Thilo Womelsdorf

Modulation of Neuronal Interactions Through Neuronal Synchronization

Brain processing depends on the interactions between neuronal groups. Those interactions are governed by the pattern of anatomical connections and by yet unknown mechanisms that modulate the effective strength of a given connection. We found that the mutual influence among neuronal groups depends on the phase relation between rhythmic activities within the groups. Phase relations supporting interactions between the groups preceded those interactions by a few milliseconds, consistent with a mechanistic role. These effects were specific in time, frequency, and space, and we therefore propose that the pattern of synchronization flexibly determines the pattern of neuronal interactions.

Dynamic functional connectivity: promise, issues, and interpretations.

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.

Resting-State Networks Show Dynamic Functional Connectivity in Awake Humans and Anesthetized Macaques

Characterization of large‐scale brain networks using blood‐oxygenation‐level‐dependent functional magnetic resonance imaging is typically based on the assumption of network stationarity across the duration of scan. Recent studies in humans have questioned this assumption by showing that within‐network functional connectivity fluctuates on the order of seconds to minutes. Time‐varying profiles of resting‐state networks (RSNs) may relate to spontaneously shifting, electrophysiological network states and are thus mechanistically of particular importance. However, because these studies acquired data from awake subjects, the fluctuating connectivity could reflect various forms of conscious brain processing such as passive mind wandering, active monitoring, memory formation, or changes in attention and arousal during image acquisition. Here, we characterize RSN dynamics of anesthetized macaques that control for these accounts, and compare them to awake human subjects. We find that functional connectivity among nodes comprising the “oculomotor (OCM) network” strongly fluctuated over time during awake as well as anaesthetized states. For time dependent analysis with short windows (<60 s), periods of positive functional correlations alternated with prominent anticorrelations that were missed when assessed with longer time windows. Similarly, the analysis identified network nodes that transiently link to the OCM network and did not emerge in average RSN analysis. Furthermore, time‐dependent analysis reliably revealed transient states of large‐scale synchronization that spanned all seeds. The results illustrate that resting‐state functional connectivity is not static and that RSNs can exhibit nonstationary, spontaneous relationships irrespective of conscious, cognitive processing. The findings imply that mechanistically important network information can be missed when using average functional connectivity as the single network measure. Hum Brain Mapp 34:2154–2177, 2013.

Attentional stimulus selection through selective synchronization between monkey visual areas.

A central motif in neuronal networks is convergence, linking several input neurons to one target neuron. In visual cortex, convergence renders target neurons responsive to complex stimuli. Yet, convergence typically sends multiple stimuli to a target, and the behaviorally relevant stimulus must be selected. We used two stimuli, activating separate electrocorticographic V1 sites, and both activating an electrocorticographic V4 site equally strongly. When one of those stimuli activated one V1 site, it gamma synchronized (60-80 Hz) to V4. When the two stimuli activated two V1 sites, primarily the relevant one gamma synchronized to V4. Frequency bands of gamma activities showed substantial overlap containing the band of interareal coherence. The relevant V1 site had its gamma peak frequency 2-3 Hz higher than the irrelevant V1 site and 4-6 Hz higher than V4. Gamma-mediated interareal influences were predominantly directed from V1 to V4. We propose that selective synchronization renders relevant input effective, thereby modulating effective connectivity.

The role of neuronal synchronization in selective attention

Attention selectively enhances the influence of neuronal responses conveying information about relevant sensory attributes. Accumulating evidence suggests that this selective neuronal modulation relies on rhythmic synchronization at local and long-range spatial scales: attention selectively synchronizes the rhythmic responses of those neurons that are tuned to the spatial and featural attributes of the attended sensory input. The strength of synchronization is thereby functionally related to perceptual accuracy and behavioural efficiency. Complementing this synchronization at a local level, attention has recently been demonstrated to regulate which locally synchronized neuronal groups phase-synchronize their rhythmic activity across long-range connections. These results point to a general computational role for selective synchronization in dynamically controlling which neurons communicate information about sensory inputs effectively.

Dynamic shifts of visual receptive fields in cortical area MT by spatial attention

Voluntary attention is the top-down selection process that focuses cortical processing resources on the most relevant sensory information. Spatial attention—that is, selection based on stimulus position—alters neuronal responsiveness throughout primate visual cortex. It has been hypothesized that it also changes receptive field profiles by shifting their centers toward attended locations and by shrinking them around attended stimuli. Here we examined, at high resolution, receptive fields in cortical area MT of rhesus macaque monkeys when their attention was directed to different locations within and outside these receptive fields. We found a shift of receptive fields, even far from the current location of attention, accompanied by a small amount of shrinkage. Thus, already in early extrastriate cortex, receptive fields are not static entities but are highly modifiable, enabling the dynamic allocation of processing resources to attended locations and supporting enhanced perception within the focus of attention by effectively increasing the local cortical magnification.

