Cortical Connectivity Analysis Research Articles

Robustness of the self-referential process under normobaric hypoxia: an fNIRS study using the GLM and homologous cortical functional connectivity analyses.

Hypoxia has been reported to impair psychological functions, such as working memory and decision-making. However, few studies have examined hypoxia's effect on social cognition. Using a self-referential task, the present study investigated normobaric hypoxia's effect on the self-referential process. Additionally, we measured brain activity during the task with fNIRS and performed conventional univariate analysis with the general linear model (GLM) as well as homologous cortical functional connectivity analysis. The results revealed that normobaric hypoxia impaired recognition of adjectives in the other-reference condition but not in the self-reference. The GLM analysis did not detect differences in brain activity between the self- and other-reference conditions, suggesting that GLM analysis may not be suitable for examining self- and other-reference conditions' neural correlates. The homologous cortical connectivity analysis revealed that the connectivity's magnitude was greater in the self-reference than in the other-reference conditions in the normoxic group. However, such a decrease in connectivity in the other-reference conditions was not observed in the hypoxic group, possibly to compensate for cognitive decline induced by the hypoxia. Considering that homologous connectivity reflects the default mode network, which is supposedly linked to continuous self-reference, stable strength of the connectivity in the self-reference condition under the hypoxia may suggest robust nature of the self-reference process under normobaric hypoxia.

OptoNet II: An Advanced MATLAB-Based Toolbox for Functional Cortical Connectivity Analysis With Surrogate Tests Using fNIRS

Cortical connectivity analysis is a widely used method for understanding the causes of neurological disorders and related brain mechanisms. Although there exist numerous activity analysis toolboxes for functional near-infrared spectroscopy (fNIRS), there are only a few cortical connectivity analysis toolboxes. In 2019, we released a MATALB toolbox named OptoNet, which has helped researchers to analyze brain networks using fNIRS. In this study, we developed an advanced MATLAB toolbox, named OptoNet II, to add new features that overcome the shortcomings of OptoNet. With these new features, OptoNet II can efficiently analyze cortical connectivity according to brain region using any fNIRS channel sets and can present the results of two connectivity analyses with auto-thresholding based on surrogate tests. To evaluate the efficacy of the new functions, the finger-tapping task experiment was carried out before and after transcranial direct current stimulation (tDCS) in the primary motor area. OptoNet II can efficiently show the effects of tDCS on functional brain region connectivity, which has been difficult to confirm by conventional methods. In this article, we propose the OptoNet II as a useful and efficient toolbox for researchers who want to perform cortical connectivity analysis using fNIRS.

Sensory Feedback in Upper Limb Amputees Impacts Cortical Activity as Revealed by Multiscale Connectivity Analysis.

Sensory feedback in upper limb amputees is crucial for improving movement decoding and also to enhance embodiment of the prosthetic limb. Recently, an increasing number of invasive and noninvasive solutions for sensory stimulation have demonstrated the capability of providing a range of sensations to upper limb amputees. However, the cortical impact of restored sensation is not clearly understood. Particularly, understanding the cortical connectivity changes at multiple scales (nodal and modular) in response to sensory stimulation, can reveal crucial information on how amputees brain process the sensory stimuli. Using Electroencephalography (EEG) signals, we compared the cortical connectivity network in response to sensory feedback provided by targeted transcutaneous electrical nerve stimulation (tTENS) in an upper limb amputee during phantom upper limb movements. We focused our cortical connectivity analysis on four functional modules comprising of 20 brain regions that are primarily associated with a visually guided motor task (visual, motor, somatosensory and multisensory integration (MI)) used in this study. At the modular level, we observed that the hubness (a graph theoretic measure quantifying the importance of brain regions in integrating brain function) of the motor module decreases whereas that of the somatosensory module increases in presence of tTENS feedback. At the nodal level, similar observations were made for the visual and MI regions. This is the first work to reveal the impact of sensory feedback at multiple scales in the cortex of amputees in response to sensory stimulation.

Dynamic visual cortical connectivity analysis based on functional magnetic resonance imaging.

