy. It takes a new approach to the problem by showing the importance of previous neonatal death, preterm birth and complication during pregnancy as risk factors for unintended pregnancy

Surfactant proteins SP-A and SP-D modulate uterine contractile events in ULTR myometrial cell line

Pulmonary surfactant proteins SP-A and SP-D are pattern recognition innate immune molecules. However, there is extrapulmonary existence, especially in the amniotic fluid and at the feto-maternal interface. There is sufficient evidence to suggest that SP-A and SP-D are involved in the initiation of labour. This is of great importance given that preterm birth is associated with increased mortality and morbidity. In this study, we investigated the effects of recombinant forms of SP-A and SP-D (rhSP-A and rhSP-D, the comprising of trimeric lectin domain) on contractile events in vitro, using a human myometrial cell line (ULTR) as an experimental model. Treatment with rhSP-A or rhSP-D increased the cell velocity, distance travelled and displacement by ULTR cells. rhSP-A and rhSP-D also affected the contractile response of ULTRs when grown on collagen matrices showing reduced surface area. We investigated this effect further by measuring contractility-associated protein (CAP) genes. Treatment with rhSP-A and rhSP-D induced expression of oxytocin receptor (OXTR) and connexin 43 (CX43). In addition, rhSP-A and rhSP-D were able to induce secretion of GROα and IL-8. rhSP-D also induced the expression of IL-6 and IL-6 Ra. We provide evidence that SP-A and SP-D play a key role in modulating events prior to labour by reconditioning the human myometrium and in inducing CAP genes and pro-inflammatory cytokines thus shifting the uterus from a quiescent state to a contractile one

A unified view on beamformers for M/EEG source reconstruction

Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging

Effects of dipole position, orientation and noise on the accuracy of EEG source localization

BACKGROUND: The electroencephalogram (EEG) reflects the electrical activity in the brain on the surface of scalp. A major challenge in this field is the localization of sources in the brain responsible for eliciting the EEG signal measured at the scalp. In order to estimate the location of these sources, one must correctly model the sources, i.e., dipoles, as well as the volume conductor in which the resulting currents flow. In this study, we investigate the effects of dipole depth and orientation on source localization with varying sets of simulated random noise in 4 realistic head models. METHODS: Dipole simulations were performed using realistic head models and using the boundary element method (BEM). In all, 92 dipole locations placed in temporal and parietal regions of the head with varying depth and orientation were investigated along with 6 different levels of simulated random noise. Localization errors due to dipole depth, orientation and noise were investigated. RESULTS: The results indicate that there are no significant differences in localization error due tangential and radial dipoles. With high levels of simulated Gaussian noise, localization errors are depth-dependant. For low levels of added noise, errors are similar for both deep and superficial sources. CONCLUSION: It was found that if the signal-to-noise ratio is above a certain threshold, localization errors in realistic head models are, on average the same for deep and superficial sources. As the noise increases, localization errors increase, particularly for deep sources

Complement in the pathogenesis of Alzheimer's disease

The emergence of complement as an important player in normal brain development and pathological remodelling has come as a major surprise to most scientists working in neuroscience and almost all those working in complement. That a system, evolved to protect the host against infection, should have these unanticipated roles has forced a rethink about what complement might be doing in the brain in health and disease, where it is coming from, and whether we can, or indeed should, manipulate complement in the brain to improve function or restore homeostasis. Complement has been implicated in diverse neurological and neuropsychiatric diseases well reviewed elsewhere, from depression through epilepsy to demyelination and dementia, in most complement drives inflammation to exacerbate the disease. Here, I will focus on just one disease, the most common cause of dementia, Alzheimer’s disease. I will briefly review the current understanding of what complement does in the normal brain, noting, in particular, the many gaps in understanding, then describe how complement may influence the genesis and progression of pathology in Alzheimer’s disease. Finally, I will discuss the problems and pitfalls of therapeutic inhibition of complement in the Alzheimer brain

Regional Differences in the Sensitivity of MEG for Interictal Spikes in Epilepsy

MEG interictal spikes as recorded in epilepsy patients are a reflection of intracranial interictal activity. This study investigates the relationship between the estimated sources of MEG spikes and the location, distribution and size of interictal spikes in the invasive ECoG of a group of 38 epilepsy patients that are monitored for pre-surgical evaluation. An amplitude/surface area measure is defined to quantify and rank ECoG spikes. It is found that all MEG spikes are associated with an ECoG spike that is among the three highest ranked in a patient. Among the different brain regions considered, the fronto-orbital, inter-hemispheric, tempero-lateral and central regions stand out. In an accompanying simulation study it is shown that for hypothesized extended sources of larger sizes, as suggested by the data, source location, orientation and curvature can partly explain the observed sensitivity of MEG for interictal spikes

Modification of EGF-Like Module 1 of Thrombospondin-1, an Animal Extracellular Protein, by O-Linked N-Acetylglucosamine

Thrombospondin-1 (TSP-1) is known to be subject to three unusual carbohydrate modifications: C-mannosylation, O-fucosylation, and O-glucosylation. We now describe a fourth: O-β-N-acetylglucosaminylation. Previously, O-β-N-acetylglucosamine (O-β-GlcNAc) was found on a threonine in the loop between the fifth and sixth cysteines of the 20th epidermal growth factor (EGF)-like module of Drosophila Notch. A BLAST search based on the Drosophila Notch loop sequence identified a number of human EGF-like modules that contain a similar sequence, including EGF-like module 1 of TSP-1 and its homolog, TSP-2. TSP-1, which has a potentially modifiable serine in the loop, reacted in immuno-blots with the CTD110.6 anti-O-GlcNAc antibody. Antibody reactivity was diminished by treatment of TSP-1 with β-N-acetylhexosaminidase. TSP-2, which lacks a potentially modifiable serine/threonine in the loop, did not react with CTD110.6. Analysis of tandem modules of TSP-1 localized reactivity of CTD110.6 to EGF-like module 1. Top-down mass spectrometric analysis of EGF-like module 1 demonstrated the expected modifications with glucose (+162 Da) and xylose (+132 Da) separately from modification with N-acetyl hexosamine (+203 Da). Mass spectrometric sequence analysis localized the +203-Da modification to Ser580 in the sequence 575CPPGYSGNGIQC586. These results demonstrate that O-β-N-acetylglucosaminylation can occur on secreted extracellular matrix proteins as well as on cell surface proteins