Improving the predictive potential of diffusion MRI in schizophrenia using normative models—Towards subject‐level classification

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group‐level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject‐level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject‐level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free‐water) dMRI measures, were calculated by means of age and sex‐adjusted z‐scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z‐scores than are found with raw values (p < .001), predictions based on summary z‐score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject‐level classification.     

Cortical localization of phase and amplitude dynamics predicting access to somatosensory awareness

Neural dynamics leading to conscious sensory perception have remained enigmatic in despite of large interest. Human functional magnetic resonance imaging (fMRI) studies have revealed that a co‐activation of sensory and frontoparietal areas is crucial for conscious sensory perception in the several second time‐scale of BOLD signal fluctuations. Electrophysiological recordings with magneto‐ and electroencephalography (MEG and EEG) and intracranial EEG (iEEG) have shown that event related responses (ERs), phase‐locking of neuronal activity, and oscillation amplitude modulations in sub‐second timescales are greater for consciously perceived than for unperceived stimuli. The cortical sources of ER and oscillation dynamics predicting the conscious perception have, however, remained unclear because these prior studies have utilized MEG/EEG sensor‐level analyses or iEEG with limited neuroanatomical coverage. We used a somatosensory detection task, magnetoencephalography (MEG), and cortically constrained source reconstruction to identify the cortical areas where ERs, local poststimulus amplitudes and phase‐locking of neuronal activity are predictive of the conscious access of somatosensory information. We show here that strengthened ERs, phase‐locking to stimulus onset (SL), and induced oscillations amplitude modulations all predicted conscious somatosensory perception, but the most robust and widespread of these was SL that was sustained in low‐alpha (6–10 Hz) band. The strength of SL and to a lesser extent that of ER predicted conscious perception in the somatosensory, lateral and medial frontal, posterior parietal, and in the cingulate cortex. These data suggest that a rapid phase‐reorganization and concurrent oscillation amplitude modulations in these areas play an instrumental role in the emergence of a conscious percept. Hum Brain Mapp 37:311–326, 2016. © 2015 Wiley Periodicals, Inc.     

Feature selection framework for functional connectome fingerprinting

The ability to uniquely characterize individual subjects based on their functional connectome (FC) is a key requirement for progress toward precision psychiatry. FC fingerprinting is increasingly studied in the neuroimaging community for this purpose, where a variety of approaches have been developed for effective FC fingerprinting. Recent independent studies showed that fingerprinting accuracy suffers at large sample sizes and when coarser parcellations are used for computing the FC. Quantifying this problem and understanding the reasons these factors impact fingerprinting accuracy is crucial to develop more accurate fingerprinting methods for large sample sizes. Part of the challenge in fingerprinting is that FC captures both generic and subject‐specific information. A systematic approach for identifying subject‐specific FC information is crucial for making progress in addressing the fingerprinting problem. In this study, we addressed three gaps in our understanding of the FC fingerprinting problem. First, we studied the joint effect of sample size and parcellation granularity. Second, we explained the reason for reduced fingerprinting accuracy with increased sample size and reduced parcellation granularity. To this end, we used a clustering quality metric from the data mining community. Third, we developed a general feature selection framework for systematically identifying resting‐state functional connectivity (RSFC) elements that capture information to uniquely identify subjects. In sum, we evaluated six different approaches from this framework by quantifying both subject‐specific fingerprinting accuracy and the decrease in accuracy with an increase in sample size to identify which approach improved quality metrics the most.     

Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models

In this article, we address technical difficulties that arise when applying Markov chain Monte Carlo (MCMC) to hierarchical models designed to perform clustering in the space of latent parameters of subject‐wise generative models. Specifically, we focus on the case where the subject‐wise generative model is a dynamic causal model (DCM) for functional magnetic resonance imaging (fMRI) and clusters are defined in terms of effective brain connectivity. While an attractive approach for detecting mechanistically interpretable subgroups in heterogeneous populations, inverting such a hierarchical model represents a particularly challenging case, since DCM is often characterized by high posterior correlations between its parameters. In this context, standard MCMC schemes exhibit poor performance and extremely slow convergence. In this article, we investigate the properties of hierarchical clustering which lead to the observed failure of standard MCMC schemes and propose a solution designed to improve convergence but preserve computational complexity. Specifically, we introduce a class of proposal distributions which aims to capture the interdependencies between the parameters of the clustering and subject‐wise generative models and helps to reduce random walk behaviour of the MCMC scheme. Critically, these proposal distributions only introduce a single hyperparameter that needs to be tuned to achieve good performance. For validation, we apply our proposed solution to synthetic and real‐world datasets and also compare it, in terms of computational complexity and performance, to Hamiltonian Monte Carlo (HMC), a state‐of‐the‐art Monte Carlo technique. Our results indicate that, for the specific application domain considered here, our proposed solution shows good convergence performance and superior runtime compared to HMC.     

