17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      MRI-Based Multiscale Model for Electromagnetic Analysis in the Human Head with Implanted DBS

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Deep brain stimulation (DBS) is an established procedure for the treatment of movement and affective disorders. Patients with DBS may benefit from magnetic resonance imaging (MRI) to evaluate injuries or comorbidities. However, the MRI radio-frequency (RF) energy may cause excessive tissue heating particularly near the electrode. This paper studies how the accuracy of numerical modeling of the RF field inside a DBS patient varies with spatial resolution and corresponding anatomical detail of the volume surrounding the electrodes. A multiscale model (MS) was created by an atlas-based segmentation using a 1 mm 3 head model (mRes) refined in the basal ganglia by a 200  μ m 2 ex-vivo dataset. Four DBS electrodes targeting the left globus pallidus internus were modeled. Electromagnetic simulations at 128 MHz showed that the peak of the electric field of the MS doubled (18.7 kV/m versus 9.33 kV/m) and shifted 6.4 mm compared to the mRes model. Additionally, the MS had a sixfold increase over the mRes model in peak-specific absorption rate (SAR of 43.9 kW/kg versus 7 kW/kg). The results suggest that submillimetric resolution and improved anatomical detail in the model may increase the accuracy of computed electric field and local SAR around the tip of the implant.

          Related collections

          Most cited references64

          • Record: found
          • Abstract: not found
          • Article: not found

          A perfectly matched layer for the absorption of electromagnetic waves

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Cellular effects of deep brain stimulation: model-based analysis of activation and inhibition.

            Deep brain stimulation (DBS) is an effective therapy for medically refractory movement disorders. However, fundamental questions remain about the effects of DBS on neurons surrounding the electrode. Experimental studies have produced apparently contradictory results showing suppression of activity in the stimulated nucleus, but increased inputs to projection nuclei. We hypothesized that cell body firing does not accurately reflect the efferent output of neurons stimulated with high-frequency extracellular pulses, and that this decoupling of somatic and axonal activity explains the paradoxical experimental results. We studied stimulation using the combination of a finite-element model of the clinical DBS electrode and a multicompartment cable model of a thalamocortical (TC) relay neuron. Both the electric potentials generated by the electrode and a distribution of excitatory and inhibitory trans-synaptic inputs induced by stimulation of presynaptic terminals were applied to the TC relay neuron. The response of the neuron to DBS was primarily dependent on the position and orientation of the axon with respect to the electrode and the stimulation parameters. Stimulation subthreshold for direct activation of TC relay neurons caused suppression of intrinsic firing (tonic or burst) activity during the stimulus train mediated by activation of presynaptic terminals. Suprathreshold stimulation caused suppression of intrinsic firing in the soma, but generated efferent output at the stimulus frequency in the axon. This independence of firing in the cell body and axon resolves the apparently contradictory experimental results on the effects of DBS. In turn, the results of this study support the hypothesis of stimulation-induced modulation of pathological network activity as a therapeutic mechanism of DBS.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Patient-specific analysis of the volume of tissue activated during deep brain stimulation.

              Despite the clinical success of deep brain stimulation (DBS) for the treatment of movement disorders, many questions remain about its effects on the nervous system. This study presents a methodology to predict the volume of tissue activated (VTA) by DBS on a patient-specific basis. Our goals were to identify the intersection between the VTA and surrounding anatomical structures and to compare activation of these structures with clinical outcomes. The model system consisted of three fundamental components: (1) a 3D anatomical model of the subcortical nuclei and DBS electrode position in the brain, each derived from magnetic resonance imaging (MRI); (2) a finite element model of the DBS electrode and electric field transmitted to the brain, with tissue conductivity properties derived from diffusion tensor MRI; (3) VTA prediction derived from the response of myelinated axons to the applied electric field, which is a function of the stimulation parameters (contact, impedance, voltage, pulse width, frequency). We used this model system to analyze the effects of subthalamic nucleus (STN) DBS in a patient with Parkinson's disease. Quantitative measurements of bradykinesia, rigidity, and corticospinal tract (CST) motor thresholds were evaluated over a range of stimulation parameter settings. Our model predictions showed good agreement with CST thresholds. Additionally, stimulation through electrode contacts that improved bradykinesia and rigidity generated VTAs that overlapped the zona incerta/fields of Forel (ZI/H2). Application of DBS technology to various neurological disorders has preceded scientific characterization of the volume of tissue directly affected by the stimulation. Synergistic integration of clinical analysis, neuroimaging, neuroanatomy, and neurostimulation modeling provides an opportunity to address wide ranging questions on the factors linked with the therapeutic benefits and side effects of DBS.
                Bookmark

                Author and article information

                Journal
                Comput Math Methods Med
                Comput Math Methods Med
                CMMM
                Computational and Mathematical Methods in Medicine
                Hindawi Publishing Corporation
                1748-670X
                1748-6718
                2013
                15 July 2013
                : 2013
                : 694171
                Affiliations
                1Athinoula A. Martinos Center For Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
                2Division of Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD 20993, USA
                3Center for Morphometric Analysis, Department of Psychiatry and Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
                4Bioengineering Department, Politecnico di Milano, 20133 Milan, Italy
                Author notes

                Academic Editor: Romas Baronas

                Article
                10.1155/2013/694171
                3727211
                23956789
                a3b6721a-5be9-4bea-846e-19b4fad37640
                Copyright © 2013 Maria Ida Iacono et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 4 April 2013
                : 29 May 2013
                Funding
                Funded by: http://dx.doi.org/10.13039/100000009 Foundation for the National Institutes of Health
                Award ID: 1R21EY020961-01
                Funded by: http://dx.doi.org/10.13039/100000009 Foundation for the National Institutes of Health
                Award ID: 1R43NS071988-01A,
                Funded by: http://dx.doi.org/10.13039/100000009 Foundation for the National Institutes of Health
                Award ID: 15P41RR014075-13
                Categories
                Research Article

                Applied mathematics
                Applied mathematics

                Comments

                Comment on this article