Variation in Reported Human Head Tissue Electrical Conductivity Values Abstract Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR.

Perceptual and Physiological Consequences of Dark Adaptation: A TMS-EEG Study Abstract Existing literature on sensory deprivation suggests that short-lasting periods of dark adaptation (DA) can cause changes in visual cortex excitability. DA cortical effects have previously been assessed through phosphene perception, i.e., the ability to report visual sensations when a transcranial magnetic stimulation (TMS) pulse is delivered over the visual cortex. However, phosphenes represent an indirect measure of visual cortical excitability which relies on a subjective report. Here, we aimed at overcoming this limitation by assessing visual cortical excitability by combining subjective (i.e., TMS-induced phosphenes) and objective (i.e., TMS-evoked potentials - TEPs) measurements in a TMS-EEG protocol after 30 min of DA. DA effects were compared to a control condition, entailing 30 min of controlled light exposure. TMS was applied at 11 intensities in order to estimate the psychometric function of phosphene report and explore the relationship between TEPs and TMS intensity. Compared to light adaptation, after DA the slope of the psychometric function was significantly steeper, and the amplitude of a TEP component (P60) was lower, only for high TMS intensities. The perceptual threshold was not affected by DA. These results support the idea that DA leads to a change in the excitability of the visual cortex, accompanied by a behavioral modification of visual perception. Furthermore, this study provides a first valuable description of the relationship between TMS intensity and visual TEPs.

How Action Context Modulates the Action-Language Relationship: A Topographic ERP Analysis Abstract The aim of the present study was to investigate how the context in which an action is presented could modulate the effect of action observation on language processing, an effect that is classically observed in the literature. To address this question, we recorded both behavioral (reaction times) and electrophysiological measures (event-related potentials) of participants performing a semantic decision task involving a verb describing an action that was congruent or incongruent with the action presented in a prime picture that had been observed. The prime picture presented an action performed in a usual or an unusual context. The results revealed different behavioral and topographical pattern responses according to the context in which an action is presented. Importantly, only in the usual context, the congruency between the prime picture and the verb stimulus facilitated the semantic processes, leading to shorter response times in this condition compared to the others. Moreover, the topographic analysis revealed that this facilitation was related to reduced processing times for the semantic access to the verb and for the motor preparation for the answer. Taken together, these findings demonstrate that the context of an action is crucial in the link between action and language.

Preterm Modulation of Connectivity by Endogenous Generators: The Theta Temporal Activities in Coalescence with Slow Waves Abstract The neuronal activity of the preterm brain is characterized by various endogenous activities whose roles in neurodevelopmental maturation processes have not been fully elucidated. The preterm EEG is characterized by discontinuities composed of short bursts of activity with dominant low frequencies. One of the earliest endogenous activities is the theta temporal activity in coalescence with slow waves (TTA-SW), which appears at 24 to 32 weeks of gestational age (wGA). The present study investigated the influence of TTA-SW on the spatial organization of the early preterm brain network. To achieve this objective, High-Density EEG data were recorded from preterm infants (29–32 wGA) and functional connectivity (FC) was estimated from the scalp EEG. TTA-SW, particularly in the theta band, induced increased FC between left temporal and left frontal areas and between left temporal and parietal areas with TTA-SW at the left temporal region, while FC was limited to the right temporal regions in the case of TTA-SW at the right temporal region. Regardless of the lateralization of TTA-SW, long-range FCs were observed between left frontal to left parietal areas, suggesting that these regions, together with the temporal region, provide a basis for coherent neuronal activation across distal cortical regions. TTA-SW dynamic features showed that brief phases of TTA-SW had an impact on both local and whole brain network organization, supporting the importance of TTA-SW as a biomarker of brain development.

