N-acetyl-aspartate levels correlate with intra-axonal compartment parameters from diffusion MRI
Introduction
Diffusion MRI (dMRI) is the imaging method of choice to probe white matter (WM) microstructure. To date, diffusion tensor imaging (DTI) has been the primary dMRI technique used to conduct in vivo investigations of WM microstructural integrity (Basser, 1995, Jones, 2010). DTI quantifies the Gaussian part of the probability distribution of molecular displacement in terms of the overall diffusion tensor from which derived metrics such as the mean, axial, and radial diffusivities (MD, D||, and D┴), and fractional anisotropy (FA) are derived. DTI metrics have been shown to be significantly altered in multiple pathologies (Horsfield and Jones, 2002, Jones, 2010). In addition, over the past decade several techniques have been proposed to assess the non-Gaussian part of the diffusion displacement distribution (Alexander et al., 2002, Jensen and Helpern, 2010, Liu et al., 2004, Maier et al., 2004, Özarslan et al., 2013, Tuch, 2004, Wedeen et al., 2005). In particular, diffusion kurtosis imaging (DKI), a clinically feasible non-Gaussian method (Jensen and Helpern, 2010, Jensen et al., 2005, Lu et al., 2006), has shown promise in several brain pathologies (Jensen and Helpern, 2010, Steven et al., 2014).
The growing list of clinical studies using both DTI and DKI demonstrates the high sensitivity of their empirical diffusion parameters to microstructural changes in WM integrity. However, such empirical measures only provide an indirect characterization of microstructure. Their physical meaning in terms of specific tissue properties still remains uncertain. Indeed, it is imperative to distinguish between mathematical models representing the diffusion signal (e.g., the cumulant expansion (Kiselev, 2010), mono-, bi-, and stretched exponential models (Assaf and Cohen, 1998, Bennett et al., 2003, Niendorf et al., 1996), and mean apparent propagator (Özarslan et al., 2013)) and true biophysical models taking into account actual neuronal structure as described below for WM. The former (e.g., DTI and DKI) are applicable in all brain voxels and do not make assumptions about the underlying microstructure, whereas the latter are specifically tailored to model the effects of microstructure on diffusion in certain regions of the brain. Hence, such biophysical models are especially useful to gain insight into the underlying pathological processes and to increase the pathophysiological specificity.
In modeling WM diffusion, the common practice has been to model axons as zero radius, infinitely long, impermeable tubes and cylinders (Assaf and Basser, 2005, Assaf et al., 2004, Kroenke et al., 2004) or sticks (Behrens et al., 2003). Another common assumption is to neglect the water exchange through the myelin sheath surrounding axons. As a result, the diffusion signal in the WM contains at least two components, which correspond to the intra- and extra-axonal spaces. While these assumptions seem plausible and form the basis for most current diffusion models of WM in the brain (Alexander et al., 2010, Assaf and Basser, 2005, Assaf et al., 2004, Basser et al., 2007, Jespersen et al., 2007, Nilsson et al., 2013, Panagiotaki et al., 2009, Panagiotaki et al., 2012, Wang et al., 2011, Zhang et al., 2012), further validation remains warranted.
Based on the assumptions of a two non-exchanging compartments model, we recently showed that for a single WM fiber bundle, a minimum set of two shells in q-space (i.e., two nonzero b-values in each direction) together with b = 0 are sufficient to discern between intra- and extra-axonal water, and allow for the description of compartment specific white matter tract integrity (WMTI) metrics from the diffusion and kurtosis tensor (Fieremans et al., 2010, Fieremans et al., 2011). Specifically, as shown in Fig. 1, these include intra-axonal diffusivity (Daxon), extra-axonal axial and radial diffusivities (De|| and De┴), axonal water fraction (AWF), and tortuosity (α) of extra-axonal space (Sen and Basser, 2005). To date, WMTI metrics have been preliminarily applied to several brain conditions and shown to provide useful information about plausible biophysical mechanisms (Benitez et al., 2014, Fieremans et al., 2013, Hui et al., 2012, Lazar et al., 2014).
