Brain Topography

Introduction: Before asking which microstate an EEG sample belongs to, there is a simpler question: can it look like a microstate at all? Yet a winning correlation mixes three different properties: how microstate-like the scalp field is, where it lies within that geometry, and how well the available templates represent that direction. We asked whether a fixed geometry, independent of any microstate solution, could explain common structure of microstate maps, constrain how strongly EEG patterns can match them, and describe that structure in interpretable terms. Methods: We introduce First-Order spherical haRMonic (FORM), projecting normalized scalp topographies onto a fixed three-dimensional subspace defined only by sensor geometry. Direction describes field geometry; projection norm, {rho}, measures FORM conformity and the maximum correlation attainable by any FORM topography. We tested simulations, literature templates, and 374 recordings from 187 LEMON participants. Results: FORM captured canonical microstate geometry despite being defined a priori of a microstate solution: FORM explained energy, {rho} squared, was 0.990-0.998 for five meta-microstates and 0.966-0.986 for pooled LEMON maps. Across 313 literature templates, FORM angular distances preserved their independently derived organization (Spearman {rho}=0.987). In continuous EEG, FORM conformity explained a median 74.4% of within-subject variance in winning-template correlation, versus 21.7% for GFP. As independently estimated dictionaries increased from k=1 to k=12, the median fitted fraction of the FORM ceiling rose from 0.613 to 0.970. Discussion: FORM places an a priori continuous geometry beneath discrete microstate labels. It separates properties intrinsic to the scalp field - whether it is microstate-like and where it lies within that geometry - from how well the chosen template dictionary represents it. FORM therefore provides an interpretable, clustering-independent reference for assignment strength, template comparison and continuous microstate dynamics.

Microstate analysis studies dynamic brain activity through a discrete representation of electroencephalographic (EEG) signals and their topography. While the body of literature describing findings in diverse experimental and clinical settings is growing rapidly, the effects of several core components of the microstate algorithm are still insufficiently understood. This study systematically investigates how two common temporal smoothing algorithms, parametric smoothing and GFP peak interpolation, influence a wide range of microstate sequence parameters, and how they shape the integrated picture of brain dynamics inferred from these metrics. We study eyes-closed resting-state EEG recordings from N=201 healthy subjects from a public EEG database. For the elementary temporal metrics, we find that widening smoothing windows markedly increase microstate duration, reduce occurrence rates, but leave coverage mostly unaffected. In terms of simple syntax properties, smoothing windows above 30 ms yield an inconsistent picture with a memoryless, first-order Markov structure at short time scales, but a positive Hurst phenomenon suggestive of long-range correlations at long time scales. Evidence of asymmetry and non-equilibrium dynamics are suppressed by smoothing. At intermediate time scales, microstate periodicity was only observed up to a window size of 28 ms. Smoothing had a strong impact on the complexity metrics entropy rate and excess entropy, decreasing the former and increasing the latter. For most metrics, the effects of GFP peak interpolation were comparable to those of smoothing windows in the range between 28 ms and 44 ms. In conclusion, elementary and higher-order microstate parameters must be interpreted in the context of preprocessing, as many parameters change continuously with smoothing intensity. This limits the comparability of microstate parameters obtained with different preprocessing settings. Strong smoothing with a window size above 30 ms or GFP peak interpolation yields a fundamentally different picture of resting state brain dynamics, i.e. that of a memoryless, aperiodic, time-reversible first-order Markov chain. Since insights from other neurophysiological modalities suggest that time series memory and time-irreversibility are markers of normal brain activity, our recommendation is to avoid intense smoothing to minimize alterations of the initial sequences.

Background: High-definition transcranial alternating current stimulation (HD-tACS) is favored by the neurostimulation community for its precision and ability to influence neuronal dynamics. Yet, the exact mechanism by which the underlying brain structures are being affected remains unclear. We believe that the investigation of the aperiodic nature of the electroencephalograph (EEG) could shed light on the modulatory effects of HD-tACS. Methods: We analyzed the EEG of 9 participants during a compensatory tracking task (CTT) in two sessions, each with different HD-tACS protocols. Every session consisted of an initial period of no stimulation, followed by 30 Hz HD-tACS in the left motor (M30) or frontal (F30) cortex. We then isolated the aperiodic component of the EEG and calculated its spectral slope {beta}. Results and Discussion: {beta} decreased during F30 mainly in the right frontal cortex, indicating a shift towards higher frequencies and an increase of the excitatory/inhibitory balance. Additionally, we found that despite the long monotonus task the accuracy of the participants did not decrease, which might be attributed to the ability of both M30 and F30 to sustain attention for prolonged time. Finally, the change of CTT accuracy during the stimulation correlated with the {beta} of specific channels before the stimulation. This indicates the potential of {beta} to be used as a screening biomarker in future studies. In conclusion, we showed the ability of HD-tACS to alter EEG aperiodic dynamics and paved the way for future exploration of such dynamics in the field.

