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

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

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

Source analysis of median nerve stimulated somatosensory evoked potentials and fields using simultaneously measured EEG and MEG signals

The sources of somatosensory evoked potentials (SEPs) and fields (SEFs), which is a standard paradigm, is investigated using multichannel EEG and MEG simultaneous recordings. The hypothesis that SEP & SEF sources are generated in the posterior bank of the central sulcus is tested, and analyses are compared based on EEG only, MEG only, bandpass filtered MEG, and both combined. To locate 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 averaged head models consisting of a set of connected volumes, typically representing the skull, scalp, and brain. The location of each dipole is then estimated using fixed MUSIC and current-density-reconstruction (CDR) algorithms. For both analyses, the results demonstrate that the band-pass filtered MEG can localize the sources accurately at the desired region as compared to only EEG and unfiltered MEG. For CDR analysis, it looks like MEG affects EEG during the combined analyses. The MUSIC algorithm gives better results than CDR, and when comparing the two head models, the averaged and the realistic head models showed the same result

Discrimination of Parkinsonian Tremor From Essential Tremor by Voting Between Different EMG Signal Processing Techniques

Parkinson's disease (PD) and essential tremor (ET) are the two most common disorders that cause involuntary muscle shaking movements, or what is called "tremor”. PD is a neurodegenerative disease caused by the loss of dopamine receptors which control and adjust the movement of the body. On the other hand, ET is a neurological movement disorder which also causes tremors and shaking, but it is not related to dopamine receptor loss; it is simply a tremor. The differential diagnosis between these two disorders is sometimes difficult to make clinically because of the similarities of their symptoms; additionally, the available tests are complex and expensive. Thus, the objective of this paper is to discriminate between these two disorders with simpler, cheaper and easier ways by using electromyography (EMG) signal processing techniques. EMG and accelerometer records of 39 patients with PD and 41 with ET were acquired from the Hospital of Kiel University in Germany and divided into a trial group and a test group. Three main techniques were applied: the wavelet-based soft-decision technique, statistical signal characterization (SSC) of the spectrum of the signal, and SSC of the amplitude variation of the Hilbert transform. The first technique resulted in a discrimination efficiency of 80% on the trial set and 85% on the test set. The second technique resulted in an efficiency of 90% on the trial set and 82.5% on the test set. The third technique resulted in an 87.5% efficiency on the trial set and 65.5% efficiency on the test set. Lastly, a final vote was done to finalize the discrimination using these three techniques, and as a result of the vote, accuracies of 92.5%, 85.0% and 88.75% were obtained on the trial data, test data and total data, respectively

The role of the dentate gyrus and adult neurogenesis in hippocampal-basal ganglia associated behaviour

The ability of the brain to continually generate new neurons throughout life is one of the most intensely researched areas of modern neuroscience. While great advancements in understanding the biochemical mechanisms of adult neurogenesis have been made, there remain significant obstacles and gaps in connecting neurogenesis with behavioural and cognitive processes such as learning and memory. The purpose of the thesis was to examine by review and laboratory experimentation the role of the dentate gyrus and of adult neurogenesis within the hippocampus in the performance of cognitive tasks dependent on the hippocampal formation and hippocampal-basal ganglia interactions. Advancement in understanding the role of neurogenesis in these processes may assist in improving treatments for common brain injury and cognitive diseases that affect this region of the brain. Mild chronic stress reduced the acquisition rate of a stimulus-response task (p=0.043), but facilitated the acquisition of a discrimination between a small and a large reward (p=0.027). In locomotor activity assays, chronic stress did not shift the dose-response to methamphetamine. Analysis of 2,5-bromodeoxyuridine incorporation showed that, overall, chronic mild stress did not effect survival of neuronal progenitors . However, learning of the tasks had a positive influence on cell survival in stressed animals (p=0.038). Microinjections of colchicine produced significant lesions of the dentate gyrus and surrounding CA1-CA3 and neocortex. Damage to these regions impaired hippocampal-dependent reference memory (p=0.054) while preserving hippocampal independent simple discrimination learning. In a delay discounting procedure, the lesions did not induce impulsive-like behaviour when delay associated with a large reward was introduced. The experiments uphold a current theory that learning acts as a buffer to mitigate the negative effects of stress on neurogenesis

Fehler in DFT und FFT Neue Aspekte in Theorie und Anwendung

SIGLETIB: DO 4529 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Different Zoom Approaches for Improving Spectral Resolution with Applications in Radar Signal Processing

Different approaches for a high-resolution analysis of narrow-band spectra are reviewed and compared. Partial-band algorithms are proved to be zoom-FFT's. In this contribution, three new modifications of the (Subband-FFT) SB-FFT are presented. In the first modification the chirp z-transform substitutes the small FFT which calculates the band of interest. In the second modification, the idea of zero-padding the input signal is applied to the SB-FFT with pruning at both input and output. Lastly zooming a small band of frequencies using a method of transforming by parts is applied for a narrow-band signal using the adaptive SB-FFT. A newly introduced version of the subband technique is included also in this work. In this version the subband decomposition technique is combined with the linear prediction method for higher spectrum resolution. Application of the SB-FFT and its modified versions and the new version in measuring the Doppler-frequency directly and indirectly for the purpose of vehicle-speed measurements is introduced in this paper. Comparison between all methods in terms of complexity and resolution is given. A new idea of channel test is included to keep the real-time successive measurements of Doppler frequency stable and consistent as well as simple