Frequency-dependent connectivity in large-scale resting-state brain networks during sleep.

Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during rapid eye movement (REM) sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. This study aimed to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3 and REM sleep (sleep stages scored using a semi-automatic procedure) in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks. In contrast, a reconnection occurred in the default mode and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier sleep cycles. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also illustrate a complex pattern of connectivity during REM sleep that is consistent with network- and frequency-specific breakdown and reconnection processes.

Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study.

Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, ß, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and ß bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, ß ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.

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.

Frequency-dependent functional connectivity in resting state networks.

Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large-scale networks, called resting-state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency-dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band-limited power correlations from high-density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.

Shared and connection-specific intrinsic interactions in the default mode network.

Electrophysiological studies revealed that different neuronal oscillations, among which the alpha (8-13 Hz) rhythm in particular, but also the beta (13-30 Hz) and gamma (30-80 Hz) rhythms, are modulated during rest in the default mode network (DMN). Little is known, however, about the role of these rhythms in supporting DMN connectivity. Biophysical studies suggest that lower and higher frequencies mediate long- and short-range connectivity, respectively. Accordingly, we hypothesized that interactions between all DMN areas are supported by the alpha rhythm, and that the connectivity between specific DMN areas is established through other frequencies, mainly in the beta and/or gamma bands. To test this hypothesis, we used high-density electroencefalographic data collected in 19 healthy volunteers at rest. We analyzed frequency-dependent functional interactions between four main DMN nodes in a broad (1-80 Hz) frequency range. In line with our hypothesis, we found that the frequency-dependent connectivity profile between pairs of DMN nodes had a peak at 9-11 Hz. Also, the connectivity profile showed other peaks at higher frequencies, which depended on the specific connection. Overall, our findings suggest that frequency-dependent connectivity analysis may be a powerful tool to better understand how different neuronal oscillations support connectivity within and between brain networks.

Neuronal dynamics enable the functional differentiation of resting state networks in the human brain.

Intrinsic brain activity is organized in spatial-temporal patterns, called resting-state networks (RSNs), exhibiting specific structural-functional architecture. These networks presumably reflect complex neurophysiological processes and have a central role in distinct perceptual and cognitive functions. In this work, we propose an innovative approach for characterizing RSNs according to their underlying neural oscillations. We investigated specific electrophysiological properties, including spectral features, fractal dimension, and entropy, associated with eight core RSNs derived from high-density electroencephalography (EEG) source-reconstructed signals. Specifically, we found higher synchronization of the gamma-band activity and higher fractal dimension values in perceptual (PNs) compared with higher cognitive (HCNs) networks. The inspection of this underlying rapid activity becomes of utmost importance for assessing possible alterations related to specific brain disorders. The disruption of the coordinated activity of RSNs may result in altered behavioral and perceptual states. Thus, this approach could potentially be used for the early detection and treatment of neurological disorders.

Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data.

INTRODUCTION: High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets. FINDINGS: Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software. CONCLUSIONS: NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows.

Motor imagery ability scores are related to cortical activation during gait imagery.

Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the θ, α, and ß band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). Positive correlations were identified between VMIQ and avgERD of the middle cingulum in the ß band and with avgERD of the left insula, right precentral area, and right middle occipital region in the θ band. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.

Are Motor Imagery Ability scores related to cortical activity during gait imagery?

Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography (hdEEG) in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the ß band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). VMIQ was positively correlated with avgERD of frontal and cingulate areas, whereas IA SCORE was positively correlated with avgERD of left inferior frontal and superior temporal regions. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.

Connectivity in Large-Scale Resting-State Brain Networks Is Related to Motor Learning: A High-Density EEG Study.

Previous research has shown that resting-state functional connectivity (rsFC) between different brain regions (seeds) is related to motor learning and motor memory consolidation. Using high-density electroencephalography (hdEEG), we addressed this question from a brain network perspective. Specifically, we examined frequency-dependent functional connectivity in resting-state networks from twenty-nine young healthy participants before and after they were trained on a motor sequence learning task. Consolidation was assessed with an overnight retest on the motor task. Our results showed training-related decreases in gamma-band connectivity within the motor network, and between the motor and functionally distinct resting-state networks including the attentional network. Brain-behavior correlation analyses revealed that baseline beta, delta, and theta rsFC were related to subsequent motor learning and memory consolidation such that lower connectivity within the motor network and between the motor and several distinct resting-state networks was correlated with better learning and overnight consolidation. Lastly, training-related increases in beta-band connectivity between the motor and the visual networks were related to greater consolidation. Altogether, our results indicate that connectivity in large-scale resting-state brain networks is related to-and modulated by-motor learning and memory consolidation processes. These finding corroborate previous seed-based connectivity research and provide evidence that frequency-dependent functional connectivity in resting-state networks is critically linked to motor learning and memory consolidation.

