Resting-state Neuronal Activity Research Articles

Electroencephalography-Derived Functional Connectivity in Sensorimotor Networks in Post Stroke Fatigue

BackgroundPoor suppression of anticipated sensory information from muscle contractions is thought to underlie high fatigue. Such diminished task-related sensory attenuation is reflected in resting state connectivity. Here we test the hypothesis ‘altered electroencephalography (EEG)-derived functional connectivity in somatosensory network in the beta band, is a signature of fatigue in post-stroke fatigue’.MethodsIn non-depressed, minimally impaired stroke survivors (n = 29), with median disease duration of 5 years, resting state neuronal activity was measured using 64-channel EEG. Graph theory-based network analysis measure of functional connectivity via small-world index (SW) was calculated focusing on right and left motor (Brodmann areas 4, 6, 8, 9, 24 and 32) and sensory (Brodmann areas 1, 2, 3, 5, 7, 40 and 43) networks, in the beta (13–30 Hz) frequency range. Fatigue was measured using Fatigue Severity Scale - FSS (Stroke), with scores of > 4, defined as high fatigue.ResultsResults confirmed the working hypothesis, with high fatigue stroke survivors showing higher small-worldness in the somatosensory networks when compared to low fatigue.ConclusionHigh levels of small-worldness in somatosensory networks indicates altered processing of somesthetic input. Such altered processing would explain high effort perception within the sensory attenuation model of fatigue.

Functional Deficiency of Interneurons and Negative BOLD fMRI Response.

The functional deficiency of the inhibitory system typically appears during development and can progress to psychiatric disorders or epilepsy, depending on its severity, in later years. It is known that interneurons, the major source of GABAergic inhibition in the cerebral cortex, can make direct connections with arterioles and participate in the regulation of vasomotion. The goal of this study was to mimic the functional deficiency of interneurons through the use of localized microinjections of the GABA antagonist, picrotoxin, in such a concentration that it did not elicit epileptiform neuronal activity. First, we recorded the dynamics of resting-state neuronal activity in response to picrotoxin injections in the somatosensory cortex of an awake rabbit; second, we assessed the altered neuronal and hemodynamic responses to whisker stimulation using BOLD fMRI and electrophysiology recordings; third, we evaluated brain tissue oxygen levels before and after picrotoxin injection. Our results showed that neuronal activity typically increased after picrotoxin administration, the BOLD responses to stimulation became negative, and the oxygen response was nearly abolished. Vasoconstriction during the resting baseline was not observed. These results indicate that picrotoxin provoked imbalanced hemodynamics either due to increased neuronal activity, decreased vascular response, or a combination of both.

Resting-State Neuronal Activity and Functional Connectivity Changes in the Visual Cortex after High Altitude Exposure: A Longitudinal Study.

Damage to the visual cortex structures after high altitude exposure has been well clarified. However, changes in the neuronal activity and functional connectivity (FC) of the visual cortex after hypoxia/reoxygenation remain unclear. Twenty-three sea-level college students, who took part in 30 days of teaching at high altitude (4300 m), underwent routine blood tests, visual behavior tests, and magnetic resonance imaging scans before they went to high altitude (Test 1), 7 days after they returned to sea level (Test 2), as well as 3 months (Test 3) after they returned to sea level. In this study, we investigated the hematological parameters, behavioral data, and spontaneous brain activity. There were significant differences among the tests in hematological parameters and spontaneous brain activity. The hematocrit, hemoglobin concentration, and red blood cell count were significantly increased in Test 2 as compared with Tests 1 and 3. As compared with Test 1, Test 3 increased amplitudes of low-frequency fluctuations (ALFF) in the right calcarine gyrus; Tests 2 and 3 increased ALFF in the right supplementary motor cortex, increased regional homogeneity (ReHo) in the left lingual gyrus, increased the voxel-mirrored homotopic connectivity (VMHC) value in the motor cortex, and decreased FC between the left lingual gyrus and left postcentral gyrus. The color accuracy in the visual task was positively correlated with ALFF and ReHo in Test 2. Hypoxia/reoxygenation increased functional connection between the neurons within the visual cortex and the motor cortex but decreased connection between the visual cortex and motor cortex.