Statistical Significance Assessment of Phase Synchrony in the Presence of Background Couplings: An ECoG Study Abstract Statistical significance testing is a necessary step in connectivity analysis. Several statistical test methods have been employed to assess the significance of functional connectivity, but the performance of these methods has not been thoroughly evaluated. In addition, the effects of the intrinsic brain connectivity and background couplings on performance of statistical test methods in task-based studies have not been investigated yet. The background couplings may exist independent of cognitive state and can be observed on both pre- and post-stimulus time intervals. The background couplings may be falsely detected by a statistical test as task-related connections, which can mislead interpretations of the task-related functional networks. The aim of this study was to investigate the relative performance of four commonly used non-parametric statistical test methods—surrogate, demeaned surrogate, bootstrap resampling, and Monte Carlo permutation methods—in the presence of background couplings and noise, with different signal-to-noise ratios (SNRs). Using simulated electrocorticographic (ECoG) datasets and phase locking value (PLV) as a measure of functional connectivity, we evaluated the performances of the statistical test methods utilizing sensitivity, specificity, accuracy, and receiver operating curve (ROC) analysis. Furthermore, we calculated optimal p values for each statistical test method using the ROC analysis, and found that the optimal p values were increased by decreasing the SNR. We also found that the optimal p value of the bootstrap resampling was greater than that of other methods. Our results from the simulation datasets and a real ECoG dataset, as an illustrative case report, revealed that the bootstrap resampling is the most efficient non-parametric statistical test for identifying the significant PLV of ECoG data, especially in the presence of background couplings.

Brain Complexity in Children with Mild and Severe Autism Spectrum Disorders: Analysis of Multiscale Entropy in EEG Abstract Multiscale entropy (MSE) model quantifies the complexity of brain functions by measuring the entropy across multiple time-scales. Although MSE model has been applied in children with Autism spectrum disorders (ASD) in previous studies, they were limited to distinguish children with ASD from those normally developed without corresponding severity level of their autistic features. Therefore, we aims  to explore and to identify the MSE features and patterns in children with mild and severe ASD by using a high dense 64-channel array EEG system. This study is a cross-sectional study, where 36 children with ASD were recruited and classified into two groups: mild and severe ASD (18 children in each). Three calculated outcomes identified brain complexity of mild and severe ASD groups: averaged MSE values, MSE topographical cortical representation, and MSE curve plotting. Averaged MSE values of children with mild ASD were higher than averaged MSE value in children with severe ASD in right frontal (0.37 vs. 0.22, respectively, p = 0.022), right parietal (0.31 vs. 0.13, respectively, p = 0.017), left parietal (0.37 vs. 0.17, respectively, p = 0.018), and central cortical area (0.36 vs. 0.21, respectively, p = 0.026). In addition, children with mild ASD showed a clear and more increase in sample entropy values over increasing values of scale factors than children with severe ASD. Obtained data showed different brain complexity (MSE) features, values and topographical representations in children with mild ASD compared with those with severe ASD. As a result of this, MSE could serve as a sensitive method for identifying the severity level of ASD.

Localization of Sensorimotor Cortex Using Navigated Transcranial Magnetic Stimulation and Magnetoencephalography Abstract The mapping of the sensorimotor cortex gives information about the cortical motor and sensory functions. Typical mapping methods are navigated transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG). The differences between these mapping methods are, however, not fully known. TMS center of gravities (CoGs), MEG somatosensory evoked fields (SEFs), corticomuscular coherence (CMC), and corticokinematic coherence (CKC) were mapped in ten healthy adults. TMS mapping was performed for first dorsal interosseous (FDI) and extensor carpi radialis (ECR) muscles. SEFs were induced by tactile stimulation of the index finger. CMC and CKC were determined as the coherence between MEG signals and the electromyography or accelerometer signals, respectively, during voluntary muscle activity. CMC was mapped during the activation of FDI and ECR muscles separately, whereas CKC was measured during the waving of the index finger at a rate of 3–4 Hz. The maximum CMC was found at beta frequency range, whereas maximum CKC was found at the movement frequency. The mean Euclidean distances between different localizations were within 20 mm. The smallest distance was found between TMS FDI and TMS ECR CoGs and longest between CMC FDI and CMC ECR sites. TMS-inferred localizations (CoGs) were less variable across participants than MEG-inferred localizations (CMC, CKC). On average, SEF locations were 8 mm lateral to the TMS CoGs (p < 0.01). No differences between hemispheres were found. Based on the results, TMS appears to be more viable than MEG in locating motor cortical areas.

