Local Field Potentials Channels Research Articles

Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure

This study offers a novel and efficient measure based on a higher order version of autocorrelative signal memory that can identify nonlinearities in a single time series. The suggested method was applied to simultaneously recorded subthalamic nucleus (STN) local field potentials (LFP) and magnetoencephalography (MEG) from fourteen Parkinson's Disease (PD) patients who underwent surgery for deep brain stimulation. Recordings were obtained during rest for both OFF and ON dopaminergic medication states. We analyzed the bilateral LFP channels that had the maximum beta power in the OFF state and the cortical sources that had the maximum coherence with the selected LFP channels in the alpha band. Our findings revealed the inherent nonlinearity in the PD data as subcortical high beta(20–30 Hz) band and cortical alpha (8–12 Hz) band activities. While the former was discernible without medication (p=0.015), the latter was induced upon the dopaminergic medication (p<6.10−4). The degree of subthalamic nonlinearity was correlated with contralateral tremor severity (r=0.45, p=0.02). Conversely, for the cortical signals nonlinearity was present for the ON medication state with a peak in the alpha band and correlated with contralateral akinesia and rigidity (r=0.46, p=0.02). This correlation appeared to be independent from that of alpha power and the two measures combined explained 34 % of the variance in contralateral akinesia scores. Our findings suggest that particular frequency bands and brain regions display nonlinear features closely associated with distinct motor symptoms and functions.

A Miniature Dual-Biomarker-Based Sensing and Conditioning Device for Closed-Loop DBS.

In this paper, a dual-biomarker-based neural sensing and conditioning device is proposed for closing the feedback loop in deep brain stimulation devices. The device explores both local field potentials (LFPs) and action potentials (APs) as measured biomarkers. It includes two channels, each having four main parts: (1) a pre-amplifier with built-in low-pass filter, (2) a ground shifting circuit, (3) an amplifier with low-pass function, and (4) a high-pass filter. The design specifications include miniature-size, light-weight, and 100 dB gain in the LFP and AP channels. This device has been validated through bench and in-vitro tests. The bench tests have been performed using different sinusoidal signals and pre-recorded neural signals. The in-vitro tests have been conducted in the saline solution that mimics the brain environment. The total weight of the device including a 3 V coin battery, and battery holder is 1.2 g. The diameter of the device is 11.2 mm. The device can be used to concurrently sense LFPs and APs for closing the feedback loop in closed-loop deep brain stimulation systems. It provides a tetherless head-mountable platform suitable for pre-clinical trials.

Multiscale modeling and decoding algorithms for spike-field activity

Objective. Behavior is encoded across multiple spatiotemporal scales of brain activity. Modern technology can simultaneously record various scales, from spiking of individual neurons to large neural populations measured with field activity. This capability necessitates developing multiscale modeling and decoding algorithms for spike-field activity, which is challenging because of the fundamental differences in statistical characteristics and time-scales of these signals. Spikes are binary-valued with a millisecond time-scale while fields are continuous-valued with slower time-scales. Approach. We develop a multiscale encoding model, adaptive learning algorithm, and decoder that explicitly incorporate the different statistical profiles and time-scales of spikes and fields. The multiscale model consists of combined point process and Gaussian process likelihood functions. The multiscale filter (MSF) for decoding runs at the millisecond time-scale of spikes while adding information from fields at their slower time-scales. The adaptive algorithm learns all spike-field multiscale model parameters simultaneously, in real time, and at their different time-scales. Main results. We validated the multiscale framework within motor tasks using both closed-loop brain–machine interface (BMI) simulations and non-human primate (NHP) spike and local field potential (LFP) motor cortical activity during a naturalistic 3D reach task. Our closed-loop simulations show that the MSF can add information across scales and that the adaptive MSF can accurately learn all parameters in real time. We also decoded the seven joint angular trajectories of the NHP arm using spike-LFP activity. These data showed that the MSF outperformed single-scale decoding, this improvement was due to the addition of information across scales rather than the dominance of one scale and was largest in the low-information regime, and the improvement was similar regardless of the degree of overlap between spike and LFP channels. Significance. This multiscale framework provides a tool to study encoding across scales and may help enhance future neurotechnologies such as motor BMIs.

