Brain Topography

Background: Electroencephalographic (EEG) potentials evoked by transcranial magnetic stimulation (TMS) offer a direct window into cortical dynamics. Yet, a systematic exploration of their morphological features, analogous to sensory-evoked potentials, is lacking, especially for stimulation outside the motor cortex. Aim: To obtain region-specific properties of frontal, parietal and occipital networks from the time course of TMS-evoked potentials (TEPs). Materials and Methods: We implemented and applied an automatic procedure to compute peak-to-peak amplitude, peak latency, and inter-peak interval of TEPs recorded from 40 neurotypical subjects stimulated over left occipital (n=25), parietal (n=25), and frontal (n=25) cortices. Results: Occipital TEPs showed the largest peak-to-peak amplitude and longest latency of the first waveform component, independently of stimulation intensity and consistent with the recruitment of a large patch of densely interconnected neurons. Concerning later components, both latency and inter-peak interval systematically decreased along the posterior-to-anterior axis, reflecting progressively faster recurrent dynamics from the alpha-dominated occipital circuitry to the tightly coupled loops between frontal cortex and subcortical structures. Parietal TEPs showed intermediate amplitude and latency measures, consistent with the heterogeneous cytoarchitectonic and connectional organization of the superior parietal cortex. Conclusions: Our findings suggest that TEP morphology is shaped by the distinct properties of the stimulated networks, with early amplitude reflecting the extent of local recruitment and later temporal features tracking the rhythm of recurrent activity. This work offers a mechanistically grounded and practically accessible approach, also released as a Python-based tool, that allows to characterize cortical reactivity across different brain-states and populations.

Neural oscillations are central to brain function and communication, yet they are typically characterized in terms of spectral power within predefined frequency bands, potentially obscuring their underlying functional organization. An alternative framework focuses on oscillatory frequency rather than power, revealing that each brain region exhibits a characteristic, or natural, frequency that can be estimated at the voxel level using a data-driven approach. Although this framework has been successfully applied to MEG, its broader use remains limited by cost and availability. Here, we extended this approach to EEG and validated it against MEG-derived maps, assessing its robustness across EEG channel densities (high-density, 64 channels; low-density, 32 channels) and physiological states (eyes open and closed). EEG-derived maps revealed a coherent spatial organization of natural frequencies across the cortex, reproducing the large-scale posterior-to-anterior and medial-to-lateral gradients of increasing frequency previously described with MEG. Differences between MEG and EEG were mainly confined to frontal and temporal regions, likely reflecting the differential sensitivity of the two techniques to neural source configurations, whereas posterior regions showed highly similar patterns. Importantly, this organization remained stable despite reductions in EEG sensor density and was modulated by physiological state, reproducing the well-known posterior alpha dominance during eyes-closed conditions. Together, these findings demonstrate that natural frequency mapping can be extended beyond specialized MEG research environments to low-density EEG settings, offering an accessible and scalable tool for investigating brain oscillations and their alterations in neuropsychiatric conditions.

Learning new information in the presence of distracters and changing conditions requires the ability to adapt. In the brain, this adaptive capability has been linked to dynamic interactions between attention and working memory, which enable the selective filtering of irrelevant input while preserving behaviorally relevant information. Specific neural oscillations have been implicated in this process. Here, we introduce a phenomenological data-driven framework for oscillatory network modeling that learns condition-dependent coupling laws directly from neural recordings and enables inference of condition-dependent directed pathways. We apply our approach to magnetoen-cephalography (MEG) data collected while participants performed a working-memory task with and without distracters. Recall dynamics in the non-distracter condition are first modeled using a linear oscillatory network in which each region of interest is represented by two alpha-band harmonic oscillators. We use universal differential equations (UDE), an extension of neural differential equations, to capture distracter-induced changes in coupling laws. Symbolic regression is then used to interpret the modifications identified by UDE as nonlinear functions, and an additional method is proposed to identify the directed pathway from the newly emerging nonlinear terms in the dynamics of brain regions of interest. Despite inter-subject variability, working memory recall data from all four participants examined under distraction showed the emergence of a pathway from the dorsolateral prefrontal cortex (dlPFC) to the primary visual cortex (V1). This finding is consistent with the established role of the dlPFC in cognitive control and suggests that distracter processing recruits a directed interaction from prefrontal to visual regions. More broadly, our results illustrate that combining linear models whose parameters are learned from the data with universal differential equations augmented by interpretability methods enables the identification of condition-dependent coupling laws, their representation as interpretable mathematical functions, and the discovery of candidate directed pathways underlying adaptive changes in oscillatory networks without requiring strong prior assumptions about the underlying mechanisms.

