Phase-based Connectivity Research Articles

Integrative role of attention networks in frequency-dependent modular organization of human brain.

Despite converging evidence of hierarchical organization in the cerebral cortex, with sensory-motor and association regions at opposite ends, the mechanism of such hierarchical interactions remains elusive. This organization was primarily investigated regarding the spatiotemporal dynamics of intrinsic connectivity networks (ICNs). However, more effort is needed to investigate network dynamics in the frequency domain. We aimed to examine the integrative role of brain regions in the frequency domain with graph metrics. Phase-based connectivity estimation was performed in three frequency bands (0.011-0.038, 0.043-0.071, and 0.076-0.103Hz) in the BOLD signal during rest. We applied modularity analysis to connectivity matrices and investigated those areas, which we called integrative regions, that showed frequency-domain flexibility. Integrative regions, mostly belonging to attention networks, were densely connected to higher-order cognitive ICNs in lower frequency bands but to sensory-motor ICNs in higher frequency bands. We compared the normalized participation coefficient (Pnorm) values of integrative and core regions with respect to their relation to higher-order cognition using a permutation-based t-test for multiple linear regression. Regression parameters of integrative regions in relation to three cognitive scores in executive functions, and working memory were significantly larger than those of core regions (Pfdr < 0.05) for salience ventral attention network. Parameters of integrative regions in relation to intelligence scores were significantly larger than those with core regions (Pfdr < 0.05) in dorsal attention network. Larger parameters of neuropsychological test scores in relation to these flexible parcels further indicate their essential role at an intermediate level in behavior. Results emphasize the importance of frequency-band analysis of brain networks.

EEG connectivity in functional brain networks supporting visuomotor integration processes in dominant and non-dominant hand movements

Objective. This study explores the changes in the organization of functional brain networks induced by performing a visuomotor integration task, as revealed by noninvasive spontaneous electroencephalographic traces (EEG). Approach. EEG data were acquired during the execution of the Nine Hole Peg Test (NHPT) with the dominant and non-dominant hands in a group of 44 right-handed volunteers. Both spectral analysis and phase-based connectivity analysis were performed in the theta (ϑ), mu ( μ ) and beta (ß) bands. Graph Theoretical Analysis (GTA) was also performed to investigate the topological reorganization induced by motor task execution. Main results. Spectral analysis revealed an increase of frontoparietal ϑ power and a spatially diffused reduction of µ and ß contribution, regardless of the hand used. GTA showed a significant increase in network integration induced by movement performed with the dominant limb compared to baseline in the ϑ band. The µ and ß bands were associated with a reduction in network integration during the NHPT. In the µ rhythm, this result was more evident for the right-hand movement, while in the ß band, results did not show dependence on the laterality. Finally, correlation analysis highlighted an association between frequency-specific topology measures and task performance for both hands. Significance. Our results show that functional brain networks reorganize during visually guided movements in a frequency-dependent manner, differently depending on the hand used (dominant/non dominant).

Increases in pre-stimulus theta and alpha oscillations precede successful encoding of crossmodal associations

A central aspect of episodic memory is the formation of associations between stimuli from different modalities. Current theoretical approaches assume a functional role of ongoing oscillatory power and phase in the theta band (3–7 Hz) for the encoding of crossmodal associations. Furthermore, ongoing activity in the theta range as well as alpha (8–12 Hz) and low beta activity (13–20 Hz) before the presentation of a stimulus is thought to modulate subsequent cognitive processing, including processes that are related to memory. In this study, we tested the hypothesis that pre-stimulus characteristics of low frequency activity are relevant for the successful formation of crossmodal memory. The experimental design that was used specifically allowed for the investigation of associative memory independent from individual item memory. Participants (n = 51) were required to memorize associations between audiovisual stimulus pairs and distinguish them from newly arranged ones consisting of the same single stimuli in the subsequent recognition task. Our results show significant differences in the state of pre-stimulus theta and alpha power between remembered and not remembered crossmodal associations, clearly relating increased power to successful recognition. These differences were positively correlated with memory performance, suggesting functional relevance for behavioral measures of associative memory. Further analysis revealed similar effects in the low beta frequency ranges, indicating the involvement of different pre-stimulus-related cognitive processes. Phase-based connectivity measures in the theta band did not differ between remembered and not remembered stimulus pairs. The findings support the assumed functional relevance of theta band oscillations for the formation of associative memory and demonstrate that an increase of theta as well as alpha band oscillations in the pre-stimulus period is beneficial for the establishment of crossmodal memory.