Robust Local Field Potential-based Neural Decoding by Actively Selecting Discriminative Channels.

Local field potentials (LFPs) have been proposed as a neural decoding signal to compensate for spike signal deterioration in invasive brain-machine interface applications. However, the presence of redundancy among LFP signals at different frequency bands across multiple channels may affect the decoding performance. In order to remove redundant LFP channels, we proposed a novel Fisher-distance ratio-based method to actively batch select discriminative channels to maximize the separation between classes. Experimental evaluation was conducted on 5 non-consecutive days of data from a non-human primate. For data from each day, the first experimental session was used to generate the training model, which was then used to perform 4-class decoding of signals from other sessions. Decoding achieved an average accuracy of 79.55%, 79.02% and 79.40% using selected LFP channels for beta, low gamma and high gamma frequency bands, respectively. Compared with decoding using full LFP channels, decoding using selected LFP channels in high gamma band resulted in an increase of 8.67% in accuracy, even if this accuracy was still 7.26% lower than that of spike-based decoding. These results demonstrate the effectiveness of the proposed method in selecting discriminative LFP channels for neural decoding.

Multiscale decoding for reliable brain-machine interface performance over time.

Recordings from invasive implants can degrade over time, resulting in a loss of spiking activity for some electrodes. For brain-machine interfaces (BMI), such a signal degradation lowers control performance. Achieving reliable performance over time is critical for BMI clinical viability. One approach to improve BMI longevity is to simultaneously use spikes and other recording modalities such as local field potentials (LFP), which are more robust to signal degradation over time. We have developed a multiscale decoder that can simultaneously model the different statistical profiles of multi-scale spike/LFP activity (discrete spikes vs. continuous LFP). This decoder can also run at multiple time-scales (millisecond for spikes vs. tens of milliseconds for LFP). Here, we validate the multiscale decoder for estimating the movement of 7 major upper-arm joint angles in a non-human primate (NHP) during a 3D reach-to-grasp task. The multiscale decoder uses motor cortical spike/LFP recordings as its input. We show that the multiscale decoder can improve decoding accuracy by adding information from LFP to spikes, while running at the fast millisecond time-scale of the spiking activity. Moreover, this improvement is achieved using relatively few LFP channels, demonstrating the robustness of the approach. These results suggest that using multiscale decoders has the potential to improve the reliability and longevity of BMIs.

Continuous Force Decoding from Local Field Potentials of the Primary Motor Cortex in Freely Moving Rats.

Local field potential (LFP) signals recorded by intracortical microelectrodes implanted in primary motor cortex can be used as a high informative input for decoding of motor functions. Recent studies show that different kinematic parameters such as position and velocity can be inferred from multiple LFP signals as precisely as spiking activities, however, continuous decoding of the force magnitude from the LFP signals in freely moving animals has remained an open problem. Here, we trained three rats to press a force sensor for getting a drop of water as a reward. A 16-channel micro-wire array was implanted in the primary motor cortex of each trained rat, and obtained LFP signals were used for decoding of the continuous values recorded by the force sensor. Average coefficient of correlation and the coefficient of determination between decoded and actual force signals were r = 0.66 and R2 = 0.42, respectively. We found that LFP signal on gamma frequency bands (30–120 Hz) had the most contribution in the trained decoding model. This study suggests the feasibility of using low number of LFP channels for the continuous force decoding in freely moving animals resembling BMI systems in real life applications.

Predicting seizures from local field potentials recorded via intracortical microelectrode arrays.

The need for new therapeutic interventions to treat pharmacologically resistant focal epileptic seizures has led recently to the development of closed-loop systems for seizure control. Once a seizure is predicted/detected by the system, electrical stimulation is delivered to prevent seizure initiation or spread. So far, seizure prediction/detection has been limited to tracking non-invasive electroencephalogram (EEG) or intracranial EEG (iEEG) signals. Here, we examine seizure prediction based on local field potentials (LFPs) from a small neocortical patch recorded via a 10×10 microelectrode array implanted in a patient with focal seizures. We formulate the seizure (ictal) prediction problem in terms of discriminating between interictal and preictal neural activity. Using deep Convolutional Neural Networks (CNNs), we show that periods of preictal activity can be successfully discriminated (80% detection; no false positives) from periods of interictal activity several (2-18) minutes prior to seizure onset. CNN input features consisted of the spectral power of LFP channels (1-second time windows) computed in 50 frequency bands (0-100 Hz; 2 Hz steps). Our preliminary results show that intracortical LFPs may be a promising neural signal for seizure prediction in focal epilepsy.

