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.

YNIMG-11269; No. of pages: 11; 4C: 4, 5, 6, 7, 8, 9 NeuroImage xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

NeuroImage journal homepage: www.elsevier.com/locate/ynimg

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S. Umesh Rudrapatna a, Tadeusz Wieloch b, Kerstin Beirup b, Karsten Ruscher b, Wouter Mol a, Pavel Yanev a, Alexander Leemans c, Annette van der Toorn a, Rick M. Dijkhuizen a

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Article history: Accepted 4 April 2014 Available online xxxx

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Keywords: Diffusion kurtosis imaging DKI DTI Diffusion Microstructure Chronic stroke

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Biomedical MR Imaging and Spectroscopy Group, Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands Laboratory for Experimental Brain Research, Department of Clinical Sciences, Division of Neurosurgery, Lund University, BMC A13, S-22184 Lund, Sweden PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands

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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 DKIbased 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 regionof-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. © 2014 Published by Elsevier Inc.

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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

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Introduction

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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

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E-mail addresses: [email protected] (S.U. Rudrapatna), [email protected] (R.M. Dijkhuizen).

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

http://dx.doi.org/10.1016/j.neuroimage.2014.04.013 1053-8119/© 2014 Published by Elsevier Inc.

Please cite this article as: Rudrapatna, S.U., et al., Can diffusion kurtosis imaging improve the sensitivity and specificity of detecting microstructural alterations in brain tissue ch..., NeuroImage (2014), http://dx.doi.org/10.1016/j.neuroimage.2014.04.013

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Stroke model

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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 diameter 0.3–0.4 mm) was introduced into the ICA via a small incision into the distal end of the CCA and advanced to occlude the origin of the MCA. The subsequent decrease in cortical blood flow during MCA occlusion was monitored with a laser-Doppler device and arterial pO2, pCO2, pH and rectal temperatures were measured. After occlusion of the MCA, the wounds were sutured and the rats were allowed to wake up. At 90 min after occlusion, the neurological score was assessed and only rats showing rotational asymmetry and dysfunctional limb placement were included in the study. At 120 min after MCA occlusion, the animals were reperfused under anesthesia by removing the nylon filament. All wounds were sutured and the rats were allowed to wake up. After a recovery period of eight weeks, the animals were perfusion-fixed with paraformaldehyde, and decapitated. After overnight post-fixation at 4 °C, brains inside intact skulls were cold-stored in phosphate-buffered saline with sodium azide (0.5 g/l). Twenty-two brain samples were collected in this manner and used for scanning with MRI.

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MRI data acquisition

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Within 15 days after perfusion-fixation, DKI acquisitions of the brains inside the intact skull were performed on a 9.4 T pre-clinical MRI scanner (Varian Inc., Palo Alto, CA, USA), equipped with a gradient insert capable of operating at 100 G/cm, with the following DKI scan parameters: 2d-multislice echo-planar imaging (EPI) sequence; repetition time (TR): 3.4 s; echo time (TE): 26 ms; interleaves: 10; field-of-view (FOV): 20 × 30 × 11 mm; matrix size: 100 × 150 × 55 (read-out × phase encoding × slices) (isotropic resolution of 200 μm). Diffusion-weighting was performed in 30 optimized directions (Cook et al., 2007) with parameters δ: 3.63 ms and Δ: 13.67 ms. Choosing the set of appropriate b-values for a DKI study needs several considerations to be taken into account, like the applicability of DKI fitting under the range of b-values in use, the achievable signalto-noise ratio (SNR) etc. To date, most in-vivo DKI studies have used maximum b-values of around 2000–3000 s/mm2 (Jensen and Helpern, 2010). In animals, the mean diffusivity in perfusion-fixed brains has been observed to be nearly half of that compared to in-vivo brain tissue (D'Arceuil et al., 2007). Thus, the b-value range for ex-vivo DKI studies in rodent brains needs to be nearly double of those used for in-vivo studies, in order to achieve levels of signal attenuation where the DKI model is relevant. Thus, we used four b-values (1125, 2250, 3375 and 4500 s/mm2) along with eight b = 0 scans for this study. Test scans were performed on one sample to ensure the suitability of this range of b-values for DKI. For each brain-sample, seven averages of such 128 (30 × 4 + 8) EPI datasets were acquired over a period of 8.5 h.