Background: Immediate transcranial evoked potentials (iTEPs) are ultra-early electroencephalographic responses elicited by single-pulse transcranial magnetic stimulation (TMS) of the primary motor cortical hand area (M1-HAND). Their characteristic latency, waveform, and sensitivity to short-interval intracortical inhibition suggest a close relationship to the cortical generators of indirect corticospinal volleys (I-waves). Here, we tested the hypothesis that iTEPs constitute a fundamental physiological response of human motor cortex and therefore generalize across motor representations, including the leg representation (M1-LEG). Methods: Single-pulse TMS was delivered over the motor cortical representation of the right tibialis anterior (TA) in 16 healthy volunteers. In a subset of participants (n=10), stimulus-response curves and paired-pulse short-interval intracortical inhibition (SICI) measures were obtained. Results: TMS of M1-LEG elicited reproducible iTEPs consisting of a slow positive deflection with superimposed high-frequency peaks with topographic maxima in electrodes adjacent to the stimulation site. M1-LEG iTEPs emerged close to active motor threshold and increased in prevalence and amplitude with increasing stimulation intensity. Paired-pulse stimulation produced consistent SICI of the second and third iTEP peaks. M1-LEG stimulation also evoked conventional later TEPs with latencies and polarities closely resembling those recorded after M1-HAND stimulation. Conclusions: TMS of the human M1-LEG elicits robust iTEPs that closely resemble those recorded from M1-HAND in their timing, intensity dependence, and sensitivity to intracortical inhibition. These findings indicate that iTEPs are not specific to M1-HAND but reflect a general physiological response of human motor cortex closely linked to corticospinal I-wave generation. This broadens their utility as a non-invasive probe of excitatory and inhibitory circuit dynamics throughout the human motor system.

The basal ganglia (BG), thalamus and cortex form the BG-thalamo-cortical (BGTC) network, which is essential for voluntary movement and centrally involved in Parkinson's disease (PD). In both patients and animal models, BG neuronal activity exhibits exaggerated oscillatory synchronization in the beta frequencies (13-30 Hz). Theoretical studies have proposed multiple network mechanisms for the generation of these abnormal beta oscillations. However, key properties, such as frequency and power distribution across BG nuclei, vary substantially among patients and between animal models (rodents vs. non-human primates). This variability complicates direct comparisons between theoretical models and experimental data, and questions whether distinct neuronal mechanisms may underlie beta oscillations across species. In an experimentally constrained BGTC network model with species-specific parameters (synaptic, neuronal, and network properties), we evaluate the features of abnormal beta oscillatory activity generated by different mechanisms. The network's negative feedback loops serve as potential sources of spontaneous oscillations. Using rodent- or primate-constrained models, we compare the spectral properties of oscillatory activity across network populations for each loop and derive the expected phase relationships between populations to align predictions with existing rodent data and propose testable hypotheses for primates. We also demonstrate how oscillation frequency can be modulated when multiple generation mechanisms interact as coupled oscillators in the full network. Our results, combined with observed cross-species beta oscillations characteristics, suggest that abnormal beta oscillations likely arise from distinct mechanisms in rodents and primates.

