Can pathway-specific LFPs be obtained in cytoarchitectonically complex structures?

Low frequency independent components: Internal neuromarkers linking cortical LFPs to behavior

Local field potentials (LFPs) in the primate motor cortex have been shown to reflect information related to volitional movements. However, LFPs are composite signals that receive contributions from multiple neural sources, producing a complex mix of component signals. Using a blind source separation approach, we examined the components of neural activity recorded using multielectrode arrays in motor areas of macaque monkeys during a grasping and lifting task. We found a set of independent components in the low-frequency LFP with high temporal and spatial consistency associated with each task stage. We observed that ICs often arise from electrodes distributed across multiple cortical areas and provide complementary information to external behavioral markers, specifically in task stage detection and trial alignment. Taken together, our results show that it is possible to separate useful independent components of the LFP associated with specific task-related events, potentially representing internal markers of transition between cortical network states.

Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent

Field potential (FP) recording is an accessible means to capture the shifts in the activity of neuron populations. However, the spatial and composite nature of these signals has largely been ignored, at least until it became technically possible to separate activities from co-activated sources in different structures or those that overlap in a volume. The pathway-specificity of mesoscopic sources has provided an anatomical reference that facilitates transcending from theoretical analysis to the exploration of real brain structures. We review computational and experimental findings that indicate how prioritizing the spatial geometry and density of sources, as opposed to the distance to the recording site, better defines the amplitudes and spatial reach of FPs. The role of geometry is enhanced by considering that zones of the active populations that act as sources or sinks of current may arrange differently with respect to each other, and have different geometry and densities. Thus, observations that seem counterintuitive in the scheme of distance-based logic alone can now be explained. For example, geometric factors explain why some structures produce FPs and others do not, why different FP motifs generated in the same structure extend far while others remain local, why factors like the size of an active population or the strong synchronicity of its neurons may fail to affect FPs, or why the rate of FP decay varies in different directions. These considerations are exemplified in large structures like the cortex and hippocampus, in which the role of geometrical elements and regional activation in shaping well-known FP oscillations generally go unnoticed. Discovering the geometry of the sources in play will decrease the risk of population or pathway misassignments based solely on the FP amplitude or temporal pattern.

Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials

We propose an evolutionary state space model (E-SSM) for analyzing high dimensional brain signals whose statistical properties evolve over the course of a non-spatial memory experiment. Under E-SSM, brain signals are modeled as mixtures of components (e.g., AR(2) process) with oscillatory activity at pre-defined frequency bands. To account for the potential non-stationarity of these components (since the brain responses could vary throughout the entire experiment), the parameters are allowed to vary over epochs. Compared with classical approaches such as independent component analysis and filtering, the proposed method accounts for the entire temporal correlation of the components and accommodates non-stationarity. For inference purpose, we propose a novel computational algorithm based upon using Kalman smoother, maximum likelihood and blocked resampling. The E-SSM model is applied to simulation studies and an application to a multi-epoch local field potentials (LFP) signal data collected from a non-spatial (olfactory) sequence memory task study. The results confirm that our method captures the evolution of the power for different components across different phases in the experiment and identifies clusters of electrodes that behave similarly with respect to the decomposition of different sources. These findings suggest that the activity of different electrodes does change over the course of an experiment in practice; treating these epoch recordings as realizations of an identical process could lead to misleading results. In summary, the proposed method underscores the importance of capturing the evolution in brain responses over the study period.

Volume-Conducted Origin of the Field Potential at the Lateral Habenula

Field potentials (FPs) are easily reached signals that provide information about the brain's processing. However, FP should be interpreted cautiously since their biophysical bases are complex. The lateral habenula (LHb) is a brain structure involved in the encoding of aversive motivational values. Previous work indicates that the activity of the LHb is relevant for hippocampal-dependent learning. Moreover, it has been proposed that the interaction of the LHb with the hippocampal network is evidenced by the synchronization of LHb and hippocampal FPs during theta rhythm. However, the origin of the habenular FP has not been analyzed. Hence, its validity as a measurement of LHb activity has not been proven. In this work, we used electrophysiological recordings in anesthetized rats and feed-forward modeling to investigate biophysical basis of the FP recorded in the LHb. Our results indicate that the FP in the LHb during theta rhythm is a volume-conducted signal from the hippocampus. This result highlight that FPs must be thoroughly analyzed before its biological interpretation and argues against the use of the habenular FP signal as a readout of the activity of the LHb.