Cortical and subcortical changes in resting-state neuronal activity and connectivity in early symptomatic ALS and advanced frontotemporal dementia

The objective of this study was to examine if patterns of resting-state brain activity and functional connectivity in cortical and subcortical regions in patients with early symptomatic amyotrophic lateral sclerosis (ALS) resemble those of behavioural variant frontotemporal dementia (bvFTD). In a cross-sectional design, eyes-closed resting-state magnetoencephalography (MEG) data of 34 ALS patients, 18 bvFTD patients and 18 age- and gender-matched healthy controls (HCs) were projected to source-space using an atlas-based beamformer. Group differences in peak frequency, band-specific oscillatory activity and functional connectivity (corrected amplitude envelope correlation) in 78 cortical regions and 12 subcortical regions were determined. False discovery rate was used to correct for multiple comparisons. BvFTD patients, as compared to ALS and HCs, showed lower relative beta power in parietal, occipital, temporal and nearly all subcortical regions. Compared to HCs, patients with ALS and patients with bvFTD had a higher delta (0.5–4 Hz) and gamma (30–48 Hz) band resting-state functional connectivity in a high number of overlapping regions in the frontal lobe and in limbic and subcortical regions. Higher delta band connectivity was widespread in the bvFTD patients compared to HCs. ALS showed a more widespread higher gamma band functional connectivity compared to bvFTD. In conclusion, MEG in early symptomatic ALS patients shows resting-state functional connectivity changes in frontal, limbic and subcortical regions that overlap considerably with bvFTD. The findings show the potential of MEG to detect brain changes in early symptomatic phases of ALS and contribute to our understanding of the disease spectrum, with ALS and bvFTD at the two extreme ends.

Wired to Punish? Electroencephalographic Study of the Resting-state Neuronal Oscillations Underlying Third-party Punishment

For over a decade, neuroimaging and brain stimulation studies have investigated neural mechanisms of third-party punishment, a key instrument for social norms enforcement. However, the neural dynamics underlying these mechanisms are still unclear. Previous electroencephalographic studies on third-party punishment have shown that inter-brain connectivity is linked to punishment behavior. However, no clear evidence was provided regarding whether the effect of inter-brain connectivity on third-party punishment is mediated by local neuronal states. In this study, we further investigate whether resting-state neuronal activity in the alpha frequency range can predict individual differences in third-party punishment. More specifically, we show that the global resting-state connectivity between the right dorsolateral prefrontal and right temporo-parietal regions is negatively correlated with the level of third-party punishment. Additionally, individuals with stronger local resting-state long-range temporal correlations in the right temporo-parietal cortices demonstrated a lower level of third-party punishment. Thus, our results further support the idea that global and local neuronal dynamics can contribute to individual differences in third-party punishment.

Critical Dynamics in Spontaneous Resting-State Oscillations Are Associated With the Attention-Related P300 ERP in a Go/Nogo Task.

Sustained attention is the ability to continually concentrate on task-relevant information, even in the presence of distraction. Understanding the neural mechanisms underlying this ability is critical for comprehending attentional processes as well as neuropsychiatric disorders characterized by attentional deficits, such as attention deficit hyperactivity disorder (ADHD). In this study, we aimed to investigate how trait-like critical oscillations during rest relate to the P300 evoked potential—a biomarker commonly used to assess attentional deficits. We measured long−range temporal correlations (LRTC) in resting-state EEG oscillations as index for criticality of the signal. In addition, the attentional performance of the subjects was assessed as reaction time variability (RTV) in a continuous performance task following an oddball paradigm. P300 amplitude and latencies were obtained from EEG recordings during this task. We found that, after controlling for individual variability in task performance, LRTC were positively associated with P300 amplitudes but not latencies. In line with previous findings, good performance in the sustained attention task was related to higher P300 amplitudes and earlier peak latencies. Unexpectedly, we observed a positive relationship between LRTC in ongoing oscillations during rest and RTV, indicating that greater criticality in brain oscillations during rest relates to worse task performance. In summary, our results show that resting-state neuronal activity, which operates near a critical state, relates to the generation of higher P300 amplitudes. Brain dynamics close to criticality potentially foster a computationally advantageous state which promotes the ability to generate higher event-related potential (ERP) amplitudes.

Study of EEG microstates in Parkinson's disease: a potential biomarker?