Background: High-density electroencephalography (EEG) improves spatial sampling but entails greater acquisition cost, preparation time, participant burden, and computational demand. However, the extent to which electrode reduction affects successive levels of EEG analysis from global signal properties to cortical network inference remains insufficiently understood. Methods: We systematically subsampled two independent resting-state EEG datasets comprising 120 children recorded with 128 channels and 98 adults recorded with 256 channels. Density-dependent effects were assessed across 128-, 256-, 64-, 32-, and 19-channel configurations using a multilevel framework encompassing signal representation and quality, global and regional spectral power, EEG microstates, scalp-level effective connectivity, traveling waves, and source-level cortical hub topology. Results: Sensitivity to electrode reduction increased with the spatial specificity of the outcome. Global relative spectral power and explained variance were comparatively stable across configurations. In contrast, signal-to-noise ratio, entropy, reconstruction error, regional spectral distributions, traveling-wave estimates, effective connectivity, and microstate dynamics became progressively more density-sensitive. Although canonical microstate classes remained broadly identifiable at lower densities, their global explained variance, temporal parameters, and transition structure were altered. Source-level hub identity and centrality ranking showed the greatest vulnerability, with markedly reduced cross-density agreement at 32 and 19 channels. Across both datasets, several spatially dependent measures showed a transition around 64 channels, whereas estimates obtained with 128 and 256 channels were generally more similar. Conclusions: Electrode density affects EEG outcomes according to the spatial scale of the intended inference. Sparse montages may preserve broad global summaries but are less reliable for regional, spatiotemporal, and source-network analyses. Under the resting-state conditions examined here, approximately 64 channels represented a pragmatic transition for several spatially dependent measures. This value should be interpreted as context-specific guidance rather than a universal threshold.

BackgroundThe balance between excitatory and inhibitory neuronal signalling (E/I balance), proposed as a key mechanism controlling neuronal activity, is disrupted in many neurodevelopmental conditions. One such condition is Neurofibromatosis Type 1, a monogenic neurodevelopmental disorder characterised by increased inhibitory tone. While previous research suggests that E/I balance may be altered in infancy, it is unknown whether E/I balance remains altered during adolescence in NF1. Here we investigated this question using the aperiodic exponent of the resting electroencephalogram (EEG) power spectrum, a proposed electrophysiological index of E/I balance. MethodThe aperiodic exponent of the EEG power spectrum ( 1/f) was estimated for 16 adolescents with NF1 diagnosis aged 11-16, and 16 age- and sex-matched neurotypical controls. General linear models (GLM) were fit to estimate effects of NF1 diagnosis, age and their interaction on aperiodic exponent. We also estimated the peak dominant frequency and power in oscillatory activity of the EEG signal and fitted the same structure of GLM models. Cognitive assessments across domains of memory, executive function and attention were also analysed with GLM to examine brain-cognition associations. ResultsThe NF1 group had higher aperiodic exponent compared to neurotypical controls, but the aperiodic exponent was not associated with cognitive performance. After accounting for aperiodic activity, the NF1 group also had lower dominant oscillatory frequency than the control group. There was no significant interaction between NF1 diagnosis and age on PAF. There were no group differences in peak alpha power. ConclusionsWe demonstrate for the first time that the alterations to aperiodic activity in NF1 are consistent with the proposed mechanism of increased inhibitory tone. These findings show the potential of this paradigm for future pharmacological and brain stimulation trials investigating treatment-related changes in cortical function.

Activity in the gamma frequency range (>30Hz) serves as a neural hallmark of stimulus encoding across multiple sensory modalities. Based on its distinct properties, this activity could be classified as broadband or narrowband oscillations. Visual stimuli, such as a grating, induce narrowband oscillations, which have shown promise as diagnostic markers for neurological disorders. However, such tasks are discomforting for the subjects. These can be made subject-friendly by swapping the stimulus for an auditory one; however, most auditory stimuli elicit a broadband activity. In search of an ideal candidate to induce auditory narrowband oscillations, we previously recorded responses to auditory gratings in electroencephalography (EEG) recordings (Gulati & Ray, 2024) using a passive listening paradigm. We observed a focal sustained broadband increase in high gamma activity (70-150Hz), which was not selective towards any stimulus attributes. Nevertheless, since EEG represents activity of a large population, it remained unclear whether observed activity represented a broadband increase or comprised multiple narrowband rhythms, and whether it was indeed untuned. To elucidate this, we recorded invasively from the regions on and around the superior temporal gyrus, closer to Heschl's gyrus of seven patients undergoing epilepsy resection surgery. Patients were either under anesthesia or fully awake while we presented auditory gratings through loudspeakers. We found a transient increase in broadband gamma activity (30-100Hz) followed by a sustained suppression of beta rhythm (14-28Hz). This broadband gamma was selective for slower spectro-temporal modulations. Our result compares the responses obtained across different recording scales and corroborates that auditory gratings produce broadband but not narrowband gamma activity.

