A study of the brain's resting state based on alpha band power, heart rate and fMRI

Individual differences in seated resting heart rate are associated with multisensory perceptual function in older adults

There is evidence that cardiovascular function can influence sensory processing and cognition, which are known to change with age. However, whether the precision of unisensory and multisensory temporal perception is influenced by cardiovascular activity in older adults is uncertain. We examined whether seated resting heart rate (RHR) was associated with unimodal visual and auditory temporal discrimination as well as susceptibility to the audio-visual Sound Induced Flash Illusion (SIFI) in a large sample of older adults (N = 3232; mean age = 64.17 years, SD = 7.74, range = 50-93; 56% female) drawn from The Irish Longitudinal Study on Ageing (TILDA). Faster seated RHR was associated with better discretization of two flashes (but not two beeps) and increased SIFI susceptibility when the audio-visual stimuli were presented close together in time but not at longer audio-visual temporal offsets. Our findings suggest a significant relationship between cardiovascular activity and the precision of visual and audio-visual temporal perception in older adults, thereby providing novel evidence for a link between cardiovascular function and perceptual function in aging.

The Impact of Stretching Intensities on Neural and Autonomic Responses: Implications for Relaxation

Stretching is an effective exercise for increasing body flexibility and pain relief. This study investigates the relationship between stretching intensity and relaxation effects, focusing on brainwaves and autonomic nervous system (ANS) activity. We used a crossover design with low- and high-intensity conditions to elucidate the impact of varying stretching intensities on neural activity associated with relaxation in 19 healthy young adults. Participants completed mood questionnaires. Electroencephalography (EEG) and plethysmography measurements were also obtained before, during, and after stretching sessions. The hamstring muscle was targeted for stretching, with intensity conditions based on the Point of Discomfort. Data analysis included wavelet analysis for EEG, plethysmography data, and repeated-measures ANOVA to differentiate mood, ANS activity, and brain activity related to stretching intensity. Results demonstrated no significant differences between ANS and brain activity based on stretching intensity. However, sympathetic nervous activity showed higher activity during the rest phases than in the stretch phases. Regarding brain activity, alpha and beta waves showed higher activity during the rest phases than in the stretch phases. A negative correlation between alpha waves and sympathetic nervous activities was observed in high-intensity conditions. However, a positive correlation between beta waves and parasympathetic nervous activities was found in low-intensity conditions. Our findings suggest that stretching can induce interactions between the ANS and brain activity.

Changes in the cortical network during sleep stage transitions

Sleep state transitions are closely related to insomnia, drowsiness, and sleep maintenance. However, how the cortical network varies during such a transition process remains unclear. Changes in the cortical interaction during the short-term process of sleep stage transitions were investigated. In all, 40 healthy young participants underwent overnight polysomnography. The phase transfer entropy of six frequency bands was obtained from 16 electroencephalography channels to assess the strength and direction of information flow between the cortical regions. Differences in the cortical network between the first and the last 10 s in a 40-s transition period across wakefulness, N1, N2, N3, and rapid eye movement were, respectively, studied. Various frequency bands exhibited different patterns during the sleep stage transitions. It was found that the mutual transitions between the sleep stages were not necessarily the opposite. More significant changes were observed in the sleep deepening process than in the process of sleep awakening. During sleep stage transitions, changes in the inflow and outflow strength of various cortical regions led to regional differences, but for the entire sleep progress, such an imbalance did not intensify, and a dynamic balance was instead observed. The detailed findings of variations in cortical interactions during sleep stage transition promote understanding of sleep mechanism, sleep process, and sleep function. Additionally, it is expected to provide helpful clues for sleep improvement, like reducing the time required to fall asleep and maintaining sleep depth.

The role of electroencephalography electrical reference in the assessment of functional brain-heart interplay: From methodology to user guidelines

BACKGROUND

The choice of EEG reference has been widely studied. However, the choice of the most appropriate re-referencing for EEG data is still debated. Moreover, the role of EEG reference in the estimation of functional Brain-Heart Interplay (BHI), together with different multivariate modelling strategies, has not been investigated yet.

METHODS

This study identifies the best methodology combining a proper EEG electrical reference and signal processing methods for an effective functional BHI assessment. The effects of the EEG reference among common average, mastoids average, Laplacian reference, Cz reference, and the reference electrode standardization technique (REST) were explored throughout different BHI methods including synthetic data generation (SDG) model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient.

RESULTS

The SDG model exhibited high robustness between EEG references, whereas the maximal information coefficient method exhibited a high sensitivity. The common average and REST references for EEG showed a good consistency in the between-method comparisons. Laplacian, and Cz references significantly bias a BHI measurement.

COMPARISON WITH EXISTING METHODS

The use of EEG reference based on a common average outperforms on the use of other references for consistency in estimating directed functional BHI. We do not recommend the use of EEG references based on analytical derivations as the experimental conditions may not meet the requirements of their optimal estimation, particularly in clinical settings.

CONCLUSION

The use of a common average for EEG electrical reference is concluded to be the most appropriate choice for a quantitative, functional BHI assessment.

fMRI-based detection of alertness predicts behavioral response variability

Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.

Emotional Reactivity and Regulation in Preschool-Age Children Who Do and Do Not Stutter: Evidence From Autonomic Nervous System Measures

Purpose: This experimental cross-sectional research study examined the emotional reactivity and emotion regulation in preschool-age children who do (CWS) and do not stutter (CWNS) by assessing their psychophysiological response during rest and while viewing pictures from the International Affective Picture System (Lang et al., 2008).

Method: Participants were 18 CWS (16 boys and two girls; mean age 4 years, 5 months) and 18 age- and gender-matched CWNS. Participants' psychophysiological responses were measured during two baselines and two picture viewing conditions. Skin conductance level (SCL) and heart rate were measured to assess emotional reactivity. Respiratory sinus arrhythmia (RSA) was measured to assess emotional regulation. Participants' shyness and executive function were assessed via parent report and considered for their effects on participants' psychophysiological responses.

Results: First, CWNS and CWS did not differ in their initial baseline SCL, heart rate, or RSA, but all participants had higher SCL and lower RSA in the second baseline, subsequent to the first challenge condition, compared to the first baseline. Second, during the challenge conditions, CWS did not differ from CWNS in their SCL, but showed a significantly higher heart rate than CWNS. Third, CWS exhibited a significantly lower RSA during the challenge conditions compared to CWNS. Lastly, the temperamental quality of shyness was associated with preschool-age children's SCL, such that participants who were rated higher in shyness had a higher SCL during the challenge conditions. Participants' executive function had a marginally significant effect on the RSA, such that the participants who had higher executive function composite scores exhibited lower RSA during the challenge conditions.

Conclusions: Our findings suggest that CWS and CWNS did not differ in their emotional reactivity and emotional regulation abilities at rest. During challenge conditions, however, CWS tended to be more emotionally reactive, as indicated by a higher heart rate, and also employed more emotional regulation, indexed by a greater decrease in RSA, compared to CWNS. Preschool-age children's behavior is largely dominated by reactivity, but there is the emergence of regulation, which can help children adjust to various contextual demands. For CWS who are more emotionally reactive, regulatory skills may be particularly critical to their prognosis and treatment.