Beta tACS of varying intensities differentially affect resting-state and movement-related M1-M1 connectivity.

Due to the interconnected nature of the brain, changes in one region are likely to affect other structurally and functionally connected regions. Emerging evidence indicates that single-site transcranial alternating current stimulation (tACS) can modulate functional connectivity between stimulated and interconnected unstimulated brain regions. However, our understanding of the network response to tACS is incomplete. Here, we investigated the effect of beta tACS of different intensities on phase-based connectivity between the left and right primary motor cortices in 21 healthy young adults (13 female; mean age 24.30 ± 4.84 years). Participants underwent four sessions of 20 min of 20 Hz tACS of varying intensities (sham, 0.5 mA, 1.0 mA, or 1.5 mA) applied to the left primary motor cortex at rest. We recorded resting-state and event-related electroencephalography (EEG) before and after tACS, analyzing changes in sensorimotor beta (13-30 Hz) imaginary coherence (ImCoh), an index of functional connectivity. Event-related EEG captured movement-related beta activity as participants performed self-paced button presses using their right index finger. For resting-state connectivity, we observed intensity-dependent changes in beta ImCoh: sham and 0.5 mA stimulation resulted in an increase in beta ImCoh, while 1.0 mA and 1.5 mA stimulation decreased beta ImCoh. For event-related connectivity, 1.5 mA stimulation decreased broadband ImCoh (4-90 Hz) during movement execution. None of the other stimulation intensities significantly modulated event-related ImCoh during movement preparation, execution, or termination. Interestingly, changes in ImCoh during movement preparation following 1.0 mA and 1.5 mA stimulation were significantly associated with participants' pre-tACS peak beta frequency, suggesting that the alignment of stimulation frequency and peak beta frequency affected the extent of neuromodulation. Collectively, these results suggest that beta tACS applied to a single site influences connectivity within the motor network in a manner that depends on the intensity and frequency of stimulation. These findings have significant implications for both research and clinical applications.

EEG source functional connectivity in patients after a recent suicide attempt

ObjectiveElectroencephalogram (EEG) based frequency measures within the alpha frequency range (AFR), including functional connectivity, show potential in assessing the underlying pathophysiology of depression and suicide-related outcomes. We investigated the association between AFR connectivity, suicidal thoughts and behaviors, and depression in a transdiagnostic sample of patients after a recent suicide attempt (SA). MethodsLagged source-based measures of linear and nonlinear whole-brain connectivity within the standard AFR ([sAFR], 8–12 Hz) and the individually referenced AFR (iAFR) were applied to 70 15-minute resting-state EEGs from patients after a SA and 70 age- and gender-matched healthy controls (HC). Hypotheses were tested using network-based statistics and multiple regression models. ResultsResults showed no significant differences between patients after a SA and HC in any of the assessed connectivity modalities. However, a subgroup analysis revealed significantly increased nonlinear connectivity within the sAFR for patients after a SA with a depressive disorder or episode ([DD], n = 53) compared to matched HC. Furthermore, a multiple regression model, including significant main effects for group and global nonlinear connectivity within the sAFR outperformed all other models in explaining variance in depressive symptom severity. ConclusionsOur study further supports the importance of the AFR in pathomechanisms of suicidality and depression. The iAFR does not seem to improve validity of phase-based connectivity. SignificanceOur results implicate distinct neurophysiological patterns in suicidal subgroups. Exploring the potential of these patterns for treatment stratification might advance targeted interventions for suicidal thoughts and behaviors.

