Elsevier

NeuroImage

Volume 97, 15 August 2014, Pages 363-373
NeuroImage

Can diffusion kurtosis imaging improve the sensitivity and specificity of detecting microstructural alterations in brain tissue chronically after experimental stroke? Comparisons with diffusion tensor imaging and histology

https://doi.org/10.1016/j.neuroimage.2014.04.013Get rights and content

Highlights

  • Sensitivity and specificity of DTI and DKI to post-stroke tissue changes were studied.

  • ROI-based statistics, data-driven analysis and detailed histology were performed.

  • DKI parameters showed greater sensitivity to brain microstructural changes than DTI.

  • DKI informs on microstructural environments that may not influence DTI parameters.

  • Histology showed no specific relation between tissue status and changes in DKI maps.

Abstract

Imaging techniques that provide detailed insights into structural tissue changes after stroke can vitalize development of treatment strategies and diagnosis of disease. Diffusion-weighted MRI has been playing an important role in this regard. Diffusion kurtosis imaging (DKI), a recent addition to this repertoire, has opened up further possibilities in extending our knowledge about structural tissue changes related to injury as well as plasticity.

In this study we sought to discern the microstructural alterations characterized by changes in diffusion tensor imaging (DTI) and DKI parameters at a chronic time point after experimental stroke. Of particular interest was the question of whether DKI parameters provide additional information in comparison to DTI parameters in understanding structural tissue changes, and if so, what their histological origins could be. Region-of-interest analysis and a data-driven approach to identify tissue abnormality were adopted to compare DTI- and DKI-based parameters in post mortem rat brain tissue, which were compared against immunohistochemistry of various cellular characteristics.

The unilateral infarcted area encompassed the ventrolateral cortex and the lateral striatum. Results from region-of-interest analysis in the lesion borderzone and contralateral tissue revealed significant differences in DTI and DKI parameters between ipsi- and contralateral sensorimotor cortex, corpus callosum, internal capsule and striatum. This was reflected by a significant reduction in ipsilateral mean diffusivity (MD) and fractional anisotropy (FA) values, accompanied by significant increases in kurtosis parameters in these regions. Data-driven analysis to identify tissue abnormality revealed that the use of kurtosis-based parameters improved the detection of tissue changes in comparison with FA and MD, both in terms of dynamic range and in being able to detect changes to which DTI parameters were insensitive. This was observed in gray as well as white matter. Comparison against immunohistochemical stainings divulged no straightforward correlation between diffusion-based parameters and individual neuronal, glial or inflammatory tissue features.

Our study demonstrates that DKI allows sensitive detection of structural tissue changes that reflect post-stroke tissue remodeling. However, our data also highlights the generic difficulty in unambiguously asserting specific causal relationships between tissue status and MR diffusion parameters.

Introduction

Discovery of curative treatments for chronic-phase stroke patients may benefit immensely from improved understanding of post-stroke structural tissue (re)organization. For the past few years, diffusion tensor imaging (DTI) has been a popular method to assess changes in tissue structure after stroke (Dijkhuizen et al., 2012, Sotak, 2002, Sundgren et al., 2004), e.g., in an experimental stroke model, it has been shown that fractional anisotropy (FA) in the corpus callosum and ipsilesional corticospinal tracts has a significant correlation with functional recovery (van Meer et al., 2012).

Recently, diffusion kurtosis imaging (DKI), an extension to DTI that can capture the non-Gaussian nature of water diffusion in tissues, has been shown to exhibit enhanced sensitivity to microstructural changes in comparison with the conventional DTI parameters, FA and mean diffusivity (MD) (Gooijers et al., 2013, Hui et al., 2008, Jensen et al., 2005, Lu et al., 2006, van Cauter et al., 2012, Wu and Cheung, 2010). While DTI has played a significant role in understanding (changes in) tissue architecture, especially in cerebral white matter studies, DKI parameters can potentially be more effective in elucidating cerebral gray matter changes (Jensen et al., 2005).