Age-Related Differences of Frequency-Dependent Functional Connectivity in Brain Networks and Their Link to Motor Performance.

Background: Aging affects the brain at the anatomical and functional levels, resulting in a decline in motor and cognitive performance. Functional magnetic resonance imaging (fMRI) studies documented lower connectivity within brain networks and higher connectivity between them, for older as compared with young adults. However, it is still unclear whether the reduced segregation between networks, as observed with fMRI, has neurophysiological underpinnings. Methods: We collected high-density electroencephalography (hdEEG) data in 24 young and 24 older adults at rest. Bimanual coordination performance was also measured in the same participants, using a computerized test. Using the hdEEG data, we reconstructed oscillatory power and functional connectivity for six large-scale brain networks, in delta, theta, alpha, beta and gamma frequency bands. We evaluated age-related differences in network power and connectivity between young and older participants, and their possible relationships with bimanual coordination performance. Results: We observed that the level of network segregation generally decreased with age, in line with fMRI findings. However, there was a relatively strong dependence on the frequency band and the brain network being considered. EEG connectivity in the sensorimotor network predicted motor performance differences across older individuals, particularly when neural oscillations in the beta frequency band were considered. Discussion: Our study provides electrophysiological evidence in support of the "de-differentiation hypothesis" for the aging brain, and for the existence of a clear link between the strength of EEG connectivity at rest and motor performance.

Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging.

Gait is a common but rather complex activity that supports mobility in daily life. It requires indeed sophisticated coordination of lower and upper limbs, controlled by the nervous system. The relationship between limb kinematics and muscular activity with neural activity, referred to as neurokinematic and neuromuscular connectivity (NKC/NMC) respectively, still needs to be elucidated. Recently developed analysis techniques for mobile high-density electroencephalography (hdEEG) recordings have enabled investigations of gait-related neural modulations at the brain level. To shed light on gait-related neurokinematic and neuromuscular connectivity patterns in the brain, we performed a mobile brain/body imaging (MoBI) study in young healthy participants. In each participant, we collected hdEEG signals and limb velocity/electromyography signals during treadmill walking. We reconstructed neural signals in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-50 Hz) frequency bands, and assessed the co-modulations of their power envelopes with myogenic/velocity envelopes. Our results showed that the myogenic signals have larger discriminative power in evaluating gait-related brain-body connectivity with respect to kinematic signals. A detailed analysis of neuromuscular connectivity patterns in the brain revealed robust responses in the alpha and beta bands over the lower limb representation in the primary sensorimotor cortex. There responses were largely contralateral with respect to the body sensor used for the analysis. By using a voxel-wise analysis of variance on the NMC images, we revealed clear modulations across body sensors; the variability across frequency bands was relatively lower, and below significance. Overall, our study demonstrates that a MoBI platform based on hdEEG can be used for the investigation of gait-related brain-body connectivity. Future studies might involve more complex walking conditions to gain a better understanding of fundamental neural processes associated with gait control, or might be conducted in individuals with neuromotor disorders to identify neural markers of abnormal gait.

Neural oscillations during motor imagery of complex gait: an HdEEG study.

The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of α- and ß-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed α and ß bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, α- and ß-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found α- and ß-band ERDs in the DT compared with the UW condition. Finally, in the ß band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related α- and ß-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases.

Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction.

In the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing - the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping - template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation - information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies.

Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks.

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrated a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks.

Hand, foot and lip representations in primary sensorimotor cortex: a high-density electroencephalography study.

The primary sensorimotor cortex plays a major role in the execution of movements of the contralateral side of the body. The topographic representation of different body parts within this brain region is commonly investigated through functional magnetic resonance imaging (fMRI). However, fMRI does not provide direct information about neuronal activity. In this study, we used high-density electroencephalography (hdEEG) to map the representations of hand, foot, and lip movements in the primary sensorimotor cortex, and to study their neural signatures. Specifically, we assessed the event-related desynchronization (ERD) in the cortical space. We found that the performance of hand, foot, and lip movements elicited an ERD in beta and gamma frequency bands. The primary regions showing significant beta- and gamma-band ERD for hand and foot movements, respectively, were consistent with previously reported using fMRI. We observed relatively weaker ERD for lip movements, which may be explained by the fact that less fine movement control was required. Overall, our study demonstrated that ERD based on hdEEG data can support the study of motor-related neural processes, with relatively high spatial resolution. An interesting avenue may be the use of hdEEG for deeper investigations into the pathophysiology of neuromotor disorders.