Microelectrode array electrical impedance tomography for fast functional imaging in the thalamus

Electrical Impedance Tomography (EIT) has the potential to be able to observe functional tomographic images of neural activity in the brain at millisecond time-scales. Prior modelling and experimental work has shown that EIT is capable of imaging impedance changes from neural depolarisation in rat somatosensory cortex. Here, we investigate the feasibility of EIT for imaging impedance changes using a stereotaxically implanted microelectrode array in the thalamus. Microelectrode array EIT was simulated using an anatomically accurate marmoset brain model. Impedance imaging was validated and detectability estimated using physiological noise recorded from the marmoset visual thalamus. The results suggest that visual-input-driven impedance changes in visual subcortical bodies within 300 μm of the implanted array could be reliably reconstructed and localised, comparable to local field potential measurements. Furthermore, we demonstrated that microelectrode array EIT could reconstruct concurrent activity in multiple subcortical bodies simultaneously.

Learning Task-Related Activities From Independent Local-Field-Potential Components Across Motor Cortex Layers

Motor cortical microcircuits receive inputs from dispersed cortical and subcortical regions in behaving animals. However, how these inputs contribute to learning and execution of voluntary sequential motor behaviors remains elusive. Here, we analyzed the independent components extracted from the local field potential (LFP) activity recorded at multiple depths of rat motor cortex during reward-motivated movement to study their roles in motor learning. Because slow gamma (30-50 Hz), fast gamma (60-120 Hz), and theta (4-10 Hz) oscillations temporally coordinate task-relevant motor cortical activities, we first explored the behavioral state- and layer-dependent coordination of motor behavior in these frequency ranges. Consistent with previous findings, oscillations in the slow and fast gamma bands dominated during distinct movement states, i.e., preparation and execution states, respectively. However, we identified a novel independent component that dominantly appeared in deep cortical layers and exhibited enhanced slow gamma activity during the execution state. Then, we used the four major independent components to train a recurrent network model for the same lever movements as the rats performed. We show that the independent components differently contribute to the formation of various task-related activities, but they also play overlapping roles in motor learning.

Local Field Potentials: Myths and Misunderstandings

The intracerebral local field potential (LFP) is a measure of brain activity that reflects the highly dynamic flow of information across neural networks. This is a composite signal that receives contributions from multiple neural sources, yet interpreting its nature and significance may be hindered by several confounding factors and technical limitations. By and large, the main factor defining the amplitude of LFPs is the geometry of the current sources, over and above the degree of synchronization or the properties of the media. As such, similar levels of activity may result in potentials that differ in several orders of magnitude in different populations. The geometry of these sources has been experimentally inaccessible until intracerebral high density recordings enabled the co-activating sources to be revealed. Without this information, it has proven difficult to interpret a century's worth of recordings that used temporal cues alone, such as event or spike related potentials and frequency bands. Meanwhile, a collection of biophysically ill-founded concepts have been considered legitimate, which can now be corrected in the light of recent advances. The relationship of LFPs to their sources is often counterintuitive. For instance, most LFP activity is not local but remote, it may be larger further from rather than close to the source, the polarity does not define its excitatory or inhibitory nature, and the amplitude may increase when source's activity is reduced. As technological developments foster the use of LFPs, the time is now ripe to raise awareness of the need to take into account spatial aspects of these signals and of the errors derived from neglecting to do so.

Unmasking local activity within local field potentials (LFPs) by removing distal electrical signals using independent component analysis

Local field potentials (LFPs) are commonly thought to reflect the aggregate dynamics in local neural circuits around recording electrodes. However, we show that when LFPs are recorded in awake behaving animals against a distal reference on the skull as commonly practiced, LFPs are significantly contaminated by non-local and non-neural sources arising from the reference electrode and from movement-related noise. In a data set with simultaneously recorded LFPs and electroencephalograms (EEGs) across multiple brain regions while rats perform an auditory oddball task, we used independent component analysis (ICA) to identify signals arising from electrical reference and from volume-conducted noise based on their distributed spatial pattern across multiple electrodes and distinct power spectral features. These sources of distal electrical signals collectively accounted for 23–77% of total variance in unprocessed LFPs, as well as most of the gamma oscillation responses to the target stimulus in EEGs. Gamma oscillation power was concentrated in volume-conducted noise and was tightly coupled with the onset of licking behavior, suggesting a likely origin of muscle activity associated with body movement or orofacial movement. The removal of distal signal contamination also selectively reduced correlations of LFP/EEG signals between distant brain regions but not within the same region. Finally, the removal of contamination from distal electrical signals preserved an event-related potential (ERP) response to auditory stimuli in the frontal cortex and also increased the coupling between the frontal ERP amplitude and neuronal activity in the basal forebrain, supporting the conclusion that removing distal electrical signals unmasked local activity within LFPs. Together, these results highlight the significant contamination of LFPs by distal electrical signals and caution against the straightforward interpretation of unprocessed LFPs. Our results provide a principled approach to identify and remove such contamination to unmask local LFPs.