The goal of the resting-state functional connectivity studies is to determine the inherent dynamics of the brain networks while the body is at rest. These networks get differentially activated when the brain is involved in various tasks such as processing of sensory inputs, initiating motor activities, or various cognitive tasks. Resting state functional connectivity networks are commonly revealed by determining Pearson Correlation Coefficients of the Blood Oxygenation Level Dependent (BOLD) signals collected from different brain regions using functional Magnetic Resonance Imaging (fMRI) while the subject is not actively performing any task. However, the functional connectivity thus determined does not correlate well with the known structural connectivity between different brain regions. Here, we used Empirical Mode decomposition (EMD), followed by Hilbert Transformation (HT), to determine the resting state functional connectivity of the somatomotor network in the human brains. We show that the time series data decomposed by this method improves correlation of the derived functional connectivity with the known structural connectivity (especially for low -TR fMRI data) as compared to the methods commonly used.

Derived from previous observations on equal and cross-frequency coupling, we evaluated the proposal that equal and cross-frequency phase synchronization may characterize the integration-segregation perspective of cerebral sensory-motor processing. Using brain recordings obtained in normal conditions and in conditions of diminished sensory input (eyes closed wakefulness, sleep and coma, when there is presumably less functional segregation of sensory-motor processing in neural networks), we assessed potential differences in partitioning of the synchrony state space linked to cross-frequency synchronization. More partitions were found in conditions of decreased sensory input. In addition, there was a less complex synchrony state space in cross-frequency as compared with equal-frequency coupling, in terms of fewer connectivity configurations. These results support the idea that equal-frequency coupling favours integration from multiple brain regions occurring in a complex synchrony state space rich in possible connectivity configurations, whereas cross-frequency coupling contributes to segregation, or localized sensory-motor transformations taking place in specific brain areas. This evidence may contribute to new considerations about the much-discussed role of multi-frequency relations in neuronal activity, and how the structural and functional modular organization of the nervous system is able to generate the coordinated activity needed for conscious and appropriate cognitive behaviors in complex environments.

Transcranial Electric stimulation (tES) is a safe and noninvasive technique increasingly used in treating brain disorders. Despite many studies on tES, there is still a lack of understanding of its mechanisms at the microscale network level. This is crucial for optimized parameter selection in therapy approaches such as the treatment of pharmacoresistant epilepsy. In this study, we made use of a recently published neuroinspired microscale model of the neocortex, known as NeoCoMM, and integrated a "Lambda E"-based model of tES. This updated version was used to investigate the acute effects of tES (tDCS and tACS), on the neural activity of various neuron types in both healthy and epileptic brain states. Results showed that in the case of healthy alpha and gamma rhythms, tACS induced electric field entrainment at the peak power frequency of the network oscillations as measured by the Local field Potentials (LFPs). This resonance-like entrainment was independent from the individual firing rate of cell types. For epileptic activity, tACS did not provide consistent results. Cathodal tDCS resulted in a promising decrease in hyperexcitable activity throughout simulations. These results advance our understanding of the impact of tES on network dynamics at both the extracellular and intracellular activity levels. Author summary

