Electrophysiological low-frequency coherence and cross-frequency coupling contribute to BOLD connectivity

Subjective pain perception mediated by alpha rhythms

Suppression of spontaneous alpha oscillatory activities, interpreted as cortical excitability, was observed in response to both transient and tonic painful stimuli. The changes of alpha rhythms induced by pain could be modulated by painful sensory inputs, experimental tasks, and top-down cognitive regulations such as attention. The temporal and spatial characteristics, as well as neural functions of pain induced alpha responses, depend much on how these factors contribute to the observed alpha event-related desynchronization/synchronization (ERD/ERS). How sensory-, task-, and cognitive-related changes of alpha oscillatory activities interact in pain perception process is reviewed in the current study, and the following conclusions are made: (1) the functional inhibition hypothesis that has been proposed in auditory and visual modalities could be applied also in pain modality; (2) the neural functions of pain induced alpha ERD/ERS were highly dependent on the cortical regions where it is observed, e.g., somatosensory cortex alpha ERD/ERS in pain perception for painful stimulus processing; (3) the attention modulation of pain perception, i.e., influences on the sensory and affective dimensions of pain experience, could be mediated by changes of alpha rhythms. Finally, we propose a model regarding the determinants of pain related alpha oscillatory activity, i.e., sensory-discriminative, affective-motivational, and cognitive-modulative aspects of pain experience, would affect and determine pain related alpha oscillatory activities in an integrated way within the distributed alpha system.

Multimodal imaging of dynamic functional connectivity

The study of large-scale functional interactions in the human brain with functional magnetic resonance imaging (fMRI) extends almost to the first applications of this technology. Due to historical reasons and preconceptions about the limitations of this brain imaging method, most studies have focused on assessing connectivity over extended periods of time. It is now clear that fMRI can resolve the temporal dynamics of functional connectivity, like other faster imaging techniques such as electroencephalography and magnetoencephalography (albeit on a different temporal scale). However, the indirect nature of fMRI measurements can hinder the interpretability of the results. After briefly summarizing recent advances in the field, we discuss how the simultaneous combination of fMRI with electrophysiological activity measurements can contribute to a better understanding of dynamic functional connectivity in humans both during rest and task, wakefulness, and other brain states.

Oscillatory correlates of autobiographical memory

Recollection of events from one's own life is referred to as autobiographical memory. Autobiographical memory is an important part of our self. Neuroimaging findings link self-referential processes with the default mode network (DMN). Much evidence coming primarily from functional magnetic resonance imaging studies shows that autobiographical memory and DMN have a common neural base. In this study, electroencephalographic data collected in 47 participants during recollection of autobiographical episodes were analyzed using temporal and spatial independent component analyses in combination with source localization. Autobiographical remembering was associated with an increase of spectral power in alpha and beta and a decrease in delta band. The increase of alpha power, as estimated by sLORETA, was most prominent in the posterior DMN, but was also observed in visual and motor cortices, prompting an assumption that it is associated with activation of DMN and inhibition of irrelevant sensory and motor areas. In line with data linking delta oscillations with aversive states, decrease of delta power was more pronounced in episodes associated with positive emotions, whereas episodes associated with negative emotions were accompanied by an increase of delta power. Vividness of recollection correlated positively with theta oscillations. These results highlight the leading role of alpha oscillations and the DMN in the processes accompanying autobiographical remembering.

Multiple interacting brain areas underlie successful spatiotemporal memory retrieval in humans

Emerging evidence suggests that our memories for recent events depend on a dynamic interplay between multiple cortical brain regions, although previous research has also emphasized a primary role for the hippocampus in episodic memory. One challenge in determining the relative importance of interactions between multiple brain regions versus a specific brain region is a lack of analytic approaches to address this issue. Participants underwent neuroimaging while retrieving the spatial and temporal details of a recently experienced virtual reality environment; we then employed graph theory to analyze functional connectivity patterns across multiple lobes. Dense, large-scale increases in connectivity during successful memory retrieval typified network topology, with individual participant performance correlating positively with overall network density. Within this dense network, the hippocampus, prefrontal cortex, precuneus, and visual cortex served as “hubs” of high connectivity. Spatial and temporal retrieval were characterized by distinct but overlapping “subnetworks” with higher connectivity within posterior and anterior brain areas, respectively. Together, these findings provide new insight into the neural basis of episodic memory, suggesting that the interactions of multiple hubs characterize successful memory retrieval. Furthermore, distinct subnetworks represent components of spatial versus temporal retrieval, with the hippocampus acting as a hub integrating information between these two subnetworks.