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Estimation of DTI and DKI parameters

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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.

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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 spatiotemporal 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, 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

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Mean kurtosis (MK), parallel kurtosis (ParK) and perpendicular kurto- 189 sis (PerK) were estimated (Tabesh et al., 2011) from the reconstructed 190



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Data-driven identification of tissue abnormality While the ROI-based procedure could show whether kurtosis parameters and DTI parameters differed in bilaterally homologous regions, it would not allow identification of other regions where these parameters were abnormal. To test this, a data-driven analysis based on support-vector-classification was used. The adopted approach involved training a v-support vector one-class classifier (Chang and Lin, 2011; Chung and Jen, 2002) with parametric data from the contralateral hemisphere, and subsequent classification of the ipsilateral parametric maps. The goal was to identify those regions in the ipsilateral hemisphere that had drastically altered parameter values as compared with those on the contralateral side. In this approach, no anatomical distinctions were made within the hemisphere, except for the broad classification of gray and white matter. Thus, regions that were classified as not belonging to the training class differed drastically from all possible parameter combinations that occur on the contralateral side. So, only largely outstanding differences would come out of this analysis. Ipsi- and contralateral hemispheres were separated by fitting a polynomial surface to manually identified control points on the midline in various slices of FA maps. Automatic thresholding of FA values was used to distinguish gray and white matter (Otsu, 1979). Gray and white matter data were trained and tested separately. The testing was performed on data from both ipsi- and contralateral hemispheres. In practical implementation, one-class classification needs a threshold (v) to be specified. In our implementation, we derived classification results over a series of v values, ranging from 0.005 to 0.2, in steps of 0.01, thus leading to 21 classification results. A tissue abnormality incidence map was created by adding all the classification results. Thus, regions with high numbers would indicate more dramatic deviations from all parametric data on the contralateral side. Since v roughly indicates the fraction of training errors and support vectors, one way to interpret the classification results is, all voxels classified as not belonging to the training class at a particular v are further away from at least v ∗ 100% of voxels with extreme parameter combinations in the training (contralateral) dataset. In order to compare DTI and kurtosis parameters, three such abnormality incidence maps were created. The first was obtained by training and testing only with DTI parameters (FA and MD) obtained from DTI fit (denoted by FA,MD[DTI]). The second was obtained by training and testing with FA and MD obtained from DKI fit (denoted by FA,MD [DKI]). The third abnormality incidence map was created by training and testing with only kurtosis parameters, namely, MK, ParK and PerK (abbreviated as K[DKI]). To visualize the spatial localization of these

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Immunohistochemistry

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Immunohistochemical analysis was performed on coronal sections from five brains after DKI scanning. In each brain, one section-ofinterest at approximately 0.05 mm posterior to bregma was chosen, and ten different stainings were prepared on 40 μm thick sections around this level. The sections were stained with established markers for neurons (NeuN, NFH), astroglia (GFAP), microglia/macrophages (OX42), myelin (LFB), synapses (APP), scar tissue (CSPGs), protein aggregates (Aβ), nuclei (HTX) and blood–brain-barrier integrity (EBA) as described in Appendix A. The information provided by these histological markers under normal and pathological conditions are summarized in the Supplementary information, Table S1. For comparison with DKI, images of the abovementioned stainings were captured using a light microscope (Olympus BX41) at two different resolutions. An overall picture of the whole section was captured with 2.5 fold magnification. In addition, five ipsilateral regions-ofinterest, i.e. intact sensorimotor cortex, corpus callosum, striatum, anterior commissure and ventral part of the basal ganglia, and their homotopic contralateral areas were imaged at 40 fold magnification factor, covering an area of about 200 × 150 μm2, corresponding to an area of about 1 pixel in the DKI/DTI maps.