Zapline‐plus: A Zapline extension for automatic and adaptive removal of frequency‐specific noise artifacts in M/EEG

Removing power line noise and other frequency‐specific artifacts from electrophysiological data without affecting neural signals remains a challenging task. Recently, an approach was introduced that combines spectral and spatial filtering to effectively remove line noise: Zapline. This algorithm, however, requires manual selection of the noise frequency and the number of spatial components to remove during spatial filtering. Moreover, it assumes that noise frequency and spatial topography are stable over time, which is often not warranted. To overcome these issues, we introduce Zapline‐plus, which allows adaptive and automatic removal of frequency‐specific noise artifacts from M/electroencephalography (EEG) and LFP data. To achieve this, our extension first segments the data into periods (chunks) in which the noise is spatially stable. Then, for each chunk, it searches for peaks in the power spectrum, and finally applies Zapline. The exact noise frequency around the found target frequency is also determined separately for every chunk to allow fluctuations of the peak noise frequency over time. The number of to‐be‐removed components by Zapline is automatically determined using an outlier detection algorithm. Finally, the frequency spectrum after cleaning is analyzed for suboptimal cleaning, and parameters are adapted accordingly if necessary before re‐running the process. The software creates a detailed plot for monitoring the cleaning. We highlight the efficacy of the different features of our algorithm by applying it to four openly available data sets, two EEG sets containing both stationary and mobile task conditions, and two magnetoencephalography sets containing strong line noise.     

Acute effects of brain-responsive neurostimulation in drug-resistant partial onset epilepsy

ObjectiveUnderstanding the acute effects of responsive stimulation (AERS) based on intracranial EEG (iEEG) recordings in ambulatory patients with drug-resistant partial epilepsy, and correlating these with changes in clinical seizure frequency, may help clinicians more efficiently optimize responsive stimulation settings.MethodsIn patients implanted with the NeuroPace® RNS® System, acute changes in iEEG spectral power following active and sham stimulation periods were quantified and compared within individual iEEG channels. Additionally, acute stimulation-induced acute iEEG changes were compared within iEEG channels before and after patients experienced substantial reductions in clinical seizure frequency.ResultsResponsive stimulation resulted in a 20.7% relative decrease in spectral power in the 2–4 second window following active stimulation, compared to sham stimulation. On several detection channels, the AERS features changed when clinical outcomes improved but were relatively stable otherwise. AERS change direction associated with clinical improvement was generally consistent within detection channels.ConclusionsIn this retrospective analysis, patients with drug-resistant partial epilepsy treated with direct brain-responsive neurostimulation showed an acute stimulation related reduction in iEEG spectral power that was associated with reductions in clinical seizure frequency.SignificanceIdentifying favorable stimulation related changes in iEEG activity could help physicians to more rapidly optimize stimulation settings for each patient.     

Using child‐friendly movie stimuli to study the development of face,place, and object regions from age 3 to 12 years

Scanning young children while they watch short, engaging, commercially‐produced movies has emerged as a promising approach for increasing data retention and quality. Movie stimuli also evoke a richer variety of cognitive processes than traditional experiments, allowing the study of multiple aspects of brain development simultaneously. However, because these stimuli are uncontrolled, it is unclear how effectively distinct profiles of brain activity can be distinguished from the resulting data. Here we develop an approach for identifying multiple distinct subject‐specific Regions of Interest (ssROIs) using fMRI data collected during movie‐viewing. We focused on the test case of higher‐level visual regions selective for faces, scenes, and objects. Adults (N = 13) were scanned while viewing a 5.6‐min child‐friendly movie, as well as a traditional localizer experiment with blocks of faces, scenes, and objects. We found that just 2.7 min of movie data could identify subject‐specific face, scene, and object regions. While successful, movie‐defined ssROIS still showed weaker domain selectivity than traditional ssROIs. Having validated our approach in adults, we then used the same methods on movie data collected from 3 to 12‐year‐old children (N = 122). Movie response timecourses in 3‐year‐old children''s face, scene, and object regions were already significantly and specifically predicted by timecourses from the corresponding regions in adults. We also found evidence of continued developmental change, particularly in the face‐selective posterior superior temporal sulcus. Taken together, our results reveal both early maturity and functional change in face, scene, and object regions, and more broadly highlight the promise of short, child‐friendly movies for developmental cognitive neuroscience.     