Most of what we currently know about brain oscillations is derived from Fourier-based spectral power methods. While widely adopted, these procedures introduce methodological limitations and inherently overlook informative neurophysiological features that often remain unreported. In this study, we characterized resting-state oscillatory activity from human magnetoencephalography (MEG) recordings (N = 128) by integrating two complementary approaches. First, oscillatory episodes were detected at the source level with sBOSC. Subsequently, the ByCycle algorithm was applied to these episodes to extract individual cycle features. Results revealed that the brain engages in oscillatory activity for only [~]25% of the recording time, with occipital and parietal regions accounting for the highest temporal prevalence across canonical frequency bands. Furthermore, oscillatory episodes lasted an average of 4.6 cycles, reinforcing the view of neural oscillations as transient bursts. Region-specific duration and power measures revealed distinct anatomical organizations offering complementary physiological information. Finally, by extracting the instantaneous amplitude, period, and waveform asymmetry of individual cycles, we successfully dissociated sinusoidal occipital alpha waves from the asymmetric sensorimotor mu rhythm. By moving beyond traditional power-centric analyses, this approach provides a comprehensive characterization of spontaneous oscillatory activity, thereby offering new insights into the spatial, temporal, and spectral structure of human brain oscillations.

Cognitive decline is a major non-motor feature of Parkinsons disease (PD), but reliable and accessible biomarkers remain limited. Resting-state electroencephalography (EEG) is a promising candidate because it is low-cost, portable, and well suited to repeated assessment. Recent work has increasingly focused on source-space functional connectivity (FC) for the prediction of cognition. However, the influence of source-modelling based on an individualized MRI-based head model relative to that based on standard template model is unknown. To compare these two source-space EEG FC methods, we analysed EEG data from the New Zealand Parkinsons Progression Programme, including 136 people with PD and 51 age-similar controls. Source reconstructed resting-state EEG was parcellated with the HCP-MMP1 atlas, and used to derive amplitude envelope correlation (AEC) and debiased weighted phase lag index (dwPLI) across six canonical frequency bands. The twenty-four FC modalities were evaluated using six machine-learning regression algorithms within a nested cross-validation framework. Theta-, alpha-, and beta-band FC showed the most consistent prediction of global cognition, with the strongest performance observed for theta- and alpha-band AEC and dwPLI features (maximum R{superscript 2} = 0.170, r = 0.439). Standard and individualized head models showed comparable predictive performance across nearly all modalities. Feature-importance patterns for Cole-Anticevic networks were also highly similar between the two head-model options. These findings show that source-space resting-state EEG FC can predict cognitive performance in PD. The comparability of the two head models suggests that the more user-friendly and less resource intense standard head model template is satisfactory. This supports feasible, scalable, and clinically accessible EEG-based biomarkers of cognition in PD.

The behavioural activity of freely moving animals is a confounding factor that affects the recording, analysis, and final results of animal EEG experiments. Along with the lack of standardisation in animal in vivo electrophysiology experiments, this could lead to huge inconsistencies, especially in the analysis of centrally acting drugs. Therefore, the main aim of this paper is to investigate the effects of behavioural activity versus inactivity on the multichannel EEG in freely moving rats. In a large sample (n = 116) of waking recordings from 12 cortical electrodes (ECoG) in Wistar rats, we evaluated behavioural activity-related changes in the power spectrum, current source density, and power-based global functional connectivity (GFC) in a 3D rat brain model, according to the TOHOKU Rat Brain Atlas. The main findings were that behavioural activity induced 1) a robust power increase in 6-8 Hz, peaking at 7 Hz with maximum changes over the parietal and temporal cortex, 2) an increase in gamma power (30-80 Hz) across the whole brain, 3) a decrease in delta (1-4 Hz) and beta (12-30 Hz) power across the whole cortex. Changes were also localised in subcortical regions, particularly in the diencephalon/thalamus. The GFC analysis showed a similar pattern of power changes across the 6-8 Hz, delta, and beta bands; however, GFC in the gamma band decreased. Again, the GFC analysis revealed changes in connectivity within subcortical structures, primarily in the thalamus. None of the measures was affected in the alpha band (8-12 Hz). These findings emphasise behavioural state as a critical factor influencing EEG outcomes, with important implications for the standardisation and translational validity of preclinical neurophysiological studies.