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Correlation between diffusion-based MRI parameters and immunohistochemical stainings

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For quantitative analysis, a notion of “amount of contrast” was defined in both MRI and histology domains. These values were analyzed in non-parametric Kendall's τ association tests to determine if any of the MRI parameters showed significant associations with any of the histological stains. For MRI parameters, the amount of contrast was defined as the mean value of the given particular parameter in a 3 × 3 voxel-sized volume covering the region-of-interest. The amount of contrast on histological sections was related to the notion of degree of staining. This was obtained by first converting a given histological image from RGB format to HSV space (hue/saturation/value), and then averaging the values in the inverted (1-V) plane. The inversion step was necessary to maintain the convention that higher values reflect a greater degree of staining. We made sure that the images used in the analysis were free of voids. Given that the diffusion-based MRI contrasts are mostly sensitive to the arrangement of microstructures rather than to the amounts of

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maps, the three abnormality incidence maps were combined into an RGB image with the red channel representing K[DKI], the green channel representing FA,MD[DKI] and the blue channel representing FA,MD[DTI]. Thus, a particular color and intensity combination would represent contributions from various channels in different proportions. Two such RGB images were obtained per sample, one from gray matter classification and the other from white matter classification. To quantitatively assess the similarity between the classifications with various parameter combinations, sequentially increasing thresholds were applied to each tissue abnormality incidence map, and Dice similarity index and Overlap coefficients were determined at each threshold for the various combinations of abnormality incidence maps, i.e., between K[DKI] and FA,MD[DKI], K[DKI] and FA,MD[DTI] and FA,MD[DKI] and FA,MD[DTI]. Dice similarity index is defined as 2 ∗ nt / (nx + ny), where nt denotes the number of overlapping voxels in the binary images and nx and ny refer to the non-zero voxels in each of the binary images, x and y. The higher the Dice similarity index, the better the overlap between the abnormality classifications. However, it is penalized if the overlap is not one-on-one. The Overlap coefficient is given by nt/min(nx, ny), with high values indicating good overlap in the classification. In this case, if one dataset forms a proper subset of the other, it would still yield the maximum coefficient, 1.0, i.e., it is only influenced by the size of the smaller classification region.

Analysis of predefined regions-of-interest In order to assess if DKI and DTI parameters were significantly altered in the ipsilateral hemisphere (as compared to the contralateral side), four regions-of-interest (ROIs), namely the forelimb region of the sensorimotor cortex (S1FL), the striatum, the internal capsule and the corpus callosum, as well as their homologous counterparts in the contralateral hemisphere were carefully delineated by an expert with knowledge of rat brain anatomy. Portions of the internal capsule that were used for the analysis ranged from ≈ 0.7 to 1.7 mm posterior to bregma (5 slices). The other three ROIs ranged from ≈0 to 1.0 mm posterior to bregma. Mean data values from these regions were expressed in ratio estimates, and statistically analyzed with pairwise t-tests to assess if the DTI and DKI parameters in the ipsilateral hemisphere were significantly different from their contralateral counterparts.

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DKI data after co-registering (Avants et al., 2008) diffusion-weighted datasets to the b = 0 dataset (to minimize eddy-current-related image distortions). Separately, DTI fits were also performed on the same data to calculate MD and FA. Both the DKI and DTI fitting routines were developed in-house using Matlab. Since deviations from the single tensor diffusion model manifest at high b-values, DTI fits were performed with data weighted at the lower three b-values, namely, 0, 1125 and 2250 s/mm2.