Navigating the world requires our brains to constantly process a massive influx of information from the environment. One of the most remarkable yet complex feats the human brain performs daily is speech perception. It is a multifaceted, hierarchical process that relies heavily on the integration of auditory and visual cues, particularly in noisy environments. Neural integration of these streams is hypothesized to depend on temporal mechanisms governed by individual alpha frequency (IAF, 8-12 Hz), which serves as a key neural correlate of the temporal binding window (TBW). While IAF has been linked to multisensory integration, its role in shaping multisensory illusions remains largely unexplored. Here, we tested this directly using transcranial alternating current stimulation (tACS) to modulate occipital activity during multisensory conflict. In a two-day protocol involving 27 healthy participants, resting-state EEG was recorded to determine IAF. On Day 2, participants then performed a multisensory speech task involving congruent (pa/, /ta/, /ka/) and incongruent McGurk stimuli (auditory /pa/, visual /ka/), both at baseline and during tACS applied at IAF and offset frequencies (IAF +/- 2 Hz) over occipital electrodes. While accuracy for congruent syllables remained high across conditions, results revealed that tACS significantly reduced the perception of illusory /ta/ percepts during McGurk trials. Finally, using a phenomenological model based on Kuramoto oscillators, we provided a mechanistic explanation for the percentage of illusory responses observed across participants under the influence of tACS at IAF and offsets. This study highlights the potential for targeted neuromodulatory interventions to influence multisensory processing in clinical or professional settings.

The glymphatic system is the waste clearance pathway in the central nervous system. Although its dysfunction has been associated with several neurological disorders, its role in multiple sclerosis (MS) remains poorly understood. This system is thought to be particularly active during sleep, yet the relationship between sleep metrics and MRI-derived markers of glymphatic activity has been scarcely explored. In this study, we aimed to perform a multimodal magnetic resonance imaging (MRI) assessment of glymphatic function in MS and investigate its association with sleep parameters, clinical disability, disease duration and MRI-derived structural markers. The study included 22 patients (34.3{+/-}10.1 years; 16 female) with relapsing-remitting MS and 26 healthy controls (32.2{+/-}5.7 years; 18 female), all of whom underwent 3.0 Tesla MRI. Glymphatic function was assessed using diffusion tensor image analysis along the perivascular space (DTI-ALPS) index and global blood-oxygen-level-dependent (BOLD) signal-cerebrospinal fluid (CSF) coupling. In addition, sleep micro- and macrostructural features, clinical disability, disease duration, along with mean diffusivity (MD), lateral ventricular, choroid plexus volumes and lesion load were evaluated. Patients showed reduced DTI-ALPS index, though this difference became only a trend after controlling for MD. No significant group differences were observed in global BOLD signal-CSF coupling. However, in patients, the weaker coupling was associated with longer total sleep time, lower arousal index, greater disability and lateral ventricle enlargement, whereas the DTI-ALPS index showed no significant correlations with any of the measures. Together, these findings challenge the assumption that DTI-ALPS can serve as a reliable marker of glymphatic function in demyelinating diseases such as MS, where disease-related alterations in white matter microstructure can confound diffusion-based measures. In contrast, gBOLD-CSF coupling showed associations with sleep, clinical disability and structural changes in patients, despite the absence of a significant group-level difference. This finding suggests that gBOLD-CSF coupling may capture individual variability in glymphatic-related dynamics relevant to sleep and the clinical expression of MS.

Neuroimaging research focusing on subcortical brain regions showed that they play a role in a wide range of cognitive functions and associated their structural alterations to various psychiatric disorders. Methods to analyse such structures were developed to observe changes, not simply in terms of volumes, but in terms of surfaces, which are able to detect subtle and local changes in shape and geometrical configuration. Such advanced analyses have however been largely restricted to command line environments with relatively limited access to Python users or researchers with low technical expertise. We present SubCortexMesh as a user-friendly toolbox which covers automated surface estimation from popular subcortical volume segmentations (FreeSurfer and the Functional Magnetic Resonance Imaging of the Brain Software Library (FSL)), computes shape-related vertex-wise metrics (thickness, surface area and curvature) for whole cohorts and includes statistical analyses. This is all done inside Python, automatically computing all subjects of a given preprocessed directory, with the explicit intent to minimise steps and manual coding required from the users. SubCortexMesh includes statistical tools which allow conventional random field theory-based cluster analyses on native subject metrics, standardised within common surface templates, thus containing a whole workflow necessary to run an up-to-date shape-wise subcortical analyses in one package.