Long-term decoding stability of local field potentials from silicon arrays in primate motor cortex during a 2D center out task

Objective. Many serious concerns exist in the long-term stability of brain–machine interfaces (BMIs) based on spike signals (single unit activity, SUA; multi unit activity, MUA). Some studies showed local field potentials (LFPs) could offer a stable decoding performance. However, the decoding stability of LFPs was examined only when high quality spike signals were recorded. Here we aim to examine the long-term decoding stability of LFPs over a larger time scale when the quality of spike signals was from good to poor or even no spike was recorded. Approach. Neural signals were collected from motor cortex of three monkeys via silicon arrays over 230, 290 and 690 days post-implantation when they performed 2D center out task. To compare long-term stability between LFPs and spike signals, we examined them in neural signals characteristics, directional tuning properties and offline decoding performance, respectively. Main results. We observed slow decreasing trends in the number of LFP channels recorded and mean LFP power in different frequency bands when spike signals quality decayed over time. The number of significantly directional tuning LFP channels decreased more slowly than that of tuning SUA and MUA. The variable preferred directions for the same signal features across sessions indicated non-stationarity of neural activity. We also found that LFPs achieved better decoding performance than SUA and MUA in retrained decoder when the quality of spike signals seriously decayed. Especially, when no spike was recorded in one monkey after 671 days post-implantation, LFPs still provided some kinematic information. In addition, LFPs outperformed MUA in long-term decoding stability in a static decoder. Significance. Our results suggested that LFPs were more durable and could provide better decoding performance when spike signals quality seriously decayed. It might be due to their resistance to recording degradation and their high redundancy among channels.

Degree of locking to network activity of neurons with similar movement tuning in the motor cortex of awake, behaving rats differs by layer

The rat motor cortex, like other areas of the mammalian neocortex, is organized in a 6-layered structure. The synaptic organization has been studied in the past by various in vitro techniques [1,2], and the movement-specific tuning properties of the neurons in forelimb area of awake, freely-behaving rats has also recently been studied [3]. In this last study, rats were trained to push down on a lever for > 1 second, and to pull back up to receive a water reward. The tuning properties of the neurons were studied by triggering on the pull-up of the lever, and neurons were classified according to the modulation of the firing rate relative to the pull-up trigger as pre-movement, movement, post-movement, hold-related, movement off, and non-related. It has been revealed that there is little difference between the tuning properties of cells in the various layers; all layers contain pretty much all tuning types, although the average firing rate of cells is higher in the deeper layers. Considering only the rate information, there does not seem to be much difference in the computations being carried out in the various layers. The local field potential (LFP) is thought to represent summed local synaptic currents, and as such, can be considered as containing information about the activity of the neural circuits, both cortical and extra-cortical. In this work, we pose the question whether there is really no laminar organization in the computations being carried out in the various layers of the forelimb area of M1. We analyzed the data presented in [3], obtained using the juxtacellular method of recording spikes from single, identifiable units in addition to multi-channel laminar LFP recordings from layers 1~6. Using this data, we analyzed the spike-LFP relationship using sliding, variable-width windows in which the LFP spike-triggered average (STA) was calculated for times between -1000 ms and 300 ms preceding and following the onset of a lever pull. The STAs were then assigned a z-score by comparing against an ensemble of STAs calculated using shuffled spike times (sSTA), effectively giving a number representing the significance of precise spike timing information. This was carried out for each of the 9 LFP channels, giving us a spatio-temporal plot of a given neuron’s relationship to the LFP. The resulting plots show a clear difference between L2/3 and L5/6 neurons. L2/3 neurons show fairly uniform and strong spike-LFP relationship throughout the analysis period, suggesting that these neurons are strongly locked to the underlying LFP regardless of the phase of movement. In contrast, cells in L5/6 often showed weaker spike-LFP relationship, and more modulation of strength throughout the analysis period, suggesting a more variable degree of spike precision to some underlying network activity.