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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 intensity/standard deviation of air signal) estimates in the demarcated ROIs were obtained at all b-values (Supplementary Fig. S1). The SNR varied between 100 and 25 over the entire b-value range (0–4500 s/mm2), suggesting that the data quality was sufficient for obtaining reliable DKI fits, assuming Gaussian noise (Veraart et al., 2011). In order to be able to assess the relative sensitivity of DKI and DTI parameters to the altered tissue status, mean ratios of DKI parameters

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Region-of-interest analysis of DKI/DTI data

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correlation tests using the differences between contralateral and ipsilateral values, obtained from bilaterally homologous ROIs. In the case of Gabor amplitude vector representation for histology, separate tests were performed for each length-scale.

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constituents, a second notion of amount of contrast was defined. We adopted an approach to quantify local differences in structural composition by decomposing images into different constituent length scales and comparing them across the length-scale decompositions. The assumption here is that any change in tissue microstructure would influence the length-scale decomposition of the corresponding histological image. To this end, we used a popular method of length-scale representation based on Gabor-filters, which has found its way into the arena of automated analysis of histological images (Kovesi, 1999; Manohar et al., 2011; Naik et al., 2008). This second notion of “amount of contrast” was based on the rotationally averaged (6 orientations) Gabor amplitude vector (Kovesi, 1999), obtained by decomposing the inverted V (value) gray-scale histological images into ten different length-scales. Given the fact that all the diffusion parameters under consideration are quantitative in nature, and that histological stainings were prepared in batches (slices from all samples were stained simultaneously), there was no need to normalize data from either of these modalities for performing Kendall's-τ association tests. The tests were performed in four different ways. First, we used data from both ipsi- and contralateral sides for the tests. Thus, for every diffusion parameter–histological staining pair, we had 50 values (10 ROIs in 5 samples) to perform the analysis. We then tested for correlations separately with the ipsilateral and contralateral values only (25 values each). Finally, we performed

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Fig. 2. Estimated ratio of ipsi-/contralateral mean values for DKI parameters and contra-/ ipsilateral mean values for DTI parameters and corresponding 95% confidence intervals in the different ROIs. Notation: CC: corpus callosum, IC: internal capsule, S: striatum, SMCX: sensorimotor cortex.

Correlation between MRI-detected diffusion parameters and histology

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Fig. 6 shows example images of various histological stainings at the level of whole coronal slices, and the regions-of-interest chosen for correlation analysis with MRI data. Fig. 7 shows magnified (40 ×) images of homologous ipsi- and contralateral cortical regions depicted in Fig. 6. Contrast differences between these regions were used in correlation analysis with MR data. Increased GFAP, APP, OX42, Aβ, and CSPG stainings could be observed in perilesional cortical and subcortical tissue, whereas LFB, NeuN, NFH and APP were mostly reduced in the lesion borderzone. Yet, no clear trend could be established between contrast on MRI-based diffusion parameters and histological stainings by visual inspection. Quantitative analysis, however, revealed some significant correlations. Fig. 8 represents results obtained from the four different association tests performed on all diffusion parameter/histological staining pairs, using both methods of contrast quantification (i.e. degree-of-stainingbased and Gabor-amplitude-based). All references to FA and MD in this table correspond to those obtained using DKI fit (similar significant values were obtained with DTI fitting). We found a large number of significant correlations when either all data from contra- and ipsilateral hemispheres were used, or when only data from the contralateral hemisphere were used. With data from the ipsilateral hemisphere only, and from difference between contra- and ipsilateral values, we found fewer significant correlations between diffusion parameters and histology. The sense of correlation (positive or negative) between the histological stains and MR parameters mostly agreed with the results from visual observations and known histopathological connections between MR parameters and tissue status. While similarities could be identified in the two quantitative methods in the case of correlations with NeuN, EBA, Aβ, CSPG and GFAP stains, notable differences were found in identifying correlations with NFH, LFB and HTX. The Gaboramplitude-based method identified several strong correlations which were not observed using the notion of degree of staining. We observed that many stains that showed a strong association with a diffusion parameter (e.g., MK) when considering data only from the contralateral side, failed to do so when data from the ipsilateral hemisphere alone was considered. Considering only kurtosis-based parameters, the most significant correlations that persisted across hemispheres were with NeuN and CSPG.