The spontaneous activity of the brain is dynamic even at rest and the deviation from this normal pattern of dynamics can lead to different pathological states. EEG microstate analysis of resting-state neuronal activity in Parkinson's disease (PD) could provide insight into altered brain dynamics of patients exhibiting dementia. Resting-state EEG microstate maps were derived from 128 channel EEG data in 20 PD without dementia (PDND), 18 PD with dementia (PDD) and 20 Healthy controls (CON) using Cartool and sLORETA softwares. Microstate map parameters including global explained variance, mean duration, frequency of occurrence (TF) and time coverage were compared statistically among the groups. Eight maps that explained 72% of the topographic variance were identified and only three maps differed significantly across the groups. TF of Map1 was lower in both PDND and PDD (p < 0.001) and that of Map3 (p = 0.02) in PDND compared to control. Cortical sources showed higher activation in precuneus, cuneus and superior parietal lobe (Threshold: Log-F = 1.74, p < 0.05) with maximum activity in the precuneus region (MNI co-ordinates: - 25, - 75, - 40; Log-F = 1.9) in PDND compared to control only for Map1. Lower TF of Map1 (prototypical microstate D) may potentially serve as a biomarker for PD with or without dementia whereas higher activation of precuneus, cuneus and superior parietal lobe at resting-state could favour signal processing, lack of which could be associated with dementia in Parkinson's disorder.

Effective brain connectivity at rest is associated with choice-induced preference formation.

Preferences can change as a consequence of making hard decisions whereby the value of chosen options increases and the value of rejected options decreases. Such choice‐induced preference changes have been associated with brain areas detecting choice conflict (anterior cingulate cortex, ACC), updating stimulus value (dorsolateral prefrontal cortex, dlPFC) and supporting memory of stimulus value (hippocampus and ventromedial prefrontal cortex, vmPFC). Here we investigated whether resting‐state neuronal activity within these regions is associated with the magnitude of individuals' preference updates. We fitted a dynamic causal model (DCM) to resting‐state neuronal activity in the spectral domain (spDCM) and estimated the causal connectivity among core regions involved in preference formation following hard choices. The extent of individuals' choice‐induced preference changes were found to be associated with a diminished resting‐state excitation between the left dlPFC and the vmPFC, whereas preference consistency was related to a higher resting‐state excitation from the ACC to the left hippocampus and vmPFC. Our results point to a model of preference formation during which the dynamic network configurations between left dlPFC, ACC, vmPFC and left hippocampus at rest are linked to preference change or stability.

Characterization of the dynamic behavior of neural activity in Alzheimer’s disease: exploring the non-stationarity and recurrence structure of EEG resting-state activity

Objective. Mild cognitive impairment (MCI) and dementia due to Alzheimer’s disease (AD) have been shown to induce perturbations to normal neuronal behavior and disrupt neuronal networks. Recent work suggests that the dynamic properties of resting-state neuronal activity could be affected by MCI and AD-induced neurodegeneration. The aim of the study was to characterize these properties from different perspectives: (i) using the Kullback–Leibler divergence (KLD), a measure of non-stationarity derived from the continuous wavelet transform; and (ii) using the entropy of the recurrence point density () and the median of the recurrence point density (), two novel metrics based on recurrence quantification analysis. Approach. KLD, and were computed for 49 patients with dementia due to AD, 66 patients with MCI due to AD and 43 cognitively healthy controls from 60 s electroencephalographic (EEG) recordings with a 10 s sliding window with no overlap. Afterwards, we tested whether the measures reflected alterations to normal neuronal activity induced by MCI and AD. Main results. Our results showed that frequency-dependent alterations to normal dynamic behavior can be found in patients with MCI and AD, both in non-stationarity and recurrence structure. Patients with MCI showed signs of patterns of abnormal state recurrence in the theta (4–8 Hz) and beta (13–30 Hz) frequency bands that became more marked in AD. Moreover, abnormal non-stationarity patterns were found in MCI patients, but not in patients with AD in delta (1–4 Hz), alpha (8–13 Hz), and gamma (30–70 Hz). Significance. The alterations in normal levels of non-stationarity in patients with MCI suggest an initial increase in cortical activity during the development of AD. This increase could possibly be due to an impairment in neuronal inhibition that is not present during later stages. MCI and AD induce alterations to the recurrence structure of cortical activity, suggesting that normal state switching during rest may be affected by these pathologies.