Understanding the neural dynamics underlying expressive musical performance remains a major challenge at the intersection of neuroscience, music cognition, and computational modeling. While EEG studies of emotion have largely focused on passive exposure to affective stimuli, comparatively little research has examined oscillatory brain activity during active musical expression. The present single-subject study investigated whether band-limited EEG activity recorded during expressive piano performance by a professional concert pianist contains sufficient discriminative structure to support supervised multi-class classification of musically defined emotional categories. MethodsEEG was recorded from 128 scalp sites while a professional concert pianist performed emotionally characterized excerpts from Bach, Beethoven, and Chopin in a continuous naturalistic session. Musical excerpts had been previously categorized and perceptually validated according to emotional valence, tempo, energy/arousal, and tonal structure. From the continuous EEG recording, 180 non-overlapping 2-second artifact-free segments were extracted, yielding 30 segments for each emotional category. Mean spectral power was computed within theta (3.5-7.5 Hz), alpha (7.5-12.5 Hz), and high-beta (24-30 Hz) frequency bands across selected centro-parietal and posterior electrodes, resulting in 24 EEG-derived features per segment. Linear Support Vector Machine, Random Forest, and Gradient Boosting classifiers were evaluated using an 80/20 train-test split combined with 5-fold cross-validation. ResultsEEG-only classification achieved above-chance performance across models, with Random Forest yielding the highest accuracy (0.42), macro F1-score (0.414), and Cohens {kappa} (0.30), exceeding the theoretical chance level of 0.167. Feature importance analysis revealed distributed contributions across theta, alpha, and high-beta oscillatory activity, particularly over parietal and occipital regions, without evidence for a single dominant neural marker. Inclusion of an additional binary arousal-related feature substantially improved Random Forest performance (accuracy = 0.58; macro F1 = 0.579; {kappa} = 0.50), indicating that arousal organization contributed strongly to category separability within the classification framework. ConclusionsThese findings suggest that oscillatory EEG activity accompanying expressive musical action contains measurable statistical structure associated with emotionally differentiated performance states. Rather than identifying discrete neural correlates of emotion, the present results provide a computational characterization of distributed oscillatory dynamics emerging during expressive motor-acoustic interaction, extending affective EEG research beyond passive perception paradigms toward ecologically grounded musical performance contexts.

Brain stimulation is increasingly recognized as an effective and important therapeutic intervention for many brain diseases. Distance between the scalp and other brain regions is a pivotal variable for neurostimulation planning and the development of new techniques, but alterations in the distance between the scalp and other regions in brain diseases are largely unknown. In this study, we developed an automatic pipeline to calculate scalp-to-region distance (SRD) values from T1 MR images and applied it to a total of 1382 participants, including patients with autism spectrum disorder (ASD), Parkinsons disease (PD), Alzheimers disease (AD), and cognitively normal controls (CNs). Cloud points were uniformly sampled on the automatically extracted scalp surface and cortex surface, on which the point-wise distance maps were generated. The brain was then coregistered with the BCI-DNI atlas, and SRD value for each brain region was extracted. Analysis of covariance (ANCOVA) was performed for SRD in each brain region, with age and sex as covariates. Compared with CNs, ASD patients showed widespread SRD decreases across the brain with prominent involvement of the frontal lobe, especially the orbitofrontal cortex and adjacent regions. In contrast, in AD patients, significantly increased SRD values were observed in various regions of the frontal gyrus. No significant SRD alteration was found in PD patients after correction. The automatic SRD calculation pipeline and the different patterns of SRD alterations in these diseases might be helpful for future neurostimulation planning in clinical practice. HighlightsO_LIAutomatic pipeline enables scalp-to-region distance (SRD) measurement, facilitates brain stimulation planning. C_LIO_LIASD patients show widespread SRD decreases, especially in the orbitofrontal cortex and adjacent regions. C_LIO_LIAD patients present increased SRD in the frontal gyrus and decreased SRD in the parahippocampal gyrus. C_LI

