KEYWORDS: Functional magnetic resonance imaging, Independent component analysis, Brain, Hemodynamics, Data modeling, Magnetic resonance imaging, Statistical analysis, Signal to noise ratio, Visualization, Brain mapping
Functional MRI is fundamentally grounded in the hemodynamic response. With an increase in neuronal activity, blood flow increases, causing an increase in blood oxygenation, leading to an increase in transverse relaxation rate T2*. This increase in blood flow is slow and highly variable and shows a considerable spatial heterogeneity. In spite of these limitations, the hemodynamic response has been proven to be exquisitely sensitive to subtle differences in neuronal activity in time, over space, and between subjects. This paper is a brief review of my Keynote address describing some of the effort coming from my group that further demonstrates methods to robustly extract ever more information from both resting state fMRI and activation-induced fMRI. Specifically, I discuss 1) our new method to use multi-echo fMRI time series data collection to separate blood oxygen level dependent (BOLD) signal from non-BOLD signal, 2) activation of the whole brain obtained using a simple task and, importantly massive averaging and a model-free analysis approach, and 3) fMRI decoding of left vs right eye ocular dominance column activation with a timing offset as low as 100 ms.
KEYWORDS: Head, Functional magnetic resonance imaging, Interference (communication), Modulation, Magnetism, Medical imaging, Image filtering, Magnetic resonance imaging, Signal detection, Data acquisition
It is commonly thought that improvements in image S/N translate directly to improvements in fMRI sensitivity. This study demonstrates that improvements in image S/N by means of increased field strength, and the use of surface coils, does not translate to similar gains in temporal S/N, due to physiological noise. An analysis of the S/N dependence on signal strength for both the imaginary and real noise components is presented. The real noise component, which is lower than the imaginary component, is a significant contributor to temporal variations in single-shot fMRI procedures. The imaginary component becomes an important contributor to time series fluctuations in multi-shot or 3D techniques. In the experiments presented here, the relationship between image and temporal S/N is examined by modulating the signal strength by means of echo time stepping, field strength modulation, and RF coil comparison. The spatial and temporal noise contributions of the resting brain are characterized by comparing phantom, subject, and thermal noise measurements. The sensitivity of both spiral and EPI single-shot acquisition methods to physiologic and systematic noise is characterized. The results suggest that the fMRI sensitivity plateaus as image S/N is increased unless physiological noise is filtered out.
Current functional MRI techniques are essentially based on detection of hemodynamic changes induced by neuronal activation. This study is an exploration of the feasibility of direct detection of neuronal current induced NMR phase and/or magnitude changes. Our analysis was based on the approximation that neurons exist in an infinite homogeneous conducting medium, and are represented by an infinitely long cylindrical conductor, carrying uniform current. Neuronal activation was modeled by a current dipole. Simulations were performed to evaluate the effects of the local neuronal magnetic fields on the MRI signal at 3T. The magnetic field changes and the corresponding phase changes induced from a current range of 2 nA to 100 (mu) A were estimated. The conductor diameter was varied from 10 micrometer to 1 mm corresponding to the sizes ranging from that of a single axon to that of a patch of functionally similar cortex. Current induced magnetic field effects were assessed as resolution and neuronal orientation with respect to Bo were modulated. Simulation results were compared with measurements obtained from a current phantom at 3T, for a current range of 10 - 100 (mu) A. Based on the dipole model formulated we found that neuronal currents can produce magnetic fields on the order of 1.3 pT; 0.0006 degree(s) to 9 nT; 4 degree(s) depending on the neuronal bundle diameter, orientation, and current capacity. The mechanism for signal changes is that of Bo shifts. This is fundamentally similar to that of BOLD contrast, but caused by current changes rather than susceptibility changes from changes in blood oxygenation. If the bundles are of random orientations within a voxel, the estimated fields and NMR magnitude changes (decrease in T2*) are on the order of present system detection levels of approximately 1nT; 0.5 degree(s) or 0.01% signal change. Conversely, if the bundle orientation is homogeneous, then neuronal current effects are above system detection levels. The spatial scale of the current distribution also determines the net phase shift and magnitude change. The simulation and phantom experiment results demonstrated the feasibility of using MRI to directly detect local magnetic field perturbations that can result from neuronal currents on the order of a few (mu) A. This study provides a simple model for the evaluation of the feasibility to directly measure neuronal currents with MRI. Additionally it gives a starting point for the design of appropriate imaging methods towards detecting low signal level neuronal currents. A more extensive modeling of cortical and neuronal geometry, tissue inhomogeneity, timing mechanisms, and current distributions, will provide further insight in the development of MRI experimental techniques.
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