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Increased myelination plays a central role in white matter neuroplasticity
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1. Introduction
White matter (WM) neuroplasticity in human motor learning has been previously tracked using multimodal magnetic resonance imaging (MRI) across both structural and functional levels (Frizzell et al., 2021, 2020; Reid et al., 2017; Sale et al., 2017). In general, neuroplasticity involves restructuring and reshaping neural networks as a result of experience, injury, learning, and healing (Sampaio-Baptista and Johansen-Berg, 2017). WM is composed of axons and associated glial cells (e.g., oligodendrocytes that produce myelin). Possible mechanisms for WM neuroplasticity include increased myelination, axon diameter, internode length, and ion channel density – all of which are thought to improve transmission efficiency of action potentials (Sampaio-Baptista and Johansen-Berg, 2017). Improved axonal transmission efficiency has recently been investigated in MRI studies and shown to account for WM neuroplasticity improvements during motor learning (Frizzell et al., 2021, 2020; Reid et al., 2017; Sale et al., 2017). Specifically, when participants underwent motor skill training, there were significantly larger motor learning effects for their non-dominant relative to dominant hands, which matched structural and functional MRI changes detected in the contralateral cortico-spinal tracts (CST) (Frizzell et al., 2021, 2020).
In a recent series of studies, we evaluated structural and functional MRI changes using diffusion tensor imaging (DTI) and functional MRI (fMRI), to non-invasively investigate WM neuroplasticity (Frizzell et al., 2021, 2020). DTI analyses examined fractional anisotropy (FA) as a measure of changes in WM structure. As expected, a significant increase in FA was detected in the right CST (i.e., non-dominant left hand). No such effect was detected in the left CST (i.e., dominant right hand). This series of studies had the primary aim of investigating the full structural-functional relationship in white matter tracts, therefore, only the CST was focused on in the current study. Emerging from the early demonstration studies (Courtemanche et al., 2018; D'Arcy et al., 2006; Gawryluk et al., 2011a; Mazerolle et al., 2008, 2010; Omura et al., 2004; Tettamanti et al., 2002; Weber et al., 2005), white matter fMRI is increasingly being reported across expanded applications (Abramian et al., 2021; Frizzell et al., 2021, 2020; Gawryluk et al., 2011b; Grajauskas et al., 2019; Huang et al., 2018; Li et al., 2019). When examining functional MRI changes, right CST increases were similarly detected for both blood-oxygen level-dependent (BOLD) contrast responses and low-frequency oscillations (LFOs). Taken together, the findings suggested improved axonal transmission efficiency. The fMRI results were particularly noteworthy, as fMRI activation in white matter has historically been thought to be undetectable (Gawryluk et al., 2014; Grajauskas et al., 2019; Li et al., 2019) and therefore provide novel non-invasive imaging window into potential WM neuroplasticity mechanisms in the human brain.
The current study represents the culmination of the planned experimental series, designed to identify the central mechanism(s) for WM neuroplasticity. The evidence from fMRI and DTI for increased axonal transmission efficiency suggested that, out of the possible mechanisms for neuroplasticity, increased myelination likely plays a central role. A recent MRI-based technique of measuring myelin levels is myelin water imaging (MWI). Different water environments in the brain can be organized based on the differing T2 relaxation properties. MWI uses multicomponent T2 relaxation from the proton signal of water in central nervous system tissue. Water between the myelin bilayers (myelin water) can be separated from intra- and extracellular water and cerebrospinal fluid water to give the myelin water fraction (MWF) (MacKay et al., 1994).