Flow, as defined by Mihalyi Csikszentmihalyi (1975), is a holistic sensation experienced when individuals are fully immersed in an activity, resulting in a mental state characterized by a diminished sense of self and altered perception of time. To investigate the global neural dynamics underlying flow, we employed EEG microstate analysis to capture the spatial and temporal properties of dominant transient global brain states (Lehmann et al., 1998). In a study involving 43 participants playing the video game Thumper for 25 minutes, we extracted three four-minute EEG segments from each session corresponding to reported experiences of flow, boredom, and frustration, as determined by self-reports and performance metrics. Across conditions, six distinct microstate topographies (A-F) accounted for most of the global variance. Given that reduced self-referential processing is a key feature of flow, we hypothesized that flow would modulate the properties of microstates C and E, which have been associated with brain regions resembling the default mode network (DMN). Compared to boredom and frustration, the flow condition showed significantly decreased global explained variance, mean duration, time coverage, and occurrence frequency of microstate E, as well as reduced mean duration and time coverage of microstate C. These findings suggest that microstates associated with self-referential processing are shorter and less frequent during flow than during boredom and frustration. This supports the notion that the flow experience modulates global brain dynamics, particularly within the DMN. Furthermore, our results align with previous research reporting reduced DMN activity during meditative and psychedelic states, reinforcing the idea of diminished self-awareness in such conditions.

Intracranial neurophysiology studies have typically ignored signals from electrodes located in white matter (WM), assuming that their information content is artifactual or related to nearby gray matter (GM). Here, we tested the electrophysiological and functional features of signals recorded from different WM locations. Signals were recorded from 19 patients undergoing intracranial monitoring for drug-resistant epilepsy by means of stereo-electroencephalography (sEEG). Each sEEG electrode was classified into WM or GM based on the surrounding tissue. We obtained recordings from a total of 1,717 sEEG electrode contacts, 36% in WM, while the patients were in awake resting state (5 minutes). For each sEEG electrode, we employed a model-based spectral decomposition to separate periodic and aperiodic components, and we computed signal complexity metrics. For a subset of participants, we computed WM structural information from diffusion-weighted magnetic resonance imaging and we evaluated functional signals during a cognitive control task. Our results show that signals recorded from WM have different spectral features and higher complexity than GM. Complexity correlates positively with fractional anisotropy, and modulations related to behavior during the task were detected in WM. Overall, this indicates that WM signals carry information that may reflect signal propagation across WM fiber tracts.

AO_SCPLOWBSTRACTC_SCPLOWThe integration of electrophysiological recordings with multimodal neuroimaging data holds great promise for advancing our understanding of brain function and neurological disorders. To facilitate this endeavour, we present electro-MICA, an open-access Python toolbox designed to project electrophysiological features from scalp and intracranial electroencephalography (EEG) onto cortical and hippocampal surfaces generated by validated multimodal imaging ecosystems. The toolbox comprises two pipelines: one for intracranial EEG (iEEG) recorded with stereo-EEG depth electrodes, and one for scalp EEG source localization. Both pipelines are grounded in numerical solutions to the electromagnetic equations governing electric activity in the brain, solved using the Boundary Element Method. A key methodological contribution is the use of a current density double layer model for neural generators, which avoids the mathematical singularities introduced by conventional dipole-based models when electrodes are near the cortical surface, a situation that can arise in iEEG. Electrode contacts are additionally modeled with non-zero length, improving physical realism. Scalp EEG source localization is performed using eLORETA on a subject-specific three-layer head model derived from the anatomical input. Validation against empirical gamma-band iEEG data from 32 subjects demonstrates that the distributed generator model outperforms both distance-based and dipole-based alternatives. An illustrative clinical example demonstrates the toolboxs capacity to reveal associations between intracranial spike rates, cortical thickness, and anatomical connectivity in an epilepsy patient. Electro-MICA requires no parameter selection from the user, facilitating straightforward multimodal analyses in both research and clinical settings. The toolbox is available at github.com/MICA-MNI/electromica with extensive online documentation at electromica.readthedocs.io.