BackgroundStudies of brain functional connectivity (FC) and effective connectivity (EC) using the functional magnetic resonance imaging (fMRI) have advanced our understanding of functional organization on visual cortex of human brain. The current studies mainly focus on static or dynamic connectivity, while the relationships between them have not been well characterized especially for static EC (sEC) and dynamic EC (dEC), as well as the consistency characteristics of changing trend of dFCs and dECs, which is of great importance to reveal the neural information processing mechanism in visual cortex region.MethodIn this study, we explore these relationships among several subareas of human visual cortex (V1–V5) by calculating the connection intensity and information flow among them over time by sliding window method, which are defined by Pearson correlation coefficient and Granger causality analysis, respectively, in each window.ResultsThe results demonstrate that there are extensive connections existing in human visual network, which are time‐varying both in resting and task‐related states. sFC intensity is negatively correlated with the variance of dFC, while sEC intensity is positively correlated with the variance of dEC. Furthermore, we also find that dFC within visual cortex at rest shows more consistency, while dEC shows less compared with task state in changing trend.ConclusionTherefore, this study provides novel findings about dynamics of connectivity in human visual cortex from the perspective of functional and effective connectivity.

Cortical connectivity analysis for assessing the impact of microgravity and the efficacy of reactive sledge jumps countermeasure to NREM 2 sleep

Cognitive performance is crucial for long-term space missions, however, environments with zero gravity such as the International Space Station (ISS) or spacecrafts induce isolation and confinement. Many studies adapt the 6° head-down tilt bed rest (HDT) protocol in order to simulate the effects of weightlessness on sleep and psychophysiology in controlled environments. We investigated how weightlessness affects the sleep physiology and whether current countermeasures are efficient for sleep quality preservation by employing the head-down tilt (6°) bed rest protocol. We focused on NREM 2 sleep stage and we employed a 3-step analysis, which includes the estimation of the features that quantify the global functional organization of the cortical networks (mean cluster coefficient and characteristic path length), a local network analysis by estimating the connectivity strength of the various cortical regions with the thalamus and, quantifying changes in the functional importance (hub strength) for 22 cortical regions. The data were collected in the premises of the ENVIHAB Medical Research Facility of the Aerospace Centre in Cologne, Germany. The participants were 23 male individuals aged between 23 and 45 years old, assigned to either the control or experimental (sledge) group. Our design involved 60 days of HDT bed rest position (with the head tilted 6° downwards), with a two-week baseline and recovery period before and after the HDT duration. Our experimental design involved polysomnographic recordings (PSG) during sleep and we analysed the data collected 14 days before experiment onset (BDC-14) and during the 21st day of the HDT period (HDT21). Our results indicate that during N2 sleep in extreme environments, global networks characteristics and their feature connectivity are impaired. Degradation of N2 phase quality affects the declarative memory, especially for motor skills. The above finding highlights the importance of sleep quality regulation in astronauts in a way that enhances cognitive functions that is crucial in extreme environments.

Improving the Nulling Beamformer Using Subspace Suppression.

Magnetoencephalography (MEG) captures the magnetic fields generated by neuronal current sources with sensors outside the head. In MEG analysis these current sources are estimated from the measured data to identify the locations and time courses of neural activity. Since there is no unique solution to this so-called inverse problem, multiple source estimation techniques have been developed. The nulling beamformer (NB), a modified form of the linearly constrained minimum variance (LCMV) beamformer, is specifically used in the process of inferring interregional interactions and is designed to eliminate shared signal contributions, or cross-talk, between regions of interest (ROIs) that would otherwise interfere with the connectivity analyses. The nulling beamformer applies the truncated singular value decomposition (TSVD) to remove small signal contributions from a ROI to the sensor signals. However, ROIs with strong crosstalk will have high separating power in the weaker components, which may be removed by the TSVD operation. To address this issue we propose a new method, the nulling beamformer with subspace suppression (NBSS). This method, controlled by a tuning parameter, reweights the singular values of the gain matrix mapping from source to sensor space such that components with high overlap are reduced. By doing so, we are able to measure signals between nearby source locations with limited cross-talk interference, allowing for reliable cortical connectivity analysis between them. In two simulations, we demonstrated that NBSS reduces cross-talk while retaining ROIs' signal power, and has higher separating power than both the minimum norm estimate (MNE) and the nulling beamformer without subspace suppression. We also showed that NBSS successfully localized the auditory M100 event-related field in primary auditory cortex, measured from a subject undergoing an auditory localizer task, and suppressed cross-talk in a nearby region in the superior temporal sulcus.