Reconstruction of respiratory variation signals from fMRI data

Functional MRI signals can be heavily influenced by systemic physiological processes in addition to local neural activity. For example, widespread hemodynamic fluctuations across the brain have been found to correlate with natural, low-frequency variations in the depth and rate of breathing over time. Acquiring peripheral measures of respiration during fMRI scanning not only allows for modeling such effects in fMRI analysis, but also provides valuable information for interrogating brain-body physiology. However, physiological recordings are frequently unavailable or have insufficient quality. Here, we propose a computational technique for reconstructing continuous low-frequency respiration volume (RV) fluctuations from fMRI data alone. We evaluate the performance of this approach across different fMRI preprocessing strategies. Further, we demonstrate that the predicted RV signals can account for similar patterns of temporal variation in resting-state fMRI data compared to measured RV fluctuations. These findings indicate that fluctuations in respiration volume can be extracted from fMRI alone, in the common scenario of missing or corrupted respiration recordings. The results have implications for enriching a large volume of existing fMRI datasets through retrospective addition of respiratory variations information.

The serotonin 2A receptor agonist 25CN-NBOH increases murine heart rate and neck-arterial blood flow in a temperature-dependent manner

BACKGROUND

Serotonin 2A receptors, the molecular target of psychedelics, are expressed by neuronal and vascular cells, both of which might contribute to brain haemodynamic characteristics for the psychedelic state.

AIM

Aiming for a systemic understanding of psychedelic vasoactivity, here we investigated the effect of N-(2-hydroxybenzyl)-2,5-dimethoxy-4-cyanophenylethylamine - a new-generation agonist with superior serotonin 2A receptor selectivity - on brain-supplying neck-arterial blood flow.

METHODS

We recorded core body temperature and employed non-invasive, collar-sensor based pulse oximetry in anesthetised mice to extract parameters of local blood perfusion, oxygen saturation, heart and respiration rate. Hypothesising an overlap between serotonergic pulse- and thermoregulation, recordings were done under physiological and elevated pad temperatures.

RESULTS

N-(2-hydroxybenzyl)-2,5-dimethoxy-4-cyanophenylethylamine (1.5 mg/kg, subcutaneous) significantly increased the frequency of heart beats accompanied by a slight elevation of neck-arterial blood flow. Increasing the animal-supporting heat-pad temperature from 37°C to 41°C enhanced the drug's effect on blood flow while counteracting tachycardia. Additionally, N-(2-hydroxybenzyl)-2,5-dimethoxy-4-cyanophenylethylamine promoted bradypnea, which, like tachycardia, quickly reversed at the elevated pad temperature. The interrelatedness of N-(2-hydroxybenzyl)-2,5-dimethoxy-4-cyanophenylethylamine's respiro-cardiovascular effects and thermoregulation was further corroborated by the drug selectively increasing the core body temperature at the elevated pad temperature. Arterial oxygen saturation was not affected by N-(2-hydroxybenzyl)-2,5-dimethoxy-4-cyanophenylethylamine at either temperature.

CONCLUSIONS

Our findings imply that selective serotonin 2A receptor activation modulates systemic cardiovascular functioning in orchestration with thermoregulation and with immediate relevance to brain-imminent neck (most likely carotid) arteries. As carotid branching is a critical last hub to channel cardiovascular output to or away from the brain, our results might have implications for the brain haemodynamics associated with psychedelia.

EEG spectro-temporal amplitude modulation as a measurement of cortical hemodynamics: an EEG-fNIRS study

Neurovascular coupling provides valuable descriptive information about neural function and communication. In this work, we propose to objectively characterize EEG sub-band modulation in an attempt to compare with local variations of fNIRS hemoglobin concentration. First, full-band EEG signals are decomposed into five well-known frequency sub-bands: delta, theta, alpha, beta, and gamma. The temporal amplitude envelope of each sub-band is then computed via Hilbert transformation. The proposed EEG 'spectro-temporal amplitude modulation' (EEG-AM) feature measures the rate at which each sub-band is modulated. Similarities between EEG-AM features and fNIRS hemoglobin concentration are computed for four neighboring channels over the occipital area during resting-state. Experiments with a database of 29 participants show statistically significant similarities between the total hemoglobin concentration and the alpha band modulating the alpha, beta, and gamma frequencies. These results support the idea that the EEG-AM can carry hemodynamic properties.Clinical relevance- This shows that the EEG spectro-temporal amplitude modulation present similarities with the hemoglobin concentration in co-placed channels.

Multimodal Parcellations and Extensive Behavioral Profiling Tackling the Hippocampus Gradient

The hippocampus displays a complex organization and function that is perturbed in many neuropathologies. Histological work revealed a complex arrangement of subfields along the medial-lateral and the ventral-dorsal dimension, which contrasts with the anterior-posterior functional differentiation. The variety of maps has raised the need for an integrative multimodal view. We applied connectivity-based parcellation to 1) intrinsic connectivity 2) task-based connectivity, and 3) structural covariance, as complementary windows into structural and functional differentiation of the hippocampus. Strikingly, while functional properties (i.e., intrinsic and task-based) revealed similar partitions dominated by an anterior-posterior organization, structural covariance exhibited a hybrid pattern reflecting both functional and cytoarchitectonic subdivision. Capitalizing on the consistency of functional parcellations, we defined robust functional maps at different levels of partitions, which are openly available for the scientific community. Our functional maps demonstrated a head-body and tail partition, subdivided along the anterior-posterior and medial-lateral axis. Behavioral profiling of these fine partitions based on activation data indicated an emotion-cognition gradient along the anterior-posterior axis and additionally suggested a self-world-centric gradient supporting the role of the hippocampus in the construction of abstract representations for spatial navigation and episodic memory.

The relationship between heart rate and functional connectivity of brain regions involved in autonomic control

The peripheral autonomic nervous system (ANS) adjusts the heart rate (HR) to intrinsic and extrinsic demands. It is controlled by a group of functionally connected brain regions assembling the so-called central autonomic network (CAN). More specifically, forebrain cortical regions, limbic and brainstem structures within the CAN have been identified as important components of circuits involved in HR regulation. The present study aimed to investigate whether functional connectivity (FC) between these regions varies in subjects with different heart rates. Thus, 84 healthy subjects were separated according to their HR in slow, medium and fast. We observed a direct association between HR and FC in CAN regions, where stronger FC was related to slower HR. This relationship, however, is non-linear, follows an exponential course and is not restricted to CAN areas only. The network-based analysis (NBS) using time series from 262 independent anatomical ROIs revealed significantly increased functional connectivity in subjects with slow HR compared to subjects with fast HR mainly in regions being part of the salience network, but also of the default-mode network. We additionally simulated the effect of aliasing on the functional connectivity using several TRs and heart rates to exclude the possibility that FC differences might be due to different aliasing effects in the data. The result of the simulation indicated that aliasing cannot explain our findings. Thus, present results imply a functionally meaningful coupling between FC and HR that need to be accounted for in future studies. Moreover, given the established link between HR and emotional, cognitive and social processes, present findings may also be considered to explain individual differences in brain activation or connectivity when using corresponding paradigms in the MR scanner to investigate such processes.

fMRI BOLD Correlates of EEG Independent Components: Spatial Correspondence With the Default Mode Network

Goal: We aimed to identify electroencephalographic (EEG) signal fluctuations within independent components (ICs) that correlate to spontaneous blood oxygenation level dependent (BOLD) activity in regions of the default mode network (DMN) during eyes-closed resting state. Methods: We analyzed simultaneously acquired EEG and functional magnetic resonance imaging (fMRI) eyes-closed resting state data in a convenience sample of 30 participants. IC analysis (ICA) was used to decompose the EEG time-series and common ICs were identified using data-driven IC clustering across subjects. The IC time courses were filtered into seven frequency bands, convolved with a hemeodynamic response function (HRF) and used to model spontaneous fMRI signal fluctuations across the brain. In parallel, group ICA analysis was used to decompose the fMRI signal into ICs from which the DMN was identified. Frequency and IC cluster associated hemeodynamic correlation maps obtained from the regression analysis were spatially correlated with the DMN. To investigate the reliability of our findings, the analyses were repeated with data collected from the same subjects 1 year later. Results: Our results indicate a relationship between power fluctuations in the delta, theta, beta and gamma frequency range and the DMN in different EEG ICs in our sample as shown by small to moderate spatial correlations at the first measurement (0.234 < |r| < 0.346, p < 0.0001). Furthermore, activity within an EEG component commonly identified as eye movements correlates with BOLD activity within regions of the DMN. In addition, we demonstrate that correlations between EEG ICs and the BOLD signal during rest are in part stable across time. Discussion: We show that ICA source separated EEG signals can be used to investigate electrophysiological correlates of the DMN. The relationship between the eye movement component and the DMN points to a behavioral association between DMN activity and the level of eye movement or the presence of neuronal activity in this component. Previous findings of an association between frontal midline theta activity and the DMN were replicated.