State-transition approaches, including EEG microstate analysis and related fMRI methods such as hidden Markov models (HMMs) and co-activation pattern (CAP) analysis, provide widely used tools for coarse-graining neural dynamics into a small set of quasi-stable states. Its utility has been demonstrated across resting-state and task paradigms, with broad applications ranging from cognitive neuroscience to candidate biomarkers for psychiatric and neurological disorders. A fundamental limitation remains, however: nearly all downstream temporal measures are conditional on the template maps defined at the outset. In the conventional pipeline, templates are derived from polarity-invariant clustering of voltage maps at global field power (GFP) peaks, making the resulting state definitions sensitive to preprocessing, sampling, initialization, clustering algorithms, and the choice of cluster number. Consequently, the method captures coarse regularities in EEG dynamics, while only weakly constraining the larger geometric organization from which those states emerge. This template dependence poses a major challenge for reproducibility and for comparisons across studies and EEG caps. Here, we revisit this problem from a topological-geometric perspective. We treat templates not as cluster centroids extracted from GFP-peak maps, but as landmarks embedded in the global structure of a state space constructed from mutual similarities among scalp voltage maps. In this formulation, microstate templates are rediscovered as discrete representatives of dominant axes that organize continuous neural-state topography. This reformulation preserves polarity as a meaningful geometric relation instead of eliminating it at the outset as analytical redundancy. It also shifts attention from isolated state labels to the terrain of the state space itself: the broader relational structure within which local states become interpretable. Using this approach, we show that landmark-based state definitions outperform conventional templates in capturing state structure and improving analytical performance. These findings suggest that the central problem in EEG microstate analysis is broader than clustering optimization: it concerns how to define valid nodes for coarse-graining continuous dynamics without discarding the topology that organizes them. By shifting the conceptual basis of microstate analysis from templates to landmarks, the present approach provides a more principled and potentially more stable foundation for state definition, including in fMRI. This topolo-geometric reappraisal extends conventional microstate analysis and opens a path toward more unified comparisons across datasets, paradigms, and recording systems.

Focusing on an organisms task at hand is instrumental for intelligent and goal-driven behavior. However, humans and other animals often fail to pay sustained attention across long time intervals. Failing to stay on-task may cause one to miss crucial task-relevant signals, leading to impaired performance, which can have serious consequences. Therefore, it is important to understand the neural basis of attentional lapses. One promising neural marker of attentional lapses is the frontal P3 (fP3) EEG component, which has been suggested to reflect the susceptibility to incoming sensory input. Following this, we hypothesized that the fP3 1) predicts imminent lapses of attention, and 2) that it should predict upcoming lapses of attention across modalities. In two experiments, we found that the fP3 reliably tracked lapses of attention of sustained attention already seconds preceding the crucial visual signal. We further extended this to the auditory domain: Already 1.5s ahead of the incoming auditory target, the fP3 revealed whether that target was detected or not. Detailed topographic analyses did, however, reveal a slight dissociation between modalities in underlying intracranial source configurations. In sum, this work revealed a supramodal neural signature of susceptibility, which tracks lapses of sustained attention seconds ahead of the critical incoming sensory input.

Intense aesthetic experiences are among the most complex responses arising from the interaction of mind, brain, and context. Observations from fMRI suggest that when viewers feel highly moved by artworks, the underlying neural states differ from those accompanying less intense responses, particularly through recruitment of the DMN. Using electroencephalography and Bayesian category-specific cumulative link mixed models, we investigated whether such putative peak aesthetic responses exhibit threshold-specific neurodynamics rather than linear scaling with intensity. Twenty-two Chilean participants viewed 113 diverse local artworks whilst rating how moved they felt on a four-point scale. We analysed both canonical oscillatory power ({theta}-{gamma}) and aperiodic components (offset and exponent) during the contemplation window and the post-elicitor window. Threshold-specific effects were found: spectral features differentiated the highest rating category from moderate responses, rather than scaling uniformly across all intensity levels. During artwork visualisation, power in the {beta}1 and {beta}2 bands, as well as the interaction of {beta}1 with the 1/f exponent, predicted the transition to the most intense response; during the post-elicitor window, the aperiodic 1/f exponent predicted the transition from very low to higher-intensity responses. Modelling individual differences in spectral signatures (in the and {gamma} bands) credibly improved predictive performance (approximate leave-one-out cross-validation; elpd_loo), suggesting that neural variability reflects meaningful mechanistic heterogeneity in aesthetic processing rather than mere noise. These findings speak to a broader question, how the brain marks the intensity of conscious experience, and, more specifically, support the hypothesis that being intensely moved constitutes a qualitatively distinct neural state, characterised by specific configurations of oscillatory dynamics and cortical excitability that modulate the transition from low and moderate to peak engagement.