Power and temporal dynamics of alpha oscillations at rest differentiate cognitive performance involving sustained and phasic cognitive control

Resting state neuronal activity in EEG/MEG recordings is primarily characterized by the presence of alpha oscillations (approx. 8–12 Hz). However, their functional significance and link to cognitive task performance remains elusive. We investigated resting state neuronal activity and its relation to task performance by assessing traditional measures of alpha activity (power and individual alpha peak frequency) and dynamic properties of the signal measured by long-range temporal correlations (LRTC). Multichannel EEG was recorded at rest in 82 healthy male adults and compared to their cognitive performance, measured by tests involving executive functions, working memory, short- and long-term memory demands. Our results showed that attention-span scores positively correlated with alpha power at rest, with corresponding neuronal sources located primarily in the left-hemispheric anterior cingulate cortex, parietal regions, and bilateral suplementary motor areas. Furthermore, better working memory performance was related to increased LRTC of alpha oscillations at rest in the right hemispheric fronto-parietal, temporal, and occipital regions. Our findings suggest that resting state neuronal activity may reflect properties of brain networks that are functionally relevant for cognitive task performance. While alpha power measured at rest might relate to tasks that employ sustained inhibitory control, LRTC are suggested to reflect the capacity of neuronal networks to perform tasks that require phasic attention and quick adaptation to changing task demands.

Macroscale variation in resting-state neuronal activity and connectivity assessed by simultaneous calcium imaging, hemodynamic imaging and electrophysiology

Functional imaging of spontaneous activity continues to play an important role in the field of connectomics. The most common imaging signal used for these experiments is the blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal, but how this signal relates to spontaneous neuronal activity remains incompletely understood. Genetically encoded calcium indicators represent a promising tool to study this problem, as they can provide brain-wide measurements of neuronal activity compared to point measurements afforded by electrophysiological recordings. However, the relationship between the calcium signal and neurophysiological parameters at the mesoscopic scale requires further systematic characterization. Therefore, we collected simultaneous resting-state measurements of electrophysiology, along with calcium and hemodynamic imaging, in lightly anesthetized mice to investigate two aims. First, we examined the relationship between each imaging signal and the simultaneously recorded electrophysiological signal in a single brain region, finding that both signals are better correlated with multi-unit activity compared to local field potentials, with the calcium signal possessing greater signal-to-noise ratio and regional specificity. Second, we used the resting-state imaging data to model the relationship between the calcium and hemodynamic signals across the brain. We found that this relationship varied across brain regions in a way that is consistent across animals, with delays increasing by600 ms towards posterior cortical regions. Furthermore, while overall functional connectivity (FC) measured by the hemodynamic signal is significantly correlated with FC measured by calcium, the two estimates were found to be significantly different. We hypothesize that these differences arise at least in part from the observed regional variation in the hemodynamic response. In total, this work highlights some of the caveats needed in interpreting hemodynamic-based measurements of FC, as well as the need for improved modeling methods to reduce this potential source of bias.

A stochastic model for EEG microstate sequence analysis

The analysis of spontaneous resting state neuronal activity is assumed to give insight into the brain function. One noninvasive technique to study resting state activity is electroencephalography (EEG) with a subsequent microstate analysis. This technique reduces the recorded EEG signal to a sequence of prototypical topographical maps, which is hypothesized to capture important spatio-temporal properties of the signal.In a statistical EEG microstate analysis of healthy subjects in wakefulness and three stages of sleep, we observed a simple structure in the microstate transition matrix. It can be described with a first order Markov chain in which the transition probability from the current state (i.e., map) to a different map does not depend on the current map. The resulting transition matrix shows a high agreement with the observed transition matrix, requiring only about 2% of mass transport (1/2 L1-distance). In the second part, we introduce an extended framework in which the simple Markov chain is used to make inferences on a potential underlying time continuous process. This process cannot be directly observed and is therefore usually estimated from discrete sampling points of the EEG signal given by the local maxima of the global field power. Therefore, we propose a simple stochastic model called sampled marked intervals (SMI) model that relates the observed sequence of microstates to an assumed underlying process of background intervals and thus, complements approaches that focus on the analysis of observable microstate sequences.

