Dirk Petersen

Demyelination reduces brain parenchymal stiffness quantified in vivo by magnetic resonance elastography.

The detection of pathological tissue alterations by manual palpation is a simple but essential diagnostic tool, which has been applied by physicians since the beginnings of medicine. Recently, the virtual “palpation” of the brain has become feasible using magnetic resonance elastography, which quantifies biomechanical properties of the brain parenchyma by analyzing the propagation of externally elicited shear waves. However, the precise molecular and cellular patterns underlying changes of viscoelasticity measured by magnetic resonance elastography have not been investigated up to date. We assessed changes of viscoelasticity in a murine model of multiple sclerosis, inducing reversible demyelination by feeding the copper chelator cuprizone, and correlated our results with detailed histological analyses, comprising myelination, extracellular matrix alterations, immune cell infiltration and axonal damage. We show firstly that the magnitude of the complex shear modulus decreases with progressive demyelination and global extracellular matrix degradation, secondly that the loss modulus decreases faster than the dynamic modulus during the destruction of the corpus callosum, and finally that those processes are reversible after remyelination.

Differential cerebellar activation related to perceived pain intensity during noxious thermal stimulation in humans: a functional magnetic resonance imaging study

Little is known about the cerebellar involvement in pain processing in spite of the fact that the cerebellum probably plays a crucial role in pain-related behavior. Using functional magnetic resonance imaging we examined the differential cerebellar activation in 18 healthy subjects in relation to their perceived pain-intensity of noxious and non-noxious thermal stimuli. In contrast to non-noxious (40 degrees C) stimuli, noxious (48.5 degrees C) stimuli revealed activation in the deep cerebellar nuclei, anterior vermis and bilaterally in the cerebellar hemispheric lobule VI. With the same noxious stimulus (48.5 degrees C) there was differential cerebellar activation depending on the perceived pain intensity: high pain intensity ratings were associated with activation in ipsilateral hemispheric lobule III-VI, deep cerebellar nuclei and in the anterior vermis (lobule III). This differential cerebellar activation pattern probably reflects not only somatosensory processing but also perceived pain intensity that may be important for cerebellar modulation of nociceptive circuits.

Disappearance of central thalamic pain syndrome after contralateral parietal lobe lesion: implications for therapeutic brain stimulation.

At present there is hardly any appropriate therapy for central pain syndromes available. We report on a unique case of a central thalamic pain syndrome that did not respond to any therapy but disappeared after an additional contralateral parietal lobe lesion. This example indicates that lesions affecting the bilateral balance of thalamo-parietal circuits may lead to pain relief in patients with central pain syndrome, which probably constitutes a bilateral disorder of functional plasticity. This should be taken into account in chronic brain stimulation for persistent pain states.

Modulation of cerebellar activation by predictive and non-predictive sequential finger movements.

We investigated the modulation of cerebellar activation by predictive and non-predictive sequential finger movements. It is hypothesized that the prediction of desired movement sequences and adaptation to new movement parameters is mediated by the cerebellum. Using functional MRI at 1.5T, seven normal subjects performed sequential finger to thumb opposition movements, either in predictive (repeatedly 2,3,4,5) or non-predictive (randomized) fashion at a constant frequency of 1 Hz. Performance and error rates were monitored by simultaneous recording of the finger movements. Predictive sequential finger opposition movements activated a cerebellar network including the lobuli IV–VI ipsilateral to the movements, the contralateral lobuli IV–VI, the vermis, and lobuli VIIB–VIII ipsilaterally. Non-predictive compared to predictive finger opposition movements activated a broader area within the ipsi- and contralateral anterior cerebellum, lobuli IV–VI, the vermis, and the ipsilateral lobuli VIIB–VIII. Additional activation foci were found in the contralateral lobuli VIIA and VIIB–VIII. Our study demonstrates a modulated information processing within the cerebellar network dependent on the predictability of movement sequences.