Sex-related differences in the aging of the human brain were studied using large array of experimental data. The open archive CamCan was used as a source of data: the magnetic encephalograms, co-registered with magnetic resonance images of the head, were obtained for each of 434 subjects (ages 18-87 years, mean age 54.7 {+/-}18.4): 217 females (ages 18-87 years, mean age 54.5 {+/-}18.4) and 217 males (ages 18-84 years, mean age 54.8 {+/-}18.3). Recordings were split in 10-year age cohorts, each cohort consisted of equal number of men and women to calculate average intersex characteristics correctly. By massively solving the inverse problem, functional tomograms were calculated - the spatial distribution of elementary spectral components. Physiological noise was eliminated by joint analysis of MEG-based functional tomogram and magnetic resonance image for each subject. Then multichannel spectra were transformed into time series of the power of elementary current dipoles. Summary electric powers were calculated in six conventional frequency bands (1-4 Hz - delta; 4-8 Hz - theta; 8-13 Hz - alpha; 13-21 Hz - beta1; 21-30 Hz - beta2; 30-48 Hz - gamma), and sex differences in age-related changes were examined. It was found that in the youngest age cohort (18-29 years) the summary electrical power of the brain for males is 1.5 times greater than such power for females. For adults (30-69 years), male and female powers are approximately equal, while in older cohorts (70-87 years), male total brain power is greater. Age dependencies in various frequency bands are generally different for men and women, excluding higher frequencies 21-48 Hz. Basic conclusion can be made that after intersex averaging total electric power of the human brain is invariant through the lifespan from 18 to 87 years. The proposed method of joint MEG and MRI analysis can be used for further study of the sex-related details of brain sources in their connection with age changes.

We present a novel astro-neural-field population model with application to migraine-related cortical spreading depolarization. The model is composed of four spatio-temporal state variables: excitatory and inhibitory membrane potentials, astrocytic potassium uptake recruitment, and extracellular potassium concentration. Extending a previous neural field model, we incorporate activity-dependent astrocytic potassium clearance via a nonlinear term coupled to astrocyte dynamics. The astrocyte transfer function, like its neural counterpart, exhibits three regimes governed by extracellular potassium, capturing its effect on clearance. This yields a more comprehensive framework, better fits experimental data, and provides new insights into the mechanisms of cortical spreading depolarization.

Substantial progress has been made in recent years on understanding how the human brain parses and processes natural speech. Much of this progress has been based on modeling how brain activity relates to the different acoustic and linguistic features of speech. By fitting and testing models based on those features, one can test hypotheses about the kinds of computations and representations the brain uses to convert speech sounds into understanding. While much of this work has focused on modeling BOLD activity using functional neuroimaging or intracranially recorded electrophysiological signals, the approach has also proven useful with MEG and EEG. Indeed, noninvasive EEG has certain advantages for studying speech processing in terms of translational research and application. Research over the last decade or so has shown that EEG can be successfully modeled based on numerous acoustic, linguistic, and paralinguistic speech features. However, an important unanswered question hangs over all of this work: namely, what constitutes a good model of EEG responses to natural speech? Or, to put it another way, how much variance in EEG recorded during natural speech listening is explainable as having derived from that speech input? The present study aims to tackle this issue. We do so under the assumption that the best model for a person's EEG response to natural speech is a set of EEG responses from other people listening to the same speech. Using this assumption, we construct inter-subject models using EEG from 19 healthy adult native speakers of English who all listened to the same audiobook. The model for each subject involves predicting their EEG data using (dimensionality-reduced) EEG from different numbers of other subjects and then extrapolating to estimate the total explainable variance in the target individual's response to speech. Following this, we show that linear models (temporal response functions) based on several commonly used acoustic and linguistic speech features can predict most - but importantly not all - of the estimated total explainable variance in EEG responses across subjects.