The advantages offered by DKI over DTI have been reported in several recent studies. In an experimental traumatic brain injury model in rats, Zhuo et al. (2012) observed the continued sensitivity of mean kurtosis (MK) to structural tissue changes even into sub-acute stages, when DTI parameters had re-normalized. Raab et al. (2010) reported that gliomas of two different grades could be separated only by using MK values. In an experimental stroke study on the spatio-temporal evolution of kurtosis parameters (Hui et al., 2012a), the differences in MK between healthy and injured tissue areas were found to persist up to seven days post stroke, when MD values had normalized. Hui et al. (2012b) also reported the distinct patterns on kurtosis and MD maps and the greater dynamic range of kurtosis parameters in comparison with MD. Recently, Vanhoutte et al. (2013) reported the suitability of DKI metrics to detect amyloid deposition in a mouse model of Alzheimer's disease, when plain DTI metrics failed to do so. These studies indicate that kurtosis parameters bring in original information, which DTI parameters may not be able to provide. Furthermore, studies on brain maturation (Cheung et al., 2009), temporal lobe epilepsy (Gao et al., 2012) and stroke lesion visualization (Grinberg et al., 2012) have reported the enhanced sensitivity of DKI parameters over DTI parameters in discerning differences in tissue status. In other studies (Blockx et al., 2012, Cheung et al., 2012, Grossman et al., 2012), a combined use of kurtosis and DTI parameters proved to be advantageous in detecting tissue changes. These observations indicate that kurtosis parameters can discriminate healthy from abnormal tissue under various pathophysiological conditions. Thus, given that perilesional white and gray matter undergo significant changes following stroke (Floyd, 2012, Schaechter et al., 2006), DKI holds promise in furthering our understanding of restructuring in both cerebral tissue types.

Although kurtosis parameters may provide additional information over DTI parameters in assessing microstructural changes after stroke (Grinberg et al., 2012, Hui et al., 2012a, Hui et al., 2012b), there are few DKI studies (Blockx et al., 2012, Zhuo et al., 2012) which empirically link the changes in diffusion kurtosis-based contrasts to possible biological underpinnings, or ascertain their specificity to particular tissue alterations. These issues are central to the application of any contrast in imaging, and especially so in the case of diffusion-based contrasts, since it is known that many different but concurrent biological processes can affect diffusion parameters in a similar manner (Beaulieu, 2002, Wang et al., 2011). In addition, there are several other factors that may further modulate diffusion metrics in a nontrivial way, such as the tensor estimation approach (Veraart et al., 2013), partial volume effects (Szczepankiewicz et al., 2013, Vos et al., 2011), and violations of model assumptions (Tournier et al., 2011, Vos et al., 2012). A better understanding of the biological processes at play and how they can affect diffusion contrasts is therefore crucial for evaluating the benefit of one contrast over the other and more importantly, in correctly interpreting what is being observed.

In this work, we chose to apply a high-resolution post-mortem MRI approach, to elucidate the possible advantages of DKI parameters over DTI parameters in characterizing altered tissue structure chronically after stroke, when injury and plasticity mechanisms are known to have induced tissue remodeling around a stroke lesion (Schaechter et al., 2006). We used discrepancies in the relative status of DTI and DKI parameters (in homologous ipsi- and contralateral regions) to deduce if DKI parameters provide enhanced sensitivity in comparison with DTI parameters under these circumstances. A data-driven approach was used to further identify regions with abnormal DKI and DTI contrasts (in comparison to the values in the contralateral hemisphere), with the aim of detecting whether DKI provides information unavailable from DTI parameters. To understand the histological underpinnings of the observed changes in diffusion parameters and thus discern what the specific information conveyed by DKI contrasts could be, we performed immunohistochemistry and compared histological stainings with various MR diffusion parameters in the same tissue samples.