Cortico-Cerebellar neurodynamics during social interaction in Autism Spectrum Disorders

BackgroundExploring neural network dynamics during social interaction could help to identify biomarkers of Autism Spectrum Disorders (ASD). A cerebellar involvement in autism has long been suspected and recent methodological advances now enable studying cerebellar functioning in a naturalistic setting. Here, we investigated the electrophysiological activity of the cerebro-cerebellar network during real-time social interaction in ASD. We focused our analysis on theta oscillations (3–8 Hz), which have been associated with large-scale coordination of distant brain areas and might contribute to interoception, motor control, and social event anticipation, all skills known to be altered in ASD. MethodsWe combined the Human Dynamic Clamp, a paradigm for studying realistic social interactions using a virtual avatar, with high-density electroencephalography (HD-EEG). Using source reconstruction, we investigated power in the cortex and the cerebellum, along with coherence between the cerebellum and three cerebral-cortical areas, and compared our findings in a sample of participants with ASD (n = 107) and with typical development (TD) (n = 33). We developed an open-source pipeline to analyse neural dynamics at the source level from HD-EEG data. ResultsIndividuals with ASD showed a significant increase in theta band power over the cerebellum and the frontal and temporal cortices during social interaction compared to resting state, along with significant coherence increases between the cerebellum and the sensorimotor, frontal and parietal cortices. However, a phase-based connectivity measure did not support a strict activity increase in the cortico-cerebellar functional network. We did not find any significant differences between the ASD and the TD group. ConclusionsThis exploratory study uncovered increases in the theta band activity of participants with ASD during social interaction, pointing at the presence of neural interactions between the cerebellum and cerebral networks associated with social cognition. It also emphasizes the need for complementary functional connectivity measures to capture network-level alterations. Future work will focus on optimizing artifact correction to include more participants with TD and increase the statistical power of group-level contrasts.

Action experience in infancy predicts visual-motor functional connectivity during action anticipation.

Despite substantial evidence indicating a close link between action production and perception in early child development, less is known about how action experience shapes the processes of perceiving and anticipating others' actions. Here, we developed a novel approach to capture functional connectivity specific to certain brain areas to investigate how action experience changes the networks involved in action perception and anticipation. Nine- and-12-month-old infants observed familiar (grasping) and novel (tool-use) actions while their brain activity was measured using EEG. Infants' motor competence of both actions was assessed. A link between action experience and connectivity patterns was found, particularly during the anticipation period. During action anticipation, greater motor competence in grasping predicted greater functional connectivity between visual (occipital alpha) and motor (central alpha) regions relative to global levels of whole-brain EEG connectivity. Furthermore, visual and motor regions tended to be more coordinated in response to familiar versus novel actions and for older than younger participants. Critically, these effects were not found in the control networks (frontal-central; frontal-occipital; parietal-central; parietal-occipital), suggesting a unique role of visual-motor networks on the link between motor skills and action encoding. HIGHLIGHTS: Infants' motor development predicted functional connectivity patterns during action anticipation. Faster graspers, and older infants, showed a stronger ratio of visual-motor neural coherence. Overall whole-brain connectivity was modulated by age and familiarity with the actions. Measuring inter-site relative to whole-brain connectivity can capture specific brain-behavior links. Measures of phase-based connectivity over time are sensitive to anticipatory action.

Classification of Tonic Pain Experience based on Phase Connectivity in the Alpha Frequency Band of the Electroencephalogram using Convolutional Neural Networks.

The complexity of brain activity involved in the generation of the experience of pain makes it hard to identify neural markers able to predict pain states. The within and between subjects variability of pain hinders the predictive potential of machine learning models trained across participants. This challenge can be tackled by implementing deep learning classifiers based on convolutional neural networks (CNNs). We targeted phase-based connectivity in the alpha band recorded with electroencephalography (EEG) during resting states and sensory conditions (eyes open [O] and closed [C] as resting states, and warm [W] and hot [H] water as sensory conditions). Connectivity features were extracted and re-organized as square matrices, because CNNs are effective in detecting the patterns from 2D data. To assess the classifier performance we implemented two complementary approaches: we 1) trained and tested the classifier with data from all participants, and 2) using a leave-one-out approach, that is excluding one participant at a time during training while using their data as a test set. The accuracy of binary classification between pain condition (H) and eyes open resting state (O) was 94.16% with the first approach, and 61.01 % with the leave-one-out approach. Clinical relevance-Further validation of the CNN classifier may help caregivers track the rehabilitation of chronic pain patients and dynamically modify the therapy. Further refinement of the model may allow its application in critical care setting with unresponsive patients to identify pain-like states otherwise incommunicable to medical personnel.

