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With careful parameter selection, these methods can produce nearly identical results Quian Quiroga et al. Functional connectivity can be estimated through spectral coherence, phase synchronization, power correlations, Granger causality, partial directed coherence, cross-correlations, etc. Some labs write in-house code for processing and analyses. The analyses used in this research are not simple, nor are the methods standardized and widely used. New methods and ideas are continuously injected into the field. Ignoring zero-phase lag synchronizations may combat this issue, although there may be biologically relevant zero-phase lag synchronizations in the brain Konig et al.


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Finally, one can estimate the cortical generators via beamforming, minimum-norm estimates, or dipole modeling, and then perform analyses in source-space. This approach is also not without drawbacks, because there is no unique inverse solution for any given cortical topography, and different methods may yield different estimates of source activity. Of course, there are advantages and limitations to every methodological approach that one must consider when interpreting results.

Recording electrophysiological or electromagnetic activity is not a perfect measurement of neurocognitive function. Mixing in the temporal or spatial domain is a critical issue. Mixing refers to when multiple spatially overlapping populations contribute to the signal recorded at a single electrode.

An example of mixing in the temporal domain that cannot be recovered through time—frequency analyses is illustrated in Figure 5. Another extreme example of mixing is if two populations of pyramidal cells are equally simultaneously active, but aligned in opposing orientation e. In this case, their electrical fields will cancel and the researcher may be left with the misleading conclusion that no neural activity has occurred. Spatial filters such as current-source-density seem to be appropriate for obtaining relatively finer spatial resolution and linking inter-regional synchronization to cognitive processes Srinivasan et al.

Independent components analysis may recover some activity from mixed sources if those sources are temporally differentiable. Complex mixing from spatially overlapping and non-stationary sources may be less mathematically tractable to separate. Figure 5. Example of how mixing in the time-domain can affect the time—frequency representation. The activities of two spatially overlapping and similarly oriented neural networks left two columns , one generating a Hz rhythm and the other generating a 0.

At right is the time—frequency representation.

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Note that neither population on its own exhibits cross-frequency coupling. Oscillation baseline shifts and amplitude asymmetries have been described before and linked to cognitively relevant event-related potentials Nikulin et al. Another theoretical limitation is that EEG measures only neural populations that are tangentially aligned to the skull, whereas MEG measures neural populations that are radially aligned to the skull.

This is mostly a theoretical argument, however, because in practice many cognitive processes recruit areas of cortex that span gyri and sulci. There is a fine balance between, on the one hand, being driven by and constraining oneself to a priori hypotheses based on theory and previous research, and, on the other hand, being open to unexpected and unpredicted but robust patterns of results in the data.

Despite the limitations, examination of neural temporal dynamics has the potential to provide insight into human neurocognitive function beyond what is possible using approaches based on spatial localization e. Arguably, these limitations and considerations reinforce the idea that analyzing the rich temporal dynamics of neural activity bring us closer to the true complexity of brain function. Accepting that information in the brain is coded precisely in time but distributed in space does not necessarily imply that space is irrelevant for neural representations and computations.

Indeed, a logical consequent of this proposition is that space-based analyses should focus on distributed patterns rather than localization. Space and time may even have similar hierarchical computational organizational principles Kiebel et al.

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The best spatial resolution currently possible is a few cubic millimeters with high-resolution fMRI, although this resolution refers to the hemodynamic response, which may be spatially dispersed from its neural origin, following vascular features Disbrow et al. Nonetheless, to the extent that representations extend over space at the level of several millimeters or centimeters, fMRI seems to be a valid tool for uncovering sparse or distributed representations.

Spatial multivariate approaches that analyze patterns of activity over space voxels have sometimes proven more sensitive than standard approaches i. Relatively low spatial resolution techniques like EEG and MEG can also be used to examine distributed spatial patterns of electrical activity. For example, spatial multivariate patterns in EEG have been used to dissociate neural computations of magnitude from valence in a feedback-driven learning task Philiastides et al. Multivariate pattern analyses may also be informative across frequencies in one brain region.

For example, visual gradient orientation can be predicted from frequency multivariate patterns Duncan et al. Distributed, multivariate spatial analyses in fMRI vs. Following is a non-exhaustive list of important foci for future research on temporal coding and processing schemes in human neurocognitive function.

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The different features are power and phase within each frequency range, and all interactions they entail e. Within each frequency band, estimates of power and phase are independent of each other with the exception of zero power, in which case it is not possible to estimate phase, although in practice this is not often observed in real data at neurocognitively relevant frequencies. Sometimes, results obtained from power and from phase are convergent; other times, divergent. How do we interpret results from power, phase, phase-power coherence, phase—phase synchrony, etc.