Resting-state hippocampal connectivity correlates with symptom severity in post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is a serious mental health injury which can manifest after experiencing a traumatic life event. The disorder is characterized by symptoms of re-experiencing, avoidance, emotional numbing and hyper-arousal. Whilst its aetiology and resultant symptomology are better understood, relatively little is known about the underlying cortical pathophysiology, and in particular whether changes in functional connectivity may be linked to the disorder. Here, we used non-invasive neuroimaging with magnetoencephalography to examine functional connectivity in a resting-state protocol in the combat-related PTSD group (n = 23), and a military control group (n = 21). We identify atypical long-range hyperconnectivity in the high-gamma-band resting-state networks in a combat-related PTSD population compared to soldiers who underwent comparable environmental exposure but did not develop PTSD. Using graph analysis, we demonstrate that apparent network connectivity of relevant brain regions is associated with cognitive-behavioural outcomes. We also show that left hippocampal connectivity in the PTSD group correlates with scores on the well-established PTSD Checklist (PCL). These findings indicate that atypical synchronous neural interactions may underlie the psychological symptoms of PTSD, whilst also having utility as a potential biomarker to aid in the diagnosis and monitoring of the disorder.

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Soldiers with PTSD display increased connectivity in high gamma resting state.

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Left frontal, temporal and hippocampus regions show hyperconnectivity in PTSD.

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Emotionally-salient stimuli induced increased connectivity in soldiers without PTSD.

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Connectivity strength in left hippocampus correlates with PTSD symptom severity.

Simultaneous EEG and fMRI: towards the characterization of structure and dynamics of brain networks

Progress in the understanding of normal and disturbed brain function is critically dependent on the methodological approach that is applied. Both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are extremely efficient methods for the assessment of human brain function. The specific appeal of the combination is related to the fact that both methods are complementary in terms of basic aspects: EEG is a direct measurement of neural mass activity and provides high temporal resolution. FMRI is an indirect measurement of neural activity and based on hemodynamic changes, and offers high spatial resolution. Both methods are very sensitive to changes of synaptic activity, suggesting that with simultaneous EEG and fMRI the same neural events can be characterized with both high temporal and spatial resolution. Since neural oscillations that can be assessed with EEG are a key mechanism for multi-site communication in the brain, EEG-fMRI can offer new insights into the connectivity mechanisms of brain networks.

Robust Long-Range Coordination of Spontaneous Neural Activity in Waking, Sleep and Anesthesia

Although the emerging field of functional connectomics relies increasingly on the analysis of spontaneous fMRI signal covariation to infer the spatial fingerprint of the brain's large-scale functional networks, the nature of the underlying neuro-electrical activity remains incompletely understood. In part, this lack in understanding owes to the invasiveness of electrophysiological acquisition, the difficulty in their simultaneous recording over large cortical areas, and the absence of fully established methods for unbiased extraction of network information from these data. Here, we demonstrate a novel, data-driven approach to analyze spontaneous signal variations in electrocorticographic (ECoG) recordings from nearly entire hemispheres of macaque monkeys. Based on both broadband analysis and analysis of specific frequency bands, the ECoG signals were found to co-vary in patterns that resembled the fMRI networks reported in previous studies. The extracted patterns were robust against changes in consciousness associated with sleep and anesthesia, despite profound changes in intrinsic characteristics of the raw signals, including their spectral signatures. These results suggest that the spatial organization of large-scale brain networks results from neural activity with a broadband spectral feature and is a core aspect of the brain's physiology that does not depend on the state of consciousness.

Attention improves transfer of motion information between V1 and MT

Selective attention modulates activity within individual visual areas; however, the role of attention in mediating the transfer of information between areas is not well understood. Here, we used fMRI to assess attention-related changes in coupled BOLD activation in two key areas of human visual cortex that are involved in motion processing: V1 and MT. To examine attention-related changes in cross-area coupling, multivoxel patterns in each visual area were decomposed to estimate the trial-by-trial response amplitude in a set of direction-selective "channels." In both V1 and MT, BOLD responses increase in direction-selective channels tuned to the attended direction of motion and decrease in channels tuned away from the attended direction. Furthermore, the modulation of cross-area correlations between similarly tuned populations is inversely related to the modulation of their mean responses, an observation that can be explained via a feedforward motion computation in MT and a modulation of local noise correlations in V1. More importantly, these modulations accompany an increase in the cross-area mutual information between direction-selective response patterns in V1 and MT, suggesting that attention improves the transfer of sensory information between cortical areas that cooperate to support perception. Finally, our model suggests that divisive normalization of neural activity in V1 before its integration by MT is critical to cross-area information coupling, both in terms of cross-area correlation as well as cross-area mutual information.