Constrained temporal parallel decomposition for EEG-fMRI fusion

OBJECTIVE

Multimodal neuroimaging has become a common practice in neuroscience research. Simultaneous EEG-fMRI is a popular multimodal recording approach due to the complementary spatiotemporal relationship between the two modalities. Several data fusion techniques have been proposed in the literature for EEG-fMRI fusion, including joint-ICA and parallel-ICA frameworks. Previous EEG-fMRI fusion approaches have used sensor-level EEG features. Recently, we introduced source-space ICA for EEG-MEG source reconstruction and component identification, which was shown to be a superior alternative to sensor-space ICA.

APPROACH

Here, we extend source-space ICA to the fusion of EEG-fMRI data. Additionally, we incorporate the use of a paradigm signal (constrained) and a lag-based signal decomposition approach to accommodate recent findings demonstrating the potentially variable lag structure between electrophysiological and BOLD signals. We evaluated this method on simulated concurrent EEG-fMRI during a boxcar task design, as well as real concurrent EEG-fMRI data from three participants performing an N-Back working memory task. The block diagram of the algorithm and corresponding source codes are provided.

MAIN RESULTS

Based on the results of the real working memory task, for all three subjects, one frontal theta component, and one right posterior alpha component had the highest contribution coefficients (~0.5) to the paradigm-related fused component. There were also two more alpha band components with contribution coefficients of 0.3. The highest contributing fMRI component (~0.8) was one known in the literature to be related to the attention network. The second fMRI component was related to the well-known default mode network, with a contribution coefficient of 0.3.

SIGNIFICANCE

The proposed EEG-fMRI fusion approach, is capable of estimating the brain maps of the EEG and fMRI for the fused components and account for the variable lag structure between electrophysiological and BOLD signals.

Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity

Oscillatory activity is crucial for information processing in the brain, and has a long history as a biomarker for psychopathology. Variation in oscillatory activity is highly heritable, but current understanding of specific genetic influences remains limited. We performed the largest genome-wide association study to date of oscillatory power during eyes-closed resting electroencephalogram (EEG) across a range of frequencies (delta 1-3.75 Hz, theta 4-7.75 Hz, alpha 8-12.75 Hz, and beta 13-30 Hz) in 8,425 subjects. Additionally, we performed KGG positional gene-based analysis and brain-expression analyses. GABRA2-a known genetic marker for alcohol use disorder and epilepsy-significantly affected beta power, consistent with the known relation between GABAA interneuron activity and beta oscillations. Tissue-specific SNP-based imputation of gene-expression levels based on the GTEx database revealed that hippocampal GABRA2 expression may mediate this effect. Twenty-four genes at 3p21.1 were significant for alpha power (FDR q < .05). SNPs in this region were linked to expression of GLYCTK in hippocampal tissue, and GNL3 and ITIH4 in the frontal cortex-genes that were previously implicated in schizophrenia and bipolar disorder. In sum, we identified several novel genetic variants associated with oscillatory brain activity; furthermore, we replicated and advanced understanding of previously known genes associated with psychopathology (i.e., schizophrenia and alcohol use disorders). Importantly, these psychopathological liability genes affect brain functioning, linking the genes' expression to specific cortical/subcortical brain regions.

How heart rate variability affects emotion regulation brain networks

Individuals with high heart rate variability tend to have better emotional well-being than those with low heart rate variability, but the mechanisms of this association are not yet clear. In this paper, we propose the novel hypothesis that by inducing oscillatory activity in the brain, high amplitude oscillations in heart rate enhance functional connectivity in brain networks associated with emotion regulation. Recent studies using daily biofeedback sessions to increase the amplitude of heart rate oscillations suggest that high amplitude physiological oscillations have a causal impact on emotional well-being. Because blood flow timing helps determine brain network structure and function, slow oscillations in heart rate have the potential to strengthen brain network dynamics, especially in medial prefrontal regulatory regions that are particularly sensitive to physiological oscillations.

Inferring multi-scale neural mechanisms with brain network modelling

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

Neuroscientists can use various techniques to measure activity within the brain without opening up the skull. One of the most common is electroencephalography, or EEG for short. A net of electrodes is attached to the scalp and reveals the patterns of electrical activity occurring in brain tissue. But while EEG is good at revealing electrical activity across the surface of the scalp, it is less effective at linking the observed activity to specific locations in the brain.

Another widely used technique is functional magnetic resonance imaging, or fMRI. A patient, or healthy volunteer, lies inside a scanner containing a large magnet. The scanner tracks changes in the level of oxygen at different regions of the brain to provide a measure of how the activity of these regions changes over time. In contrast to EEG, fMRI is good at pinpointing the location of brain activity, but it is an indirect measure of brain activity as it depends on blood flow and several other factors. In terms of understanding how the brain works, EEG and fMRI thus provide different pieces of the puzzle. But there is no easy way to fit these pieces together.

Other areas of science have used computer models to merge different sources of data to obtain new insights into complex processes. Schirner et al. now adopt this approach to reveal the workings of the brain that underly signals like EEG and fMRI.

After recording structural MRI data from healthy volunteers, Schirner et al. built a computer model of each person’s brain. They then ran simulations with each individual model stimulating it with the person’s EEG to predict the fMRI activity of the same individual. Comparing these predictions with real fMRI data collected at the same time as the EEG confirmed that the predictions were accurate. Importantly, the brain models also displayed many features of neural activity that previously could only be measured by implanting electrodes into the brain.

This new approach provides a way of combining experimental data with theories about how the nervous system works. The resulting models can help generate and test ideas about the mechanisms underlying brain activity. Building models of different brains based on data from individual people could also help reveal the biological basis of differences between individuals. This could in turn provide insights into why some individuals are more vulnerable to certain brain diseases and open up new ways to treat these diseases.

Threat of shock increases excitability and connectivity of the intraparietal sulcus

Anxiety disorders affect approximately 1 in 5 (18%) Americans within a given 1 year period, placing a substantial burden on the national health care system. Therefore, there is a critical need to understand the neural mechanisms mediating anxiety symptoms. We used unbiased, multimodal, data-driven, whole-brain measures of neural activity (magnetoencephalography) and connectivity (fMRI) to identify the regions of the brain that contribute most prominently to sustained anxiety. We report that a single brain region, the intraparietal sulcus (IPS), shows both elevated neural activity and global brain connectivity during threat. The IPS plays a key role in attention orienting and may contribute to the hypervigilance that is a common symptom of pathological anxiety. Hyperactivation of this region during elevated state anxiety may account for the paradoxical facilitation of performance on tasks that require an external focus of attention, and impairment of performance on tasks that require an internal focus of attention.

