Peter J. Basser, NIH, “Characterizing brain microstructure, architecture and organization with diffusion MRI”

Jump to:


“Characterizing brain microstructure, architecture and organization with diffusion MRI”

Dr. Basser probably best known as the principal inventor of diffusion tensor NMR and MRI (DTI), and has worked at the NIH for more than two decades with colleague Carlo Pierpaoli and others to translate these and other quantitative MRI technologies clinically, as well as develop them as basic research tools in the neurosciences, to probe brain architecture, organization, and function. Other MRI technologies Dr. Basser has co-invented, including CHARMED, AxCaliber, and MAP-MRI, which are used to examine features of the intra-axonal and extracellular milieu in white matter. Multiple pulsed-field gradient-based (mPFG) MRI methods developed in his lab are used to detect microstructural features in gray matter, primarily for in vivo cytoarchitectonic parcellation of the cortex.

For most of his career, Dr. Basser has pursued dual interests in the Neurosciences and what is now called “Tissue Sciences”, a field he helped create. Much of his work tries to merge these two disciplines. Even prior to his involvement in MRI, he and colleague, Brad Roth were the first to explain the physical basis of electromagnetic stimulation of axons, later called Transcranial Magnetic Stimulation (TMS). Dr. Basser also helped pioneer the use of this technique in the treatment of depression as an alternative to Electroconvulsive Therapy (ECT). Since then, he has continued to develop analytical and finite element method (FEM) models of electromagnetic field interactions with brain tissue with colleague, Pedro Miranda, to explore different possible mechanisms of action, and possible diagnostic and therapeutic applications. Dr. Basser wrote the first paper describing the delivery of chemotherapeutic agents (and other biological agents) into brain parenchyma by direct infusion, which was subsequently named “Convection Enhanced Delivery” (CED). He has worked to explain the physical basis of observed scaling laws among morphological parameters of axons, more recently explaining the observed axon diameter distribution (ADD) in terms of maximizing information flow along nerve fascicles.

In the area of Tissue Biophysics he has worked to explain the biophysical basis of the functional properties of cartilage. With Alan Grodzinsky, he developed a theory relating and establishing the equivalence of the molecular scale Poisson-Boltzmann model describing mutual electrostatic repulsion among proteoglycans (PG), and the macroscopic Donnan model that explains ion partitioning at the tissue length scale. With Alice Maroudas, he developed a theory and experimental design to measure the individual contributions of PG swelling pressure and collagen network retraction stress, which balance each other, and together determine the stiffness of cartilage. With Ferenc Horkay, he has worked to elucidate the underlying physical principles governing the properties and interactions among ECM biomacromolecules, water, and ions, to provide a needed scientific (biophysical) foundation for Tissue Engineering of ECM and articular cartilage. His more recent work has focused upon using this knowledge of water/biopolymer/ion interactions to improve MRI assessment of ECM.

Webcast IconClick here to view webcast


“Characterizing brain microstructure, architecture and organization with diffusion MRI”

Diffusion imaging comprises a set of MRI methods that has provided a new window on the brain and other organs. Starting with early diffusion MRI that could reveal white matter pathways, subsequent advances have allowed neuroradiologists and neuroscientists to “drill down into the voxel” to be able determine important functional features of white matter microstructure, such as the distribution of axon diameters. This talk will provide a brief overview of diffusion MRI methods and the high-dimensional data they produce and describe some of the research we have conducted at the NIH to help us better understand brain function from studies of brain microstructure, architecture, and organization.



JHU - Institute for Computational Medicine