CMC is more than a measure of corticospinal tract integrity in acute stroke patients

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CMC is weaker and occurs at lower frequencies in acute stroke patients.

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Both afferent and efferent input signals contribute to CMC.

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CMC should not be used as a direct measure of corticospinal tract integrity.

In healthy subjects, motor cortex activity and electromyographic (EMG) signals from contracting contralateral muscle show coherence in the beta (15–30 Hz) range. Corticomuscular coherence (CMC) is considered a sign of functional coupling between muscle and brain. Based on prior studies, CMC is altered in stroke, but functional significance of this finding has remained unclear. Here, we examined CMC in acute stroke patients and correlated the results with clinical outcome measures and corticospinal tract (CST) integrity estimated with diffusion tensor imaging (DTI). During isometric contraction of the extensor carpi radialis muscle, EMG and magnetoencephalographic oscillatory signals were recorded from 29 patients with paresis of the upper extremity due to ischemic stroke and 22 control subjects. CMC amplitudes and peak frequencies at 13–30 Hz were compared between the two groups. In the patients, the peak frequency in both the affected and the unaffected hemisphere was significantly (p < 0.01) lower and the strength of CMC was significantly (p < 0.05) weaker in the affected hemisphere compared to the control subjects. The strength of CMC in the patients correlated with the level of tactile sensitivity and clinical test results of hand function. In contrast, no correlation between measures of CST integrity and CMC was found. The results confirm the earlier findings that CMC is altered in acute stroke and demonstrate that CMC is bidirectional and not solely a measure of integrity of the efferent corticospinal tract.

Vacancy-donor complexes in highly n-type Ge doped with As, P and Sb

Positron annihilation spectroscopy was performed to study defects in Ge doped with As, P and Sb. In each case, the samples had approximately the same dopant concentration  ∼10(19) cm(-3). Results from the Doppler broadening and positron lifetime spectroscopies were compared to electronic structure calculations. The positron lifetime results show that the open volume related to the defect centers is not larger than a monovacancy. The results suggest that in the As doped sample the dominant trap at room temperature is a complex consisting of a vacancy and at least three dopant atoms. In the case of P doped Ge the results indicate that two defect complexes compete in positron trapping. Complexes with a higher number of P atoms around the vacancy seem to dominate at room temperature whereas at low temperature positron trapping at centers with fewer P atoms around the vacancy becomes more significant. The complexes with fewer P atoms are more negatively charged. In Sb doped Ge the results suggest that several types of traps are simultaneously competing in positron trapping at all measurement temperatures.

Optimal spatial filtering for brain oscillatory activity using the Relevance Vector Machine

Over the past decade, various techniques have been proposed for localization of cerebral sources of oscillatory activity on the basis of magnetoencephalography (MEG) or electroencephalography recordings. Beamformers in the frequency domain, in particular, have proved useful in this endeavor. However, the localization accuracy and efficacy of such spatial filters can be markedly limited by bias from correlation between cerebral sources and short duration of source activity, both essential issues in the localization of brain data. Here, we evaluate a method for frequency-domain localization of oscillatory neural activity based on the relevance vector machine (RVM). RVM is a Bayesian algorithm for learning sparse models from possibly overcomplete data sets. The performance of our frequency-domain RVM method (fdRVM) was compared with that of dynamic imaging of coherent sources (DICS), a frequency-domain spatial filter that employs a minimum variance adaptive beamformer (MVAB) approach. The methods were tested both on simulated and real data. Two types of simulated MEG data sets were generated, one with continuous source activity and the other with transiently active sources. The real data sets were from slow finger movements and resting state. Results from simulations show comparable performance for DICS and fdRVM at high signal-to-noise ratios and low correlation. At low SNR or in conditions of high correlation between sources, fdRVM performs markedly better. fdRVM was successful on real data as well, indicating salient focal activations in the sensorimotor area. The resulting high spatial resolution of fdRVM and its sensitivity to low-SNR transient signals could be particularly beneficial when mapping event-related changes of oscillatory activity.

Is Small-Fenestra Stapedotomy a Safer Outpatient Procedure than Total Stapedectomy?