Mediofrontal theta‐band oscillations reflect top‐down influence in the ventriloquist illusion

In the ventriloquist illusion, spatially disparate visual signals can influence the perceived location of simultaneous sounds. Previous studies have shown asymmetrical responses in auditory cortical regions following perceived peripheral sound shifts. Moreover, higher‐order cortical areas perform inferences on the sources of disparate audiovisual signals. Recent studies have also highlighted top‐down influence in the ventriloquist illusion and postulated a governing function of neural oscillations for crossmodal processing. In this EEG study, we analyzed source‐reconstructed neural oscillations to address the question of whether perceived sound shifts affect the laterality of auditory responses. Moreover, we investigated the modulation of neural oscillations related to the occurrence of the illusion more generally. With respect to the first question, we did not find evidence for significant changes in the laterality of auditory responses due to perceived sound shifts. However, we found a sustained reduction of mediofrontal theta‐band power starting prior to stimulus onset when participants perceived the illusion compared to when they did not perceive the illusion. We suggest that this effect reflects a state of diminished cognitive control, leading to reliance on more readily discriminable visual information and increased crossmodal influence. We conclude that mediofrontal theta‐band oscillations serve as a neural mechanism underlying top‐down modulation of crossmodal processing in the ventriloquist illusion.     

A library of quantitative markers of seizure severity

Objective

Understanding fluctuations in seizure severity within individuals is important for determining treatment outcomes and responses to therapy, as well as assessing novel treatments for epilepsy. Current methods for grading seizure severity rely on qualitative interpretations from patients and clinicians. Quantitative measures of seizure severity would complement existing approaches to electroencephalographic (EEG) monitoring, outcome monitoring, and seizure prediction. Therefore, we developed a library of quantitative EEG markers that assess the spread and intensity of abnormal electrical activity during and after seizures.

Methods

We analyzed intracranial EEG (iEEG) recordings of 1009 seizures from 63 patients. For each seizure, we computed 16 markers of seizure severity that capture the signal magnitude, spread, duration, and postictal suppression of seizures.

Results

Quantitative EEG markers of seizure severity distinguished focal versus subclinical seizures across patients. In individual patients, 53% had a moderate to large difference (rank sum $r>.3$, $p<.05$) between focal and subclinical seizures in three or more markers. Circadian and longer term changes in severity were found for the majority of patients.

Significance

We demonstrate the feasibility of using quantitative iEEG markers to measure seizure severity. Our quantitative markers distinguish between seizure types and are therefore sensitive to established qualitative differences in seizure severity. Our results also suggest that seizure severity is modulated over different timescales. We envisage that our proposed seizure severity library will be expanded and updated in collaboration with the epilepsy research community to include more measures and modalities.     

Phase-amplitude coupling of ripple activities during seizure evolution with theta phase

ObjectiveHigh-frequency activities (HFAs) and phase-amplitude coupling (PAC) are key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures.MethodsWe used intracranial electrodes to record seizures of focal epilepsy (12 focal-to-bilateral tonic-clonic seizures and three focal-aware seizures in seven patients). The synchronization index, representing PAC, was used to analyze the coupling between the amplitude of ripples (80–250 Hz) and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were scaled linearly to the power changes of ripple.ResultsThe main low frequency band modulating ictal-ripple activities was the θ band (4–8 Hz), and after completion of ictal-ripple burst, δ (1–4 Hz)-ripple PAC occurred. The ripple power increased simultaneously with rhythmic fluctuations from the seizure onset zone, and spread to other regions.ConclusionsRipple activities during seizure evolution were modulated by the θ phase. The PAC phenomenon was visualized as rhythmic fluctuations.SignificanceRipple power associated with seizure evolution increased and spread with fluctuations. The θ oscillations related to the fluctuations might represent the common neurophysiological processing involved in seizure generation.     

Prediction of seizure freedom after epilepsy surgery – Critical reappraisal of significance of intracranial EEG parameters

ObjectiveTo analyze the significance of intracranial electroencephalography (iEEG) parameters such as seizure onset patterns (SOP) and size of seizure onset zone (SOZ) with respect to prediction of seizure freedom after resective epilepsy surgery.MethodsAll patients who underwent iEEG with subdural electrodes between January 2006 and December 2015 in our epilepsy-center were included. Various iEEG parameters were retrospectively analyzed regarding their predictive value to post-operative seizure freedom. Furthermore, associations of specific SOPs with underlying histopathology and brain regions of the SOZ were examined.ResultsEighty-one patients (34 female) with 324 seizures were assessed. Low-voltage fast activity (37%) and sharp activity <13 Hz (30%) were the most frequent SOPs. Focal SOZ (≤2 cm) was the only iEEG parameter independently associated with 1-year post-operative seizure freedom (OR 4.1, 95% CI 1.433–11.679). While no SOP was linked to specific histopathologies, some associations between SOPs and anatomical regions of SOZ were found.ConclusionsA circumscribed SOZ, but no specific SOP was predictive for seizure freedom after epilepsy surgery.SignificanceIntracranial EEG may be helpful to predict post-operative seizure freedom. Multicenter studies with larger numbers of patients are required to reliably assess the significance of specific SOPs for successful resective epilepsy surgery.     