Effects of perceived and exerted pain control on neural activity during pain relief in experimental heat hyperalgesia: A fMRI study

Perceived control over pain can attenuate pain perception by mechanisms of endogenous pain control and emotional reappraisal irrespective of whether this control is exerted or only perceived. Self‐initiated termination of pain elicits different expectations of subsequent pain relief as compared to perceived pain control. It is unknown whether and how this perceived vs. exerted control on pain differs and affects subsequent pain relief. Using fMRI, we studied two factors of pain control on pain relief: the (i) sense of control (perceived control but no execution) and (ii) the execution of control (exerted control). To account for the impact of factual execution of pain control on pain relief we applied bearable short and hardly bearable long contact‐heat stimuli which were applied either controllable or not. Using controllability as factor, there was dissociable neural activity during pain relief: following the perceived control condition neural activity was found in the orbitofrontal and mediofrontal cortex and, following the exerted control condition, in the anterolateral and dorsolateral prefrontal cortex and posterior parietal cortex.

Grading system for the selection of patients with congenital aural atresia for active middle ear implants

IntroductionActive middle ear implants (aMEI) are being increasingly used for hearing restoration in congenital aural atresia. The existing gradings used for CT findings do not meet the requirements for these implants. Some items are expendable, whereas other important imaging factors are missing. We aimed to create a new grading system that could describe the extent of the malformation and predict the viability and challenges of implanting an aMEI.MethodsOne hundred three malformed ears were evaluated using HRCT of the temporal bone. The qualitative items middle ear and mastoid pneumatization, oval window, stapes, round window, tegmen mastoideum displacement and facial nerve displacement were included. An anterior- and posterior round window corridor, oval window and stapes corridor were quantified and novelly included. They describe the size of the surgical field and the sight towards the windows.ResultsThe ears were graded on a 16-point scale (16–13 easy, 12–9 moderate, 8–5 difficult, 4–0 high risk). The strength of agreement between the calculated score and the performed implantations was good. The comparison of the new 16-point scale with the Jahrsdoerfer score showed that both were able to conclusively detect the high-risk group; however, the new 16-point scale was able to further determine which malformed ears were favorable for aMEI, which the Jahrsdoerfer score could not do.ConclusionThe Active Middle Ear Implant Score for aural atresia (aMEI score) allows more precise risk stratification and decision making regarding the implantation. The use of operative corridors seems to have significantly better prognostic accuracy than the Jahrsdoerfer score.

Biophysical modeling of brain tumor progression: From unconditionally stable explicit time integration to an inverse problem with parabolic PDE constraints for model calibration

PURPOSE A novel unconditionally stable, explicit numerical method is introduced to the field of modeling brain cancer progression on a tissue level together with an inverse problem (IP) based on optimal control theory that allows for automated model calibration with respect to observations in clinical imaging data. METHODS Biophysical models of cancer progression on a tissue level are in general based on the assumption that the spatiotemporal spread of cancerous cells is determined by cell division and net migration. These processes are typically described in terms of a parabolic partial differential equation (PDE). In the present work a parallelized implementation of an unconditionally stable, explicit Euler (EE(⋆)) time integration method for the solution of this PDE is detailed. The key idea of the discussed EE(⋆) method is to relax the strong stability requirement on the spectral radius of the coefficient matrix by introducing a subdivision regime for a given outer time step. The performance is related to common implicit numerical methods. To quantify the numerical error, a simplified model that has a closed form solution is considered. To allow for a systematic, phenomenological validation a novel approach for automated model calibration on the basis of observations in medical imaging data is developed. The resulting IP is based on optimal control theory and manifests as a large scale, PDE constrained optimization problem. RESULTS The numerical error of the EE(⋆) method is at the order of standard implicit numerical methods. The computing times are well below those obtained for implicit methods and by that demonstrate efficiency. Qualitative and quantitative analysis in 12 patients demonstrates that the obtained results are in strong agreement with observations in medical imaging data. Rating simulation success in terms of the mean overlap between model predictions and manual expert segmentations yields a success rate of 75% (9 out of 12 patients). CONCLUSIONS The discussed EE(⋆) method provides desirable features for image-based model calibration or hybrid image registration algorithms in which the model serves as a biophysical prior. This is due to (i) ease of implementation, (ii) low memory requirements, (iii) efficiency, (iv) a straightforward interface for parameter updates, and (v) the fact that the method is inherently matrix-free. The explicit time integration method is confirmed via experiments for automated model calibration. Qualitative and quantitative analysis demonstrates that the proposed framework allows for recovering observations in medical imaging data and by that phenomenological model validity.PURPOSE A novel unconditionally stable, explicit numerical method is introduced to the field of modeling brain cancer progression on a tissue level together with an inverse problem (IP) based on optimal control theory that allows for automated model calibration with respect to observations in clinical imaging data. METHODS Biophysical models of cancer progression on a tissue level are in general based on the assumption that the spatiotemporal spread of cancerous cells is determined by cell division and net migration. These processes are typically described in terms of a parabolic partial differential equation (PDE). In the present work a parallelized implementation of an unconditionally stable, explicit Euler (EE⋆ ) time integration method for the solution of this PDE is detailed. The key idea of the discussed EE⋆ method is to relax the strong stability requirement on the spectral radius of the coefficient matrix by introducing a subdivision regime for a given outer time step. The performance is related to common implicit numerical methods. To quantify the numerical error, a simplified model that has a closed form solution is considered. To allow for a systematic, phenomenological validation a novel approach for automated model calibration on the basis of observations in medical imaging data is developed. The resulting IP is based on optimal control theory and manifests as a large scale, PDE constrained optimization problem. RESULTS The numerical error of the EE⋆ method is at the order of standard implicit numerical methods. The computing times are well below those obtained for implicit methods and by that demonstrate efficiency. Qualitative and quantitative analysis in 12 patients demonstrates that the obtained results are in strong agreement with observations in medical imaging data. Rating simulation success in terms of the mean overlap between model predictions and manual expert segmentations yields a success rate of 75% (9 out of 12 patients). CONCLUSIONS The discussed EE⋆ method provides desirable features for image-based model calibration or hybrid image registration algorithms in which the model serves as a biophysical prior. This is due to (i) ease of implementation, (ii) low memory requirements, (iii) efficiency, (iv) a straightforward interface for parameter updates, and (v) the fact that the method is inherently matrix-free. The explicit time integration method is confirmed via experiments for automated model calibration. Qualitative and quantitative analysis demonstrates that the proposed framework allows for recovering observations in medical imaging data and by that phenomenological model validity.

Risk Assessment of Magnetic Resonance Imaging in Chronically Implanted Paddle Electrodes for Cortical Stimulation

Background: Cortical epidural stimulation is used for the treatment of different neuropsychiatric disorders such as chronic neuropathic pain, tinnitus, movement disorders, and psychiatric diseases. While preoperative magnetic resonance imaging (MRI) is considered the imaging tool of choice for planning the approach and electrode placement, postoperative MRI is still a contraindication with implanted paddle leads due to the risk of thermal damage or current induction creating seizures or neurological deficits. Objectives: In this feasibility in vitro study the temperature changes and induction were determined as well as the artifacts caused by 2 parallel paddle leads (Resume II, Model 3587 A; Medtronic, Minneapolis, Minn., USA), commonly used in clinical practice with and without a pulse generator (Prime Advanced, Model 7489; Medtronic). Methods: An ultrasound gel-filled head phantom with 2 paddle leads mimicking the surgical scenario was used to evaluate temperature changes as well as induced currents in a 1.5- and 3-tesla MR scanner. In addition, 1 patient underwent a 3-tesla MRI with an implanted subdural paddle lead. Results: Negligible temperature changes were detected with turbo spin echo sequences in the 1.5- and 3-tesla scanner using a head and body coil. Induced voltages up to 6 V were measured. The imaging artifacts in the phantom were well tolerable. The patients imaging was uneventful under the settings which are accepted for deep brain stimulation imaging. Conclusion: MRI under the conditions described here seems to be safe with the implants used in this study. In particular, the induced temperature is much lower with paddle compared to conventional leads due to the different electrode design. The induced voltage does not carry any risks. However, these findings cannot automatically be transferred to other implants or other scanning conditions, and further studies are needed. The biomedical companies should be encouraged to develop MR-conditional paddle leads. Also, further research is necessary to study the mechanism of action of cortical stimulation in the future.