Cognitive conflict increases processing of negative, task-irrelevant stimuli

The detection of cognitive conflict is thought to trigger adjustments in executive control. It has been recently shown that cognitive conflict increases processing of stimuli that are relevant to the ongoing task and that these modulations are exerted by the dorsolateral prefrontal cortex (DLPFC). However, it is still unclear whether such control influences are unspecific and might also affect the processing of task-irrelevant stimuli.The aim of the study was to examine if cognitive conflict affects processing of neutral and negative, task-irrelevant pictures. Participants responded to congruent (non-conflict) or to incongruent (conflict-eliciting) trials of a modified flanker task. Each response was followed by a presentation of a neutral or negative picture. The late positive potential (LPP) in response to picture presentation was used to assess the level of picture processing after conflict vs non-conflict trials. Connectivity between the DLPFC and attentional and perceptual areas during picture presentation was analysed to check if the DLPFC might be a source of these modulations.ERP results showed an effect of cognitive conflict only on processing of negative pictures: LPP in response to negative pictures was increased after conflict trials, whereas LPP in response to neutral pictures remained unchanged. Cortical connectivity analysis showed that conflict trials intensified information flow from the DLPFC towards attentional and perceptual regions.Results suggest that cognitive conflict increases processing of task-irrelevant stimuli; however, they must display high biological salience. Increase in cognitive control exerted by the DLPFC over attentional and perceptual regions is a probable mechanism of the effect.

EEG Cortical Connectivity Analysis of Working Memory Reveals Topological Reorganization in Theta and Alpha Bands.

Numerous studies have revealed various working memory (WM)-related brain activities that originate from various cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the brain network in WM in the cortical space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize the topological properties of the brain functional network in the theta and alpha frequency bands during WM tasks. Twenty-eight subjects performed visual n-back tasks at two difficulty levels, i.e., 0-back (control task) and 2-back (WM task). After preprocessing, Electroencephalogram (EEG) signals were projected into the source space and 80 cortical brain regions were selected for further analysis. Subsequently, the theta- and alpha-band networks were constructed by calculating the Pearson correlation coefficients between the power series (obtained by concatenating the power values of all epochs in each session) of all pairs of brain regions. Graph theoretical approaches were then employed to estimate the topological properties of the brain networks at different WM tasks. We found higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task. Moreover, compared to the 0-back task, altered regional centrality was revealed in the 2-back task in various brain regions that mainly resided in the frontal, temporal and occipital lobes, with distinct presentations in the theta and alpha bands. In addition, significant negative correlations were found between the reaction time with the average path length of the theta-band network and the local clustering of the alpha-band network, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks.

Analysis of cortical connectivity based on time-frequency transfer entropy during unimanual movement

Analysis of cortical connectivity based on time-frequency transfer entropy during unimanual movement

Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG.

We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification. The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system. Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 ±0.09) and LCMV (0.72 ±0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74 ±0.16) but significantly better for LCMV (0.82 ±0.12) . We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.

7. Physiological and pathological aging: A cortical connectivity analysis by graph theory model applied to brain network

Cortical connectivity in physiological and pathological aging. Physiological aging was evaluated in 113 EEGs, divided in three groups: Young (18–45 years), Adult (50–70) and Elderly (>70). Pathological aging was evaluated in 378 EEGs, divided in: Alzheimer disease (AD), mild cognitive impaired (MCI) and normal elderly subjects. Graph theory parameters were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA in delta, theta, alpha1, alpha2, beta1, beta2 and gamma bands. In physiological aging, normalized Characteristic Path Length ( λ ) presented the pattern Young > Adult > Elderly in the higher alpha band, the pattern Elderly > Young in delta and theta bands. Normalized Clustering coefficient ( γ ) and Small-World ( σ ) showed no significant age-modulation. In pathological aging, λ presented differences between healthy and dementia in theta band while γ showed a significant increment for AD in delta, theta and alpha1 bands; finally, σ presented a significant rising in MCI respect to AD in theta band.Functional integration was prejudiced in both physiological and pathological aging, while functional segregation of brain networks is more evident only in the pathological aging. Graph theory can disclose brain network connectivity in physiological and pathological aging, allowing understanding functional integration and segregation processes.

Investigating the neural basis of cooperative joint action. An EEG hyperscanning study.

The aim of the present study is to investigate the neurophysiological basis of the cognitive functions underlying the execution of joint actions, by means of the recent technique called hyperscanning. Neuroelectrical hyperscanning is based on the simultaneous recording of brain activity from multiple subjects and includes the analysis of the functional relation between the brain activity of all the interacting individuals. We recorded simultaneous high density electroencephalography (hdEEG) from 16 pairs of subjects involved in a computerized joint action paradigm, with controlled levels of cooperation. Results of cortical connectivity analysis returned significant differences, in terms of inter-brain functional causal links, between the condition of cooperative joint action and a condition in which the subjects were told they were interacting with a PC, while actually interacting with another human subject. Such differences, described by selected brain connectivity indices, point toward an integration between the two subjects' brain activity in the cooperative condition, with respect to control conditions.

Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data.

Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15-45 years), 46 Adult (50-70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks.