Continuous Decoding of Hand Movement From EEG Signals Using Phase-Based Connectivity Features.

The principal goal of the brain-computer interface (BCI) is to translate brain signals into meaningful commands to control external devices or neuroprostheses to restore lost functions of patients with severe motor disabilities. The invasive recording of brain signals involves numerous health issues. Therefore, BCIs based on non-invasive recording modalities such as electroencephalography (EEG) are safer and more comfortable for the patients. The BCI requires reconstructing continuous movement parameters such as position or velocity for practical application of neuroprostheses. The BCI studies in continuous decoding have extensively relied on extracting features from the amplitude of brain signals, whereas the brain connectivity features have rarely been explored. This study aims to investigate the feasibility of using phase-based connectivity features in decoding continuous hand movements from EEG signals. To this end, the EEG data were collected from seven healthy subjects performing a 2D center-out hand movement task in four orthogonal directions. The phase-locking value (PLV) and magnitude-squared coherence (MSC) are exploited as connectivity features along with multiple linear regression (MLR) for decoding hand positions. A brute-force search approach is employed to find the best channel pairs for extracting features related to hand movements. The results reveal that the regression models based on PLV and MSC features achieve the average Pearson correlations of 0.43 ± 0.03 and 0.42 ± 0.06, respectively, between predicted and actual trajectories over all subjects. The delta and alpha band features have the most contribution in regression analysis. The results also demonstrate that both PLV and MSC decoding models lead to superior results on our data compared to two recently proposed feature extraction methods solely based on the amplitude or phase of recording signals (p < 0.05). This study verifies the ability of PLV and MSC features in the continuous decoding of hand movements with linear regression. Thus, our findings suggest that extracting features based on brain connectivity can improve the accuracy of trajectory decoder BCIs.

Two mechanisms facilitate regional independence between brain regions based on an examination of alpha-band activity in healthy control adult males

BackgroundAt rest, 8 to 12 Hz alpha rhythms are the dominant rhythm in the brain, with a common peak alpha frequency (PAF = the frequency at which alpha generators show maximum power) observed across brain regions. Although a common PAF across brain regions should result in high between-region connectivity, especially connectivity measures assessing the phase-similarity between alpha generators, high inter-regional alpha connectivity has not been observed. The present study was conducted as an initial step toward identifying mechanisms that allow brain regions to maintain functional independence in the presence of a common PAF. MethodsMEG data were obtained from 16 healthy control male adults (mean age = 24 years; range 21 to 30 years). A task requiring participants to alternate between a 10 s eyes-closed condition and a 5 s eyes-open condition was used to drive parietal-occipital alpha generators, with the 10 s eyes-closed condition eliciting large-amplitude alpha activity and thus providing alpha measures with good signal-to-noise ratio for source localization. Alpha source-space measures were obtained using Vector-based Spatial-Temporal Analysis using L1-minimum-norm. In each participant, the four strongest parietal-occipital alpha generators were identified. Connectivity between sources was assessed via a measure of phase-based connectivity called inter-site phase clustering (ISPC). ResultsIntra-class correlations (ICC) showed very high similarity in the average PAF (=computed using all eyes-closed data) between the four alpha sources (ICC single measure = 0.88, p < 0.001). Despite a common average PAF, across participants, significant ISPC was often observed no more than that expected by chance. Examination of the alpha time course data indicated that low ISPC was often due to instantaneous changes in alpha phase (phase slips). ISPC analyses removing data with phase slips indicated that low ISPC was also due to slight continuous changes in the alpha frequency, with frequency drift more likely in non-significant than significant ISPC trials. ConclusionsThe present exploratory effort suggested two processes underlying the lack of observed inter-regional alpha phase coherence that may help maintain regional functional independence even in the presence of a common PAF. In particular, although the alpha generators were observed to oscillate at the same rate on average, across time each alpha generator oscillated a little slower or faster, and about every one and a half seconds an alpha generator abruptly lost the beat. Because of this, functional independence among alpha generators (and thus brain regions) was the rule rather than the exception. Studies replicating these processes that allow brain regions to maintain functional independence, using different source localization methods and in different conditions (e.g., a true resting state), are warranted. Impact statementUsing source localization to measure parietal-occipital alpha generator activity, two properties that limit between-region alpha functional connectivity are proposed. In particular, a model of alpha generator activity is offered where via transient phase slips occurring approximately every 1.5 s, as well as slight non-stationarity in the alpha frequency, brain regions retain a common alpha frequency while also maintaining regional identity and presumably functionality. Findings also suggest novel markers for use in studies examining changes in alpha activity across maturation as well as in studies examining alpha activity in patient populations where alpha abnormalities have been reported.