Section snippets

Stroke model

Transient middle cerebral artery occlusion (MCAO) was induced in adult male Wistar rats (320–400 g), housed under diurnal light conditions as described in Memezawa et al. (1992). They were anesthetized (initially with 4% Isoflurane, Intervet, Schering Plough in N2O/O2 (70:30), and during surgery with 2% Isoflurane in N2O/O2). The right common carotid artery (CCA) and external carotid artery were occluded permanently and the internal carotid artery (ICA) was exposed. A nylon filament (top

Region-of-interest analysis of DKI/DTI data

Fig. 1A shows example parametric maps obtained from a typical brain sample. The unilateral tissue infarct encompassed the ventrolateral cortex and the lateral striatum. It was characterized by high MD, low FA and reduced kurtosis parameters, indicative of destructed tissue. The intact perilesional tissue regions displayed altered contrast as compared to homologous contralateral areas. Fig. 1B shows typical bilaterally homologous ROIs demarcated in the same dataset. SNR (mean signal

Discussion

In the current study, we assessed the potential of DKI to measure contrast changes in brain regions that are known to undergo significant structural alterations after stroke, such as perilesional cortex, striatum, internal capsule and corpus callosum, which may affect post-stroke functional status (van Meer et al., 2012).

Results from our study indicate that DKI can be more sensitive than DTI in detecting microstructural changes throughout the brain at a chronic time point after stroke. Analysis

Acknowledgments

We are grateful to the following funding agencies for supporting this research. Utrecht University's High Potential program; The European Union's Seventh Framework Programme (FP7/2007–2013) under grant agreements no. 201024 and no. 202213 (European Stroke Network); The Swedish Research Council (No. 2008-3134); The Swedish Brain Fund and the Wachtmeister Foundation.

References (59)

  • F. Szczepankiewicz et al.

    Variability in diffusion kurtosis imaging: impact on study design, statistical power and interpretation

    NeuroImage

    (2013)
  • Y. Takatsuru et al.

    Contribution of neuronal and glial circuit in intact hemisphere for functional remodeling after focal ischemia

    Neurosci. Res.

    (2014)
  • J. Veraart et al.

    Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls

    NeuroImage

    (2013)
  • S.B. Vos et al.

    Partial volume effect as a hidden covariate in DTI analyses

    NeuroImage

    (2011)
  • S.B. Vos et al.

    The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain

    NeuroImage

    (2012)
  • J. Zhuo et al.

    Diffusion kurtosis as an in vivo imaging marker for reactive astrogliosis in traumatic brain injury

    NeuroImage

    (2012)
  • G. Andsberg et al.

    Amelioration of ischaemia-induced neuronal death in the rat striatum by NGF-secreting neural stem cells

    Eur. J. Neurosci.

    (1988)
  • P.J. Basser

    Inferring microstructural features and the physiological state of tissues from diffusion-weighted images

    NMR Biomed.

    (1995)
  • C. Beaulieu

    The basis of anisotropic water diffusion in the nervous system — a technical review

    NMR Biomed.

    (2002)
  • H.J. Bidmon et al.

    Structural alterations and changes in cytoskeletal proteins and proteoglycans after focal cortical ischemia

    Neuroscience

    (1998)
  • C.C. Chang et al.

    LIBSVM: a library for support vector machines

    ACM Trans. Intell. Syst. Technol.

    (2011)
  • J.S. Cheung et al.

    Stratification of heterogeneous diffusion MRI ischemic lesion with kurtosis imaging: evaluation of mean diffusion and kurtosis MRI mismatch in an animal model of transient focal ischemia

    Stroke

    (2012)
  • C.C. Chung et al.

    Training v-support vector regression: theory and algorithms

    Neural Comput.

    (2002)
  • P.A. Cook et al.

    Optimal acquisition orders of diffusion-weighted MRI measurements

    J. Magn. Reson. Imaging

    (2007)
  • R.M. Dijkhuizen et al.

    Functional MRI and diffusion tensor imaging of brain reorganization after experimental stroke

    Transl. Stroke Res.

    (2012)
  • C.L. Floyd

    Animal models of acute neurological injuries II

    Springer Protocols Handbooks

    (2012)
  • Y. Gao et al.

    Diffusion abnormalities in temporal lobes of children with temporal lobe epilepsy: a preliminary diffusional kurtosis imaging study and comparison with diffusion tensor imaging

    NMR Biomed.

    (2012)
  • J. Gooijers et al.

    White matter organization in relation to upper limb motor control in healthy subjects: exploring the added value of diffusion kurtosis imaging

    Brain Struct. Funct

    (2013)
  • F. Grinberg et al.

    Diffusion kurtosis imaging and log-normal distribution function imaging enhance the visualisation of lesions in animal stroke models

    NMR Biomed.

    (2012)
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