We have been investigating several biophysical processes potentially associated with nerve excitation and their relationship to the measured MR signal. Uri Nevo, a former STBB post-doctoral fellow, and now an Senior Lecturer at Tel Aviv University, successfully constructed and tested an experimental system in our lab to interrogate organotypic cultured brain slices using diffusion MRI. This work showed promising preliminary results relating changes in the measured apparent diffusion coefficient (ADC) map to environmental challenges to which cultured tissues were subjected. One hypothesis that emerged from these studies is that active processes occurring at many different length scales (cell streaming, water flow across membranes, etc.) are responsible for a portion of the loss in the diffusion weighted MRI signal in stroke. This insight prompted the development of a theory to explain how microscopic fluid flows affect the measured diffusion weighted MRI signal and possibly the ADC measured in tissues (i.e., pseudo-diffusion) as well as an experimental model test system, an modified Rheo-NMR instrument, in which well-characterized flow field distributions can be produced that result in a known amount of pseudo-diffusion. The importance of these combined theoretical and experimental studies is that if such microscopic motions, like streaming, water flow across membranes, etc., manifest themselves as additional signal loss in diffusion weighted MRI, then we could use this information to infer distinct aspects of cell function and vitality, including features of excitability by a judicious analysis of the MRI data. This idea represents a significant refinement and of the Intravoxel Incoherent Motion concept proposed by LeBihan et al, which only considers the effect of random motion caused by microcirculatory water flow as contributing to pseudo-diffusion. We are now continuing these studies with a rising third-year doctoral student in Biophysics from the University of Maryland, Ruiliang Bai. We have also been involved in companion studies in the area of Transcranial Magnetic Stimulation (TMS) to understand how induced electric and magnetic fields are distributed within the brain and how they could selectively affect different populations of neurons. Pedro Miranda and his research group in Lisbon, in association with STBB, has performed detailed calculations using the finite element method (FEM) to predict the electric field and current density distributions induced in the brain during TMS. Previously, we found that both tissue heterogeneity and anisotropy of the electrical conductivity (i.e., the electrical conductivity tensor field) contribute significantly to distort these induced fields, and even to create excitatory or inhibitory "hot spots" in some regions that were previously not predicted. More recently, we have been developing realistic FEM models of cortical folds, containing gyri and sulci, and showed that this more complicated cortical anatomy can significantly affect the distribution of induced electric field distribution within the tissue, and the location and types of nerve cells that could be excited or depressed by such stimuli. We are beginning to marry our macroscopic FEM models of TMS with microscopic models of nerve excitability in the CNS in order to predict the locus of excitation in TMS and even the populations of neurons that are excited or depressed. This knowledge is important to have in addressing, for instance, the safety and basis of efficacy of TMS for the treatment of clinical depression--an application we helped pioneer in the '90s with our NINDS and NIMH colleagues. Despite its growing use, it is still not known what the action of induced electromagnetic fields are in the brain in therapeutic TMS, and specifically which and what populations of nerves it might trigger or depress. Our research can provide a basis for understanding the physics and physiology of this and other clinical applications of TMS. More recent studies of ours have focused on the microscopic effects of these electric and magnetic fields on cells in the nervous system, moving from the macro to the microscale in our modeling activities. Recently, we have also been applying these advanced FEM models to explain the physical basis for Direct Current Excitation (DCE) as well as therapeutic uses of AC electric fields on the brain.

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Wenger, Cornelia; Salvador, Ricardo; Basser, Peter J et al. (2015) The electric field distribution in the brain during TTFields therapy and its dependence on tissue dielectric properties and anatomy: a computational study. Phys Med Biol 60:7339-57
Fields, R Douglas; Woo, Dong Ho; Basser, Peter J (2015) Glial Regulation of the Neuronal Connectome through Local and Long-Distant Communication. Neuron 86:374-86
Bai, Ruiliang; Basser, Peter J; Briber, Robert M et al. (2014) NMR Water Self-Diffusion and Relaxation Studies on Sodium Polyacrylate Solutions and Gels in Physiologic Ionic Solutions. J Appl Polym Sci 131:
Bai, Ruiliang; Koay, Cheng Guan; Hutchinson, Elizabeth et al. (2014) A framework for accurate determination of the Tâ‚‚ distribution from multiple echo magnitude MRI images. J Magn Reson 244:53-63
Pajevic, S; Basser, P J; Fields, R D (2014) Role of myelin plasticity in oscillations and synchrony of neuronal activity. Neuroscience 276:135-47
Salvador, R; Silva, S; Basser, P J et al. (2011) Determining which mechanisms lead to activation in the motor cortex: a modeling study of transcranial magnetic stimulation using realistic stimulus waveforms and sulcal geometry. Clin Neurophysiol 122:748-58
Nevo, Uri; Ozarslan, Evren; Komlosh, Michal E et al. (2010) A system and mathematical framework to model shear flow effects in biomedical DW-imaging and spectroscopy. NMR Biomed 23:734-44
Roth, Bradley J; Basser, Peter J (2009) Mechanical model of neural tissue displacement during Lorentz effect imaging. Magn Reson Med 61:59-64
Silva, S; Basser, P J; Miranda, P C (2008) Elucidating the mechanisms and loci of neuronal excitation by transcranial magnetic stimulation using a finite element model of a cortical sulcus. Clin Neurophysiol 119:2405-13