The purpose of the current study is to examine the in vivo relationship between these WMTI parameters and concentrations of the metabolites N-acetylaspartate (NAA), creatine (Cr), choline (Cho), and myo-Inositol (mI) measured using 1H-MRS. Our results could help clarify their meaning and shed light on the validity of the assumptions typically made when modeling the diffusion signal in WM. In particular, we hypothesized that NAA, as an endogenous probe of the neuronal intracellular space (Kroenke et al., 2004), would correlate specifically with the WMTI parameters related to axonal density and diffusion inside the axons.
The relationship between FA and NAA, Cr, and Cho has been evaluated in the WM of healthy adults in a previous study which showed that NAA concentrations explained most of the variance in FA (Wijtenburg et al., 2012). Here, we evaluate the relationship between DTI, DKI, model-specific WMTI parameters, and 1H-MRS metabolites (NAA, Cr, Cho, and mI absolute concentrations) in a cohort of patients with mild traumatic brain injury (MTBI). This cohort has already been compared to age-matched controls using DTI (Grossman et al., 2013), DKI (Grossman et al., 2013), and 1H-MRS (Kirov et al., 2013a, Kirov et al., 2013b). By combining the results from both diffusion and spectroscopy measurements in MTBI, we aim (i) to investigate the specificity of diffusion parameters for 1H-MRS-detectable metabolites and (ii) to elucidate specific biophysical mechanisms that influence structural and metabolic changes following MTBI.
Section snippets
Subjects
Approval for the study was obtained from the Institutional Review Board of the New York University School of Medicine and all participants provided informed written consent. Twenty-five adult patients with MTBI (20 male, 5 female; mean age = 33.6 years ± 11.2) prospectively recruited in our previous studies (Grossman et al., 2013, Kirov et al., 2013a) were examined retrospectively. Patients had been included if they were within 1 month following injury (mean interval = 21.2 days ± 14.3) and classified
Results
With respect to the diffusivity metrics, significant positive correlation was found in the global WM for NAA with FA (ρ = 0.69, p < 0.001), and significant negative correlation with D┴ (ρ = − 0.45, p = 0.028), as shown in Fig. 5. Similarly for the corpus callosum ROIs, positive correlation was found with FA (ρ = 0.48, p = 0.028) and a negative trend with D┴ (ρ = − 0.38, p = 0.081) in the splenium, and a positive trend between FA and NAA (ρ = 0.38, p = 0.073). In addition, no correlations were found between DTI
Discussion
While the diffusion signal is very sensitive to changes in microstructural WM integrity, empirical dMRI measures lack specificity. Here we have shown that biophysical modeling of the diffusion signal in the WM provides insight into microstructural changes and underlying pathological processes. Using the common WM modeling assumption that myelinated axons can be represented as impermeable cylinders, we were recently able to separate between intra- and extra-axonal diffusion in terms of
Conclusion
The relationship between compartment-specific white matter tract integrity (WMTI) parameters from diffusion MRI and concentrations of 1H-MRS-detectable metabolites was investigated in vivo in a cohort of patients with mild traumatic brain injury (MTBI). Results demonstrated significant correlations associating NAA with those WMTI parameters that are affected by intra-axonal diffusion and axonal density, providing arguments for the validity of a two-compartment non-exchange model of intra- and
Acknowledgments
The authors thank Jelle Veraart for assistance with image postprocessing and James Babb for fruitful discussions on statistical analyses. This research was supported in part by the National Institutes of Health grant numbers EB01015, NS050520, NS039135, and NS051623, the Noto Foundation to M.I., and the Center for Advanced Imaging Innovation and Research, a National Institute for Biomedical Imaging and Bioengineering Biomedical Technology Resource Center, for grant number P41 EB017183.
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2022, NeuroImage: ClinicalCitation Excerpt :It should be noted that the present study is the first to examine the association between the left DLPFC neurometabolite levels and neurite properties in healthy older adults and aMCI patients. A few studies have previously investigated the relationship between conventional white matter parameters (e.g., FA) and brain metabolites in healthy older adults and patients with schizophrenia and mild traumatic brain injury (Caprihan et al., 2015; Grossman et al., 2015; Reid et al., 2016). One of these studies also reported a positive association between NAA and intra-axonal diffusivity in patients with mild traumatic brain injury (Grossman et al., 2015).