It is tempting to speculate that the number of simultaneously active neurons drives power whereas the timing of the activity of those neurons drives phase. However, this is likely overly simplistic. For example, both power and phase information can be used to predict spike-timing Rasch et al. For human neuroscience, perhaps the best way to dissociate the roles of power and phase for neurocomputation may come from careful and clever experimental design in which different predictions are made for how measures of power vs.

Dynamic and oscillatory neural activity can be measured at a large range of spatial scales, from within a single neuron to populations of millions of neurons Varela et al. What is the appropriate spatial scale for neurocognitive function? Are different scales more appropriate for different cognitive functions? Are multi-spatial-scale interactions relevant for cognition? There have been few investigations into how electrophysiological measurements at different spatial scales are related to each other. Thus, surface EEG may reflect a complex mixture of spatiotemporal dynamics from widespread areas.

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Whether and to what extent the divergence between activities recorded from multiple spatial scales is meaningful for cognitive function deserves more empirical attention. Synchronous activity across widespread brain regions is believed to reflect functionally unified networks, such that physically separate neural ensembles are co-processing the same information or transferring information back and forth. Presumably, functional interactions — the nature and strength with which different nodes in a brain network communicate with each other — are shaped by the anatomical connections bridging those nodes.

But what aspects of functional connectivity are shaped by anatomical connectivity: The strength of connectivity?

Frequency range of synchronous interactions? Timing and phase delay? This question may be best addressed by linking EEG measures to white matter properties, measured through diffusion tensor imaging DTI; Johansen-Berg and Rushworth, , which takes advantage of the fact that the diffusion of water molecules in the brain is constrained by white matter fiber bundles. DTI data provides meaningful information about local white matter integrity and also the strength of tracts connecting different brain regions Johansen-Berg, For example, visual stimulus-evoked gamma oscillations are correlated across subjects with corpus callosum white matter integrity Zaehle and Herrmann, Similar findings have been observed with resting state EEG connectivity Teipel et al.

Establishing causation is critical to science. To date, much of the current work on the role of oscillations in human neurocognitive function has been correlative. Although this is a necessary initial step, once spatial—temporal-frequency characteristics of neurocognitive processes are characterized, oscillation dynamics should be experimentally manipulated, ideally without explicitly manipulating the cognitive process thought to rely on those dynamics.

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There are several tools for addressing issues of causality, including transcranial magnetic stimulation, which has been shown to transiently perturb ongoing oscillations Van Der Werf and Paus, that are dominant to each cortical region Rosanova et al. Pharmacological manipulations may also be useful, although pharmacological agents may have complex effects on several brain systems and functions, so it may be difficult to interpret such results solely in the context of oscillations.

Oscillations can also be exogenously manipulated through stimulus flicker: When a visual stimulus is flashed at a particular frequency like a strobe-light , regions of the brain that process that stimulus begin to oscillate at that frequency. Time—frequency decomposition is often used because of visually observable oscillations in EEG data, the link to animal research examining local field potential oscillations Buzsaki and Draguhn, , the fact that time—frequency methods are becoming increasingly common in the field, and the continuous advances in computing power, which facilitate analyses.

But is all or even most time-based information in the brain contained in oscillation dynamics? In fact, in time—frequency decomposition analyses such as wavelet convolution or Fourier transform, oscillations themselves in different frequency bands are not directly measured; instead, what is measured is the extent to which the time-domain signal correlates with wavelets or sine waves at specific frequency bands with specific windows in time. Because any time-domain signal can be represented as a sum of sine waves of different phases, frequencies, and amplitudes, non-oscillatory responses will be captured by time—frequency decomposition.

Yeung et al. Indeed, even in absence of sharp peaks in EEG power spectra over extended recording periods, frequency band-specific temporal dynamics are apparent He et al. Broadband activity also seems to be relevant for some aspects of sensory—motor functioning Onton and Makeig, ; Miller et al. One could argue that whether the neural dynamics are truly oscillatory is not important; rather, what is important is that a time—frequency approach to analyzing electrophysiology data may provide new insights into neurocognitive function beyond what could be learned from simple time-domain averaging.

However, whether the dynamics are truly oscillatory in nature might be relevant to linking human work to in vivo animal recordings, computational models, etc. There are other ways in which information can be encoded in time that are not necessarily oscillatory.


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If the brain uses time-based information coding and processing schemes, what is the nature of this information? Or does it reflect the passive transmission of information from one population of cells to another? The stomatogastric ganglion is a nucleus in the crustacean stomach that comprises approximately 30 neurons and controls the stomach and parts of the digestive system.