Frequency specificity of regional homogeneity in the resting-state human brain

Resting state-fMRI studies have found that the inter-areal correlations in cortical networks concentrate within ultra-low frequencies (0.01–0.04 Hz) while long-distance connections within subcortical networks distribute over a wider frequency range (0.01–0.14 Hz). However, the frequency characteristics of regional homogeneity (ReHo) in different areas are still unclear. To examine the ReHo properties in different frequency bands, a data-driven method, Empirical Mode Decomposition (EMD), was adopted to decompose the time series of each voxel into several components with distinct frequency bands. ReHo values in each of the components were then calculated. Our results showed that ReHo in cortical areas were higher and more frequency-dependent than those in the subcortical regions. BOLD oscillations of 0.02–0.04 Hz mainly contributed to the cortical ReHo, whereas the ReHo in limbic areas involved a wider frequency range and were dominated by higher-frequency BOLD oscillations (>0.08 Hz). The frequency characteristics of ReHo are distinct between different parts of the striatum, with the frequency band of 0.04–0.1 Hz contributing the most to ReHo in caudate nucleus, and oscillations lower than 0.02 Hz contributing more to ReHo in putamen. The distinct frequency-specific ReHo properties of different brain areas may arise from the assorted cytoarchitecture or synaptic types in these areas. Our work may advance the understanding of the neural-physiological basis of local BOLD activities and the functional specificity of different brain regions.

Resting state low-frequency fluctuations in prefrontal cortex reflect degrees of harm avoidance and novelty seeking: an exploratory NIRS study

Harm avoidance (HA) and novelty seeking (NS) are temperament dimensions defined by Temperament and Character Inventory (TCI), respectively, reflecting a heritable bias for intense response to aversive stimuli or for excitement in response to novel stimuli. High HA is regarded as a risk factor for major depressive disorder and anxiety disorder. In contrast, higher NS is linked to increased risk for substance abuse and pathological gambling disorder. A growing body of evidence suggests that patients with these disorders show abnormality in the power of slow oscillations of resting-state brain activity. It is particularly interesting that previous studies have demonstrated that resting state activities in medial prefrontal cortex (MPFC) are associated with HA or NS scores, although the relation between the power of resting state slow oscillations and these temperament dimensions remains poorly elucidated. This preliminary study investigated the biological bases of these temperament traits by particularly addressing the resting state low-frequency fluctuations in MPFC. Regional hemodynamic changes in channels covering MPFC during 5-min resting states were measured from 22 healthy participants using near-infrared spectroscopy (NIRS). These data were used for correlation analyses. Results show that the power of slow oscillations during resting state around the dorsal part of MPFC is negatively correlated with the HA score. In contrast, NS was positively correlated with the power of resting state slow oscillations around the ventral part of MPFC. These results suggest that the powers of slow oscillation at rest in dorsal or ventral MPFC, respectively, reflect the degrees of HA and NS. This exploratory study therefore uncovers novel neural bases of HA and NS. We discuss a neural mechanism underlying aversion-related and reward-related processing based on results obtained from this study.

Functional interactions as big data in the human brain

Noninvasive studies of human brain function hold great potential to unlock mysteries of the human mind. The complexity of data generated by such studies, however, has prompted various simplifying assumptions during analysis. Although this has enabled considerable progress, our current understanding is partly contingent upon these assumptions. An emerging approach embraces the complexity, accounting for the fact that neural representations are widely distributed, neural processes involve interactions between regions, interactions vary by cognitive state, and the space of interactions is massive. Because what you see depends on how you look, such unbiased approaches provide the greatest flexibility for discovery.

Resting state functional connectivity: its physiological basis and application in neuropharmacology

Brain structures do not work in isolation; they work in concert to produce sensory perception, motivation and behavior. Systems-level network activity can be investigated by resting state magnetic resonance imaging (rsMRI), an emerging neuroimaging technique that assesses the synchrony of the brain's ongoing spontaneous activity. Converging evidence reveals that rsMRI is able to consistently identify distinct spatiotemporal patterns of large-scale brain networks. Dysregulation within and between these networks has been implicated in a number of neurodegenerative and neuropsychiatric disorders, including Alzheimer's disease and drug addiction. Despite wide application of this approach in systems neuroscience, the physiological basis of these fluctuations remains incompletely understood. Here we review physiological studies in electrical, metabolic and hemodynamic fluctuations that are most pertinent to the rsMRI signal. We also review recent applications to neuropharmacology - specifically drug effects on resting state fluctuations. We speculate that the mechanisms governing spontaneous fluctuations in regional oxygenation availability likely give rise to the observed rsMRI signal. We conclude by identifying several open questions surrounding this technique. This article is part of the Special Issue Section entitled 'Neuroimaging in Neuropharmacology'.