The interaction between endogenous GABA, functional connectivity, and behavioral flexibility is critically altered with advanced age.

The flexible adjustment of ongoing behavior challenges the nervous system’s dynamic control mechanisms and has shown to be specifically susceptible to age-related decline. Previous work links endogenous gamma-aminobutyric acid (GABA) with behavioral efficiency across perceptual and cognitive domains, with potentially the strongest impact on those behaviors that require a high level of dynamic control. Our analysis integrated behavior and modulation of interhemispheric phase-based connectivity during dynamic motor-state transitions with endogenous GABA concentration in adult human volunteers. We provide converging evidence for age-related differences in the preferred state of endogenous GABA concentration for more flexible behavior. We suggest that the increased interhemispheric connectivity observed in the older participants represents a compensatory neural mechanism caused by phase-entrainment in homotopic motor cortices. This mechanism appears to be most relevant in the presence of a less optimal tuning of the inhibitory tone as observed during healthy aging to uphold the required flexibility of behavioral action. Future work needs to validate the relevance of this interplay between neural connectivity and GABAergic inhibition for other domains of flexible human behavior.

Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences

BackgroundFunctional connectomes have been proven to be able to predict an individual’s traits, acting as a fingerprint. A majority of studies use the amplitude information of fMRI signals to construct the connectivity but it remains unknown whether phase synchronization can be incorporated for improved prediction of individual cognitive behaviors. MethodsIn this paper, we address the issue by extracting phase information from the fMRI time series with a phase locking approach, followed by the construction of functional connectomes. ResultsWe first examine the identification and prediction performance using phase-based profiles in comparison with amplitude-based connectomes. We then combine both phase-based and amplitude-based connectivity to extract subject-specific information enabled by the phase synchronization. Results show that high individual identification rates (from 82.7% to 92.6%) can be achieved by phase-based connectomes. Phase-based connectivity offers unique information complementary to amplitude-based signals. Intra-network phase-locking appears more informative for individual prediction. In addition, phase synchronization can be used to predict cognitive behaviors. Comparison with existing method: The amplitude-based connectivity cannot capture the subject-specific information due to neural synchronization. The comparison with other phase-based methods has been involved in the discussion session. ConclusionsOur findings suggest that neural synchronization carries subject-specific information, which can be captured by phase locking value. The incorporation of phase information into connectomes presents a promising approach to understand each individual brain’s uniqueness.

Propofol-induced Unresponsiveness Is Associated with a Brain Network Phase Transition.