The pairwise phase consistency: A bias-free measure of rhythmic neuronal synchronization

Oscillatory activity is a widespread phenomenon in nervous systems and has been implicated in numerous functions. Signals that are generated by two separate neuronal sources often demonstrate a consistent phase-relationship in a particular frequency-band, i.e., they demonstrate rhythmic neuronal synchronization. This consistency is conventionally measured by the PLV (phase-locking value) or the spectral coherence measure. Both statistical measures suffer from significant bias, in that their sample estimates overestimate the population statistics for finite sample sizes. This is a significant problem in the neurosciences where statistical comparisons are often made between conditions with a different number of trials or between neurons with a different number of spikes. We introduce a new circular statistic, the PPC (pairwise phase consistency). We demonstrate that the sample estimate of the PPC is a bias-free and consistent estimator of its corresponding population parameter. We show, both analytically and by means of numerical simulations, that the population statistic of the PPC is equivalent to the population statistic of the squared PLV. The variance and mean squared error of the PPC and PLV are compared. Finally, we demonstrate the practical relevance of the method in actual neuronal data recorded from the orbitofrontal cortex of rats that engage in a two-odour discrimination task. We find a strong increase in rhythmic synchronization of spikes relative to the local field potential (as measured by the PPC) for a wide range of low frequencies (including the theta-band) during the anticipation of sucrose delivery in comparison to the anticipation of quinine delivery.

A Microsaccadic Rhythm Modulates Gamma-Band Synchronization and Behavior

Rhythms occur both in neuronal activity and in behavior. Behavioral rhythms abound at frequencies at or below 10 Hz. Neuronal rhythms cover a very wide frequency range, and the phase of neuronal low-frequency rhythms often rhythmically modulates the strength of higher-frequency rhythms, particularly of gamma-band synchronization (GBS). Here, we study stimulus-induced GBS in awake monkey areas V1 and V4 in relation to a specific form of spontaneous behavior, namely microsaccades (MSs), small fixational eye movements. We found that MSs occur rhythmically at a frequency of ∼3.3 Hz. The rhythmic MSs were predicted by the phase of the 3.3 Hz rhythm in V1 and V4 local field potentials. In turn, the MSs modulated both visually induced GBS and the speed of visually triggered behavioral responses. Fast/slow responses were preceded by a specific temporal pattern of MSs. These MS patterns induced perturbations in GBS that in turn explained variability in behavioral response speed. We hypothesize that the 3.3 Hz rhythm structures the sampling and exploration of the environment through building and breaking neuronal ensembles synchronized in the gamma-frequency band to process sensory stimuli.

Dynamic circuit motifs underlying rhythmic gain control, gating and integration

Brain circuitry processes information by rapidly and selectively engaging functional neuronal networks. The dynamic formation of networks is often evident in rhythmically synchronized neuronal activity and tightly correlates with perceptual, cognitive and motor performances. But how synchronized neuronal activity contributes to network formation and how it relates to the computation of behaviorally relevant information has remained difficult to discern. Here we structure recent empirical advances that link synchronized activity to the activation of so-called dynamic circuit motifs. These motifs explicitly relate (1) synaptic and cellular properties of circuits to (2) identified timescales of rhythmic activation and to (3) canonical circuit computations implemented by rhythmically synchronized circuits. We survey the ubiquitous evidence of specific cell and circuit properties underlying synchronized activity across theta, alpha, beta and gamma frequency bands and show that their activation likely implements gain control, context-dependent gating and state-specific integration of synaptic inputs. This evidence gives rise to the dynamic circuit motifs hypothesis of synchronized activation states, with its core assertion that activation states are linked to uniquely identifiable local circuit structures that are recruited during the formation of functional networks to perform specific computational operations.

Theta-Activity in anterior cingulate cortex predicts task rules and their adjustments following errors

Accomplishing even simple tasks depend on neuronal circuits to configure how incoming sensory stimuli map onto responses. Controlling these stimulus-response (SR) mapping rules relies on a cognitive control network comprising the anterior cingulate cortex (ACC). Single neurons within the ACC convey information about currently relevant SR mapping rules and signal unexpected action outcomes, which can be used to optimize behavioral choices. However, its functional significance and the mechanistic means of interaction with other nodes of the cognitive control network remain elusive and poorly understood. Here, we report that core aspects of cognitive control are encoded by rhythmic theta-band activity within neuronal circuits in the ACC. Throughout task performance, theta-activity predicted which of two SR mapping rules will be established before processing visual target information. Task-selective theta-activity emerged particularly early during those trials, which required the adjustment of SR rules following an erroneous rule representation in the preceding trial. These findings demonstrate a functional correlation of cognitive control processes and oscillatory theta-band activity in macaque ACC. Moreover, we report that spike output of a subset of cells in ACC is synchronized to predictive theta-activity, suggesting that the theta-cycle could serve as a temporal reference for coordinating local task selective computations across a larger network of frontal areas and the hippocampus to optimize and adjust the processing routes of sensory and motor circuits to achieve efficient sensory-motor control.