DOI:http://dx.doi.org/10.7554/eLife.23608.001

Anxiety disorders affect around one in five Americans, and in many cases people experience anxiety so intensely that they have difficulties performing day-to-day activities. To help these people, it is important to understand how anxiety works. Current research suggests that anxiety disorders are caused when the connections in the brain that control our response to threat are either excessively or inappropriately activated. However, it was not clear what causes the anxiety to last for long periods. To better understand this phenomenon, Balderston et al. studied the brains of over 30 volunteers using two types of measurements called magnetoencephalography and fMRI.

In the each experiment, participants experienced periods of threat, where they could receive unpredictable electric shocks. In the first experiment, Balderston et al. measured the brain activity by recording the magnetic fields generated in the brain. In the second experiment, they used fMRI to record changes in the blood flow throughout the brain to measure how the different regions in the brain communicate.

The recordings identified a single part of the brain that increased its activity and changed its communication pattern with the other regions in the brain, when people are anxious. This region in a part of the brain called parietal lobe, is also important for processing attention, which suggests that anxiety might make people also more aware of their surroundings. However, this extra awareness might also make it more difficult for people to concentrate.

Future studies may be able to stimulate this area of the brain through the scalp to potentially reduce anxiety, as the affected area is close to the skull.

DOI:http://dx.doi.org/10.7554/eLife.23608.002

Dissociated patterns of anti-correlations with dorsal and ventral default-mode networks at rest

Previous studies of resting state functional connectivity have demonstrated that the default-mode network (DMN) is negatively correlated with a set of brain regions commonly activated during goal-directed tasks. However, the location and extent of anti-correlations are inconsistent across different studies, which has been posited to result largely from differences in whether or not global signal regression (GSR) was applied as a pre-processing step. Notably, coordinates of seed regions-of-interest defined within the posterior cingulate cortex (PCC)/precuneus, an area often employed to study functional connectivity of the DMN, have been inconsistent across studies. Taken together with recent observations that the DMN contains functionally heterogeneous subdivisions, it is presently unclear whether these seeds map to different DMN subnetworks, whose patterns of anti-correlation may differ. If so, then seed location may be a non-negligible factor that, in addition to differences in preprocessing steps, contributes to the inconsistencies reported among published studies regarding DMN correlations/anti-correlations. In this study, they examined anti-correlations of different subnetworks within the DMN during rest using both seed-based and point process analyses, and discovered that: (1) the ventral branch of the DMN (vDMN) yielded significantly weaker anti-correlations than that associated with the dorsal branch of the DMN (dDMN); (2) vDMN anti-correlations introduced by GSR were distinct from dDMN anti-correlations; (3) PCC/precuneus seeds employed by earlier studies mapped to different DMN subnetworks, which may explain some of the inconsistency (in addition to preprocessing steps) in the reported DMN anti-correlations. Hum Brain Mapp 38:2454-2465, 2017. © 2017 Wiley Periodicals, Inc.

Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses

Accurate characterization of the spatiotemporal relationship between two of the most prominent neuroimaging measures of neuronal activity, the 8-13Hz, occipito-parietal EEG alpha oscillation and the BOLD fMRI signal, must encompass the intrinsically dynamic nature of both alpha power and brain function. Here, during the eyes-open resting state, we use a 16s sliding-window analysis and demonstrate that the mean spatial network of dynamic alpha-BOLD correlations is highly comparable to the static network calculated over six minutes. However, alpha-BOLD correlations showed substantial spatiotemporal variability within-subjects and passed through many different configurations such that the static network was fully represented in only ~10% of 16s epochs, with visual and parietal regions (coherent on average) often opposingly correlated with each other or with alpha. We find that the common assumption of static-alpha BOLD correlations greatly oversimplifies temporal variation in brain network dynamics. Fluctuations in alpha-BOLD coupling significantly depended upon the instantaneous amplitude of alpha power, and primary and lateral visual areas were most strongly negatively correlated with alpha during different alpha power states, possibly suggesting the action of multiple alpha mechanisms. Dynamic alpha-BOLD correlations could not be explained by eye-blinks/movements, head motion or non-neuronal physiological variability. Individual's mean alpha power and frequency were found to contribute to between-subject variability in alpha-BOLD correlations. Additionally, application to a visual stimulation dataset showed that dynamic alpha-BOLD correlations provided functional information pertaining to the brain's response to stimulation by exhibiting spatiotemporal fluctuations related to variability in the trial-by-trial BOLD response magnitude. Significantly weaker visual alpha-BOLD correlations were found both preceding and following small amplitude BOLD response trials compared to large response trials.

Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects

Several strategies have been proposed to model and remove physiological noise from resting-state fMRI (rs-fMRI) data, particularly at ultrahigh fields (7 T), including contributions from respiratory volume (RV) and heart rate (HR) signal fluctuations. Recent studies suggest that these contributions are highly variable across subjects and that physiological noise correction may thus benefit from optimization at the subject or even voxel level. Here, we systematically investigated the impact of the degree of spatial specificity (group, subject, newly proposed cluster, and voxel levels) on the optimization of RV and HR models. For each degree of spatial specificity, we measured the fMRI signal variance explained (VE) by each model, as well as the functional connectivity underlying three well-known resting-state networks (RSNs) obtained from the fMRI data after removal of RV+HR contributions. Whole-brain, high-resolution rs-fMRI data were acquired from twelve healthy volunteers at 7 T, while simultaneously recording their cardiac and respiratory signals. Although VE increased with spatial specificity up to the voxel level, the accuracy of functional connectivity measurements improved only up to the cluster level, and subsequently decreased at the voxel level. This suggests that voxelwise modeling over-fits to local fluctuations with no physiological meaning. In conclusion, our results indicate that 7 T rs-fMRI connectivity measurements improve if a cluster-based physiological noise correction approach is employed in order to take into account the individual spatial variability in the HR and RV contributions.

Multimodal Imaging Evidence for a Frontoparietal Modulation of Visual Cortex during the Selective Processing of Conditioned Threat

Emotionally salient cues are detected more readily, remembered better, and evoke greater visual cortical responses compared with neutral stimuli. The current study used concurrent EEG-fMRI recordings to identify large-scale network interactions involved in the amplification of visual cortical activity when viewing aversively conditioned cues. To generate a continuous neural signal from pericalcarine visual cortex, we presented rhythmic (10/sec) phase-reversing gratings, the orientation of which predicted the presence (CS+) or absence (CS-) of a cutaneous electric shock (i.e., the unconditioned stimulus). The resulting single trial steady-state visual evoked potential (ssVEP) amplitude was regressed against the whole-brain BOLD signal, resulting in a measure of ssVEP-BOLD coupling. Across all trial types, ssVEP-BOLD coupling was observed in both primary and extended visual cortical regions, the rolandic operculum, as well as the thalamus and bilateral hippocampus. For CS+ relative to CS- trials during the conditioning phase, BOLD-alone analyses showed CS+ enhancement at the occipital pole, superior temporal sulci, and the anterior insula bilaterally, whereas ssVEP-BOLD coupling was greater in the pericalcarine cortex, inferior parietal cortex, and middle frontal gyrus. Dynamic causal modeling analyses supported connectivity models in which heightened activity in pericalcarine cortex for threat (CS+) arises from cortico-cortical top-down modulation, specifically from the middle frontal gyrus. No evidence was observed for selective pericalcarine modulation by deep cortical structures such as the amygdala or anterior insula, suggesting that the heightened engagement of pericalcarine cortex for threat stimuli is mediated by cortical structures that constitute key nodes of canonical attention networks.