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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 of perilesional regions, exhibiting clear glial, neuronal and inflammatory changes on immunohistochemistry, displayed relatively larger changes in kurtosis as compared to FA or MD. The observed simultaneous decrease in FA and MD, and concurrent increases in kurtosis parameters in the ipsilateral

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Fig. 3 shows results from one-class classification of parameters in gray matter as obtained from five random brain samples. Bright white spots indicate regions where all three abnormality incidence maps (K[DKI], FA,MD[DKI] and FA,MD[DTI]) showed very high values, indicating highly altered parameters in comparison with values in the contralateral hemisphere. Predominantly red regions show where only the kurtosis parameters drastically differed. Similarly, predominantly green and blue regions show where significant FA and MD changes in DKI and DTI fits were found, respectively. We found relatively large areas showing altered kurtosis parameters, as compared to areas with changes in FA or MD. A large increase in kurtosis parameter values (and accompanied decrease in FA and MD) was observed in the ipsilateral basal ganglia in 18 of the 22 samples. Significant changes in perilesional cortical tissue were observed in 11 samples. The abnormality incidence maps in Fig. 3 have been thresholded at 16 (corresponding to v = 0.05) indicating that the values in the highlighted regions differed from the rest by a large margin. Fig. 4 shows the variation of Dice similarity index and Overlap coefficient as a function of applied threshold (i.e., varying v) on the gray matter abnormality incidence maps. These results indicate that the classifications based on kurtosis parameters differed drastically from those based on FA and MD (obtained from DKI or DTI fits), as evidenced by the low Dice indices, and the low Overlap coefficients. In cortical regions, we observed mostly kurtosis-related changes, and not as much FA and MD changes. As expected, FA,MD[DKI] and FA,MD[DTI] essentially identified similar regions.

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For the same brain samples, the results obtained from white matter classification are shown in Figs. 5 and S3. 16 out of 22 brain samples revealed clear differences in kurtosis parameters between ipsi- and contralateral hemispheres, especially in the corpus callosum and internal capsule. We observed that kurtosis parameters are more adept at showing interhemispheric white matter differences as compared with FA and MD, which displayed a more patchy pattern. However, it should be noted that the white matter abnormality incidence maps shown in Fig. 5 were not thresholded, indicating that these results are not as robust as those obtained from gray matter abnormality analysis.

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(ipsilateral/contralateral) and mean ratios of DTI parameters (contralateral/ipsilateral) from various regions were compared. Given the reversed nature of signal changes, this swapping of ratios allowed for a more direct interpretation of sensitivity of the different parameters. Estimated ratios and their 95% confidence intervals (Schaarschmidt and Gerhard, 2009) are given in Fig. 2. In general, FA and MD were reduced in the ipsilateral ROIs, whereas kurtosis was increased. FA and MD measurements obtained with DTI and DKI fits were similar. In nearly all cases, the confidence intervals were either above or below 1.0, indicating significant differences between ipsi- and contralateral regions. A greater dynamic range was observed for kurtosis-based parameters in comparison to FA and MD in striatum, perilesional cortex and corpus callosum. Bar plots for the same data, and results from paired t-tests are presented in Supplementary information Fig. S2.

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Fig. 3. Results from gray matter one-class classification showing areas with the most significant abnormality, overlaid on the corresponding FA maps of five randomly selected multislice brain datasets (each row represents an individual dataset). The gray matter abnormality incidence maps were thresholded at a value of 16 (up to v = 0.05). FA maps were scaled down by a factor of 3 to highlight the classification results. For visualization purposes, K[DKI], FA,MD[DKI] and FA,MD[DTI] were stacked (as R, G and B channels) and converted to an RGB image. The slice positions (left to right) ranged approximately from −4.16 mm to 0.4 mm with respect to Bregma (posterior–anterior direction).