The results from MWF can be subsequently compared to DTI. DTI measurements of FA, radial diffusivity (RD) and axial diffusivity (AD) have been used to characterize microstructural changes during motor learning (Frizzell et al., 2021; Reid et al., 2017; Sale et al., 2017; Scholz et al., 2009; Taubert et al., 2010; Wang et al., 2014). RD measures the amount of water traveling perpendicular to the tract, making it more sensitive to the cross-sectional extent of myelination (i.e., increased myelination leads to reduction of RD) (Winklewski et al., 2018). By comparison, AD measures the amount of water diffusing along the tracts in a voxel, which makes it more sensitive to factors effecting axonal tract integrity (i.e., axonal injury or reduced axonal caliber may lead to a decrease of AD) (Winklewski et al., 2018). While both are expected to be sensitive to myelin levels, RD is proposed to have a greater sensitivity (Winklewski et al., 2018). Therefore, RD should have a greater correlation with MWI results than FA or AD. While DTI measurements have been proved useful for analyzing microstructural changes, they are an indirect measure of myelin levels, as they compare all water in the brain and do not specify different water environments. Specifying different environments of water, as MWI does, results in a more direct measurement of myelin levels and can be more sensitive to a change in myelination.
Using MRI to measure myelin is not necessarily a new idea as several reviews break down its different techniques. More specifically, there are several reviews on MWI that speak to its validity as a measure of myelin levels (Alonso-Ortiz et al., 2015; MacKay et al., 2006; MacKay and Laule, 2016). MacKay and Laule, 2016 review the imaging acquisition, analysis, findings in animal and human work, and importantly, post-mortem validation work. More recently, Lee et al, 2021 created an extensive review and practical guide for MWI, which concluded that even with the technical and physiological limitations, MWI is an effective stand-in biomarker of myelination. MWF has been shown to correlate strongly with the histological staining of myelin (Laule et al., 2006; Moore et al., 2000). Therefore, MWF can quantify increased myelination in the CST and evaluate whether this plays a central role in WM neuroplasticity.
Numerous studies have demonstrated a measurable change of FA or MWF during longitudinal monitoring of motor training tasks, as well as correlation of FA and its components to MWI results (Baumeister et al., 2020; Frizzell et al., 2021; Lakhani et al., 2016; Mädler et al., 2008; Scholz et al., 2009; Song et al., 2005; Taubert et al., 2010; Tu et al., 2016; Wang et al., 2014). Notably similar to this study, Lakhani, et al., 2016 used MWI to conclude that motor task acquisition led to myelination in the contralateral brain. Mädler, et al., 2008 and Baumeister et al., 2020, found correlation of MWI results to FA. Deeper correlation studies by Song et al., 2005 and Tu et al., 2016, were able to find myelin compactness in rats correlated stronger with the RD, than FA or AD. Across different regions of interests, Kiely et al, 2022 showed that across the adult lifespan, RD shows the strongest correlation with MWF over any other DTI-derived measurement (Kiely et al., 2022). The current study is able to expand measurable MWF, FA, and RD changes along the segmented CST during motor learning, as well as measure the relationship of these changes with each other.
The current study built upon prior investigations (Frizzell et al., 2021, 2020), which localized WM neuroplasticity changes to the internal capsule of the CST using DTI FA, BOLD fMRI, and LFOs. The current study included myelin water-based analyses along the entire CST. Specifically, the MWF, FA, RD, and AD mean value at 15 nodes along the CST was determined and compared between baseline and endpoint scans following motor control training. As in previous studies, the analyses focused on right greater than left CST comparisons in correspondence with the functional motor learning results showing significant improvement for non-dominant (left) greater than dominant (right) hand performance.
The primary hypothesis tested whether increased axonal transmission efficiency was due to increased myelination. Myelination was measured with MWI, which directly evaluated the CST during neuroplasticity-related improvements in motor control. To further connect myelin levels to DTI FA, the secondary hypothesis predicted that RD, rather than AD, would show a strong relationship with MWF as a function of increased myelination. Specifically, RD would decrease with increased myelination in the right CST, with no change in the left CST. MWI and RD changes taken together, would demonstrate that directly increased myelination plays a central role in WM neuroplasticity.
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Irini Patsaki
Nefeli Dimitriadi
Akylina Despoti1, 
Argyro Papathanasiou
Serafeim Nanas
Zi-Ai Zhao
Lin Tao
Meng Li
Hui-Sheng Chen
