7,903 research outputs found
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.This work was supported by the Spanish Ministry of Economy and Competitiveness (BFU2013-41533R).Peer reviewedPeer Reviewe
Model of Low-pass Filtering of Local Field Potentials in Brain Tissue
Local field potentials (LFPs) are routinely measured experimentally in brain
tissue, and exhibit strong low-pass frequency filtering properties, with high
frequencies (such as action potentials) being visible only at very short
distances (10~) from the recording electrode. Understanding
this filtering is crucial to relate LFP signals with neuronal activity, but not
much is known about the exact mechanisms underlying this low-pass filtering. In
this paper, we investigate a possible biophysical mechanism for the low-pass
filtering properties of LFPs. We investigate the propagation of electric fields
and its frequency dependence close to the current source, i.e. at length scales
in the order of average interneuronal distance. We take into account the
presence of a high density of cellular membranes around current sources, such
as glial cells. By considering them as passive cells, we show that under the
influence of the electric source field, they respond by polarisation, i.e.,
creation of an induced field. Because of the finite velocity of ionic charge
movement, this polarization will not be instantaneous. Consequently, the
induced electric field will be frequency-dependent, and much reduced for high
frequencies. Our model establishes that with respect to frequency attenuation
properties, this situation is analogous to an equivalent RC-circuit, or better
a system of coupled RC-circuits. We present a number of numerical simulations
of induced electric field for biologically realistic values of parameters, and
show this frequency filtering effect as well as the attenuation of
extracellular potentials with distance. We suggest that induced electric fields
in passive cells surrounding neurons is the physical origin of frequency
filtering properties of LFPs.Comment: 10 figs, revised tex file and revised fig
Brain Control of Movement Execution Onset Using Local Field Potentials in Posterior Parietal Cortex
The precise control of movement execution onset is essential for safe and autonomous cortical motor prosthetics. A recent study from the parietal reach region (PRR) suggested that the local field potentials (LFPs) in this area might be useful for decoding execution time information because of the striking difference in the LFP spectrum between the plan and execution states (Scherberger et al., 2005). More specifically, the LFP power in the 0–10 Hz band sharply rises while the power in the 20–40 Hz band falls as the state transitions from plan to execution. However, a change of visual stimulus immediately preceded reach onset, raising the possibility that the observed spectral change reflected the visual event instead of the reach onset. Here, we tested this possibility and found that the LFP spectrum change was still time locked to the movement onset in the absence of a visual event in self-paced reaches. Furthermore, we successfully trained the macaque subjects to use the LFP spectrum change as a "go" signal in a closed-loop brain-control task in which the animals only modulated the LFP and did not execute a reach. The execution onset was signaled by the change in the LFP spectrum while the target position of the cursor was controlled by the spike firing rates recorded from the same site. The results corroborate that the LFP spectrum change in PRR is a robust indicator for the movement onset and can be used for control of execution onset in a cortical prosthesis
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Developing robust movement decoders for local field potentials
textBrain Computer Interfaces (BCI) are devices that translate acquired neural signals to command and control signals. Applications of BCI include neural rehabilitation and neural prosthesis (thought controlled wheelchair, thought controlled speller etc.) to aid patients with disabilities and to augment human computer interaction. A successful practical BCI requires a faithful acquisition modality to record high quality neural signals; a signal processing system to construct appropriate features from these signals; and an algorithm to translate these features to appropriate outputs. Intracortical recordings like local field potentials provide reliable high SNR signals over long periods and suit BCI applications well. However, the non-stationarity of neural signals poses a challenge in robust decoding of subject behavior. Most BCI research focuses either on developing daily re-calibrated decoders that require exhaustive training sessions; or on providing cross-validation results. Such results ignore the variation of signal characteristics over different sessions and provide an optimistic estimate of BCI performance. Specifically, traditional BCI algorithms fail to perform at the same level on chronological data recordings. Neural signals are susceptible to variations in signal characteristics due to changes in subject behavior and learning, and variability in electrode characteristics due to tissue interactions. While training day-specific BCI overcomes signal variability, BCI re-training causes user frustration and exhaustion. This dissertation presents contributions to solve these challenges in BCI research. Specifically, we developed decoders trained on a single recording session and applied them on subsequently recorded sessions. This strategy evaluates BCI in a practical scenario with a potential to alleviate BCI user frustration without compromising performance. The initial part of the dissertation investigates extracting features that remain robust to changes in neural signal over several days of recordings. It presents a qualitative feature extraction technique based on ranking the instantaneous power of multichannel data. These qualitative features remain robust to outliers and changes in the baseline of neural recordings, while extracting discriminative information. These features form the foundation in developing robust decoders. Next, this dissertation presents a novel algorithm based on the hypothesis that multiple neural spatial patterns describe the variation in behavior. The presented algorithm outperforms the traditional methods in decoding over chronological recordings. Adapting such a decoder over multiple recording sessions (over 6 weeks) provided > 90% accuracy in decoding eight movement directions. In comparison, performance of traditional algorithms like Common Spatial Patterns deteriorates to 16% over the same time. Over time, adaptation reinforces some spatial patterns while diminishing others. Characterizing these spatial patterns reduces model complexity without user input, while retaining the same accuracy levels. Lastly, this dissertation provides an algorithm that overcomes the variation in recording quality. Chronic electrode implantation causes changes in signal-to-noise ratio (SNR) of neural signals. Thus, some signals and their corresponding features available during training become unavailable during testing and vice-versa. The proposed algorithm uses prior knowledge on spatial pattern evolution to estimate unknown neural features. This algorithm overcomes SNR variations and provides up to 93% decoding of eight movement directions over 6 weeks. Since model training requires only one session, this strategy reduces user frustration. In a practical closed-loop BCI, the user learns to produce stable spatial patterns, which improves performance of the proposed algorithms.Electrical and Computer Engineerin
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