New uses of LFPs: Pathway-specific threads obtained through spatial discrimination

Local field potentials (LFPs) reflect the coordinated firing of functional neural assemblies during information coding and transfer across neural networks. As such, it was proposed that the extraordinary variety of cytoarchitectonic elements in the brain is responsible for the wide range of amplitudes and for the coverage of field potentials, which in most cases receive contributions from multiple pathways and populations. The influence of spatial factors overrides the bold interpretations of customary measurements, such as the amplitude and polarity, to the point that their cellular interpretation is one of the hardest tasks in Neurophysiology. Temporal patterns and frequency bands are not exclusive to pathways but rather, the spatial configuration of the voltage gradients created by each pathway is highly specific and may be used advantageously. Recent technical and analytical advances now make it possible to separate and then reconstruct activity for specific pathways. In this review, we discuss how spatial features specific to cells and populations define the amplitude and extension of LFPs, why they become virtually indecipherable when several pathways are co-activated, and then we present the recent advances regarding their disentanglement using spatial discrimination techniques. The pathway-specific threads of LFPs have a simple cellular interpretation, and the temporal fluctuations obtained can be applied to a variety of new experimental objectives and improve existing approaches. Among others, they facilitate the parallel readout of activity in several populations over multiple time scales correlating them with behavior. Also, they access information contained in irregular fluctuations, facilitating the testing of ongoing plasticity. In addition, they open the way to unravel the synaptic nature of rhythmic oscillations, as well as the dynamic relationships between multiple oscillatory activities. The challenge of understanding which waves belong to which populations, and the pathways that provoke them, may soon be overcome.

Diversity of LFPs Activated in Different Target Regions by a Common CA3 Input

Identifying the pathways contributing to local field potential (LFP) events and oscillations is essential to determine whether synchronous interregional patterns indicate functional connectivity. Here, we studied experimentally and numerically how different target structures receiving input from a common population shape their LFPs. We focused on the bilateral CA3 that sends gamma-paced excitatory packages to the bilateral CA1, the lateral septum, and itself (recurrent input). The CA3-specific contribution was isolated from multisite LFPs in target regions using spatial discrimination techniques. We found strong modulation of LFPs by target-specific features, including the morphology and population arrangement of cells, the timing of CA3 inputs, volume conduction from nearby targets, and co-activated inhibition. Jointly they greatly affect the LFP amplitude, profile, and frequency characteristics. For instance, ipsilateral (Schaffer) LFPs occluded contralateral ones, and septal LFPs arise mostly from remote sources while local contribution from CA3 input was minor. In the CA3 itself, gamma waves have dual origin from local networks: in-phase excitatory and nearly antiphase inhibitory. Also, waves may have different duration and varying phase in different targets. These results indicate that to explore the cellular basis of LFPs and the functional connectivity between structures, besides identifying the origin population/s, target modifiers should be considered.

Marginally subcritical dynamics explain enhanced stimulus discriminability under attention

Recent experimental and theoretical work has established the hypothesis that cortical neurons operate close to a critical state which describes a phase transition from chaotic to ordered dynamics. Critical dynamics are suggested to optimize several aspects of neuronal information processing. However, although critical dynamics have been demonstrated in recordings of spontaneously active cortical neurons, little is known about how these dynamics are affected by task-dependent changes in neuronal activity when the cortex is engaged in stimulus processing. Here we explore this question in the context of cortical information processing modulated by selective visual attention. In particular, we focus on recent findings that local field potentials (LFPs) in macaque area V4 demonstrate an increase in γ-band synchrony and a simultaneous enhancement of object representation with attention. We reproduce these results using a model of integrate-and-fire neurons where attention increases synchrony by enhancing the efficacy of recurrent interactions. In the phase space spanned by excitatory and inhibitory coupling strengths, we identify critical points and regions of enhanced discriminability. Furthermore, we quantify encoding capacity using information entropy. We find a rapid enhancement of stimulus discriminability with the emergence of synchrony in the network. Strikingly, only a narrow region in the phase space, at the transition from subcritical to supercritical dynamics, supports the experimentally observed discriminability increase. At the supercritical border of this transition region, information entropy decreases drastically as synchrony sets in. At the subcritical border, entropy is maximized under the assumption of a coarse observation scale. Our results suggest that cortical networks operate at such near-critical states, allowing minimal attentional modulations of network excitability to substantially augment stimulus representation in the LFPs.