The central oscillatory network of essential tremor

The responsible pathological mechanisms of essential tremor are not yet clear. In order to understand the mechanisms of the central network its sources need to be found. The cortical sources of both the basic and first “harmonic” frequency of essential tremor are addressed in this paper. The power and coherence were estimated using the multitaper method for EEG and EMG data from 6 essential tremor patients. The Dynamic Imaging of Coherent Sources (DICS) was used to find the coherent sources in the brain. Before hand this method was validated for the application of finding multiple sources for the same oscillation in the brain by using two model simulations which indicated the accuracy of the method. In all the essential tremor patients the corticomuscular coherence was also present in the basic and the first harmonic frequency of the tremor. The source for the basic frequency and the first harmonic frequency was in the region of primary sensory motor cortex, prefrontal and in the diencephalon on the contralateral side for all the patients. Thus the generation of these two oscillations involves the same cortical areas and indicates the oscillation at double the tremor frequency is a harmonic of the basic tremor frequency

Imaging coherent sources of tremor related EEG activity in patients with Parkinson's disease

The cortical sources of both the basic and first 'harmonic' frequency of Parkinsonian tremor are addressed in this paper. The power and coherence was estimated using the multitaper method for EEG and EMG data from 6 Parkinsonian patients with a classical rest tremor. The Dynamic Imaging of Coherent Sources (DICS) was used to find the coherent sources in the brain. Before hand this method was validated for the application to the EEG by showing in 3 normal subjects that rhythmic stimuli (1-5Hz) to the median nerve leads to almost identical coherent sources for the basic and first harmonic frequency in the contralateral sensorimotor cortex which is the biologically plausible result. In all the Parkinson patients the corticomuscular coherence was also present in the basic and the first harmonic frequency of the tremor. However, the source for the basic frequency was close to the frontal midline and the first harmonic frequency was in the region of premotor and sensory motor cortex on the contralateral side for all the patients. Thus the generation of these two oscillations involves different cortical areas and possibly follows different pathways to the periphery

A neural network approach to distinguish Parkinsonian tremor from advanced essential tremor

A new technique for discrimination between Parkinsonian tremor and essential tremor is investigated in this paper. The method is based on spectral analysis of both accelerometer and surface EMG signals with neural networks. The discrimination system consists of two parts: feature extraction part and classification (distinguishing) part. The feature extraction part uses the method of approximate spectral density estimation of the data by implementing the wavelet-based soft decision technique. In the classification part, a machine learning approach is implemented using back-propagation supervised neural network. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the important features used for distinguishing between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance

Differentiating phase shift and delay in narrow band coherent signals

Objective Differentiating between a fixed activation pattern (phase shift) and conduction time (time delay) in rhythmic signals has important physiological implications but is methodologically difficult. Methods Delay was estimated by the maximising coherence method and phase spectra calculated between (i) a narrow band-pass filtered AR2 process and its delayed copy for different phase shifts, (ii) the surface EMGs from two antagonistic forearm muscles with reciprocal alternating activity, and (iii) EEG and EMG data from 11 recordings in five Parkinsonian tremor patients. Results Estimated delays between the versions of the AR2 process resembled the real delay and were not significantly biased by the phase-shifts. The reciprocal alternating pattern of muscle activation was shown to be a pure phase-shift without any time delay. The phase between tremor-coherent cortical electrodes and EMG showed opposite signs and differed by 3pi/4-pi between the antagonistic muscles. Bidirectional delays between contralateral cortex and EMG did not differ between the antagonists and were in keeping with fast corticospinal transmission and feedback to the cortex for both muscles. Conclusions Phase shifts and delays reflect different mechanisms in tremor related oscillatory interactions. Significance The maximising coherence method can differentiate between them

Cortico-muscular coherence on artifact corrected EEG-EMG data recorded with a MRI scanner

Simultaneous recording of electroencephalogram (EEG) and electromyogram (EMG) with magnetic resonance imaging (MRI) provides great potential for studying human brain activity with high temporal and spatial resolution. But, due to the MRI, the recorded signals are contaminated with artifacts. The correction of these artifacts is important to use these signals for further spectral analysis. The coherence can reveal the cortical representation of peripheral muscle signal in particular motor tasks, e.g. finger movements. The artifact correction of these signals was done by two different algorithms the Brain vision analyzer (BVA) and the Matlab FMRIB plug-in for EEGLAB. The Welch periodogram method was used for estimating the cortico-muscular coherence. Our analysis revealed coherence with a frequency of 5Hz in the contralateral side of the brain. The entropy is estimated for the calculated coherence to get the distribution of coherence in the scalp. The significance of the paper is to identify the optimal algorithm to rectify the MR artifacts and as a first step to use both these signals EEG and EMG in conjunction with MRI for further studies