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areas that may be structurally altered. Our data-driven approach was shown to be effective in detecting prominent differences between ipsi- and contralateral tissues in gray and white matter areas, especially based on kurtosis parameters. These areas included the a priori selected regions for ROI analysis, but also exposed other areas, displaying the spatial extent to which the brain may structurally remodel after stroke. Moreover, abnormal regions highlighted by kurtosis and tensor

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ROIs indicate reduction in mobility and directionality of diffusing water, combined with formation of barriers to diffusion. This picture is consistent with the known formation of glial scars on the ipsilateral side after stroke, wherein, glial processes intertwine heavily, reducing and hindering diffusion in a non-orientation-specific manner. In addition to ROI analysis, we applied a data-driven machinelearning-based methodology for unbiased identification of other brain

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Fig. 4. Dice similarity index and Overlap coefficient between thresholded gray matter abnormality incidence map pairs at various thresholds calculated from different DKI and DTI fits (K[DKI], FA,MD[DKI] and FA,MD[DTI]). A threshold of 1 corresponds to all v ≤ 0.2 and a threshold of 20 corresponds to v = 0.005.


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Previous studies that compared DKI with DTI have concluded that DKI parameters either provide increased sensitivity for diffusion characteristics that inform on tissue structure, or provide complementary information on the microenvironment in which tissue water diffuses (Cheung et al., 2009; Gao et al., 2012; Grinberg et al., 2012; Hui et al., 2012a, 2012b; Raab et al., 2010; Zhuo et al., 2012). The notion of

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complementary information conjures the possibility of DKI parameters being sensitive to microstructural tissue features that would not influence DTI parameters. Results obtained from our data-driven analysis may shed light on this important aspect. The observed mismatches between tissue regions with abnormal kurtosis and DTI parameter values in post-stroke brain suggest that kurtosis parameters may indeed be sensitive to microstructural environments that would have little influence on DTI parameters and vice versa. Nevertheless, certain brain regions, particularly in the ipsilateral basal ganglia, displayed strong overlap between kurtosis and DTI parameter abnormalities. Moreover, regions highlighted by abnormal kurtosis parameter values often encompassed regions highlighted by abnormal FA and MD. Our results therefore seem to indicate that kurtosis parameters inform on microstructural environments that may not influence DTI parameters, as well as provide enhanced sensitivity to structural changes that may also be detected with DTI parameters. In this context, it is important to mention that we also found a few regions highlighted by DTI parameter abnormalities without DKI parameter abnormalities. However, given that DKI data also provide DTI information, it would be possible

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parameters did not necessarily overlap, which indicates that kurtosis parameters could be sensitive to certain microstructural characteristics that would not influence DTI parameters. Immunohistochemical analysis of various cellular features to identify the histological origins of the DKI and DTI contrasts indicated inconsistent association between MRI contrasts and histological stains in the contralateral- and the stroke-affected ipsilateral hemisphere. The substantial differences in the association between histology and MR parameters depending on the hemisphere under study call for careful interpretation of such results.

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Fig. 5. Results from white matter one-class classification showing areas with most significant abnormality, overlaid on the corresponding FA maps of five randomly selected multislice brain datasets (each row represents an individual dataset). The incidence maps were not thresholded. FA maps were scaled down by a factor of 3 to highlight the classification results. For visualization purposes, K[DKI], FA,MD[DKI] and FA,MD[DTI] were stacked (as R, G and B channels) and converted to an RGB image.

GFAP

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FA Map with ROI

Fig. 6. Example of histological images at the whole slice level, from one of the brain samples, along with ROIs chosen for correlation analysis superimposed on FA map corresponding to the same slice in MR acquisitions (il: ipsilateral; cl: contralateral).


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Fig. 7. Magnified (40×) images obtained with various histological stains from ipsilateral (il) and contralateral (cl) perilesional cortex, corresponding to the cortical regions marked in Fig. 6.