Physiological noise correction using ECG-derived respiratory signals for enhanced mapping of spontaneous neuronal activity with simultaneous EEG-fMRI

The study of spontaneous brain activity based on BOLD-fMRI may be seriously compromised by the presence of signal fluctuations of non-neuronal origin, most prominently due to cardiac and respiratory mechanisms. Methods used for modeling and correction of the so-called physiological noise usually rely on the concurrent measurement of cardiac and respiratory signals. In simultaneous EEG-fMRI recordings, which are primarily aimed at the study of spontaneous brain activity, the electrocardiogram (ECG) is typically measured as part of the EEG setup but respiratory data are not generally available. Here, we propose to use the ECG-derived respiratory (EDR) signal estimated by Empirical Mode Decomposition (EMD) as a surrogate of the respiratory signal, for retrospective physiological noise correction of typical simultaneous EEG-fMRI data. A physiological noise model based on these physiological signals (P-PNM) complemented with fMRI-derived noise regressors was generated, and evaluated, for 17 simultaneous EEG-fMRI datasets acquired from a group of seven epilepsy patients imaged at 3T. The respiratory components of P-PNM were found to explain BOLD variance significantly in addition to the cardiac components, suggesting that the EDR signal was successfully extracted from the ECG, and P-PNM outperformed an image-based model (I-PNM) in terms of total BOLD variance explained. Further, the impact of the correction using P-PNM on fMRI mapping of patient-specific epileptic networks and the resting-state default mode network (DMN) was assessed in terms of sensitivity and specificity and, when compared with an ICA-based procedure and a standard pre-processing pipeline, P-PNM achieved the best performance. Overall, our results support the feasibility and utility of extracting physiological noise models of the BOLD signal resorting to ECG data exclusively, with substantial impact on the simultaneous EEG-fMRI mapping of resting-state networks, and, most importantly, epileptic networks where sensitivity and specificity are still limited.

EEG-fMRI Gradient Artifact Correction by Multiple Motion-Related Templates

OBJECTIVES

In simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), artifacts on the EEG arise from the switching of magnetic field gradients in the MR scanner. These artifacts depend on head position, and are, therefore, difficult to remove in the presence of subject motion. In this study, gradient artifacts are modeled by multiple templates extracted from externally recorded motion information.

METHODS

Gradient artifact correction was performed in EEG-fMRI recordings by estimating artifactual templates modulated by slowly varying splines, as well as head position information. The EEG signal quality was then compared following two common methods: averaged artifact subtraction (AAS) and optimal basis sets (OBS).

RESULTS

Artifact correction using multiple templates estimated from splines or motion time courses outperformed the existing AAS and OBS approaches, as quantified by root-mean-square power across gradient epochs. Improvements were mostly seen in posterior EEG channels, where most of the residual artifacts are seen following the AAS and OBS methods. Residual spectral power was comparable to that of EEG signals recorded without fMRI scanning.

CONCLUSION

Gradient artifacts can be well modeled by multiple templates estimated from head position information, resulting in an effective artifact removal.

SIGNIFICANCE

This method can facilitate EEG-fMRI of uncooperative subjects in whom motion is inevitable, for example, to investigate high-frequency EEG activity in which gradient artifacts are particularly prominent.

Brain-heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field

Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths (7 T and above) offers unique opportunities for studying the human brain with increased spatial resolution, contrast and sensitivity. However, its reliability can be compromised by factors such as head motion, image distortion and non-neural fluctuations of the functional MRI signal. The objective of this review is to provide a critical discussion of the advantages and trade-offs associated with UHF imaging, focusing on the application to studying brain-heart interactions. We describe how UHF MRI may provide contrast and resolution benefits for measuring neural activity of regions involved in the control and mediation of autonomic processes, and in delineating such regions based on anatomical MRI contrast. Limitations arising from confounding signals are discussed, including challenges with distinguishing non-neural physiological effects from the neural signals of interest that reflect cardiorespiratory function. We also consider how recently developed data analysis techniques may be applied to high-field imaging data to uncover novel information about brain-heart interactions.

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement

Simultaneous EEG-fMRI provides an increasingly attractive research tool to investigate cognitive processes with high temporal and spatial resolution. However, artifacts in EEG data introduced by the MR scanner still remain a major obstacle. This study, employing commonly used artifact correction steps, shows that head motion, one overlooked major source of artifacts in EEG-fMRI data, can cause plausible EEG effects and EEG-BOLD correlations. Specifically, low-frequency EEG (<20Hz) is strongly correlated with in-scanner movement. Accordingly, minor head motion (<0.2mm) induces spurious effects in a twofold manner: Small differences in task-correlated motion elicit spurious low-frequency effects, and, as motion concurrently influences fMRI data, EEG-BOLD correlations closely match motion-fMRI correlations. We demonstrate these effects in a memory encoding experiment showing that obtained theta power (~3-7Hz) effects and channel-level theta-BOLD correlations reflect motion in the scanner. These findings highlight an important caveat that needs to be addressed by future EEG-fMRI studies.

Global signal modulation of single-trial fMRI response variability: Effect on positive vs negative BOLD response relationship

In functional magnetic resonance imaging (fMRI), the relationship between positive BOLD responses (PBRs) and negative BOLD responses (NBRs) to stimulation is potentially informative about the balance of excitatory and inhibitory brain responses in sensory cortex. In this study, we performed three separate experiments delivering visual, motor or somatosensory stimulation unilaterally, to one side of the sensory field, to induce PBR and NBR in opposite brain hemispheres. We then assessed the relationship between the evoked amplitudes of contralateral PBR and ipsilateral NBR at the level of both single-trial and average responses. We measure single-trial PBR and NBR peak amplitudes from individual time-courses, and show that they were positively correlated in all experiments. In contrast, in the average response across trials the absolute magnitudes of both PBR and NBR increased with increasing stimulus intensity, resulting in a negative correlation between mean response amplitudes. Subsequent analysis showed that the amplitude of single-trial PBR was positively correlated with the BOLD response across all grey-matter voxels and was not specifically related to the ipsilateral sensory cortical response. We demonstrate that the global component of this single-trial response modulation could be fully explained by voxel-wise vascular reactivity, the BOLD signal standard deviation measured in a separate resting-state scan (resting state fluctuation amplitude, RSFA). However, bilateral positive correlation between PBR and NBR regions remained. We further report that modulations in the global brain fMRI signal cannot fully account for this positive PBR-NBR coupling and conclude that the local sensory network response reflects a combination of superimposed vascular and neuronal signals. More detailed quantification of physiological and noise contributions to the BOLD signal is required to fully understand the trial-by-trial PBR and NBR relationship compared with that of average responses.

Physical Activity Is Linked to Greater Moment-To-Moment Variability in Spontaneous Brain Activity in Older Adults

Higher cardiorespiratory fitness (CRF) and physical activity (PA) in old age are associated with greater brain structural and functional integrity, and higher cognitive functioning. However, it is not known how different aspects of lifestyle such as sedentariness, light PA (LI-PA), or moderate-to-vigorous physical activity (MV-PA) relate to neural activity in aging. In addition, it is not known whether the effects of PA on brain function differ or overlap with those of CRF. Here, we objectively measured CRF as oxygen consumption during a maximal exercise test and measured PA with an accelerometer worn for 7 days in 100 healthy but low active older adults (aged 60-80 years). We modeled the relationships between CRF, PA, and brain functional integrity using multivariate partial least squares analysis. As an index of functional brain integrity we used spontaneous moment-to-moment variability in the blood oxygenation level-dependent signal (SDBOLD), known to be associated with better cognitive functioning in aging. We found that older adults who engaged more in LI-PA and MV-PA had greater SDBOLD in brain regions that play a role in integrating segregated functional domains in the brain and benefit from greater CRF or PA, such as precuneus, hippocampus, medial and lateral prefrontal, and temporal cortices. Our results suggest that engaging in higher intensity PA may have protective effects on neural processing in aging. Finally, we demonstrated that older adults with greater overall WM microstructure were those showing more LI-PA and MV-PA and greater SDBOLD. We conclude that SDBOLD is a promising correlate of functional brain health in aging. Future analyses will evaluate whether SDBOLD is modifiable with interventions aimed to increase PA and CRF in older adults.