Background:The wakeful brain can easily access and coordinate a large repertoire of different states—dynamics suggestive of “criticality.” Anesthesia causes loss of criticality at the level of electroencephalogram waveforms, but the criticality of brain network connectivity is less well studied. The authors hypothesized that propofol anesthesia is associated with abrupt and divergent changes in brain network connectivity for different frequencies and time scales—characteristic of a phase transition, a signature of loss of criticality.Methods:As part of a previously reported study, 16 volunteers were given propofol in slowly increasing brain concentrations, and their behavioral responsiveness was assessed. The network dynamics from 31-channel electroencephalogram data were calculated from 1 to 20 Hz using four phase and envelope amplitude–based functional connectivity metrics that covered a wide range of time scales from milliseconds to minutes. The authors calculated network global efficiency, clustering coefficient, and statistical complexity (using the Jensen–Shannon divergence) for each functional connectivity metric and compared their findings with those from an in silico Kuramoto network model.Results:The transition to anesthesia was associated with critical slowing and then abrupt profound decreases in global network efficiency of 2 Hz power envelope metrics (from mean ± SD of 0.64 ± 0.15 to 0.29 ± 0.28 absolute value, P < 0.001, for medium; and from 0.47 ± 0.13 to 0.24 ± 0.21, P < 0.001, for long time scales) but with an increase in global network efficiency for 10 Hz weighted phase lag index (from 0.30 ± 0.20 to 0.72 ± 0.06, P < 0.001). Network complexity decreased for both the 10 Hz hypersynchronous (0.44 ± 0.13 to 0.23 ± 0.08, P < 0.001), and the 2 Hz asynchronous (0.73 ± 0.08 to 0.40 ± 0.13, P < 0.001) network states. These patterns of network coupling were consistent with those of the Kuramoto model of an order–disorder phase transition.Conclusions:Around loss of behavioral responsiveness, a small increase in propofol concentrations caused a collapse of long time scale power envelope connectivity and an increase in 10 Hz phase-based connectivity—suggestive of a brain network phase transition.

Time-frequency analysis methods and their application in developmental EEG data

EEG provides a rich measure of brain activity that can be characterized as neuronal oscillations. However, most developmental EEG work to date has focused on analyzing EEG data as Event-Related Potentials (ERPs) or power based on the Fourier transform. While these measures have been productive, they do not leverage all the information contained within the EEG signal. Namely, ERP analyses ignore non-phase-locked signals and Fourier-based power analyses ignore temporal information. Time-frequency analyses can better characterize the oscillations contained in the EEG data. By separating power and phase information across different frequencies, time-frequency measures provide a closer interpretation of the neurophysiological mechanisms, facilitate translation across neurophysiology disciplines, and capture processes not observed by ERP or Fourier-based analyses (e.g., connectivity). Despite their unique contributions, a literature review of this journal reveals that time-frequency analyses of EEG are yet to be embraced by the developmental cognitive neuroscience field. This manuscript presents a conceptual introduction to time-frequency analyses for developmental researchers. To facilitate the use of time-frequency analyses, we include a tutorial of accessible scripts, based on Cohen (2014), to calculate time-frequency power (signal strength), inter-trial phase synchrony (signal consistency), and two types of phase-based connectivity (inter-channel phase synchrony and weighted phase lag index).

Modulation of Sustained Attention by Theta-tACS over the Lateral and Medial Frontal Cortices.

Theta oscillations over the posterior medial frontal cortex (pMFC) and lateral prefrontal cortex (LPFC) play vital roles in sustained attention. Specifically, pMFC power and pMFC-LPFC synchronization correlate with cognitive control in sustained-attention-related tasks, but the causal relationships remain unknown. In the present study, we first analyzed the correlation between EEG theta oscillations (characterized by time-frequency power and phase-based connectivity) and the level of sustained attention (Experiment 1) and then utilized transcranial alternating current stimulation (tACS) to modulate theta oscillations and in turn observed its effects on sustained attention (Experiment 2). In Experiment 1, two time-frequency regions of interest (ROIs) were determined, in which high/low time-frequency power and high/low phase-based connectivity corresponded to high/low-level sustained attention. In Experiment 2, time-frequency power and phase-based connectivity of theta oscillations were compared between the sham and tACS groups within the time-frequency ROIs determined in Experiment 1. Results showed that phase-based connectivity between pMFC and LPFC significantly decreased in the tACS group compared with the sham group during the first five minutes of the poststimulation period. Moreover, a marginal trend existed that sustained attention was downregulated by tACS in the same time interval, suggesting that theta phase synchronization between pMFC and LPFC may play a causal role in sustained attention.