Independent component analysis (ICA) is widely used to remove artefacts from EEG and TMS-EEG recordings, but the components that correspond to artefacts are usually identified by manual inspection, which is subjective and difficult to reproduce. Existing automated classifiers are either designed for ordinary EEG and do not target the artefacts specific to concurrent transcranial magnetic stimulation, or are built on EEGLAB, so none is available for FieldTrip-based pipelines. We present TEFAR, a lightweight, configurable framework for semi-automatic artefact-component classification that applies the same detectors to ordinary EEG and TMS-EEG. TEFAR is built on FieldTrip and requires no additional MATLAB toolboxes. Each artefact class (line noise, blinks, lateral eye movements, cranial muscle, cardiac activity, and the TMS decay and recharge artefacts) is identified from the spectral, spatial, or temporal signature that its components are known to express. Detection thresholds use robust statistics (the median and the median absolute deviation rather than the mean and standard deviation), so that a single dominant artefact component cannot raise the threshold that is meant to detect it. In 30 ground-truth simulations, TEFAR reached a specificity of 1.000 and a sensitivity of 0.96-0.97 for both profiles. An application of TEFAR to real EEG and TMS-EEG recordings is the next step and will be reported separately.

Developmental dyslexia is associated with impaired cortical tracking of continuous speech and difficulties in perceiving within-word prosodic structure in children and adults. For adults and continuous speech, both low-frequency delta- and theta-band impairments and high-frequency gamma- and beta-band differences are found. To date, perceptual difficulties with intra-word prosody have not been studied in direct relation to neural speech encoding. Here neurotypical adults and adults with dyslexia listened to multisyllabic words. EEG was recorded, and cortical encoding was quantified using temporal response function (TRF) modelling. Four speech representations were explored: lexical stress pattern (intra-word prosodic prominence), the low-frequency speech envelope, word onsets and syllable onsets. Adults with dyslexia showed significantly reduced encoding of intra-word prosodic prominence (delta band) and of the speech envelope (delta and theta bands). In contrast, encoding of word and syllable onsets was preserved. Pinpointing delta-band cortical tracking as central to encoding intra-word prosodic prominence opens new avenues for research.

Dance is increasingly delivered as an intervention for social and affective functioning, but a live dyadic encounter confounds several distinct ingredients - social attention, verbal engagement, coordinated movement, and in-the-moment generation - so its neural effects cannot be assigned to any one of them. We recorded dual-brain mobile EEG from adult human participants of both sexes (16 female, 2 male) and a dance instructor across six five-minute conditions that vary these ingredients independently: solitary rest, direct eye gaze, conversation, teacher-led and participant-led mirroring, and free improvisation, with concurrent head-mounted accelerometry. Referencing each condition to eye gaze - socially engaging but nearly motionless - rather than to solitary rest partitioned the response. The components proved dissociable. Nonverbal social attention alone reorganized the power spectrum, flattening the aperiodic exponent (g = -0.97) and desynchronizing alpha-band oscillations (g = -0.90), while leaving inter-regional connectivity unchanged (amplitude g = +0.15; phase g = +0.11). Verbal engagement added high-frequency amplitude coupling (g = +2.61) without further spectral change. Coordinated movement added a decrease in theta/alpha coupling involving the frontoparietal-control network (g = -0.59), the signature of an externally directed state, alongside a further spectral shift (g = -0.61). Movement-vigor covariates, corticokinematic coherence, and aperiodic-adjusted power indicated that the spectral effects were cortical rather than myogenic. Four weeks of improvisational dance training increased empathic responsiveness to others' distress relative to a movie control (g = 0.82). Coordinated social movement thus acts on the brain componentially, with mutual attention rather than motor load establishing the engaged state.