Human Brain Networks in Cognitive Decline: A Graph Theoretical Analysis of Cortical Connectivity from EEG Data

The aim of this study was to investigate the neuronal network characteristics in physiological and pathological brain aging. A database of 378 participants divided in three groups was analyzed: Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal elderly (Nold) subjects. Through EEG recordings, cortical sources were evaluated by sLORETA software, while graph theory parameters (Characteristic Path Length λ, Clustering coefficient γ, and small-world network σ) were computed to the undirected and weighted networks, obtained by the lagged linear coherence evaluated by eLORETA software. EEG cortical sources from spectral analysis showed significant differences in delta, theta, and alpha 1 bands. Furthermore, the analysis of eLORETA cortical connectivity suggested that for the normalized Characteristic Path Length (λ) the pattern differences between normal cognition and dementia were observed in the theta band (MCI subjects are find similar to healthy subjects), while for the normalized Clustering coefficient (γ) a significant increment was found for AD group in delta, theta, and alpha 1 bands; finally, the small world (σ) parameter presented a significant interaction between AD and MCI groups showing a theta increase in MCI. The fact that AD patients respect the MCI subjects were significantly impaired in theta but not in alpha bands connectivity are in line with the hypothesis of an intermediate status of MCI between normal condition and overt dementia.

P37: Human brain networks in physiological and pathological aging: a graph theoretical analysis of cortical connectivity from EEG data

P37: Human brain networks in physiological and pathological aging: a graph theoretical analysis of cortical connectivity from EEG data

Functional cortical connectivity analysis of mental fatigue unmasks hemispheric asymmetry and changes in small-world networks

Changes in functional connectivity across mental states can provide richer information about human cognition than simpler univariate approaches. Here, we applied a graph theoretical approach to analyze such changes in the lower alpha (8–10Hz) band of EEG data from 26 subjects undergoing a mentally-demanding test of sustained attention: the Psychomotor Vigilance Test. Behavior and connectivity maps were compared between the first and last 5min of the task. Reaction times were significantly slower in the final minutes of the task, showing a clear time-on-task effect. A significant increase was observed in weighted characteristic path length, a measure of the efficiency of information transfer within the cortical network. This increase was correlated with reaction time change. Functional connectivity patterns were also estimated on the cortical surface via source localization of cortical activities in 26 predefined regions of interest. Increased characteristic path length was revealed, providing further support for the presence of a reshaped global topology in cortical connectivity networks under fatigue state. Additional analysis showed an asymmetrical pattern of connectivity (right>left) in fronto-parietal regions associated with sustained attention, supporting the right-lateralization of this function. Interestingly, in the fatigue state, significance decreases were observed in left, but not right fronto-parietal connectivity. Our results indicate that functional network organization can change over relatively short time scales with mental fatigue, and that decreased connectivity has a meaningful relationship with individual difference in behavior and performance.

Seeing with the mind's eye: top-down, bottom-up, and conscious awareness

With the advent of functional brain imaging techniques and recent developments in the analysis of cortical connectivity, the focus of mental imagery studies has shifted from a semi-modular approach to an integrated cortical network perspective. Functional magnetic resonance imaging studies of visual imagery of faces and objects show that activation of content-specific representations stored in the ventral visual stream is top-down-modulated by parietal and frontal regions. Recent findings in patients with conscious awareness disorders reveal that mental imagery can be used to map patterns of residual cognitive function in their brain and to provide diagnostic and prognostic indicators.

Seeing faces and objects with the "mind's eye".

With the advent of functional brain imaging techniques and recent developments in the analysis of cortical connectivity, the focus of mental imagery studies has shifted from a semi-modular approach to a more realistic, integrated, cortical networks perspective. Recent studies of visual imagery of faces and objects suggest that activation of content-specific representations stored in the ventral visual stream is top-down modulated by parietal and frontal regions. The relation of these findings to other cognitive functions is discussed, as well as their clinical implications for patients with impaired states of conscious awareness.

Analysis of cortical connectivity using Hopfield neural network

Functional magnetic resonance imaging (fMRI) is increasingly recognized as a standard technique for brain mapping and determining the connectivity between cortical regions. Statistical approaches to determining cortical connectivity, e.g. structural equation modeling between various regions of interest in the active brain can be computationally inefficient. This study explores the utility of a Hopfield neural network to determine cortical connectivity in an fMRI data set.

Analysis of cortical connectivity using Hopfield neural network

Functional magnetic resonance imaging (fMRI) is increasingly recognized as a standard technique for brain mapping and determining the connectivity between cortical regions. Statistical approaches to determining cortical connectivity, e.g. structural equation modeling between various regions of interest in the active brain can be computationally inefficient. This study explores the utility of a Hopfield neural network to determine cortical connectivity in an fMRI data set.