Tolga Esat Özkurt

Distinct oscillatory STN-cortical loops revealed by simultaneous MEG and local field potential recordings in patients with Parkinson's disease

Neuronal oscillations are assumed to play a pivotal role in the pathophysiology of Parkinsons disease (PD). Neurons in the subthalamic nucleus (STN) generate oscillations which are coupled to rhythmic population activity both in other basal ganglia nuclei and cortical areas. In order to localize these cortical areas, we recorded local field potentials (LFPs) and magnetoencephalography (MEG) simultaneously in PD patients undergoing surgery for deep brain stimulation (DBS). Patients were withdrawn from antiparkinsonian medication and recorded at rest. We scanned the entire brain for oscillations coherent with LFPs recorded from the STN with a frequency domain beamformer. Coherent activity in the low (12-20 Hz) and high (20-35 Hz) beta range was found in the ipsilateral sensorimotor and the premotor cortex. Coherence in the alpha range (7-12 Hz) was observed at various locations in the ipsilateral temporal lobe. In a subset of subjects, the superior temporal gyrus consistently showed coherent alpha oscillations. Our findings provide new insights into patterns of frequency-specific functional connectivity between basal ganglia and cortex and suggest that simultaneous inter-regional interactions may be segregated in the frequency domain. Furthermore, they demonstrate that simultaneous MEG-LFP recordings are a powerful tool to study interactions between brain areas in PD patients undergoing surgery for DBS.

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 Parkinsons 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 Parkinsons 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 Parkinsons 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 Parkinsons 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 Parkinsons disease.

A direct relationship between oscillatory subthalamic nucleus–cortex coupling and rest tremor in Parkinson’s disease

Electrophysiological studies suggest that rest tremor in Parkinsons disease is associated with an alteration of oscillatory activity. Although it is well known that tremor depends on cortico-muscular coupling, it is unclear whether synchronization within and between brain areas is specifically related to the presence and severity of tremor. To tackle this longstanding issue, we took advantage of naturally occurring spontaneous tremor fluctuations and investigated cerebral synchronization in the presence and absence of rest tremor. We simultaneously recorded local field potentials from the subthalamic nucleus, the magnetoencephalogram and the electromyogram of forearm muscles in 11 patients with Parkinsons disease (all male, age: 52-74 years). Recordings took place the day after surgery for deep brain stimulation, after withdrawal of anti-parkinsonian medication. We selected epochs containing spontaneous rest tremor and tremor-free epochs, respectively, and compared power and coherence between subthalamic nucleus, cortex and muscle across conditions. Tremor-associated changes in cerebro-muscular coherence were localized by Dynamic Imaging of Coherent Sources. Subsequently, cortico-cortical coupling was analysed by computation of the imaginary part of coherency, a coupling measure insensitive to volume conduction. After tremor onset, local field potential power increased at individual tremor frequency and cortical power decreased in the beta band (13-30 Hz). Sensor level subthalamic nucleus-cortex, cortico-muscular and subthalamic nucleus-muscle coherence increased during tremor specifically at tremor frequency. The increase in subthalamic nucleus-cortex coherence correlated with the increase in electromyogram power. On the source level, we observed tremor-associated increases in cortico-muscular coherence in primary motor cortex, premotor cortex and posterior parietal cortex contralateral to the tremulous limb. Analysis of the imaginary part of coherency revealed tremor-dependent coupling between these cortical areas at tremor frequency and double tremor frequency. Our findings demonstrate a direct relationship between the synchronization of cerebral oscillations and tremor manifestation. Furthermore, they suggest the feasibility of tremor detection based on local field potentials and might thus become relevant for the design of closed-loop stimulation systems.

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 Parkinsons disease patient.

Parkinsonian Rest Tremor Is Associated With Modulations of Subthalamic High‐Frequency Oscillations

High frequency oscillations (>200 Hz) have been observed in the basal ganglia of PD patients and were shown to be modulated by the administration of levodopa and voluntary movement.

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.

Principal Component Analysis of the Fractional Brownian Motion for 0 l H l 0.5

Principal component analysis (PCA) has been proposed for the estimation of the self-similarity parameter H, namely the Hurst parameter of 1/f processes, and an analytical proof is provided only for H/=0.5 in a recent study [I]. In our paper, we extend this study by deriving explicit expressions and presenting an analytical proof for the range of 0 < H < 0.5 (the anti-persistent part of the fractional Brownian motion). We also show via simulations that the accuracy of the estimated H values may decrease considerably as the theoretical H value increases towards the persistent part (0.5<H<1)

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.

Beamspace Magnetoencephalographic Signal Decomposition in Spherical Harmonics Domain

The recently proposed signal space separation (SSS) method can transform the multichannel magnetic measurements of brain (MEG) into parts that correspond to inner sources and undesired external interferences. In this paper, we extend this method by decomposing the signal into deep and superficial regions. This is realized by manipulating the SSS coefficients using a scheme that exploits beamspace methodology. It relies on estimating a linear transformation which maximizes the power of the source space of interest over the power of remaining part. We demonstrate that this method yields a simple and direct way to decompose the signal into deep and/or superficial parts

Decomposition of MEG signals with sparse representations

We suggest an iterative method for the decomposition of MEG signals into some user-specified parts. It is based on a technique called morphological component analysis (MCA), which seeks sparse representations. A numerical simulation is carried out to reveal the performance characteristics of this method.