Frontal functional connectivity and disease duration interactively predict cognitive decline in Parkinson's disease

ObjectiveCognitive decline does not always follow a predictable course in Parkinson’s disease (PD), with some patients remaining stable while others meet criteria for dementia from early stages. Functional connectivity has been proposed as a good correlate of cognitive decline in PD, although it has not been explored whether the association between this connectivity and cognitive ability is influenced by disease duration, which was our objective. MethodsWe included 30 patients with PD and 15 healthy controls (HC). Six cognitive domains were estimated based on neuropsychological assessment. Phase-based connectivity at frontal and posterior cortical regions was estimated from a resting EEG. ResultsThe PD group showed significant impairment for the executive, visuospatial, and language domains compared with HC. Increased connectivity at frontal regions was also found in the PD group. Frontal delta and theta connectivity negatively influenced general cognition and visuospatial performance, but this association was moderated by disease duration, with increased connectivity predicting worse performance after 8 years of disease duration. ConclusionSubtle neurophysiological changes underlie cognitive decline along PD progression, especially around a decade after motor symptoms onset. SignificanceConnectivity of EEG slow waves at frontal regions might be used as a predictor of cognitive decline in PD.

Dynamic Adjustments of Midfrontal Control Signals in Adults and Adolescents

During task performance, our level of cognitive control is dynamically adjusted to task demands as reflected, for example, by the congruency sequence effect (CSE) in conflict tasks. Although brain areas related to cognitive control show protracted maturation across adolescence, previous studies found that adolescents show similar behavioral CSEs to adults. In the present study, we investigated whether there are age-related changes in the neural underpinnings of dynamic control adjustments using electroencephalography. Early adolescents (ages 12-14, N = 30) and young adults (ages 25-27, N = 29) completed a confound-minimized flanker task optimized for the detection of sequential control adjustments. The CSE was observed in midfrontal theta power thought to capture anterior cingulate cortex-mediated monitoring processes but was not modulated significantly by age. Adolescents, however, showed a smaller congruency effect in the power and cross-trial temporal consistency of midfrontal theta oscillations than adults. No age differences were observed in phase-based connectivity between midfrontal and lateral frontal regions in the theta band. These findings provide strong support for the role of midfrontal theta oscillations in conflict monitoring and reactive control and suggest that the cognitive system of early adolescents initially responds less reliably to the occurrence of conflict than that of adults.

Gas–Liquid Flow Pattern Analysis Based on Graph Connectivity and Graph-Variate Dynamic Connectivity of ERT

Two-phase flow is widely encountered in process engineering and related scientific research. Understanding flow patterns and their transitions is important to discover the fluid mechanics of two-phase flow. In order to investigate the complexity of horizontal gas–water two-phase flow and accurately identify the flow pattern, a 16-electrode electrical resistance tomography was used to collect the spatial distribution of phase fraction. The experimental data are compressed and treated as a 16-D time series corresponding to the average response of the phase distribution in the field of each exciting electrode, which can be studied with graph-based techniques. Three connectivity metrics—correlation, coherence, and the phase-lag index are extracted from the multivariate time series, which correspond to the amplitude, power, and phase-based connectivity among signals, respectively. Together, these connectivity metrics make a comprehensive description of the characteristics of each flow pattern and reveal the transition process of flow patterns. The dynamic characteristics of typical flow patterns are then analyzed using the method of graph-variate signal analysis named graph-variate dynamic connectivity.

Comparing Measures of Active and Passive Field Spread between non-, partially, and fully invasive human brain recordings