Whole brain functional connectivity using phase locking measures of resting state magnetoencephalography.

The analysis of spontaneous functional connectivity (sFC) reveals the statistical connections between regions of the brain consistent with underlying functional communication networks within the brain. In this work, we describe the implementation of a complete all-to-all network analysis of resting state neuronal activity from magnetoencephalography (MEG). Using graph theory to define networks at the dipole level, we established functionally defined regions by k-means clustering cortical surface locations using Eigenvector centrality (EVC) scores from the all-to-all adjacency model. Permutation testing was used to estimate regions with statistically significant connections compared to empty room data, which adjusts for spatial dependencies introduced by the MEG inverse problem. In order to test this model, we performed a series of numerical simulations investigating the effects of the MEG reconstruction on connectivity estimates. We subsequently applied the approach to subject data to investigate the effectiveness of our method in obtaining whole brain networks. Our findings indicated that our model provides statistically robust estimates of functional region networks. Application of our phase locking network methodology to real data produced networks with similar connectivity to previously published findings, specifically, we found connections between contralateral areas of the arcuate fasciculus that have been previously investigated. The use of data-driven methods for neuroscientific investigations provides a new tool for researchers in identifying and characterizing whole brain functional connectivity networks.

Regional homogeneity and resting state functional connectivity: Associations with exposure to early life stress

Early life stress (ELS) confers risk for psychiatric illness. Previous literature suggests ELS is associated with decreased resting-state functional connectivity (rs-FC) in adulthood, but there are no studies of resting-state neuronal activity in this population. This study investigated whether ELS-exposed individuals demonstrate resting-state activity patterns similar to those found in PTSD. Twenty-seven adults (14 with at least moderate ELS), who were medication-free and without psychiatric or medical illness, underwent MRI scans during two 4-minute rest periods. Resting-state activity was examined using regional homogeneity (ReHo), which estimates regional activation patterns through indices of localized concordance. ReHo values were compared between groups, followed by rs-FC analyses utilizing ReHo-localized areas as seeds to identify other involved regions. Relative to controls, ELS subjects demonstrated diminished ReHo in the inferior parietal lobule (IPL) and superior temporal gyrus (STG). ReHo values were inversely correlated with ELS severity. Secondary analyses revealed decreased rs-FC between the IPL and right precuneus/posterior cingulate, left fusiform gyrus, cerebellum and caudate in ELS subjects. These findings indicate that ELS is associated with altered resting-state activity and connectivity in brain regions involved in trauma-related psychiatric disorders. Future studies are needed to evaluate whether these associations represent potential imaging biomarkers of stress exposure.

Fractal-based linear model of resting state hemodynamic response in fMRI

Understanding endogenous dynamics of the brain has been one of crucial issues in neuroscience since it will disclose a huge default-mode functional network hidden behind resting state signals. Recent studies have shown that a resting state fMRI time series tend to exhibit the fractal property such as 1/f-type spectral density which is characterized by a fractal exponent. There exist indirect evidences supporting that the fractal exponent is associated with neurophysiological activities, however the relevance of fractal behavior with functional connectivity has been still unclear. Recently it was observed that the spontaneous fluctuation of cerebral blood volume also exhibits fractal properties. Since fMRI is a non-invasive measurement of hemodynamic activities, it leads us to consider attributing the fractal behavior of BOLD signals to cerebral hemodynamics as well as neuronal activities. Motivated by this idea, we propose a fractal-based linear model of resting state hemodynamic response function (rs-HRF) whose behavior is summarized by a fractal exponent. It comprises the infinite or large number of density functions with slowly decaying coefficients according to a fractal exponent while the classical HRF consists of just two density functions. We showed that the special condition of rs-HRF causes long memory in BOLD signals. The rs-HRF model also implies that a resting state BOLD signal can be well approximated as a fractionally integrated process (FIP) rather than the typical fractional Gaussian noise (FGN). The simulation studies based on the rs-HRF model enables us to figure out the influence of hemodynamic fractal behavior on functional connectivity of resting state BOLD signals. First, we simulated a multivariate autoregressive process as an approximation of stationary resting state neuronal activity, and obtained its corresponding BOLD signals by convolving them with rs-HRF. Then, we observed the dissimilarity of wavelet correlation matrices between neuronal activities and BOLD signals on the basis of the symmetric Kullback-Leibler divergence which can be utilized as a measure of difference between two distributions. The dissimilarity of wavelet correlation increases in high frequency scales as the variance of fractal exponents increases while the dissimilarity approaches to zero in low frequency scales. These results suggest that the difference of fractal exponents between brain regions may cause apparent discrepancy of functional connectivity between neuronal activities and BOLD signals in high frequency scales. All of these results may give us insight into the dependence of functional connectivity on fractal behavior, and direct us to the default-mode functional network dominated by neuronal activities beyond fractal behavior of BOLD signals. While the linear relationship between neuronal activities and BOLD signals in evoked state has been well represented by HRF, it is questionable that the classical HRF is appropriate even for resting state since the model does not well reflect the fractal properties of cerebral hemodynamics as well as BOLD signals. In the future work, it will be valuable to constitute the nonlinear model of fractal behavior induced by cerebral hemodynamics as an extension of the classical Balloon model.

