Essential constraints for detecting deep sources in EEG - application to orthostatic tremor

The hypotheses that orthostatic tremor is generated by a central oscillator is been tested in this paper. 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 orthostatic tremor are addressed in this paper. The Dynamic Imaging of Coherent Sources (DICS) was used to find the coherent sources in the brain. The three essential constraints for detecting deep sources in the brain using EEG data were tested by three model simulations. The optimal number of electrodes, length of the data and the signal to noise ratio required for error-free localization was tested. In all the orthostatic tremor patients the corticomuscular coherence was also present in the basic and the first harmonic frequency of the tremor. The basic frequency constituted a network of primary leg area, supplementary motor area, primary motor cortex, two pre-motor sources, diencephalon and cerebellum. The first harmonic frequency was in the region of primary leg area, supplementary motor area, primary motor cortex, diencephalon and cerebellum. Thus the generation of these two oscillations involves the same network structure and indicates the oscillation at double the tremor frequency is a harmonic of the basic tremor frequency. The orthostatic tremor could have the central oscillator in the brain

Dynamic imaging of coherent sources reveals different network connectivity underlying the generation and perpetuation of epileptic seizures

The concept of focal epilepsies includes a seizure origin in brain regions with hyper synchronous activity (epileptogenic zone and seizure onset zone) and a complex epileptic network of different brain areas involved in the generation, propagation, and modulation of seizures. The purpose of this work was to study functional and effective connectivity between regions involved in networks of epileptic seizures. The beginning and middle part of focal seizures from ictal surface EEG data were analyzed using dynamic imaging of coherent sources (DICS), an inverse solution in the frequency domain which describes neuronal networks and coherences of oscillatory brain activities. The information flow (effective connectivity) between coherent sources was investigated using the renormalized partial directed coherence (RPDC) method. In 8/11 patients, the first and second source of epileptic activity as found by DICS were concordant with the operative resection site; these patients became seizure free after epilepsy surgery. In the remaining 3 patients, the results of DICS / RPDC calculations and the resection site were discordant; these patients had a poorer post-operative outcome. The first sources as found by DICS were located predominantly in cortical structures; subsequent sources included some subcortical structures: thalamus, Nucl. Subthalamicus and cerebellum. DICS seems to be a powerful tool to define the seizure onset zone and the epileptic networks involved. Seizure generation seems to be related to the propagation of epileptic activity from the primary source in the seizure onset zone, and maintenance of seizures is attributed to the perpetuation of epileptic activity between nodes in the epileptic network. Despite of these promising results, this proof of principle study needs further confirmation prior to the use of the described methods in the clinical praxis

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

Discrimination of Parkinsonian tremor from essential tremor by implementation of a wavelet-based soft-decision technique on EMG and accelerometer signals

A wavelet-decomposition with soft-decision algorithm is used to estimate an approximate power-spectral density (PSD) of both accelerometer and surface EMG signals for the purpose of discrimination of Parkinson tremor from essential tremor. A soft-decision wavelet-based PSD estimation is used with 256 bands for a signal sampled at 800 Hz. The sum of the entropy of the PSD in band 6 (7.8125–9.375 Hz) and band 11 (15.625–17.1875 Hz) is used as a classification factor. 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 threshold value of the classification factor differentiating 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. A “voting” between three results obtained from accelerometer signal and two EMG signals is applied to obtain the final discrimination. A total accuracy of discrimination of 85% is obtained

The cortical and sub-cortical network of sensory evoked response in healthy subjects

The aim of this study was to find the cortical and sub-cortical network responsible for the sensory evoked coherence in healthy subjects during electrical stimulation of right median nerve at wrist. The multitaper method was used to estimate the power and coherence spectrum followed by the source analysis method dynamic imaging of coherent sources (DICS) to find the highest coherent source for the basic frequency 3Hz and the complete cortical and sub-cortical network responsible for the sensory evoked coherence in healthy subjects. The highest coherent source for the basic frequency was in the posterior parietal cortex for all the subjects. The cortical and sub-cortical network comprised of the primary sensory motor cortex (SI), secondary sensory motor cortex (SII), frontal cortex and medial pulvinar nucleus in the thalamus. The cortical and sub-cortical network responsible for the sensory evoked coherence was found successfully with a 64-channel EEG system. The sensory evoked coherence is involved with a thalamo-cortical network in healthy subjects

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

Dynamical correlation of non-stationary signals in time domain — a comparative study

In a thorough study, the multitaper (MTM) and the extended continuous wavelet-transform (CWT) coherence-analysis methods were compared in terms of there application in determining the dynamics from the electroencephalogram (EEG) and electromyogram (EMG) signals of patients with Parkinsonian tremor. The main aim of the study in a biological point of view is to analyze whether the basic tremor frequency and its “first harmonic” frequency of Parkinsonian tremor are really harmonically related or are in fact distinct processes. The extension of the CWT is achieved by using a Morlet wavelet as the analysis window with an adjustable relative bandwidth which gives the flexibility in setting a desired frequency resolution. In order to obtain a perspective view of the two methods, they were applied to two different model signals to determine their actual threshold in detecting short-lived changes in the analysis of non-stationary signals and to determine their noise thresholds by adding external noise to the signals to test the reduction in coherence to be not merely due to the random fluctuations in stochastic signals. Beyond applying an autoregressive 2nd-order and a coupled van der Pol model system, however, also true EEG and EMG data from five Parkinson patients were used. The results were compared in terms of the time and frequency resolutions of these two methods, and it was determined that the multitaper method was able to detect reduction in power and coherence as short as 1 s. The extended CWT analysis only revealed gaps that were longer than 3 s. The time gaps in the coherence indicate the loss of connection between the cortex and muscle during the respective time intervals. This more accurate analysis of the MTM was also seen in the dynamical EEG–EMG coherence at the tremor frequency and its “first harmonic” of Parkinsonian patients. In terms of our “biological” aim, this shows distinct prevalence of the corticomuscular coupling at those frequencies over time. Applying this method to biological data reveals important aspects about their dynamics, e.g., the distinct dynamics between basic frequency and “first harmonic” frequency over time in Parkinsonian tremor

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