We have been investigating a number of biophysical processes associated with nerve excitation and their relationship to the MR signal. Uri Nevo, a former STBB post-doctoral fellow, and now an Assistant Professor 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 results relating changes in the measured apparent diffusion coefficient (ADC) map to changes in environmental conditions 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 some signal loss in the diffusion weighted MRI signal. This insight prompted the development of a theory to explain how microscopic fluid flows affect the measured diffusion weighted MRI signal and the ADC measured in tissues (i.e., pseudo-diffusion) and an experimental model system, the Rheo-NMR, in which well-characterized flow fields can be produced, which create known amounts of pseudo-diffusion. The importance of these combined studies is that if such microscopic motions, like streaming, water flow across membranes, etc., manifest themselves as an additional signal loss in diffusion weighted MRI experiments, then we can use this information to infer different aspects of cell function and vitality, including excitability. We are now continuing these studies with a recently hired graduate student from the University of Maryland, Ruiliang Bai. We have also been collaborating with Bradley Roth to try to examine different physical mechanisms that could be exploited or used to detect neural currents directly using MRI. One approach we examined previously was whether small displacements caused by Lorentz forces produced in strong magnetic fields (like those within a large clinical MRI scanner) could be employed to measure neural currents in vivo using MRI. Our calculations showed that the induced displacements of nerves caused by Lorentz forces in tissues would be too small to be detectable by MRI using existing technology. In the area of Transcranial Magnetic Stimulation (TMS), Pedro Miranda and his 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 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 more realistic FEM models of cortical folds, containing gyri and sulci. We showed that this more complicated cortical anatomy also significantly affects the distribution of induced electric fields within the tissue, and the location and types of nerve cells that could be excited or depressed by such stimuli. These phenomena could have significant clinical consequences both in interpreting or inferring the region or locus of excitation and in determining the source of nerve excitation. We are beginning to marry our macroscopic 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. Recently, we have also been applying these advance FEM models to explain the physical basis for Direct Current Excitation (DCE).

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Bai, Ruiliang; Stewart, Craig V; Plenz, Dietmar et al. (2016) Assessing the sensitivity of diffusion MRI to detect neuronal activity directly. Proc Natl Acad Sci U S A 113:E1728-37
Benjamini, Dan; Komlosh, Michal E; Holtzclaw, Lynne A et al. (2016) White matter microstructure from nonparametric axon diameter distribution mapping. Neuroimage 135:333-44
Wenger, Cornelia; Salvador, Ricardo; Basser, Peter J et al. (2016) Improving Tumor Treating Fields Treatment Efficacy in Patients With Glioblastoma Using Personalized Array Layouts. Int J Radiat Oncol Biol Phys 94:1137-43
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; Koay, Cheng Guan; Hutchinson, Elizabeth et al. (2014) A framework for accurate determination of the T? distribution from multiple echo magnitude MRI images. J Magn Reson 244:53-63
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:
Miranda, Pedro C; Mekonnen, Abeye; Salvador, Ricardo et al. (2014) Predicting the electric field distribution in the brain for the treatment of glioblastoma. Phys Med Biol 59:4137-47
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

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