Cortical representation of different motor rhythms during bimanual movements

The cortical control of bimanual and unimanual movements involves complex facilitatory and inhibitory interhemispheric interactions. We analysed the part of the cortical network directly related to the motor output by corticomuscular (64 channel EEG–EMG) and cortico-cortical (EEG–EEG) coherence and delays at the frequency of a voluntarily maintained unimanual and bimanual rhythm and in the 15–30-Hz band during isometric contractions. Voluntary rhythms of each hand showed coherence with lateral cortical areas in both hemispheres and occasionally in the frontal midline region (60–80 % of the recordings and 10–30 %, respectively). They were always coherent between both hands, and this coherence was positively correlated with the interhemispheric coherence (p < 0.01). Unilateral movements were represented mainly in the contralateral cortex (60–80 vs. 10–30 % ipsilateral, p < 0.01). Ipsilateral coherence was more common in left-hand movements, paralleled by more left–right muscle coherence. Partial corticomuscular coherence most often disappeared (p < 0.05) when the contralateral cortex was the predictor, indicating a mainly indirect connection of ipsilateral/frontomesial representations with the muscle via contralateral cortex. Interhemispheric delays had a bimodal distribution (1–10 and 15–30 ms) indicating direct and subcortical routes. Corticomuscular delays (mainly 12–25 ms) indicated fast corticospinal projections and musculocortical feedback. The 15–30-Hz corticomuscular coherence during isometric contractions (60–70 % of recordings) was strictly contralaterally represented without any peripheral left–right coherence. Thus, bilateral cortical areas generate voluntary unimanual and bimanual rhythmic movements. Interhemispheric interactions as detected by EEG–EEG coherence contribute to bimanual synchronization. This is distinct from the unilateral cortical representation of the 15–30-Hz motor rhythm during isometric movements

Oscillating central motor networks in pathological tremors and voluntary movements: what makes the difference?

Parkinsonian tremor (PD), essential tremor (ET) and voluntarily mimicked tremor represent fundamentally different motor phenomena, yet, magnetoencephalographic and imaging data suggest their origin in the same motor centers of the brain. Using EEG–EMG coherence and coherent source analysis we found a different pattern of corticomuscular delays, time courses and central representations for the basic and double tremor frequencies typical for PD suggesting a wider range defective oscillatory activity. For the basic tremor frequency similar central representations in primary sensorimotor, prefrontal/premotor and diencephalic (e.g. thalamic) areas were reproduced for all three tremors. But renormalized partial directed coherence of the spatially filtered (source) signals revealed a mainly unidirectional flow of information from the diencephalon to cortex in voluntary tremor, e.g. a thalamocortical relay, as opposed to a bidirectional subcortico-cortical flow in PD and ET promoting uncontrollable, e.g. thalamocortical, loop oscillations. Our results help to understand why pathological tremors although originating from the physiological motor network are not under voluntary control and they may contribute to the solution of the puzzle why high frequency thalamic stimulation has a selective effect on pathological tremor leaving voluntary movement performance almost unaltered

NEW METHOD FOR FAST IMAGE EDGE DETECTION BASED ON SUBBAND DECOMPOSITION

ABSTRACT A new method of detection the edges of an image is presented in this article. The method uses a kind of twodimensional subband spectrum analysis (2D-SSA) filter that is based on subband decomposition, and it is very convenient to get the edge frequency spectrum of an image after certain preprocessing. Comparing with spatial methods, the method is less sensitive to noise. It is also superior to the conventional frequency methods. In conventional frequency methods, the bandwidth and central frequency of filter are fixed, and it needs to transform the whole image into frequency domain. While in this method, the bandwidth and central frequency can be adjusted flexibly, and it only uses a few pixels to implement FFT. So this method is a fast way to extract the edges of an image. The simulation results show its efficiency

Comparison of EEG and MEG in source localization of induced human gamma-band oscillations during visual stimulus

High frequency gamma oscillations are indications of information processing in cortical neuronal networks. Recently, non-invasive detection of these oscillations have become one of the main research areas in magnetoencephalography (MEG) and electroencephalography (EEG) studies. The aim of this study, which is a continuation of our previous MEG study, is to compare the capability of the two modalities (EEG and MEG) in localizing the source of the induced gamma activity due to a visual stimulus, using a spatial filtering technique known as dynamic imaging of coherent sources (DICS). To do this, the brain activity was recorded using simultaneous MEG and EEG measurement and the data were analyzed with respect to time, frequency, and location of the strongest response. The spherical head modeling technique, such as, the three-shell concentric spheres and an overlapping sphere (local sphere) have been used as a forward model to calculate the external electromagnetic potentials and fields recorded by the EEG and MEG, respectively. Our results from the time-frequency analysis, at the sensor level, revealed that the parieto-occipital electrodes and sensors from both modalities showed a clear and sustained gamma-band activity throughout the post-stimulus duration and that both modalities showed similar strongest gamma-band peaks. It was difficult to interpret the spatial pattern of the gamma-band oscillatory response on the scalp, at the sensor level, for both modalities. However, the source analysis result revealed that MEG3 sensor type, which measure the derivative along the longitude, showed the source more focally and close to the visual cortex (cuneus) as compared to that of the EEG

Dipole source analysis for readiness potential and field using simultaneously measured EEG and MEG signals

Various source localization techniques have indicated the generators of each identifiable component of movement-related cortical potentials, since the discovery of the surface negative potential prior to self-paced movement by Kornhuber and Decke. Readiness potentials and fields preceding self-paced finger movements were recorded simultaneously using multichannel electroencephalography (EEG) and magnetoencephalography (MEG) from five healthy subjects. The cortical areas involved in this paradigm are the supplementary motor area (SMA) (bilateral), pre-SMA (bilateral), and contralateral motor area of the moving finger. This hypothesis is tested in this paper using the dipole source analysis independently for only EEG, only MEG, and both combined. To localize the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for spherical head models consisting of a set of connected volumes, typically representing the scalp, skull, and brain. In the source reconstruction it is to be expected that EEG predominantly localizes radially oriented sources while MEG localizes tangential sources at the desired region of the cortex. The effect of MEG on EEG is also observed when analyzing both combined data. When comparing the two head models, the spherical and the realistic head models showed similar results. The significant points for this study are comparing the source analysis between the two modalities (EEG and MEG) so as to assure that EEG is sensitive to mostly radially orientated sources while MEG is only sensitive to only tangential sources, and comparing the spherical and individual head models