Network analysis of EEG related functional MRI changes due to medication withdrawal in focal epilepsy

Anti-epileptic drugs (AEDs) have a global effect on the neurophysiology of the brain which is most likely reflected in functional brain activity recorded with EEG and fMRI. These effects may cause substantial inter-subject variability in studies where EEG correlated functional MRI (EEG-fMRI) is used to determine the epileptogenic zone in patients who are candidate for epilepsy surgery. In the present study the effects on resting state fMRI are quantified in conditions with AED administration and after withdrawal of AEDs. EEG-fMRI data were obtained from 10 patients in the condition that the patient was on the steady-state maintenance doses of AEDs as prescribed (condition A) and after withdrawal of AEDs (condition B), at the end of a clinically standard pre-surgical long term video-EEG monitoring session. Resting state networks (RSN) were extracted from fMRI. The epileptic component (ICE) was identified by selecting the RSN component with the largest overlap with the EEG-fMRI correlation pattern. Changes in RSN functional connectivity between conditions A and B were quantified. EEG-fMRI correlation analysis was successful in 30% and 100% of the cases in conditions A and B, respectively. Spatial patterns of ICEs are comparable in conditions A and B, except for one patient for whom it was not possible to identify the ICE in condition A. However, the resting state functional connectivity is significantly increased in the condition after withdrawal of AEDs (condition B), which makes resting state fMRI potentially a new tool to study AED effects. The difference in sensitivity of EEG-fMRI in conditions A and B, which is not related to the number of epileptic EEG events occurring during scanning, could be related to the increased functional connectivity in condition B.

White matter integrity supports BOLD signal variability and cognitive performance in the aging human brain

Decline in cognitive performance in old age is linked to both suboptimal neural processing in grey matter (GM) and reduced integrity of white matter (WM), but the whole-brain structure-function-cognition associations remain poorly understood. Here we apply a novel measure of GM processing-moment-to-moment variability in the blood oxygenation level-dependent signal (SDBOLD)-to study the associations between GM function during resting state, performance on four main cognitive domains (i.e., fluid intelligence, perceptual speed, episodic memory, vocabulary), and WM microstructural integrity in 91 healthy older adults (aged 60-80 years). We modeled the relations between whole-GM SDBOLD with cognitive performance using multivariate partial least squares analysis. We found that greater SDBOLD was associated with better fluid abilities and memory. Most of regions showing behaviorally relevant SDBOLD (e.g., precuneus and insula) were localized to inter- or intra-network "hubs" that connect and integrate segregated functional domains in the brain. Our results suggest that optimal dynamic range of neural processing in hub regions may support cognitive operations that specifically rely on the most flexible neural processing and complex cross-talk between different brain networks. Finally, we demonstrated that older adults with greater WM integrity in all major WM tracts had also greater SDBOLD and better performance on tests of memory and fluid abilities. We conclude that SDBOLD is a promising functional neural correlate of individual differences in cognition in healthy older adults and is supported by overall WM integrity.

The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults

In functional magnetic resonance imaging (fMRI) research one is typically interested in neural activity. However, the blood‐oxygenation level‐dependent (BOLD) signal is a composite of both neural and vascular activity. As factors such as age or medication may alter vascular function, it is essential to account for changes in neurovascular coupling when investigating neurocognitive functioning with fMRI. The resting‐state fluctuation amplitude (RSFA) in the fMRI signal (rsfMRI) has been proposed as an index of vascular reactivity. The RSFA compares favourably with other techniques such as breath‐hold and hypercapnia, but the latter are more difficult to perform in some populations, such as older adults. The RSFA is therefore a candidate for use in adjusting for age‐related changes in vascular reactivity in fMRI studies. The use of RSFA is predicated on its sensitivity to vascular rather than neural factors; however, the extent to which each of these factors contributes to RSFA remains to be characterized. The present work addressed these issues by comparing RSFA (i.e., rsfMRI variability) to proxy measures of (i) cardiovascular function in terms of heart rate (HR) and heart rate variability (HRV) and (ii) neural activity in terms of resting state magnetoencephalography (rsMEG). We derived summary scores of RSFA, a sensorimotor task BOLD activation, cardiovascular function and rsMEG variability for 335 healthy older adults in the population‐based Cambridge Centre for Ageing and Neuroscience cohort (Cam‐CAN; http://www.cam-can.com). Mediation analysis revealed that the effects of ageing on RSFA were significantly mediated by vascular factors, but importantly not by the variability in neuronal activity. Furthermore, the converse effects of ageing on the rsMEG variability were not mediated by vascular factors. We then examined the effect of RSFA scaling of task‐based BOLD in the sensorimotor task. The scaling analysis revealed that much of the effects of age on task‐based activation studies with fMRI do not survive correction for changes in vascular reactivity, and are likely to have been overestimated in previous fMRI studies of ageing. The results from the mediation analysis demonstrate that RSFA is modulated by measures of vascular function and is not driven solely by changes in the variance of neural activity. Based on these findings we propose that the RSFA scaling method is articularly useful in large scale and longitudinal neuroimaging studies of ageing, or with frail participants, where alternative measures of vascular reactivity are impractical. Hum Brain Mapp 36:2248–2269, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

A reliable and time-saving semiautomatic spike-template-based analysis of interictal EEG-fMRI

OBJECTIVE

A prerequisite for the implementation of interictal electroencephalography-correlated functional magnetic resonance imaging (EEG-fMRI) in the presurgical work-up for epilepsy surgery is straightforward processing. We propose a new semi-automatic method as alternative for the challenging and time-consuming visual spike identification.

METHODS

Our method starts from a patient-specific spike-template, built by averaging spikes recorded on the EEG outside the scanner. Spatiotemporal cross-correlations between the template and the EEG measured during fMRI were calculated. To minimize false-positive detections, this time course of cross-correlations was binarized by means of a spike-template-specific threshold determined in healthy controls. To inform our model for statistical parametric mapping, this binarized regressor was convolved with the canonical hemodynamic response function. We validated our "template-based" method in 21 adult patients with refractory focal epilepsy with a well-defined epileptogenic zone and interictal spikes during EEG-fMRI. Sensitivity and specificity for detecting the epileptogenic zone were calculated and represented in receiver operating characteristic (ROC) curves. Our approach was compared with a previously proposed semiautomatic "topography-based" method that used the topographic amplitude distribution of spikes as a starting point for correlation-based fitting.

RESULTS

Good diagnostic performance could be reached with our template-based method. The optimal area under the ROC curve was 0.77. Diagnostic performance of the topography-based method was overall low.

SIGNIFICANCE

Our new template-based method is more standardized and time-saving than visual spike identification on intra-scanner EEG recordings, and preserves good diagnostic performance for detecting the epileptogenic zone.

Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: spatial specificity, test-retest reliability and effect of fMRI sampling rate

The blood oxygenation level dependent (BOLD) signal measures brain function indirectly through physiological processes and hence is susceptible to global physiological changes. Specifically, fluctuations in end-tidal CO2 (PETCO2), in addition to cardiac rate variation (CRV), and respiratory volume per time (RVT) variations, have been known to confound the resting-state fMRI (rs-fMRI) signal. Previous studies addressed the resting-state fMRI response function to CRV and RVT, but no attempt has been made to directly estimate the voxel-wise response function to PETCO2. Moreover, the potential interactions among PETCO2, CRV, and RVT necessitate their simultaneous inclusion in a multi-regression model to estimate the PETCO2 response. In this study, we use such a model to estimate the voxel-wise PETCO2 response functions directly from rs-fMRI data of nine healthy subjects. We also characterized the effect of sampling rate (TR=2seconds vs. 323ms) on the temporal and spatial variability of the PETCO2 response function in addition to that of CRV and RVT. In addition, we assess the test-retest reproducibility of the response functions to PETCO2, CRV and RVT. We found that despite overlaps across their spatial patterns, PETCO2 explains a unique portion of the rs-fMRI signal variance compared to RVT and CRV. We also found the shapes of the estimated responses are very similar between long- and short-TR data, although responses estimated from short-TR data have higher reproducibility.

