scholarly journals Application of Paramagnetically Tagged Molecules for Magnetic Resonance Imaging of Biofilm Mass Transport Processes

2010 ◽  
Vol 76 (12) ◽  
pp. 4027-4036 ◽  
Author(s):  
B. Ramanan ◽  
W. M. Holmes ◽  
W. T. Sloan ◽  
V. R. Phoenix

ABSTRACT Molecules become readily visible by magnetic resonance imaging (MRI) when labeled with a paramagnetic tag. Consequently, MRI can be used to image their transport through porous media. In this study, we demonstrated that this method could be applied to image mass transport processes in biofilms. The transport of a complex of gadolinium and diethylenetriamine pentaacetic acid (Gd-DTPA), a commercially available paramagnetic molecule, was imaged both in agar (as a homogeneous test system) and in a phototrophic biofilm. The images collected were T 1 weighted, where T 1 is an MRI property of the biofilm and is dependent on Gd-DTPA concentration. A calibration protocol was applied to convert T 1 parameter maps into concentration maps, thus revealing the spatially resolved concentrations of this tracer at different time intervals. Comparing the data obtained from the agar experiment with data from a one-dimensional diffusion model revealed that transport of Gd-DTPA in agar was purely via diffusion, with a diffusion coefficient of 7.2 × 10−10 m2 s−1. In contrast, comparison of data from the phototrophic biofilm experiment with data from a two-dimensional diffusion model revealed that transport of Gd-DTPA inside the biofilm was by both diffusion and advection, equivalent to a diffusion coefficient of 1.04 × 10−9 m2 s−1. This technology can be used to further explore mass transport processes in biofilms, either by using the wide range of commercially available paramagnetically tagged molecules and nanoparticles or by using bespoke tagged molecules.

2013 ◽  
Vol 66 (5) ◽  
pp. 456-461 ◽  
Author(s):  
Baheerathan Ramanan ◽  
William M. Holmes ◽  
William T. Sloan ◽  
Vernon R. Phoenix

2021 ◽  
Author(s):  
Yusi Chen ◽  
Qasim Bukhari ◽  
Tiger Wutu Lin ◽  
Terrence J Sejnowski

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.


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