Event detection and classification from multimodal time series with application to neural data.

The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering.

Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.

From mechanisms to functions: The role of theta and gamma coherence in the intrahippocampal circuits.

Brain rhythms are essential for information processing in neuronal networks. Oscillations recorded in different brain regions can be synchronized and have a constant phase difference, that is, they can be coherent. Coherence between local field potential (LFP) signals from different brain regions may be correlated with the performance of cognitive tasks, indicating that these regions of the brain are jointly involved in the information processing. Why does coherence occur and how is it related to the information transfer between different regions of the hippocampal formation? In this article, we discuss possible mechanisms of theta and gamma coherence and its role in the hippocampus-dependent attention and memory processes, since theta and gamma rhythms are most pronounced in these processes. We review in vivo studies of interactions between different regions of the hippocampal formation in theta and gamma frequency bands. The key propositions of the review are as follows: (1) coherence emerges from synchronous postsynaptic currents in principal neurons as a result of synchronization of neuronal spike activity; (2) the synchronization of neuronal spike patterns in two regions of the hippocampal formation can be realized through induction or resonance; (3) coherence at a specific time point reflects the transfer of information between the regions of the hippocampal formation; (4) the physiological roles of theta and gamma coherence are different due to their different functions and mechanisms of generation. All hippocampal neurons are involved in theta activity, and theta coherence arranges the firing order of principal neurons throughout the hippocampal formation. In contrast, gamma coherence reflects the coupling of active neuronal ensembles. Overall, the coherence of LFPs between different areas of the brain is an important physiological process based on the synchronized neuronal firing, and it is essential for cooperative information processing.

Differential Effects of Pathological Beta Burst Dynamics Between Parkinson's Disease Phenotypes Across Different Movements.

Background: Resting state beta band (13-30 Hz) oscillations represent pathological neural activity in Parkinson's disease (PD). It is unknown how the peak frequency or dynamics of beta oscillations may change among fine, limb, and axial movements and different disease phenotypes. This will be critical for the development of personalized closed loop deep brain stimulation (DBS) algorithms during different activity states. Methods: Subthalamic (STN) and local field potentials (LFPs) were recorded from a sensing neurostimulator (Activa® PC + S, Medtronic PLC.) in fourteen PD participants (six tremor-dominant and eight akinetic-rigid) off medication/off STN DBS during 30 s of repetitive alternating finger tapping, wrist-flexion extension, stepping in place, and free walking. Beta power peaks and beta burst dynamics were identified by custom algorithms and were compared among movement tasks and between tremor-dominant and akinetic-rigid groups. Results: Beta power peaks were evident during fine, limb, and axial movements in 98% of movement trials; the peak frequencies were similar during each type of movement. Burst power and duration were significantly larger in the high beta band, but not in the low beta band, in the akinetic-rigid group compared to the tremor-dominant group. Conclusion: The conservation of beta peak frequency during different activity states supports the feasibility of patient-specific closed loop DBS algorithms driven by the dynamics of the same beta band during different activities. Akinetic-rigid participants had greater power and longer burst durations in the high beta band than tremor-dominant participants during movement, which may relate to the difference in underlying pathophysiology between phenotypes.

Controlling Clinical States Governed by Different Temporal Dynamics With Closed-Loop Deep Brain Stimulation: A Principled Framework.

Closed-loop strategies for deep brain stimulation (DBS) are paving the way for improving the efficacy of existing neuromodulation therapies across neurological disorders. Unlike continuous DBS, closed-loop DBS approaches (cl-DBS) optimize the delivery of stimulation in the temporal domain. However, clinical and neurophysiological manifestations exhibit highly diverse temporal properties and evolve over multiple time-constants. Moreover, throughout the day, patients are engaged in different activities such as walking, talking, or sleeping that may require specific therapeutic adjustments. This broad range of temporal properties, along with inter-dependencies affecting parallel manifestations, need to be integrated in the development of therapies to achieve a sustained, optimized control of multiple symptoms over time. This requires an extended view on future cl-DBS design. Here we propose a conceptual framework to guide the development of multi-objective therapies embedding parallel control loops. Its modular organization allows to optimize the personalization of cl-DBS therapies to heterogeneous patient profiles. We provide an overview of clinical states and symptoms, as well as putative electrophysiological biomarkers that may be integrated within this structure. This integrative framework may guide future developments and become an integral part of next-generation precision medicine instruments.

Spontaneous and Perturbational Complexity in Cortical Cultures.

Dissociated cortical neurons in vitro display spontaneously synchronized, low-frequency firing patterns, which can resemble the slow wave oscillations characterizing sleep in vivo. Experiments in humans, rodents, and cortical slices have shown that awakening or the administration of activating neuromodulators decrease slow waves, while increasing the spatio-temporal complexity of responses to perturbations. In this study, we attempted to replicate those findings using in vitro cortical cultures coupled with micro-electrode arrays and chemically treated with carbachol (CCh), to modulate sleep-like activity and suppress slow oscillations. We adapted metrics such as neural complexity (NC) and the perturbational complexity index (PCI), typically employed in animal and human brain studies, to quantify complexity in simplified, unstructured networks, both during resting state and in response to electrical stimulation. After CCh administration, we found a decrease in the amplitude of the initial response and a marked enhancement of the complexity during spontaneous activity. Crucially, unlike in cortical slices and intact brains, PCI in cortical cultures displayed only a moderate increase. This dissociation suggests that PCI, a measure of the complexity of causal interactions, requires more than activating neuromodulation and that additional factors, such as an appropriate circuit architecture, may be necessary. Exploring more structured in vitro networks, characterized by the presence of strong lateral connections, recurrent excitation, and feedback loops, may thus help to identify the features that are more relevant to support causal complexity.

Theta Oscillations at Subthalamic Region Predicts Hypomania State After Deep Brain Stimulation in Parkinson's Disease.

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective treatment for the motor impairments of patients with advanced Parkinson's disease. However, mood or behavioral changes, such as mania, hypomania, and impulsive disorders, can occur postoperatively. It has been suggested that these symptoms are associated with the stimulation of the limbic subregion of the STN. Electrophysiological studies demonstrate that the low-frequency activities in ventral STN are modulated during emotional processing. In this study, we report 22 patients with Parkinson's disease who underwent STN DBS for treatment of motor impairment and presented stimulation-induced mood elevation during initial postoperative programming. The contact at which a euphoric state was elicited by stimulation was termed as the hypomania-inducing contact (HIC) and was further correlated with intraoperative local field potential recorded during the descending of DBS electrodes. The power of four frequency bands, namely, θ (4-7 Hz), α (7-10 Hz), ß (13-35 Hz), and γ (40-60 Hz), were determined by a non-linear variation of the spectrogram using the concentration of frequency of time (conceFT). The depth of maximum θ power is located approximately 2 mm below HIC on average and has significant correlation with the location of contacts (r = 0.676, p < 0.001), even after partializing the effect of α and ß, respectively (r = 0.474, p = 0.022; r = 0.461, p = 0.027). The occurrence of HIC was not associated with patient-specific characteristics such as age, gender, disease duration, motor or non-motor symptoms before the operation, or improvement after stimulation. Taken together, these data suggest that the location of maximum θ power is associated with the stimulation-induced hypomania and the prediction of θ power is frequency specific. Our results provide further information to refine targeting intraoperatively and select stimulation contacts in programming.

Precise Targeting of Single Microelectrodes to Orientation Pinwheel Centers.

In the mammalian visual system, early stages of visual form perception begin with orientation selective neurons in primary visual cortex (V1). In many species (including humans, monkeys, tree shrews, cats, and ferrets), these neurons are organized in pinwheel-like orientation columns. To study the functional organization within orientation pinwheels, it is important to target pinwheel subdomains precisely. We therefore developed a technique to provide a quantitative determination of the location of pinwheel centers (PCs). Previous studies relied solely on blood vessel images of the cortical surface to guide electrode penetrations to PCs in orientation maps. However, considerable spatial error remained using this method. In the present study, we improved the accuracy of targeting PCs by ensuring perpendicularity of electrodes and by utilizing the orientation tuning of local field potentials (LFP) recorded at or near the optically determined positions.

Implications of asymmetric neural activity patterns in the basal ganglia outflow in the integrative neural network model for cervical dystonia.

Cervical dystonia (CD) is characterized by abnormal twisting and turning of the head with associated head oscillations. It is the most common form of dystonia, which is a third most common movement disorder. Despite frequent occurrence there is paucity in adequate therapy, much of which is attributed to its uncertain pathophysiology. Recently we proposed a unifying network model highlighting the role of head neural integrator (hNI) for the pathophysiology of CD. According to our hypothesis the CD is due to abnormal output of hNI; the latter itself is not affected but its dysfunction is secondary to abnormal feedback. We hypothesized that asymmetry in the feedback to hNI is associated with severity in CD; the feedback asymmetry is greater in CD with lateralized head postures, such as turning of head in yaw plane (torticollis) or roll plane (laterocollis). The hypothesis also specifies that feedback to hNI-cerebellum, proprioception, and basal ganglia outflow (pallidus) are connected in a network; thus asymmetry is distributed through the feedback network. In 15 CD patients undergoing deep brain stimulation (DBS) surgery, with their informed consent, we used the opportunity to collect single unit neural responses and local field potential from the globus pallidus to measure whether feedback to hNI is asymmetric. Using machine learning algorithms developed to analyze single unit data, we found: (1) globus pallidus interna (GPi) firing rate, discharge pattern and gamma oscillation were asymmetric in patients with robust torticollis; (2) there was no asymmetry in these parameters in retrocollis; and (3) in those patients with oppositely directed laterocollis and torticollis. Firing rate was higher in GPi cells ipsilateral to the direction of head rotation; the asymmetry was more pronounced in tonic cells compared to burst neurons. In addition to confirming that CD is associated with an asymmetric pallidal activity, our data showed that neuronal asymmetry correlated with the degree of involuntary head turning. We propose that asymmetric pallidal activity results in asymmetric feedback to hNI causing its dysfunction.

Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets.

Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

A Marked Point Process Framework for Extracellular Electrical Potentials.

Neuromodulations are an important component of extracellular electrical potentials (EEP), such as the Electroencephalogram (EEG), Electrocorticogram (ECoG) and Local Field Potentials (LFP). This spatially temporal organized multi-frequency transient (phasic) activity reflects the multiscale spatiotemporal synchronization of neuronal populations in response to external stimuli or internal physiological processes. We propose a novel generative statistical model of a single EEP channel, where the collected signal is regarded as the noisy addition of reoccurring, multi-frequency phasic events over time. One of the main advantages of the proposed framework is the exceptional temporal resolution in the time location of the EEP phasic events, e.g., up to the sampling period utilized in the data collection. Therefore, this allows for the first time a description of neuromodulation in EEPs as a Marked Point Process (MPP), represented by their amplitude, center frequency, duration, and time of occurrence. The generative model for the multi-frequency phasic events exploits sparseness and involves a shift-invariant implementation of the clustering technique known as k-means. The cost function incorporates a robust estimation component based on correntropy to mitigate the outliers caused by the inherent noise in the EEP. Lastly, the background EEP activity is explicitly modeled as the non-sparse component of the collected signal to further improve the delineation of the multi-frequency phasic events in time. The framework is validated using two publicly available datasets: the DREAMS sleep spindles database and one of the Brain-Computer Interface (BCI) competition datasets. The results achieve benchmark performance and provide novel quantitative descriptions based on power, event rates and timing in order to assess behavioral correlates beyond the classical power spectrum-based analysis. This opens the possibility for a unifying point process framework of multiscale brain activity where simultaneous recordings of EEP and the underlying single neuron spike activity can be integrated and regarded as marked and simple point processes, respectively.

Clinical Outcome and Characterization of Local Field Potentials in Holmes Tremor Treated with Pallidal Deep Brain Stimulation.

BACKGROUND: Holmes tremor (HT) is an irregular, low-frequency rest tremor associated with prominent action and postural tremors. Currently, the most effective stereotactic target and neurophysiologic characterization of HT, specifically local field potentials (LFPs) are uncertain. We present the outcome, intraoperative neurophysiologic analysis with characterization of LFPs in a patient managed with left globus pallidus interna deep brain stimulation (Gpi DBS). CASE REPORT: A 24-year-old male underwent left Gpi DBS for medically refractory HT. LFPs demonstrated highest powers in the delta range in Gpi. At the 6-month follow-up, a 90% reduction in tremor was observed. DISCUSSION: Pallidal DBS should be considered as an alternative target for management of refractory HT. LFP demonstrated neuronal activity associated with higher power in the delta region, similarly seen in patients with generalized dystonia.

An aberrant link between gamma oscillation and functional connectivity in Aß1-42-mediated memory deficits in rats.

Alzheimer's disease (AD) is a neurodegenerative disease that gradually induces cognitive deficits in the elderly. Impairment of working memory was typically observed in AD. As amyloid-ß peptide (Aß) is a causative factor for the cognitive impairments in AD, developing a mechanistic understanding of the contribution of Aß to cognitive impairments may yield insights into the pathophysiology of AD. Gamma oscillations as well as functional connectivity have been recognized to play important roles in various cognitive functions. Gamma oscillation is hypothesized as the results of the coordinated interaction of neural network. However, the temporal link between gamma oscillation and functional connectivity in working memory has remained largely unexplored. Moreover, whether Aß-induced working memory deficit is due to the abnormal link between gamma oscillation and functional connectivity? Therefore, in the present study, local field potentials (LFPs) were recorded via multi-electrodes implanted in rat prefrontal cortex (PFC) for the control group and Aß-injected group (Aß group) when they performed a Y-maze working memory task. Gamma oscillations and functional connectivity among LFPs were respectively estimated. Compared with the control group, the Aß group showed significantly weaker oscillations and functional connectivity among LFPs. Moreover, the time gap between gamma oscillation and functional connectivity was examined. The precise temporal link between the gamma oscillation and functional connectivity in the control group and the Aß-induced abnormal link in the Aß group were noticed. Our results indicate that the link between gamma oscillation and functional connectivity may provide a potential mechanism for the Aß1-42-induced working memory deficits.

Laminar Distribution of Phase-Amplitude Coupling of Spontaneous Current Sources and Sinks.

Although resting-state functional connectivity is a commonly used neuroimaging paradigm, the underlying mechanisms remain unknown. Thalamo-cortical and cortico-cortical circuits generate oscillations at different frequencies during spontaneous activity. However, it remains unclear how the various rhythms interact and whether their interactions are lamina-specific. Here we investigated intra- and inter-laminar spontaneous phase-amplitude coupling (PAC). We recorded local-field potentials using laminar probes inserted in the forelimb representation of rat area S1. We then computed time-series of frequency-band- and lamina-specific current source density (CSD), and PACs of CSD for all possible pairs of the classical frequency bands in the range of 1-150 Hz. We observed both intra- and inter-laminar spontaneous PAC. Of 18 possible combinations, 12 showed PAC, with the highest measures of interaction obtained for the pairs of the theta/gamma and delta/gamma bands. Intra- and inter-laminar PACs involving layers 2/3-5a were higher than those involving layer 6. Current sinks (sources) in the delta band were associated with increased (decreased) amplitudes of high-frequency signals in the beta to fast gamma bands throughout layers 2/3-6. Spontaneous sinks (sources) of the theta and alpha bands in layers 2/3-4 were on average linked to dipoles completed by sources (sinks) in layer 6, associated with high (low) amplitudes of the beta to fast-gamma bands in the entire cortical column. Our findings show that during spontaneous activity, delta, theta, and alpha oscillations are associated with periodic excitability, which for the theta and alpha bands is lamina-dependent. They further emphasize the differences between the function of layer 6 and that of the superficial layers, and the role of layer 6 in controlling activity in those layers. Our study links theories on the involvement of PAC in resting-state functional connectivity with previous work that revealed lamina-specific anatomical thalamo-cortico-cortical connections.

Brain-state dependent uncoupling of BOLD and local field potentials in laminar olfactory bulb.

The neural activities of the olfactory bulb (OB) can be modulated significantly by internal brain states. While blood oxygenation level dependent functional MRI (BOLD-fMRI) has been extensively applied to study OB in small animals, the relationship between BOLD signals and electrophysiological signals remains to be elucidated. Our recent study has revealed a complex relationship between BOLD and local field potentials (LFP) signals in different OB layers during odor stimulation. However, no study has been performed to compare these two types of signals under global brain states. Here, the changes of BOLD and LFP signals in the glomerular, mitral cell, and granular cell layers of the OB under different brain states, which were induced by different concentrations of isoflurane, were sequentially acquired using electrode array and high-resolution MRI. It was found that under deeper anesthesia, the LFP powers in all layers were decreased but the BOLD signals were unexpectedly increased. Furthermore, the decreases of LFP powers were layer-independent, but the increases of BOLD signal were layer-specific, with the order of glomerular>mitral cell>granular cell layer. The results provide new evidence that the direct neural activity levels might not be correlated well with BOLD signals in some cases, and remind us that cautions should be taken to use BOLD signals as the index of neural activities.

A neurochemical closed-loop controller for deep brain stimulation: toward individualized smart neuromodulation therapies.

Current strategies for optimizing deep brain stimulation (DBS) therapy involve multiple postoperative visits. During each visit, stimulation parameters are adjusted until desired therapeutic effects are achieved and adverse effects are minimized. However, the efficacy of these therapeutic parameters may decline with time due at least in part to disease progression, interactions between the host environment and the electrode, and lead migration. As such, development of closed-loop control systems that can respond to changing neurochemical environments, tailoring DBS therapy to individual patients, is paramount for improving the therapeutic efficacy of DBS. Evidence obtained using electrophysiology and imaging techniques in both animals and humans suggests that DBS works by modulating neural network activity. Recently, animal studies have shown that stimulation-evoked changes in neurotransmitter release that mirror normal physiology are associated with the therapeutic benefits of DBS. Therefore, to fully understand the neurophysiology of DBS and optimize its efficacy, it may be necessary to look beyond conventional electrophysiological analyses and characterize the neurochemical effects of therapeutic and non-therapeutic stimulation. By combining electrochemical monitoring and mathematical modeling techniques, we can potentially replace the trial-and-error process used in clinical programming with deterministic approaches that help attain optimal and stable neurochemical profiles. In this manuscript, we summarize the current understanding of electrophysiological and electrochemical processing for control of neuromodulation therapies. Additionally, we describe a proof-of-principle closed-loop controller that characterizes DBS-evoked dopamine changes to adjust stimulation parameters in a rodent model of DBS. The work described herein represents the initial steps toward achieving a "smart" neuroprosthetic system for treatment of neurologic and psychiatric disorders.

Complex relationship between BOLD-fMRI and electrophysiological signals in different olfactory bulb layers.

Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI), one of the most powerful technologies in neuroscience, measures neural activity indirectly. Therefore, systematic correlation of BOLD signals with other neural activity measurements is critical to understanding and then using the technology. Numerous studies have revealed that the BOLD signal is determined by many factors and is better correlated with local field potentials (LFP) than single/multiple unit firing. The relationship between BOLD and LFP signals under higher spatial resolution is complex and remains unclear. Here, changes of BOLD and LFP signals in the glomerular (GL), mitral cell (MCL), and granular cell layers (GCL) of the olfactory bulb were evoked by odor stimulation and sequentially acquired using high-resolution fMRI and electrode array. The experimental results revealed a rather complex relationship between BOLD and LFP signals. Both signal modalities were increased layer-dependently by odor stimulation, but the orders of signal intensity were significantly different: GL>MCL>GCL and GCL>GL>MCL for BOLD and LFP, respectively. During odor stimulation, the temporal features of LFPs were similar for a given band in different layers, but different for different frequency bands in a given layer. The BOLD and LFP signals in the low gamma frequency band correlated the best. This study provides new evidence for the consistency between structure and function in understanding the neurophysiological basis of BOLD signals, but also reminds that caution must be taken in interpreting of BOLD signals in regard to neural activity.