Are Epilepsy-Related fMRI Components Dependent on the Presence of Interictal Epileptic Discharges in Scalp EEG?

Spatial independent component analysis (ICA) is increasingly being used to extract resting-state networks from fMRI data. Previous studies showed that ICA also reveals independent components (ICs) related to the seizure onset zone. However, it is currently unknown how these epileptic ICs depend on the presence of interictal epileptic discharges (IEDs) in the EEG. The goal of this study was to explore the relation between ICs obtained from fMRI epochs during the occurrence of IEDs in the EEG and those without IEDs. fMRI data sets with co-registered EEG were retrospectively selected of patients from whom the location of the epileptogenic zone was confirmed by outcome of surgery (n = 8). The fMRI data were split into two epochs: one with IEDs visible in scalp EEG and one without. Spatial ICA was applied to the fMRI data of each part separately. The maps of all resulting components were compared to the resection area and the EEG-fMRI correlation pattern by computing a spatial correlation coefficient to detect the epilepsy-related component. For all patients, except one, there was a remarkable resemblance between the epilepsy-related components selected during epochs with IEDs and those without IEDs. These findings suggest that epilepsy-related ICs are not dependent on the presence of IEDs in scalp EEG. Since these epileptic ICs showed partial overlap with resting-state networks of healthy volunteers (n = 10), our study supports the need for new ways to classify epileptic ICs.

Dynamic spectral indices of the electroencephalogram provide new insights into tonic pain

OBJECTIVE

This study aimed to investigate reliability of electroencephalography (EEG) during rest and tonic pain. Furthermore, changes in EEG between the two states as well as dynamics and relation to pain ratings were investigated.

METHODS

On two separate days EEG was recorded in 39 subjects during rest and tonic pain (cold pressor test: left hand held in 2°C water for 2 min.) while pain intensity was rated continuously. Dynamic spectral analysis was performed on the EEG. Between-day reliability of spectral indices was assessed and correlations to pain ratings were investigated.

RESULTS

EEG reliability was high during both states. The relative spectral indices increased in delta (1-4 Hz; P=0.0002), beta3 (18-32 Hz; P<0.0001) and gamma (32-70 Hz; P<0.0001) bands during tonic pain, and decreased in theta (4-8 Hz; P<0.0001), alpha1 (8-10 Hz; P<0.0001), alpha2 (10-12 Hz; P<0.0001) bands. Theta, beta3 and gamma bands correlated significantly to the area-under-curve of pain ratings, but only theta was dynamic and correlated to the pain ratings (R=0.88, P<0.0001).

CONCLUSIONS

EEG assessed during tonic pain is a valid experimental pain model both in terms of reliability between days and in connection between cortical activity and pain perception.

SIGNIFICANCE

EEG during tonic pain is more pain-specific and should be used in future basic and pharmacological studies.

The default mode network and EEG regional spectral power: a simultaneous fMRI-EEG study

Electroencephalography (EEG) frequencies have been linked to specific functions as an “electrophysiological signature” of a function. A combination of oscillatory rhythms has also been described for specific functions, with or without predominance of one specific frequency-band. In a simultaneous fMRI-EEG study at 3 T we studied the relationship between the default mode network (DMN) and the power of EEG frequency bands. As a methodological approach, we applied Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) and dual regression analysis for fMRI resting state data. EEG power for the alpha, beta, delta and theta-bands were extracted from the structures forming the DMN in a region-of-interest approach by applying Low Resolution Electromagnetic Tomography (LORETA). A strong link between the spontaneous BOLD response of the left parahippocampal gyrus and the delta-band extracted from the anterior cingulate cortex was found. A positive correlation between the beta-1 frequency power extracted from the posterior cingulate cortex (PCC) and the spontaneous BOLD response of the right supplementary motor cortex was also established. The beta-2 frequency power extracted from the PCC and the precuneus showed a positive correlation with the BOLD response of the right frontal cortex. Our results support the notion of beta-band activity governing the “status quo” in cognitive and motor setup. The highly significant correlation found between the delta power within the DMN and the parahippocampal gyrus is in line with the association of delta frequencies with memory processes. We assumed “ongoing activity” during “resting state” in bringing events from the past to the mind, in which the parahippocampal gyrus is a relevant structure. Our data demonstrate that spontaneous BOLD fluctuations within the DMN are associated with different EEG-bands and strengthen the conclusion that this network is characterized by a specific electrophysiological signature created by combination of different brain rhythms subserving different putative functions.

Fast eigenvector centrality mapping of voxel-wise connectivity in functional magnetic resonance imaging: implementation, validation, and interpretation

Eigenvector centrality mapping (ECM) has recently emerged as a measure to spatially characterize connectivity in functional brain imaging by attributing network properties to voxels. The main obstacle for widespread use of ECM in functional magnetic resonance imaging (fMRI) is the cost of computing and storing the connectivity matrix. This article presents fast ECM (fECM), an efficient algorithm to estimate voxel-wise eigenvector centralities from fMRI time series. Instead of explicitly storing the connectivity matrix, fECM computes matrix-vector products directly from the data, achieving high accelerations for computing voxel-wise centralities in fMRI at standard resolutions for multivariate analyses, and enabling high-resolution analyses performed on standard hardware. We demonstrate the validity of fECM at cluster and voxel levels, using synthetic and in vivo data. Results from synthetic data are compared to the theoretical gold standard, and local centrality changes in fMRI data are measured after experimental intervention. A simple scheme is presented to generate time series with prescribed covariances that represent a connectivity matrix. These time series are used to construct a 4D dataset whose volumes consist of separate regions with known intra- and inter-regional connectivities. The fECM method is tested and validated on these synthetic data. Resting-state fMRI data acquired after real-versus-sham repetitive transcranial magnetic stimulation show fECM connectivity changes in resting-state network regions. A comparison of analyses with and without accounting for motion parameters demonstrates a moderate effect of these parameters on the centrality estimates. Its computational speed and statistical sensitivity make fECM a good candidate for connectivity analyses of multimodality and high-resolution functional neuroimaging data.

EEG correlates of self-referential processing

Self-referential processing has been principally investigated using functional magnetic resonance imaging (fMRI). However, understanding of the brain functioning is not possible without careful comparison of the evidence coming from different methodological domains. This paper aims to review electroencephalographic (EEG) studies of self-referential processing and to evaluate how they correspond, complement, or contradict the existing fMRI evidence. There are potentially two approaches to the study of EEG correlates of self-referential processing. Firstly, because simultaneous registration of EEG and fMRI has become possible, the degree of overlap between these two signals in brain regions related to self-referential processing could be determined. Second and more direct approach would be the study of EEG correlates of self-referential processing per se. In this review, I discuss studies, which employed both these approaches and show that in line with fMRI evidence, EEG correlates of self-referential processing are most frequently found in brain regions overlapping with the default network, particularly in the medial prefrontal cortex. In the time domain, the discrimination of self- and others-related information is mostly associated with the P300 ERP component, but sometimes is observed even earlier. In the frequency domain, different frequency oscillations have been shown to contribute to self-referential processing, with spontaneous self-referential mentation being mostly associated with the alpha frequency band.