Targeting brain regions of interest in functional near‐infrared spectroscopy—Scalp‐cortex correlation using subject‐specific light propagation models

Targeting specific brain regions of interest by the accurate positioning of optodes (emission and detection probes) on the scalp remains a challenge for functional near‐infrared spectroscopy (fNIRS). Since fNIRS data does not provide any anatomical information on the brain cortex, establishing the scalp‐cortex correlation (SCC) between emission‐detection probe pairs on the scalp and the underlying brain regions in fNIRS measurements is extremely important. A conventional SCC is obtained by a geometrical point‐to‐point manner and ignores the effect of light scattering in the head tissue that occurs in actual fNIRS measurements. Here, we developed a sensitivity‐based matching (SBM) method that incorporated the broad spatial sensitivity of the probe pair due to light scattering into the SCC for fNIRS. The SCC was analyzed between head surface fiducial points determined by the international 10–10 system and automated anatomical labeling brain regions for 45 subject‐specific head models. The performance of the SBM method was compared with that of three conventional geometrical matching (GM) methods. We reveal that the light scattering and individual anatomical differences in the head affect the SCC, which indicates that the SBM method is compulsory to obtain the precise SCC. The SBM method enables us to evaluate the activity of cortical regions that are overlooked in the SCC obtained by conventional GM methods. Together, the SBM method could be a promising approach to guide fNIRS users in designing their probe arrangements and in explaining their measurement data.     

Validating dynamicity in resting state fMRI with activation‐informed temporal segmentation

Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test–retest reliability. We hypothesize that time‐varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data‐driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole‐brain functional activation, rather than a fixed‐length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block‐design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject‐specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole‐brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time‐varying FC in rest.     

Age of onset modulates resting‐state brain network dynamics in Friedreich Ataxia

This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. For that purpose, 5 min of resting‐state MEG activity were recorded in 16 FRDA patients (mean age: 27 years, range: 12–51 years; 10 females) and matched healthy subjects. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). Post hoc median‐split, nonparametric permutations and Spearman rank correlations were used for statistics. In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states compared with FRDA patients with an ASO > 11 years or healthy subjects. The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. This study demonstrates that ASO is the main determinant of alterations in the sub‐second dynamics of posterior associative neocortices in FRDA patients and substantiates a direct link between sub‐second network activity and functional brain integration over long timescales.     

Construction and validation of a database of head models for functional imaging of the neonatal brain

The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age‐specific head models. Although an age‐appropriate population‐level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual‐level datasets at ages 29–44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra‐cerebral tissue against manual segmentation. We have also conducted a leave‐one‐out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best‐matching individual from the database in place of a subject‐specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub.     

Seizure prediction with spectral power of EEG using cost-sensitive support vector machines

Purpose: We propose a patient‐specific algorithm for seizure prediction using multiple features of spectral power from electroencephalogram (EEG) and support vector machine (SVM) classification. Methods: The proposed patient‐specific algorithm consists of preprocessing, feature extraction, SVM classification, and postprocessing. Preprocessing removes artifacts of intracranial EEG recordings and they are further preprocessed in bipolar and/or time‐differential methods. Features of spectral power of raw, or bipolar and/or time‐differential intracranial EEG (iEEG) recordings in nine bands are extracted from a sliding 20‐s–long and half‐overlapped window. Nine bands are selected based on standard EEG frequency bands, but the wide gamma bands are split into four. Cost‐sensitive SVMs are used for classification of preictal and interictal samples, and double cross‐validation is used to achieve in‐sample optimization and out‐of‐sample testing. We postprocess SVM classification outputs using the Kalman Filter and it removes sporadic and isolated false alarms. The algorithm has been tested on iEEG of 18 patients of 20 available in the Freiburg EEG database who had three or more seizure events. To investigate the discriminability of the features between preictal and interictal, we use the Kernel Fisher Discriminant analysis. Key findings: The proposed patient‐specific algorithm for seizure prediction has achieved high sensitivity of 97.5% with total 80 seizure events and a low false alarm rate of 0.27 per hour and total false prediction times of 13.0% over a total of 433.2 interictal hours by bipolar preprocessing (92.5% sensitivity, a false positive rate of 0.20 per hour, and false prediction times of 9.5% by time‐differential preprocessing). This high prediction rate demonstrates that seizures can be predicted by the patient‐specific approach using linear features of spectral power and nonlinear classifiers. Bipolar and/or time‐differential preprocessing significantly improves sensitivity and specificity. Spectral powers in high gamma bands are the most discriminating features between preictal and interictal. Significance: High sensitivity and specificity are achieved by nonlinear classification of linear features of spectral power. Power changes in certain frequency bands already demonstrated their possibilities for seizure prediction indicators, but we have demonstrated that combining those spectral power features and classifying them in a multivariate approach led to much higher prediction rates. Employing only linear features is advantageous, especially when it comes to an implantable device, because they can be computed rapidly with low power consumption.     