Event Abstract Back to Event Comparing Measures of Active and Passive Field Spread between non-, partially, and fully invasive human brain recordings Mansoureh Fahimi Hnazaee1*, Benjamin Wittevrongel1, Evelien Carrette2, Ine Dauwe2, Alfred Meurs2, Paul Boon2, Dirk Van Roost2 and Marc Van Hulle1 1 Faculty of Medicine, KU Leuven, Belgium 2 Ghent University Hospital, Belgium BACKGROUND Precise details of the propagation of neural activity on remote cortical areas are still poorly understood; this propagation refers to both electric diffusion as a result of physiological propagation of action potentials along axons (active spread) and the passive spread of the electric field over the cortical surface, also referred to as field spread, or as more commonly known, electrical volume conduction. Principles of volume conduction is of significant interest for researchers interested in solving the M/EEG inverse problem and increasing the accuracy of source localization. Therefore we need measures that can separate instantaneous signal propagation from genuine causal and functional interactions, such as methods that can reveal the temporal delay. However, no study has yet attempted to compare the influence of volume conduction for different levels of invasiveness. In this study, by using measures of phase-based and amplitude based correlations both on actual data and reconstructed sources obtained from the data for each recording modality, we try to quantify the level of volume conduction, comparing between subcortical, cortical, and scalp recordings. METHODS We recorded 3 minutes of resting state (with eyes fixating) and a rich stimuli task in 5 patients (2 female and 3 male patients) from simultaneously implanted subdural grids, depth electrodes, and scalp EEG. Areas of subdural recording were spread over left and right temporal and frontal cortex, and depth electrodes were implanted in left and/or right hippocampus in 4 patients and left insula in 1 patient. The data is decomposed into traditional frequency bands using Morlet wavelet function. Phase-based connectivity measures were used to quantify the interactions include Phase Locking Value (PLV), and imaginary part of Phase Locking Value (iPLV) whereas for Amplitude-based we used Spearman correlation coefficient. The connectivity analysis is done on both the data and on the estimated sources obtained using Independent Components Analysis (ICA). The independent sources were estimated with statistical and algorithmic reliability with the help of bootstrapping and random initialization using the Icasso software package. The calculated measures from both the original data and on the estimated independent sources are then analyzed statistically using ANOVA. CONCLUSION Our study provides a comprehensive account of the spatial spread between SEEG, ECoG and EEG. As can be expected, IPLV consistently decreases for higher frequency bands. Additionally, the lower PLV and Spearman correlation for ECoG compared to SEEG can probably be attributed to the larger average distances (larger spatial coverage) spanned by ECoG. Using ICA analysis on partially invasive and fully invasive brain recording we consistently found between 4 and 7 cortical generators, which is in accordance with previous studies on functional networks during resting state. Similar overall trends in connectivity can be seen for both raw data and reconstructed sources, however PLV and Spearman are now higher for SEEG compared to ECoG, which is likely attributed to a higher number of cortical generators discovered from ECoG. FUTURE WORK - Current results show the analysis of a single subject, further analysis would include normalizing and pooling results from all subjects in order to investigate connectivity as a function of anatomical location. When pooling results we can also account for inter-electrode distances and control these for modalities. - It has been shown that correlations are more variable and on average higher during rest than when the patient is engaged in a listening task. In this study data has been recorded from the same patients engaged during a rich stimuli task (an audio-visual movie to ensure engagement of multi-sensory and higher-level cognitive functions). A future step would be to compare task and non-task connectivity measures.

Exploring the alpha desynchronization hypothesis in resting state networks with intracranial electroencephalography and wiring cost estimates

This paper addresses a fundamental question, are eyes closed and eyes open resting states equivalent baseline conditions, or do they have consistently different electrophysiological signatures? We compare the functional connectivity patterns in an eyes closed resting state with an eyes open resting state to investigate the alpha desynchronization hypothesis. The change in functional connectivity from eyes closed to eyes open, is here, for the first time, studied with intracranial recordings. We perform network connectivity analysis in iEEG and we find that phase-based connectivity is sensitive to the transition from eyes closed to eyes open only in interhemispheral and frontal electrodes. Power based connectivity, on the other hand, consistently discriminates between the two conditions in temporal and interhemispheral electrodes. Additionally, we provide a calculation for the wiring cost, defined in terms of the connectivity between electrodes weighted by distance. We find that the wiring cost variation from eyes closed to eyes open is sensitive to the eyes closed and eyes open conditions. We extend the standard network-based approach using the filtration method from algebraic topology which does not rely on the threshold selection problem. Both the wiring cost measure defined here and this novel methodology provide a new avenue for understanding the electrophysiology of resting state.