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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.

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. •TESS builds on and enhances state-of-the-art EEG source imaging methods.•TESS estimates significant sources corresponding to given topographies.•Topographies can be group maps, simulated maps, simulated artifact maps, etc.•TESS provides direct estimate of sources' amplitude and significance.•TESS is best suited for source analysis of low SNR spontaneous brain activity.

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 18mm in Euclidean space. The corresponding error when the subject's own MRI is employed is 9.1mm. 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 describe a neuroimaging 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 subject-specific MRI to improve the anatomical interpretation of diffuse optical tomography images of brain activation.

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by “eyeballing” of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.