EEG-fMRI integration for the study of human brain function

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have proved to be extremely valuable tools for the non-invasive study of human brain function. Moreover, due to a notable degree of complementarity between the two modalities, the combination of EEG and fMRI data has been actively sought in the last two decades. Although initially focused on epilepsy, EEG-fMRI applications were rapidly extended to the study of healthy brain function, yielding new insights into its underlying mechanisms and pathways. Nevertheless, EEG and fMRI have markedly different spatial and temporal resolutions, and probe neuronal activity through distinct biophysical processes, many aspects of which are still poorly understood. The remarkable conceptual and methodological challenges associated with EEG-fMRI integration have motivated the development of a wide range of analysis approaches over the years, each relying on more or less restrictive assumptions, and aiming to shed further light on the mechanisms of brain function along with those of the EEG-fMRI coupling itself. Here, we present a review of the most relevant EEG-fMRI integration approaches yet proposed for the study of brain function, supported by a general overview of our current understanding of the biophysical mechanisms coupling the signals obtained from the two modalities.

PHYCAA+: an optimized, adaptive procedure for measuring and controlling physiological noise in BOLD fMRI

The presence of physiological noise in functional MRI can greatly limit the sensitivity and accuracy of BOLD signal measurements, and produce significant false positives. There are two main types of physiological confounds: (1) high-variance signal in non-neuronal tissues of the brain including vascular tracts, sinuses and ventricles, and (2) physiological noise components which extend into gray matter tissue. These physiological effects may also be partially coupled with stimuli (and thus the BOLD response). To address these issues, we have developed PHYCAA+, a significantly improved version of the PHYCAA algorithm (Churchill et al., 2011) that (1) down-weights the variance of voxels in probable non-neuronal tissue, and (2) identifies the multivariate physiological noise subspace in gray matter that is linked to non-neuronal tissue. This model estimates physiological noise directly from EPI data, without requiring external measures of heartbeat and respiration, or manual selection of physiological components. The PHYCAA+ model significantly improves the prediction accuracy and reproducibility of single-subject analyses, compared to PHYCAA and a number of commonly-used physiological correction algorithms. Individual subject denoising with PHYCAA+ is independently validated by showing that it consistently increased between-subject activation overlap, and minimized false-positive signal in non gray-matter loci. The results are demonstrated for both block and fast single-event task designs, applied to standard univariate and adaptive multivariate analysis models.

EEG correlates of time-varying BOLD functional connectivity

Recent resting-state fMRI studies have shown that the apparent functional connectivity (FC) between brain regions may undergo changes on time-scales of seconds to minutes, the basis and importance of which are largely unknown. Here, we examine the electrophysiological correlates of within-scan FC variations during a condition of eyes-closed rest. A sliding window analysis of simultaneous EEG-fMRI data was performed to examine whether temporal variations in coupling between three major networks (default mode; DMN, dorsal attention; DAN, and salience network; SN) are associated with temporal variations in mental state, as assessed from the amplitude of alpha and theta oscillations in the EEG. In our dataset, alpha power showed a significant inverse relationship with the strength of connectivity between DMN and DAN. In addition, alpha power covaried with the spatial extent of anticorrelation between DMN and DAN, with higher alpha power associated with larger anticorrelation extent. Results suggest an electrical signature of the time-varying FC between the DAN and DMN, potentially reflecting neural and state-dependent variations.

Correlated slow fluctuations in respiration, EEG, and BOLD fMRI

Low-frequency temporal fluctuations of physiological signals (<0.1 Hz), such as the respiration and cardiac pulse rate, occur naturally during rest and have been shown to be correlated with blood-oxygenation-level-dependent (BOLD) signal fluctuation. Such physiological signal modulations have been considered as sources of noise and their effects on BOLD signal are commonly removed in functional magnetic resonance imaging (fMRI) studies. However, possible neural correlates of the physiological fluctuations have not been considered nor examined in detail. In the present study we investigated this possibility by simultaneously acquiring electroencephalogram (EEG) with BOLD fMRI data, respiratory and cardiac waveforms in healthy human subjects at eyes-closed and eyes-open resting. We quantified the concurrent changes of the EEG power in the alpha frequency band, the respiration volume, and the cardiac pulse rate, then assessed the temporal correlations between alpha EEG power and physiological signal fluctuations. In addition, time-shifted time courses of alpha EEG power or physiological data were included as regressors to examine their correlations with the whole-brain BOLD fMRI signals. We observed a significant correlation between alpha EEG global field power and respiration, particularly at eyes-closed resting condition. Similar spatial patterns were observed between the correlation maps of BOLD with alpha EEG power and respiration, with negative correlations coinciding in the visual cortex, superior/middle temporal gyrus, inferior frontal gyrus, and inferior parietal lobule and positive correlations in the thalamus and caudate. Regressing out the physiological variations in the BOLD signal resulted in reduced correlation between BOLD and alpha EEG power. These results suggest a mutual link of neuronal origin between alpha EEG power, respiration, and BOLD signals.

Resting-state fMRI confounds and cleanup

The goal of resting-state functional magnetic resonance imaging (fMRI) is to investigate the brain's functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain "at rest" as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of fMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state fMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO₂ concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state fMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state fMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline.

Ballistocardiographic artifact removal from simultaneous EEG-fMRI using an optical motion-tracking system

The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allows the investigation of neuronal activity with high temporal and spatial resolution. While much progress has been made to overcome the multiple technical challenges associated with the recording of EEG inside the MR scanner, the ballistocardiographic (BCG) artifact, which is caused by cardiac-related motion inside the magnetic field, remains a major issue affecting EEG quality. The BCG is difficult to remove by standard average artifact subtraction (AAS) methods due to its variability across cardiac cycles. We thus investigate the possibility of directly recording the BCG motion using an optical motion-tracking system. In 5 subjects, the system is shown to accurately measure BCG motion. Regressing out linear and quadratic functions of the measured motion parameters resulted in a significant reduction (p<0.05) in root-mean-square (RMS) amplitudes across cardiac cycles compared to AAS. A further significant RMS reduction was obtained when applying the regression and AAS methods sequentially, resulting in RMS amplitudes that were not significantly different from those of EEG recorded outside the scanner, although with higher residual variability. The large contributions of pure translational parameters and of non-linear terms to the BCG waveforms indicate that non-rigid motion of the EEG wires (originating from rigid head motion) is likely an important cause of the artifact.

Coupling between resting cerebral perfusion and EEG

While several studies have investigated interactions between the electroencephalography (EEG) and functional magnetic resonance imaging BOLD signal fluctuations, less is known about the associations between EEG oscillations and baseline brain haemodynamics, and few studies have examined the link between EEG power outside the alpha band and baseline perfusion. Here we compare whole-brain arterial spin labelling perfusion MRI and EEG in a group of healthy adults (n = 16, ten females, median age: 27 years, range 21-48) during an eyes closed rest condition. Correlations emerged between perfusion and global average EEG power in low (delta: 2-4 Hz and theta: 4-7 Hz), middle (alpha: 8-13 Hz), and high (beta: 13-30 Hz and gamma: 30-45 Hz) frequency bands in both cortical and sub-cortical regions. The correlations were predominately positive in middle and high-frequency bands, and negative in delta. In addition, central alpha frequency positively correlated with perfusion in a network of brain regions associated with the modulation of attention and preparedness for external input, and central theta frequency correlated negatively with a widespread network of cortical regions. These results indicate that the coupling between average EEG power/frequency and local cerebral blood flow varies in a frequency specific manner. Our results are consistent with longstanding concepts that decreasing EEG frequencies which in general map onto decreasing levels of activation.