Diffusion imaging

Diffusion imaging principles

Diffusion weighted imaging (DWI) is a type of MRI sequence designed to assess the mobil-ity of water particles. In free space, water molecules would travel randomly in a Brownian movement, a process well-characterized by Einstein in 1905. In the brain, however, the diffusion of water is restricted by biological tissues such as cell membranes, fibers and macromolecules (Le Bihan, 2003). The sum of contributions of these biological barriers limits the free movement of the water particles and determines the apparent diffusion coefficient.

Diffusion tensor imaging (DTI) is a DWI sequence that measures the diffusion in a number of different directions of the space (typical number of directions range from 16 to 64).

Using this multidimensional information of diffusion in each direction, each voxel can be represented with an ellipsoid representing the preferential direction of the diffusion of the water molecules (Fig1.3left). This ellipsoid, in turn, can be represented mathematically by a tensor (Fig1.3mid). Typical DTI measures are directly derived from the tensor, such as the fractional anisotropy (FA) or the mean diffusivity (MD) (Fig 1.3 right). The FA represents the degree of directionality of the voxel. When the water diffusion is restricted to a certain direction the FA is high, as in axons, where the water diffusion depends to the tract direction. On the contrary, in areas where there is no preferential diffusion direction (or isotropy), such as CSF, the FA is low. Another measure that can be extracted is MD, which measures the total diffusion of the voxel, no matter the directionality. In free water, such as the CSF for example, diffusion is not restricted and the MD is high. In locations where water diffusion is determined by biological barriers, such as inside the neurons and in the interstitial space, the MD is low (Weston et al., 2015).

Thus, in the CSF MD is high and FA is low, in the white matter MD is intermediate and FA is high and in the gray matter MD and FA are intermediate and low respectively (Figure1.4).

The main advantage of DWI is that it is able to detect changes at the microstructural level (Weston et al., 2015). In neurodegenerative diseases it is thought that the breakdown of biological barriers like myelin cell membranes or organelles would produce a measurable change in the diffusion properties of the tissue (Uluğ et al., 1999). Thus, DWI has been demonstrated as a powerful tool in neurology to assess brain changes in multiple neurologic diseases, such as AD, dementia with Lewy bodies, Frontotemporal lobar degeneration or

1.2. Alzheimer’s disease biomarkers 13

Figure 1.3: Diffusion Tensor Imaging Principles. Left- Diffusion of water parti-cles in each direction inside a determinate voxel. Mid-Tensor fitting. The diffu-sion information is used to estimate a mathematical object called tensor. Right.

DTI metrics estimation.

Parkinson’s disease (Agosta et al., 2017; Bozzali and Cherubini, 2007). The potential con-tribution of diffusion imaging to an early and more accurate diagnosis has received special attention in the last few years. As explained in the previous paragraph, different measures can be extracted from DTI analyses. Typically, diffusion studies in neurodegenerative disease have focused on the study of the white matter (Amlien and Fjell, 2014). Reduc-tions of FA and increases of MD in white matter tracts have been systematically reported in temporal and frontal lobes, as well as in the corpus callosum and posterior cingulate (Agosta et al., 2017). Diffusion has also been studied in the hippocampus (Cherubini et al., 2010) or, anectdotically, in the gray matter (Weston et al., 2015).

Another promising measure that can be derived from DWI data is the free water fraction (FW) (Pasternak et al., 2009). This proposed bi-compartment model differentiates the contribution of the extracellular water from the tissue-restricted water. Specifically, the FW is defined as water molecules that are not hindered or restricted, consequently extra-cellular, with a diffusion coefficient of water in body temperature. Recent evidences in the literature suggest that the FW component provides high sensitivity to detect extracellular processes like atrophy, cerebral edema or even inflammation (Lyall et al., 2017).

Diffusion imaging and microstructure in healthy controls and AD

In the last decade, there has been a growing interest in DWI as it was hypothesized that subtle microstructural changes could precede macrostructural alterations (Alexander et al., 2007; Müller et al., 2005; Ringman et al., 2007). As explained before, in AD, diffusion has been widely studied in the WM and in the hippocampus (Amlien and Fjell, 2014;

Cherubini et al., 2010; Eustache et al., 2016; Hanyu et al., 1997, 1998). The typical

Figure 1.4: Diffusion properties of the different brain tissues: gray matter, white matter and cerebrospinal fluid.

diffusion signature in the prodromal and dementia AD phases consist of a decrease in FA and an increase in MD. Hippocampal diffusivity has also demonstrated good power in predicting the MCI to AD conversion (Douaud et al., 2013; Kantarci et al., 2005; Müller et al., 2005).

While diffusion can also be studied in the GM, there is a scarcity of published studies on the subject (Weston et al., 2015). In contrast to the white matter, the most common metric used in the cortex is MD, due to the absence of preferential diffusion direction (Fortea et al., 2010; Weston et al., 2015). The results in the literature often report increased MD in MCI and AD patients (Jacobs et al., 2013; Rose et al., 2008). Conversely, MD decreases were found in presymptomatic AD mutation carriers (Fortea et al., 2010; Ryan et al., 2013). Finally, cortical MD correlates with the neuropsychological performance in controls and MCI (Kantarci et al., 2011). However, the reports in the literature that studied gray matter alterations in AD are anecdotal, with small sample sizes and do not include the whole AD continuum. Consequently, more research is required to understand the early gray matter microstructural alterations in AD (Weston et al., 2015).

The FW model has been recently proposed as an interesting tool for different diseases (Hoy et al., 2017; Johanna et al., 2017; Lyall et al., 2017; Maier-Hein et al., 2015). Specifically, the FW compartment could add complementary information to typical DTI measures such as MD. To date, there are no published studies assessing FW in the early AD phases.