Using electroencephalography (EEG) to elucidate the spontaneous activation of brain resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting-state topographies, so-called microstates). We estimated seven resting-state topographies explaining the EEG data set with k-means clustering (N = 164, 256 electrodes). Using a method specifically designed to localize the sources of broadband EEG scalp topographies by matching sensor and source space temporal patterns, we demonstrated that we can estimate the EEG RSNs reliably by measuring the reproducibility of our findings. After subtracting their mean from the seven EEG RSNs, we identified seven state-specific networks. The mean map includes regions known to be densely anatomically and functionally connected (superior frontal, superior parietal, insula, and anterior cingulate cortices). While the mean map can be interpreted as a "router," crosslinking multiple functional networks, the seven state-specific RSNs partly resemble and extend previous functional magnetic resonance imaging-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.

No abstract

Abstract.Efficient computation of the time-dependent forward solution for photon transport in a head model is a key capability for performing accurate inversion for functional Diffuse Optical Imaging (DOI) of the brain. The diffusion approximation to photon transport is much faster to simulate than the physically-correct radiative transport equation (RTE), however, it is commonly assumed that scattering lengths must be much smaller than all system dimensions and all absorption lengths for the approximation to be accurate. Neither of these conditions is satisfied in the cerebrospinal fluid (CSF). Since line-of-sight distances in the CSF are small, of the order a few mm, we explore the idea that the CSF scattering coefficient may be modeled by any value from zero up to the order of the typical inverse line-of-sight distance, or about 0.3 mm -1 , without significantly altering 2 calculated detector signals or partial pathlengths relevant for functional measurements. We demonstrate this in detail using Monte Carlo simulation of the RTE in a three-dimensional head model based on clinical MRI data, with realistic optode geometries. Our findings lead us to expect that the diffusion approximation will be valid even in the presence of the CSF, with consequences for faster solution of the inverse problem.

We describe a neuro imaging protocol that utilizes an anatomical atlas of the human head to guide Diffuse optical tomography of human brain activation. The protocol is demonstrated by imaging the hemodynamic response to median nerve stimulation in three healthy subjects, and comparing the images obtained using a head atlas with the images obtained using the subject-specific head anatomy. The results indicate that using the head atlas anatomy it is possible to reconstruct the location of the brain activation to the expected gyrus of the brain, in agreement with the results obtained with the subject-specific head anatomy. The benefits of this novel method derive from eliminating the need for subject-specific head anatomy and thus obviating the need for a subjectspecific MRI to improve the anatomical interpretation of Diffuse optical tomography images of brain activation.

We describe the validation of an anatomical brain atlas approach to the analysis of diffuse optical tomography (DOT). Using MRI data from 32 subjects, we compare the diffuse optical images of simulated cortical activation reconstructed using a registered atlas with those obtained using a subject’s true anatomy. The error in localization of the simulated cortical activations when using a registered atlas is due to a combination of imperfect registration, anatomical differences between atlas and subject anatomies and the localization error associated with diffuse optical image reconstruction. When using a subject-specific MRI, any localization error is due to diffuse optical image reconstruction only. In this study we determine that using a registered anatomical brain atlas results in an average localization error of approximately 18 mm in Euclidean space. The corresponding error when the subject’s own MRI is employed is 9.1 mm. In general, the cost of using atlas-guided DOT in place of subject-specific MRI-guided DOT is a doubling of the localization error. Our results show that despite this increase in error, reasonable anatomical localization is achievable even in cases where the subject-specific anatomy is unavailable.