Cross-Species Investigation on Resting State Electroencephalogram Abstract Resting state electroencephalography (EEG) during eyes-closed and eyes-open conditions is widely used to evaluate brain states of healthy populations and brain dysfunctions in clinical conditions. Although several results have been obtained by measuring these brain activities in humans, it remains unclear whether the same results can be replicated in animals, i.e., whether the physiological properties revealed by these findings are phylogenetically conserved across species. In the present study, we describe a paradigm for recording resting state EEG activities during eyes-closed and eyes-open conditions from rats, and investigated the differences between eyes-closed and eyes-open conditions for humans and rats. We found that compared to the eyes-open condition, human EEG spectral amplitude in the eyes-closed condition was significantly higher at 8–12 Hz and 18–22 Hz in the occipital region, but significantly lower at 18–22 Hz and 30–100 Hz in the frontal region. In contrast, rat EEG spectral amplitude was significantly higher in the eyes-closed condition than in the eyes-open condition at 1–4 Hz, 8–12 Hz, and 13–17 Hz in the frontal-central region. In both species, the 1/f-like power spectrum scaling of resting state EEG activities was significantly higher in the eyes-closed condition than in the eyes-open condition at parietal-occipital and frontal regions. These results provided a neurophysiological basis for future translational studies from experimental animal findings to human psychophysiology, since the validity of such translation critically relies on a well-established experimental paradigm and a carefully-examined signal characteristic to bridge the gaps across different species.

Accuracy of Estimating the Area of Cortical Muscle Representations from TMS Mapping Data Using Voronoi Diagrams Abstract Motor evoked potentials (MEPs) caused by transcranial magnetic stimulation (TMS) provide a possibility of noninvasively mapping cortical muscle representations for clinical and research purposes. The interpretation of such results is complicated by the high variability in MEPs and the lack of a standard optimal mapping protocol. Comparing protocols requires the determination of the accuracy of estimated representation parameters (such as the area), which is problematic without ground truth data. We addressed this problem and obtained two main results: (1) the development of a bootstrapping-based approach for estimating the within-session variability and bias of representation parameters and (2) estimations of the area and amplitude-weighted area accuracies for motor representations using this approach. The method consists in the simulation of TMS mapping results by subsampling MEPs from a single map with a large number of stimuli. We studied the extensor digitorum communis (EDC) and flexor digitorum superficialis (FDS) muscle maps of 15 healthy subjects processed using Voronoi diagrams. We calculated the (decreasing) dependency of the errors in the area and weighted area on the number of stimuli. This result can be used to choose a number of stimuli sufficient for studying the effects of a given size (e.g., the protocol with 150 stimuli leads to relative errors of 7% for the area and 11% for the weighted area in 90% of the maps). The approach is applicable to other parameters (e.g., the center of gravity) and other map processing methods, such as spline interpolation.

Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study Abstract Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over time, not the brain-state transition itself. Here, we propose a novel dynamic functional connectivity analysis method, brain-state extraction algorithm based on state transition (BEST), which constructs connectivity matrices from the duration of brain-states and decodes the proper number of brain-states in a data-driven way. To set the duration of each brain-state, we detected brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time. Furthermore, we also used Bayesian information criterion to the clustering method to estimate and extract the number of brain-states. Through validations, it was proved that BEST could find brain-state transition time-points and could estimate the proper number of brain-states without any a priori knowledge. It has also shown that BEST can be applied to resting state fMRI data and provide stable and consistent results.