Perceptual learning of tone patterns changes the effective connectivity between Heschl's gyrus and planum temporale

Learning of complex auditory sequences such as music can be thought of as optimizing an internal model of regularities through unpredicted events (or “prediction errors”). We used dynamic causal modeling (DCM) and parametric empirical Bayes on functional magnetic resonance imaging (fMRI) data to identify modulation of effective brain connectivity that takes place during perceptual learning of complex tone patterns. Our approach differs from previous studies in two aspects. First, we used a complex oddball paradigm based on tone patterns as opposed to simple deviant tones. Second, the use of fMRI allowed us to identify cortical regions with high spatial accuracy. These regions served as empirical regions‐of‐interest for the analysis of effective connectivity. Deviant patterns induced an increased blood oxygenation level‐dependent response, compared to standards, in early auditory (Heschl''s gyrus [HG]) and association auditory areas (planum temporale [PT]) bilaterally. Within this network, we found a left‐lateralized increase in feedforward connectivity from HG to PT during deviant responses and an increase in excitation within left HG. In contrast to previous findings, we did not find frontal activity, nor did we find modulations of backward connections in response to oddball sounds. Our results suggest that complex auditory prediction errors are encoded by changes in feedforward and intrinsic connections, confined to superior temporal gyrus.     

A Systems-Level Approach to Human Epileptic Seizures

Epileptic seizures are due to the pathological collective activity of large cellular assemblies. A better understanding of this collective activity is integral to the development of novel diagnostic and therapeutic procedures. In contrast to reductionist analyses, which focus solely on small-scale characteristics of ictogenesis, here we follow a systems-level approach, which combines both small-scale and larger-scale analyses. Peri-ictal dynamics of epileptic networks are assessed by studying correlation within and between different spatial scales of intracranial electroencephalographic recordings (iEEG) of a heterogeneous group of patients suffering from pharmaco-resistant epilepsy. Epileptiform activity as recorded by a single iEEG electrode is determined objectively by the signal derivative and then subjected to a multivariate analysis of correlation between all iEEG channels. We find that during seizure, synchrony increases on the smallest and largest spatial scales probed by iEEG. In addition, a dynamic reorganization of spatial correlation is observed on intermediate scales, which persists after seizure termination. It is proposed that this reorganization may indicate a balancing mechanism that decreases high local correlation. Our findings are consistent with the hypothesis that during epileptic seizures hypercorrelated and therefore functionally segregated brain areas are re-integrated into more collective brain dynamics. In addition, except for a special sub-group, a highly significant association is found between the location of ictal iEEG activity and the location of areas of relative decrease of localised EEG correlation. The latter could serve as a clinically important quantitative marker of the seizure onset zone (SOZ).     

Investigating the spectral features of the brain meso‐scale structure at rest

Recent studies provide novel insights into the meso‐scale organization of the brain, highlighting the co‐occurrence of different structures: classic assortative (modular), disassortative, and core‐periphery. However, the spectral properties of the brain meso‐scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso‐scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source‐localized high‐density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block‐model (WSBM) to capture the landscape of meso‐scale structures across the frequency domain. We found that different meso‐scale modalities co‐exist and are diversely organized over the frequency spectrum. Specifically, we found a core‐periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30–50 Hz). We further described other features of the meso‐scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and core‐peripheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity‐invariance). In conclusion, we observed that the brain spontaneous activity shows frequency‐specific meso‐scale organization, which may support spatially distributed and local information processing.     

Frequency‐dependent modulation of neural oscillations across the gait cycle

Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait‐related modulations of neural activity, we collected high‐density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event‐related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG‐based brain–body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders.