Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation.

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large-scale cortical networks. In this technical report, we develop a novel denoising approach for suppressing the leakage of interference source activity into the activity representing a brain region of interest. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We apply this denoising approach to reconstruct simulated evoked response topographies to deep brain stimulation (DBS) in a phantom recording. We highlight the advantages of our approach compared to the benchmark-spatiotemporal signal space separation-and show that it can more accurately reveal brain stimulation-evoked response topographies. Finally, we apply our method to MEG recordings from a single patient with Parkinson's disease, to reveal early cortical-evoked responses to DBS of the subthalamic nucleus.

Abnormally low sensorimotor α band nonlinearity serves as an effective EEG biomarker of Parkinson's disease.

Biomarkers obtained from the neurophysiological signals of patients with Parkinson's disease (PD) have objective value in assessing their motor condition for effective diagnosis, monitoring, and clinical intervention. Prominent cortical biomarkers of PD have typically been derived from various ß band wave features. This study approached the topic from an alternative perspective and attempted to estimate a recently suggested measure representing α band nonlinear autocorrelative memory from a publicly available EEG dataset that involves 15 patients with earlier-stage PD (dopaminergic medication OFF and ON states) and 16 age-matched healthy controls. The cortical nonlinearity was elevated for the PD ON state compared with the OFF state for bilateral sensorimotor channels C3 and C4 (n = 26; P = 0.003). A similar statistical difference was also identified between PD OFF state and healthy subjects (n = 26; P = 0.049). Analysis over all channels revealed that the α band nonlinearity induced upon medication was constrained to sensorimotor regions. The α nonlinearity measure was compared with a well-accepted cortical biomarker of ß-γ phase-amplitude coupling (PAC). They were in moderate negative correlation (r = -0.412; P = 0.036) for only healthy subjects, but not for the patients. The nonlinearity measure was found to be insusceptible to the nonstationary variations within the particular data. Our study provides further evidence that the α band nonlinearity measure can serve as a promising cortical biomarker of PD. The suggested measure can be estimated from a noninvasive low-resolution single scalp EEG channel of patients with relatively early-stage PD, who did not yet need to undergo deep brain stimulation operation.NEW & NOTEWORTHY This study suggests a nonlinearity measure that differentiates Parkinson's disease (PD) dopamine OFF-state scalp EEG data from those of dopamine ON-state patients and healthy subjects. Unlike typical PD cortical biomarkers based on ß band activity, this metric shows elevation upon dopaminergic medication in the α band. We provide evidence supporting its potential as an early-stage promising PD biomarker that can be estimated from noninvasive EEG recordings with low resolution and SNR.

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.

N1-P2: Neural markers of temporal expectation and response discrimination in interval timing.

Humans use temporal regularities in their daily life to act in accordance with future events in the most efficient way. To achieve this, humans build temporal expectations and determine a template action that is in line with those expectations. In this temporal trisection study, we aimed to study the neurophysiological counterparts of temporal expectation and response discrimination. We investigated amplitude variations of early event-related potentials (ERPs) while manipulating time intervals. We measured temporal expectation-related attenuation of neural activity and response discrimination processes in N1 and P2 ERP components. Results showed that the amplitude of the N1 component was attenuated for the predicted task-relevant temporal location of a response decision. The P2 amplitude, in contrast, was enhanced for a discriminated response in comparison to a template response. The present study supports a link between the different functional associations of the N1 and P2 components within the requirements of a timing task. N1-related amplitude modulation can determine a change in expectation level during timing. The amplitude regulation of the P2 component, in contrast, explains temporal discrimination in both expected and unexpected temporal locations. In addition to expectation­related modulation, our results suggest an additional regulation of the N1 amplitude that is linked to attention. The effect was observed in instances that included a prediction error of a task-relevant temporal location for a response decision. In conclusion, our study contributes to the growing neurocognitive literature on interval timing by capturing different aspects of a timing task; namely, N1-related expectation and P2-related response discrimination processes.

Brain Oscillatory Correlates of Visual Short-Term Memory Errors.

Brain dynamics of memory formation were explored during encoding and retention intervals of a visual working memory task. EEG data were acquired while subjects were exposed to grayscale images of widely known object categories (e.g., "luggage," "chair," and "car"). Following a short delay, two probes were shown to test memory accuracy. Oscillatory portraits of successful and erroneous memories were contrasted. Where significant differences were identified, oscillatory traits of false memories (i.e., when a novel probe item of the same category is recognized as familiar) were compared with those of successful and erroneous memories. Spectral analysis revealed theta (6-8 Hz) power over occipital channels for encoding of successful and false memories that was smaller when compared to other types of memory errors. The reduced theta power indicates successful encoding and reflects the efficient activation of the underlying neural assemblies. Prominent alpha-beta (10-26 Hz) activity belonging to the right parieto-occipital channels was identified during the retention interval. It was found to be larger for false memories and errors than that of correctly answered trials. High levels of alpha-beta oscillatory activity for errors correspond to poor maintenance leading to inefficient allocation of WM resources. In case of false memories, this would imply necessary cognitive effort to manage the extra semantic and perceptual load induced by the encoded stimuli.

Unilateral deep brain stimulation suppresses alpha and beta oscillations in sensorimotor cortices.

Deep brain stimulation (DBS) is an established therapy to treat motor symptoms in movement disorders such as Parkinson's disease (PD). The mechanisms leading to the high therapeutic effectiveness of DBS are poorly understood so far, but modulation of oscillatory activity is likely to play an important role. Thus, investigating the effect of DBS on cortical oscillatory activity can help clarifying the neurophysiological mechanisms of DBS. Here, we aimed at scrutinizing changes of cortical oscillatory activity by DBS at different frequencies using magnetoencephalography (MEG). MEG data from 17 PD patients were acquired during DBS of the subthalamic nucleus (STN) the day after electrode implantation and before implanting the pulse generator. We stimulated the STN unilaterally at two different stimulation frequencies, 130 Hz and 340 Hz using an external stimulator. Data from six patients had to be discarded due to strong artefacts and two other datasets were excluded since these patients were not able to finalize the paradigm. After DBS artefact removal, power spectral density (PSD) values of MEG were calculated for each individual patient and averaged over the group. DBS at both 130 Hz and 340 Hz led to a widespread suppression of cortical alpha/beta band activity (8-22 Hz) specifically over bilateral sensorimotor cortices. No significant differences were observed between the two stimulation frequencies. Our finding of a widespread suppression of cortical alpha/beta band activity is particularly interesting as PD is associated with pathologically increased levels of beta band activity in the basal ganglia-thalamo-cortical circuit. Therefore, suppression of such oscillatory activity might be an essential effect of DBS for relieving motor symptoms in PD and can be achieved at different stimulation frequencies above 100 Hz.

Modulation of alpha oscillations is required for the suppression of semantic interference.

Recent findings on alpha band oscillations suggest their important role in memory consolidation and suppression of external distractors such as environmental noise. However, less attention was given to the phenomenon of internal distracting information being solely inherent to the stimuli content. Human memory may be prone to internal distractions caused by semantic relatedness between the meaning of words (e.g., atom, neutron, nucleus, etc.) to be encoded, i.e., semantic interference. Our study investigates the brain oscillatory dynamics behind the semantic interference phenomenon, whose possible outcome is known as false memories. In this direction, Deese-Roediger-McDermott word lists were appropriated for a modified Sternberg paradigm in auditory modality. Participants received semantically related and unrelated word lists via headphones while EEG data were acquired. Semantic interference triggered the false memory rates to be higher than those of other types of memory errors. Analysis demonstrated that the upper part of alpha band (∼10-12Hz) power decreases on parieto-occipital channels in the retention interval, prior to the probe item for semantically related condition. Our study elucidates the oscillatory mechanisms behind semantic interference by relying on alpha functional inhibition theory.

Statistically reliable and fast direct estimation of phase-amplitude cross-frequency coupling.

There is growing interest in phase-amplitude cross-frequency coupling (PAC), which is widely observed in human and animal brain recordings. The choice of the estimation method is vital while extracting accurate PAC parameters from data. Two desired properties of PAC estimators are reliability and computational efficiency. This study offers a methodology called normalized direct PAC (ndPAC) for the rapid and statistically reliable estimation of PAC strength. A plain confidence limit formula, depending solely on data length and confidence level, is derived. Confidence level derivation is validated numerically. It is shown through simulations that ndPAC exhibits high specificity and sensitivity performances. The suggested methodology is also demonstrated on monkey electrocorticogram recorded during a visual task.

A critical note on the definition of phase-amplitude cross-frequency coupling.

Recent studies have observed the ubiquity of phase-amplitude coupling (PAC) phenomenon in human and animal brain recordings. While various methods were performed to quantify it, a rigorous analytical definition of PAC is lacking. This paper yields an analytical definition and accordingly offers theoretical insights into some of the current methods. A direct PAC estimator based on the given definition is presented and shown theoretically to be superior to some of the previous methods such as general linear model (GLM) estimator. It is also shown that the proposed PAC estimator is equivalent to GLM estimator when a constant term is removed from its formulation. The validity of the derivations is demonstrated with simulated data of varying noise levels and local field potentials recorded from the subthalamic nucleus of a Parkinson's disease patient.

High frequency oscillations in the subthalamic nucleus: a neurophysiological marker of the motor state in Parkinson's disease.

Increasing evidence suggests that abnormal oscillatory activity in basal ganglia and cortex plays a pivotal role in the pathophysiology of Parkinson's disease. Recordings of local field potentials from subthalamic nucleus of patients undergoing deep brain stimulation have focused on oscillations occurring at frequencies below 100 Hz in the alpha, beta and gamma range and suggested that, in particular, an increase of beta band oscillations underlies slowing of movement in Parkinson's disease. Recent findings showing that the amplitude of high frequency oscillations (>200 Hz) couples with the phase of beta activity have raised the important question about the role of subthalamic high frequency oscillations in Parkinson's disease. To investigate functional characteristics and clinical relevance of high frequency oscillations, we recorded local field potentials from 18 subthalamic nuclei of 9 akinetic-rigid Parkinsonian patients with implanted deep brain stimulation electrodes and still externalised leads before and after intake of levodopa. We identified two distinct bands of high frequency oscillations, one centred around 250 Hz and another one around 350 Hz that show characteristic levodopa dependent amplitude and coupling behaviours. Administration of levodopa changed the power ratio between the two high frequency bands towards the component centred around 350 Hz in all 18 nuclei under study (p<10(-4)). Moreover, this power ratio correlated significantly with the Unified Parkinson's Disease Rating Scale hemibody akinesia/rigidity subscore (r=0.3618, p=0.015), but interestingly not with beta peak power (p=0.1) suggesting that levodopa induced changes in high frequency and beta oscillations are at least potentially independent of each other. Accordingly, a combined parameter composed of power ratio of high frequency oscillations and beta peak power significantly increased the correlation with the motor state (r=0.45, p=0.004). These results indicate that a shift from slower to faster frequencies of the spectrum greater than 200 Hz represents a prokinetic neurophysiological marker underlying levodopa induced motor improvement in Parkinson's disease.

Spatial filtering of MEG signals for user-specified spherical regions.

We introduce a spatial filtering method in the spherical harmonics domain for constraining magnetoencephalographic (MEG) multichannel measurements to any user-specified spherical region of interest (ROI) inside the head. The method relies on a linear transformation of the signal space separation inner coefficients that represent the MEG signal generated by sources located inside the head. The spatial filtering is achieved effectively by constructing a spherical harmonics basis vector that is dependent on the center of the targeted ROI and it does not require any discrete division of the headspace into grids like the traditional MEG spatial filtering approaches. The validation and the performance of the method are demonstrated through both simulated and actual bilateral auditory-evoked data experiments.

Decomposition of magnetoencephalographic data into components corresponding to deep and superficial sources.

We extend the signal space separation (SSS) method to decompose multichannel magnetoencephalographic (MEG) data into regions of interest inside the head. It has been shown that the SSS method can transform MEG data into a signal component generated by neurobiological sources and a noise component generated by external sources outside the head. In this paper, we show that the signal component obtained by the SSS method can be further decomposed by a simple operation into signals originating from deep and superficial sources within the brain. This is achieved by using a scheme that exploits the beamspace methodology that relies on a linear transformation that maximizes the power of the source space of interest. The efficiency and accuracy of the algorithm are demonstrated by experiments utilizing both simulated and real MEG data.