We present two wide-field (%5 0 ; 3A5), diffraction-limited (k=D ' 0B5 at 10 m), broadband 10 and 20 m images of the Orion Nebula, plus six 7-13 m narrowband (k=Ák ' 1) images of the BN/ KL complex taken at the 3.8 m UKIRT telescope with the MPIA MAX camera. The wide-field images, centered on the Trapezium and BN/ KL regions, are mosaics of 35 00 ; 35 00 frames obtained with standard chopping and nodding techniques and reconstructed using a new restoration method developed for this project. They show the filamentary structure of the dust emission from the walls of the H ii region and reveal a new remarkable group of arclike structures %1 0 to the south of the Trapezium. The morphology of the Ney-Allen Nebula, produced by wind-wind interaction in the vicinity of the Trapezium stars, suggests a complex kinematical structure at the center of the cluster. We find indications that one of the most massive members of the cluster, the B0.5 V star 1 Ori D, is surrounded by a photoevaporated circumstellar disk. Among the four historic Trapezium OB stars, this is the only one without a binary companion, suggesting that stellar multiplicity and the presence of massive circumstellar disks may be mutually exclusive. In what concerns the BN / KL complex, we find evidence for extended optically thin silicate emission on top of the deep 10 m absorption feature. Assuming a simple two-component model, we map with '0B5 spatial resolution the foreground optical depth, color temperature, and mid-IR luminosity of the embedded sources. We resolve a conspicuous point source at the location of the IRc2-A knot, approximately 0B5 north of the deeply embedded H ii region ''I.'' We analyze the spectral profile of the 10 m silicate absorption feature and find indication for grain crystallization in the harsh nebular environment. In the OMC-1 South region, we detect several point sources and discuss their association with the mass-loss phenomenology observed at optical and millimeter wavelengths. Finally, we list the position and photometry of 177 point sources, the large majority of which are detected for the first time in the mid-IR. Twenty-two of them lack a counterpart at shorter wavelengths and are therefore candidates for deeply embedded protostars. The comparison of photometric data obtained at two different epochs reveals that source variability at 10 m is present up to a level of %1 mag on a timescale of $2 yr. With the possible exception of a pair of OB stars, all point sources detected at shorter wavelengths display 10 m emission well above the photospheric level, which we attribute to disk circumstellar emission. The recent model of Robberto et al. provides the simplest explanation for the observed mid-IR excess.

No abstract

Relating measures of electroencephalography (EEG) back to the underlying sources is a long-standing inverse problem. Here we propose a new method to estimate the EEG sources of identified electrophysiological states that represent spontaneous activity, or are evoked by a stimulus, or caused by disease or disorder. Our method has the unique advantage of seamlessly integrating a statistical significance of the source estimate while efficiently eliminating artifacts (e.g., due to eye blinks, eye movements, bad electrodes). After determining the electrophysiological states in terms of stable topographies using established methods (e.g.: ICA, PCA, k-means, epoch average), we propose to estimate these states' time courses through spatial regression of a General Linear Model (GLM). These time courses are then used to find EEG sources that have a similar time-course (using temporal regression of a second GLM). We validate our method using both simulated and experimental data. Simulated data allows us to assess the difference between source maps obtained by the proposed method and those obtained by applying conventional source imaging of the state topographies. Moreover, we use data from 7 epileptic patients (9 distinct epileptic foci localized by intracranial EEG) and 2 healthy subjects performing an eyes-open/eyes-closed task to elicit activity in the alpha frequency range. Our results indicate that the proposed EEG source imaging method accurately localizes the sources for each of the electrical brain states. Furthermore, our method is particularly suited for estimating the sources of EEG resting states or otherwise weak spontaneous activity states, a problem not adequately solved before.© 2014 Elsevier Inc. All rights reserved. IntroductionThe study of brain function has benefited enormously from modern neuroimaging techniques to reveal localization and dynamics of neuronal activity during evoked and spontaneous states. One of the most widely used methodologies to analyze data from functional magnetic resonance imaging (fMRI), is the General Linear Model (GLM) where pre-defined hemodynamic responses are used in a linear regression model and contrasts of interest are evaluated by statistical hypothesis testing (Bandettini et al., 1992;Friston et al., 1995;Kwong et al., 1992;Ogawa et al., 1992). As the fMRI signal is related to neuronal activity via neurovascular coupling, it only provides a (slow) proxy for neuronal activity. Electroencephalography (EEG), on the other hand, directly records the fast changes of current potential related to neuronal activity. Recent advances in high-density recording and 3D source analysis have increased EEG accuracy as a brain imaging method with the inherent advantage of high temporal resolution (Michel and Murray, 2012) It is fairly natural to conceive the application of conventional GLM analysis as used for fMRI to the Electrical Source Images of the EEG (or ESI: with this general term we indicate any method mapping scalp measurements into source space), as some previous papers (Bro...