Alteration of functional connectivity in tinnitus brain revealed by resting-state fMRI?: A pilot study

Objective: We report a case study on possible alterations in resting-state functional connectivity between the auditory network and non-auditory brain regions in tinnitus patients. Design: Independent component analyses were performed to evaluate coherent spontaneous activity in distributed brain networks. The resting-state functional connectivity scores between the right and left auditory networks were measured. Direct comparison of resting-state connectivity between tinnitus patients and controls was made using a two-sample t-test. Study sample: Four patients (three male, one female; mean age 45 ± 3.92 years) with chronic tinnitus lateralized to the left ear, and six age-matched controls (four male, two female; mean age 45 ± 2.76 years) participated in this case study. Results: The average resting-state functional connectivity (FC) score between the left and right auditory cortical regions was significantly lower in tinnitus patients than in controls (P < 0.05). Direct comparison between patients and controls showed that increased functional connectivity caused by tinnitus occurred predominantly in the left amygdala and in the dorsomedial prefrontal cortex. Conclusions: Our pilot study suggested that resting state functional magnetic resonance imaging (fMRI) could be useful to investigate possible alterations in resting-state neuronal activity between the auditory and non-auditory networks in tinnitus patients.

Neuronal Activity of the Human Subthalamic Nucleus in the Parkinsonian and Nonparkinsonian State

We recorded resting-state neuronal activity from the human subthalamic nucleus (STN) during functional stereotactic surgeries. By inserting up to five parallel microelectrodes for single- or multiunit recordings and applying statistical spike-sorting methods, we were able to isolate a total of 351 single units in 65 patients with Parkinson's disease (PD) and 33 single units in 9 patients suffering from essential tremor (ET). Among these were 93 pairs of simultaneously recorded neurons in PD and 17 in ET, which were detected either by the same (n = 30) or neighboring microelectrodes (n = 80). Essential tremor is a movement disorder without any known basal ganglia pathology and with normal dopaminergic brain function. By comparing the neuronal activity of the STN in patients suffering from PD and ET we intended to characterize, for the first time, changes of basal ganglia activity in the human disease state that had previously been described in animal models of Parkinson's disease. We found a significant increase in the mean firing rate of STN neurons in PD and a relatively larger fraction of neurons exhibiting burstlike activity compared with ET. The overall proportion of neurons exhibiting intrinsic oscillations or interneuronal synchronization as defined by significant spectral peaks in the auto- or cross-correlations functions did not differ between PD and ET when considering the entire frequency range of 1-100 Hz. The distribution of significant oscillations across the theta (1-8 Hz), alpha (8-12 Hz), beta (12-35 Hz), and gamma band (>35 Hz), however, was uneven in ET and PD, as indicated by a trend in Fisher's exact test (P = 0.05). Oscillations and pairwise synchronizations within the 12- to 35-Hz band were a unique feature of PD. Our results confirm the predictions of the rate model of Parkinson's disease. In addition, they emphasize abnormalities in the patterning and dynamics of neuronal discharges in the parkinsonian STN, which support current concepts of abnormal motor loop oscillations in Parkinson's disease.