We compared two stapedoplasty techniques to evaluate whether one technique is safer than the other as an outpatient procedure and to demonstrate possible reasons for outpatient failures. We performed a retrospective study of patient records of 94 operated adult patients who were all initially scheduled for outpatient surgery for otosclerosis (47 total stapedectomies and 47 small-fenestra stapedotomies). Six patients (13%) with stapedectomy and 1 patient (2%) with stapedotomy had to stay overnight at the hospital due to postoperative vertigo and nausea. The number of outpatient failures was statistically significantly different between the stapedoplasty techniques (p = 0.05). Five patients (11%) with stapedectomy and 2 patients (4%) with stapedotomy had a drop in bone conduction threshold between 5 and 8 dB pre- to postoperatively (n.s.). The short-term hearing improvement did not differ statistically significantly between the techniques when compared to the preoperative values. Small-fenestra stapedotomy is the safer procedure to be performed as outpatient setting than total stapedectomy.

Neuromagnetic localization of rhythmic activity in the human brain: a comparison of three methods

Cortical rhythmic activity is increasingly employed for characterizing human brain function. Using MEG, it is possible to localize the generators of these rhythms. Traditionally, the source locations have been estimated using sequential dipole modeling. Recently, two new methods for localizing rhythmic activity have been developed, Dynamic Imaging of Coherent Sources (DICS) and Frequency-Domain Minimum Current Estimation (MCE(FD)). With new analysis methods emerging, the researcher faces the problem of choosing an appropriate strategy. The aim of this study was to compare the performance and reliability of these three methods. The evaluation was performed using measured data from four healthy subjects, as well as with simulations of rhythmic activity. We found that the methods gave comparable results, and that all three approaches localized the principal sources of oscillatory activity very well. Dipole modeling is a very powerful tool once appropriate subsets of sensors have been selected. MCE(FD) provides simultaneous localization of sources and was found to give a good overview of the data. With DICS, it was possible to separate close-by sources that were not retrieved by the other two methods.

Properties of MEG tomographic maps obtained with spatial filtering

Magnetoencephalography (MEG) has, in comparison with other functional imaging modalities, unique properties which makes it the prime candidate for the noninvasive investigation of long-range oscillatory interactions in the human brain. Recent methodological developments based on spatial filtering introduced the computation of functional tomographic maps covering the entire brain and representing the distribution of coherence to a given reference signal or the distribution of power. Because of the spatially inhomogeneous sensitivity profile of the MEG sensors, the spatial resolution of the resulting functional maps is not isotropic across the brain. Here, we introduce a convenient analytic expression for the computation of the spatial resolution at any given point in the brain. We derive the dependence of the resolution on the signal-to-noise ratio and on the changes of the leadfields. The resolution map can be displayed on anatomical MRI in the same way as the functional maps. In addition, we establish a procedure for computing a confidence volume of local maxima which is based on a bootstrap method. The confidence volume is a measure for the uncertainty of the localization. It is important for assigning local maxima of activation to specific anatomical structures and may be used to test for differences in localization between different experimental conditions.

The neural basis of intermittent motor control in humans

The basic question of whether the human brain controls continuous movements intermittently is still under debate. Here we show that 6- to 9-Hz pulsatile velocity changes of slow finger movements are directly correlated to oscillatory activity in the motor cortex, which is sustained by cerebellar drive through thalamus and premotor cortex. Our findings suggest that coupling of 6- to 9-Hz oscillatory activity in the cerebello-thalamo-cortical loop represents the neural mechanism for the intermittent control of continuous movements.

Dynamic imaging of coherent sources: Studying neural interactions in the human brain

Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept ("binding") is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto- or electroencephalography (MEG/EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.

Lateralization of speech processing in the brain as indicated by mismatch negativity and dichotic listening

The goal of the present study was to evaluate the differences between dichotic listening and mismatch negativity as measures of speech lateralization in the human brain. For this purpose, we recorded the magnetic equivalent of the mismatch negativity, elicited by consonant-vowel syllable change, and tested the same subjects in the dichotic listening procedure. The results showed that both methods indicated left-hemisphere dominance in speech processing. However, the mismatch negativity, as compared to the right-ear advantage, suggested slightly stronger left-hemisphere dominance in speech processing. No clear correlation was found between the laterality indexes of mismatch negativity and right-ear advantage calculated from dichotic listening results. The possible explanation for this finding would be that these two measures reflect different stages of speech processing in the human brain.