From a statistical point of view, clustering spike time-windows from multi-electrode arrays (MEAs) recordings is a challenging high-dimensional, unsupervised clustering task where stationarity, commonly assumed in standard time-series analysis, is often violated and for which a gold standard is currently unavailable. Here we aim at providing practical guidance on how to cluster this type of data by systematically comparing 108 clustering pipelines differing along three main dimensions: (i) the data feature space; (ii) the metrics used to quantify distance between data points; and (iii) the clustering algorithm. For our benchmark we used both labeled synthetic data, mimicking four physiologically-inspired classes of spike time-windows, and a set of real MEAs recordings from a two-dimensional in-vitro neural culture. The performance of the competing pipelines was evaluated in terms of balanced accuracy and computational time when analyzing the synthetic datasets, while the Silhouette score was used for the real dataset, where no ground-truth is available. Overall, our analysis shows that the best combination is formed by k-means with Euclidean distance applied after Principal Component Analysis (PCA) of the spike time-windows. Conversely, hierarchical clustering showed the highest computational burden, while Independent Component Analysis and kernel PCA provided less effective noise suppression.

Drug-resistant focal epilepsy (DRFE) is understood as a complex brain network disorder. As a result, conventional pair-wise functional connectivity may fail to capture the higher-order interactions between groups of epileptogenic regions. We applied multivariate information theory to stereotactic electroencephalography-defined networks in retrospective resting-state fMRI analysis to determine whether the overall strength and dominant type of interdependencies between three or more regions, represented by the S-information and O-information respectively, could offer clinically relevant insights prior to surgical intervention in DRFE. We found networks involving the epileptogenic zone to be mostly dominated by re-dundant information-sharing, which was predicted by stronger structural and functional connections between regions. Non-temporal lobe epilepsy patients demonstrated greater redundancy and stronger comparative interdependencies than temporal lobe epilepsy patients, in line with a more complex network architecture. Among non-temporal lobe cases with postoperative follow-up, not seizure-free patients displayed more positive O-information deviation scores than seizure-free patients, suggesting elevated redundancy in networks associated with poorer surgical outcomes. Our findings potentiate the S-information and O-information as complementary higher-order descriptors of the epileptogenic network within personalised presurgical assessments.

We investigated how auditory and visual presentation of small numerical magnitudes and non- numerical magnitudes are passively processed at the neural level. Earlier research suggests automatic processing of quantities occurs rapidly in the brain. However, there are no studies on passive, pre-attentive change detection of magnitudes utilizing mismatch negativity (MMN) across sensory modalities in same participants, important to understand amodal processing of magnitudes. We recorded EEG in passive auditory and visual multi-feature oddball and control paradigms from the same adult individuals (N=104/105, respectively). In the auditory paradigm, the standard stimulus was a 2-part sound, while the deviant stimuli varied in frequency, intensity, gap length, and number of sounds (1-part and 3-part stimuli). In the visual paradigm, the standard stimulus was a dot array containing two medium-sized dots, while the deviant stimuli varied in numerosity (1 or 3 dots) and dot size (small or large). The results for the auditory paradigm showed significant MMN across all features, including comparisons with deviant-alone control stimuli, except for the 2-part vs. 1-part contrast. The results for the visual paradigm suggested differences in processing across all features apart from large and medium-size stimuli. These results replicate earlier MMN findings that the brain distinguishes pre-attentively changes in sound frequency, amplitude and gap. Auditorily presented small numerical magnitudes also showed pre-attentive processing but less robustly to the contrast 2 vs. 1. Visual pre-attentive processing of small numerical magnitudes was also observed. Together these findings could be interpreted to support the existence of abstract, pre- attentive processing of numerical magnitudes. Highlights* Auditory and visual processing of numerical and non-numerical magnitudes was examined with EEG * Auditory non-numerical magnitudes and medium vs. small dots replicated earlier MMN studies * aMMN and vMMN showed pre-attentive processing of changes in small numerosities 2 vs. 3 and 2 vs. 1 * 2 vs. 1 tone processing was less robust than 2 vs. 3 or the processing of the visual contrasts * The brain processes changes in small non-symbolic magnitudes pre-attentively