Brain resting-state functional MRI connectivity: morphological foundation and plasticity

Despite the immense ongoing efforts to map brain functional connections and organizations with resting-state functional MRI (rsfMRI), the mechanisms governing the temporally coherent rsfMRI signals remain unclear. In particular, there is a lack of direct evidence regarding the morphological foundation and plasticity of these rsfMRI derived connections. In this study, we investigated the role of axonal projections in rsfMRI connectivity and its plasticity. Well-controlled rodent models of complete and posterior corpus callosotomy were longitudinally examined with rsfMRI at 7T in conjunction with intracortical EEG recording and functional MRI tracing of interhemispheric neuronal pathways by manganese (Mn(2+)). At post-callosotomy day 7, significantly decreased interhemispheric rsfMRI connectivity was observed in both groups in the specific cortical areas whose callosal connections were severed. At day 28, the disrupted connectivity was restored in the partial callosotomy group but not in the complete callosotomy group, likely due to the compensation that occurred through the remaining interhemispheric axonal pathways. This restoration - along with the increased intrahemispheric functional connectivity observed in both groups at day 28 - highlights the remarkable adaptation and plasticity in brain rsfMRI connections. These rsfMRI findings were paralleled by the intracortical EEG recording and Mn(2+) tracing results. Taken together, our experimental results directly demonstrate that axonal connections are the indispensable foundation for rsfMRI connectivity and that such functional connectivity can be plastic and dynamically reorganized atop the morphological connections.

Multifaceted roles for low-frequency oscillations in bottom-up and top-down processing during navigation and memory

A prominent and replicated finding is the correlation between running speed and increases in low-frequency oscillatory activity in the hippocampal local field potential. A more recent finding concerns low-frequency oscillations that increase in coherence between the hippocampus and neocortical brain areas such as prefrontal cortex during memory-related behaviors (i.e., remembering the correct location to visit). In this review, we tie together movement-related and memory-related low-frequency oscillations in the rodent with similar findings in humans. We argue that although movement-related low-frequency oscillations, in particular, may have slightly different characteristics in humans than rodents, placing important constraints on our thinking about this issue, both phenomena have similar functional foundations. We review four prominent theoretical models that provide partially conflicting accounts of movement-related low-frequency oscillations. We attempt to tie together these theoretical proposals, and existing data in rodents and humans, with memory-related low-frequency oscillations. We propose that movement-related low-frequency oscillations and memory-related low-frequency oscillatory activity, both of which show significant coherence with oscillations in other brain regions, represent different facets of "spectral fingerprints," or different resonant frequencies within the same brain networks underlying different cognitive processes. Together, movement-related and memory-related low-frequency oscillatory coupling may be linked by their distinct contributions to bottom-up, sensorimotor driven processing and top-down, controlled processing characterizing aspects of memory encoding and retrieval.

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.

Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure

Resting state networks (RSNs) are sets of brain regions exhibiting temporally coherent activity fluctuations in the absence of imposed task structure. RSNs have been extensively studied with fMRI in the infra-slow frequency range (nominally <10(-1)Hz). The topography of fMRI RSNs reflects stationary temporal correlation over minutes. However, neuronal communication occurs on a much faster time scale, at frequencies nominally in the range of 10(0)-10(2)Hz. We examined phase-shifted interactions in the delta (2-3.5 Hz), theta (4-7 Hz), alpha (8-12 Hz) and beta (13-30 Hz) frequency bands of resting-state source space MEG signals. These analyses were conducted between nodes of the dorsal attention network (DAN), one of the most robust RSNs, and between the DAN and other networks. Phase shifted interactions were mapped by the multivariate interaction measure (MIM), a measure of true interaction constructed from the maximization of imaginary coherency in the virtual channels comprised of voxel signals in source space. Non-zero-phase interactions occurred between homologous left and right hemisphere regions of the DAN in the delta and alpha frequency bands. Even stronger non-zero-phase interactions were detected between networks. Visual regions bilaterally showed phase-shifted interactions in the alpha band with regions of the DAN. Bilateral somatomotor regions interacted with DAN nodes in the beta band. These results demonstrate the existence of consistent, frequency specific phase-shifted interactions on a millisecond time scale between cortical regions within RSN as well as across RSNs.

The θ-γ neural code

Theta and gamma frequency oscillations occur in the same brain regions and interact with each other, a process called cross-frequency coupling. Here, we review evidence for the following hypothesis: that the dual oscillations form a code for representing multiple items in an ordered way. This form of coding has been most clearly demonstrated in the hippocampus, where different spatial information is represented in different gamma subcycles of a theta cycle. Other experiments have tested the functional importance of oscillations and their coupling. These involve correlation of oscillatory properties with memory states, correlation with memory performance, and effects of disrupting oscillations on memory. Recent work suggests that this coding scheme coordinates communication between brain regions and is involved in sensory as well as memory processes.

Patterns of Cortical Oscillations Organize Neural Activity into Whole-Brain Functional Networks Evident in the fMRI BOLD Signal

Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG/MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks.