Myelin packing marker through in vivo water gap mapping
Poster No:
2175
Submission Type:
Abstract Submission
Authors:
Rona Shaharabani1, Aviv Mezer1
Institutions:
1The Hebrew University of Jerusalem, Jerusalem, Israel
First Author:
Co-Author:
Introduction:
Multiple sclerosis (MS) is characterized by loss of membrane adhesion, swelling across the water gaps, vesiculation, and eventual disintegration of the myelin structure1.
Myelin water imaging (MWI) methods are sensitive to MS demyelination processes2. This T2-based quantitative magnetic resonance imaging (qMRI) method is sensitive to myelin content and its integrity. MWI is a multiexponential T2 model that estimates the fraction of the signal that arises from the water trapped between the myelin membranes and that arises from other tissue water3,4. The macromolecular and lipid tissue volume (MTV), defined as 1-water fraction (WF), was shown to approximate the total myelin fraction5. MWI and MTV are sensitive to the reduction of myelin in MS but not to the changes in myelin organization, such as the size of the water gap between the myelin membranes.
We hypothesize that combining the MWI technique with myelin content fraction from other qMRI measurements will allow characterizing the size of the water gap between the myelin.
Myelin water imaging (MWI) methods are sensitive to MS demyelination processes2. This T2-based quantitative magnetic resonance imaging (qMRI) method is sensitive to myelin content and its integrity. MWI is a multiexponential T2 model that estimates the fraction of the signal that arises from the water trapped between the myelin membranes and that arises from other tissue water3,4. The macromolecular and lipid tissue volume (MTV), defined as 1-water fraction (WF), was shown to approximate the total myelin fraction5. MWI and MTV are sensitive to the reduction of myelin in MS but not to the changes in myelin organization, such as the size of the water gap between the myelin membranes.
We hypothesize that combining the MWI technique with myelin content fraction from other qMRI measurements will allow characterizing the size of the water gap between the myelin.
Methods:
Our model describes the T2 exponential decay as the sum of the myelin water (MW) and any other tissue water (TW) contributions. Our modeling relies on multi-echo spin-echo or gradient multi-echo data. In the dependency of the T2 multi-compartment model on the myelin water gap ratio equation there are three unknown parameters: T2MW, T2TW, and the ratio (dw/dm).
We used a novel lipid phantom to characterize and validate the water gap model. Our specifically designed in vitro biological systems6,7 mimic the biological assembly of myelin and are well-designed multi-lamellar vesicles (like the myelin sheaths) with well-characterized water gap thickness. We suspended the membranes in NaCl solutions at different concentrations (0-500 mM) to change the water gap between the membranes (dw). We used cryo-TEM to measure the water gap and the membrane thicknesses in the phantom system.
Next, we tested our biophysical model on healthy young adults (N=25) in four white matter regions (lateral-occipital, superior-temporal, superior-frontal, and superior-parietal).
The phantoms and human subjects were scanned in a 3T Skyra Siemens scanner with a 32-channel receive head-coil for R27, R15, and WF5 mapping.
We used a novel lipid phantom to characterize and validate the water gap model. Our specifically designed in vitro biological systems6,7 mimic the biological assembly of myelin and are well-designed multi-lamellar vesicles (like the myelin sheaths) with well-characterized water gap thickness. We suspended the membranes in NaCl solutions at different concentrations (0-500 mM) to change the water gap between the membranes (dw). We used cryo-TEM to measure the water gap and the membrane thicknesses in the phantom system.
Next, we tested our biophysical model on healthy young adults (N=25) in four white matter regions (lateral-occipital, superior-temporal, superior-frontal, and superior-parietal).
The phantoms and human subjects were scanned in a 3T Skyra Siemens scanner with a 32-channel receive head-coil for R27, R15, and WF5 mapping.
Results:
The lipids' membrane thickness is measured to be 4.5-5 nm, which is about the same size as the myelin membrane. The water gap between the membranes decreased from ~15 nm to ~ 4 nm (Fig. 1a) depending on the salt concentration. Our in vitro measurements validate our biophysical model and the water gap thickness fits very well with the cryo-TEM measured water gap. The ratio between the water gap thickness to the membrane thickness (dw/dm) is a measure of the membrane packing. The model-fitted ratio correlates well with the cryo-TEM measured ratio (r=0.99, p<10-2; Fig. 1b).
In healthy young adults, the model estimates of the TW T2 was ~90 msec and the MW T2 values ranged between 20-40 msec (Fig. 2a) across subjects and regions. Importantly, these values agree with the myelin water T2 previously estimated using the MWI technique8,9. All the areas yielded similar membrane packing ratios between 0.7-1 across all subjects and regions. Assuming a constant membrane thickness10 (4.5 nm), the estimated water gap thickness is between 3-4.5 nm, which agrees with ex vivo and animal models estimation10 (Fig. 2b).
In healthy young adults, the model estimates of the TW T2 was ~90 msec and the MW T2 values ranged between 20-40 msec (Fig. 2a) across subjects and regions. Importantly, these values agree with the myelin water T2 previously estimated using the MWI technique8,9. All the areas yielded similar membrane packing ratios between 0.7-1 across all subjects and regions. Assuming a constant membrane thickness10 (4.5 nm), the estimated water gap thickness is between 3-4.5 nm, which agrees with ex vivo and animal models estimation10 (Fig. 2b).
Conclusions:
We developed a new model to study the myelination packing status in vivo using clinical multi-echo sequences and simple WF estimates. Until now, the water gap could only be calculated in postmortem axonal analysis. We established this approach with a biophysical model and validated it with a specifically designed in vitro phantom system and in vivo human data. Our phantom in vitro system and in vivo human data showed high agreement for the predicated signal and the extracted parameters.
Modeling and Analysis Methods:
Methods Development 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Keywords:
Degenerative Disease
Demyelinating
Modeling
MRI
Myelin
Structures
White Matter
1|2Indicates the priority used for review
Provide references using author date format
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