This study presents a systematic investigation of the brain's own microwave emissions in the 0.85-5.0 GHz range, aiming to validate microwave encephalography (MWEG) as a novel tool for cognitive state assessment. Early experiments (2016-2019) using comparisons with background noise, a control phantom, and different antenna types confirmed the endogenous origin of the recorded signals. In a 2021 experiment (118 measurements on one subject, right and left occipital areas), we observed a significant functional hemispheric asymmetry: the number of "substantial spikes" was significantly higher in darkness for the right hemisphere (p<0.001) and higher in light for the left hemisphere (p<0.001). A 2025 group study (three subjects) confirmed the asymmetry (p<0.05 for right, p<0.01 for left). We describe the signal processing pipeline, including spike detection and statistical analysis. Two complementary mathematical models are presented: an energy-based model linking optical input to microwave emission via neural activity modes, and an information-based model interpreting spectral changes in terms of structural pattern overlap with memory engrams. An estimation of the physical dimensions of possible brain resonators (2-22 mm) is provided. The results are discussed in the context of hemispheric specialization and the information commutation theory. This work opens new perspectives for developing low cost, contactless brain monitoring systems.

Intracranial EEG (iEEG) affords a unique opportunity to assess human neural activity with millisecond temporal resolution and millimeter spatial precision. However, group-level statistical analyses remain challenging due to sparse and heterogeneous electrode coverage, anatomical variability, and the difficulty of integrating spatiotemporal responses across individuals. Most iEEG studies therefore define brain regions of interest for the group analysis, sacrificing a spatial resolution strength of iEEG and excluding data outside those regions of interest. Here, we present a spatiotemporal analysis and mapping with permutation-based linear mixed-effects models (STAMP-LME), a cortical-mapping framework for population-level iEEG analysis independent of predefined cortical regions of interest. STAMP-LME is a complete processing pipeline spanning electrode localization, mapping to a standardized surface of the cortical mantle, including medial, orbitofrontal, and insular regions, and statistical inference at the cortical level. Electrode-level data from each participant is mapped to a standardized cortical surface using weights that account for electrode-to-cortex distance and confidence in cortical sampling. Brain signals are then combined with a spatial smoothing kernel and entered into LME models at each vertex-node and time point. Data-derived null distributions are generated through controlled permutations and used for spatiotemporal inference with threshold-free cluster enhancement (TFCE) or false-discovery-rate (FDR) correction. We validated STAMP-LME using simulations with known cortical ground truth while varying effect size, interparticipant source-location variability, electrode coverage, and spatial smoothing. The framework reliably detected simulated effects and recovered their spatiotemporal distribution while maintaining low localization error and limiting false-positive detections across different sampling conditions. We then applied the method to intracranial recordings from nine individuals performing an auditory oddball task. The empirical analysis revealed distributed auditory cortical responses consistent with conventional channel-level analyses, while providing more fine-grained group-level activation maps across sampled cortical regions. STAMP-LME provides a flexible, open-source framework for data-driven population-level cortical iEEG analysis that preserves the spatiotemporal strengths of intracranial recordings while accounting for sparse and heterogeneous electrode coverage. The structured workflow enables reproducible and anatomically precise group-level inference on a standardized cortical surface and facilitates comparison with surface-based fMRI and source-localized M/EEG results, therefore providing a practical tool for cognitive and clinical neuroscience research.

We explored the role of proprioceptive feedback from Ia afferents in intra-muscle synergies stabilizing muscle force with the help of muscle vibration, which is known to increase presynaptic inhibition of those afferents to alpha-motoneurons. Healthy young adults performed an accurate three-finger force production task by pressing with the index, middle and ring fingers of the dominant (right) hand. This was done without vibration and with vibration applied to the extrinsic finger flexors, extensors, both muscles, the palm, or biceps brachii. High-density electromyography was used to identify motor units in both finger flexors and extensors. The raster plots of motor units were converted into firing frequency functions and then subjected to principal component analysis to extract stable motor unit groups (MU-modes). The uncontrolled manifold (UCM) analysis was used to quantify the variance components affecting and not affecting force, and the synergy index in the MU-mode space. Contrary to our expectations, the synergy index did not drop under any vibration condition and showed an increase during vibration applied simultaneously to both flexor and extensor muscles. This was primarily associated with a drop in the component of variance affecting the force magnitude. The results speak against an important role of monosynaptic Ia-afferent projections to alpha-motoneurons in the formation of intra-muscle synergies. They suggest that muscle vibration can play the role of a noisy signal decreasing performance-affecting variance via stochastic resonance effects.