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Correlation between MR diffusion parameters and histological features

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In order to establish the relationship between various diffusion contrasts and underlying histological characteristics, an informed choice of immunohistochemical stainings was made based on various cellular features that allow characterization of post-stroke tissue status. The immunohistochemical data showed clear signs of neuronal degeneration (reduced NeuN and NFH), synaptic deterioration (reduced APP), myelin loss (reduced LFB), gliosis (increased GFAP), inflammatory cell activation (increased OX42), scar formation (increased CSPGs) and

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to detect these regions with DKI fitting, as we have confirmed by comparison of FA and MD calculations from DTI- and DKI-based measurements. Thus, the application of DKI to assess (changes in) brain tissue structure can provide a unique set of parameters whose combined range of microstructural sensitivity forms a superset of that provided by DTI.

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extracellular modifications (increased Aβ) in perilesional tissue, in line with previous studies (Andsberg et al., 1988; Bidmon et al., 1998; Davies et al., 1998; Irving et al., 2001; van Groen et al., 2005). Although we measured significant associations between various diffusion parameters (including kurtosis parameters) and histological features, we found that most of the correlations were not unequivocally parameterspecific. e.g., NeuN was found to correlate significantly with most diffusion parameters. While we did find exceptions, e.g., significant correlations between OX42 and ParK, NeuN, CSPG and OX42 with MK, the lack of agreement between ipsi- and contralateral tissues underscores the complexity of the relationship(s) between tissue architecture and diffusion behavior. Astrogliosis, identified by increased GFAP expression, has been associated earlier with increases in kurtosis-related parameters (Zhuo et al., 2012). Zhuo et al. (2012) reported increased contralateral expression of GFAP and increased MK in the same regions seven days after induction of traumatic brain injury. We found no such significant association in


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our data. However, we do find a strong positive correlation between ParK and to a lesser extent, MK, with OX42, a marker for microglia and macrophages (Hughes et al., 2010), in the contralateral hemisphere (Fig. 8). The differences in the nature of injuries under study (7 days after traumatic brain injury, i.e. in the subacute phase vs chronic state 8 weeks after stroke) could explain the differing observations. While our results indicate that diffusion parameters in the relatively unaffected contralateral brain tissue are more straightforwardly associated with underlying tissue structure, data from the ipsilateral hemisphere reflect loss of such a correlation in the case of significant tissue abnormality as a result of stroke injury. This brings us to the question as to whether diffusion-related contrasts should, for some reason, be expected to show specific behavior vis-a-vis histological stainings at all, and if so why? From the perspective of MRI acquisition, diffusion-weighted scans probe tissue microstructure. The fact that diffusion signal is mostly influenced by the structural organization of the different tissue constituents rather than by the sheer amount of different substances is fairly well established (Basser, 1995). The diffusion-weighted signal from a voxel of MRI data represents the summation of the effects that all the microstructures in that voxel have on diffusing water molecules. However, many different micro-environments may lead to similar diffusion-weighted signals, making it a many-to-one mapping (Mulkern et al., 2009; Wang et al., 2011; Yablonskiy and Sukstanskii, 2010). Besides, the obtained voxel signal is an average of all such micro-structural effects. Thus, the inverse problem of teasing out the sources from diffusion-weighted signals is complicated, unless, only few microstructures dominate the signal formation process. Hence, unless one of the constituents depicted by the stains had the most profound influence on the diffusion signal and the rest in combination or otherwise had none, results from such an analysis would be hard to

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Fig. 8. Significant associations (p b 0.05) between MRI-based diffusion parameters and histological stainings using Kendall's-τ test from all ipsi- and contralateral data (‘Contra & Ipsi’), with data from only the contralateral (‘Contra Only’) or the ipsilateral side (‘Ipsi Only’), or with the difference of values in bilaterally homologous regions (‘Contra–Ipsi’). Top row: results from analysis using degree of staining (DoS), Bottom row: results from analysis using Gabor-amplitude vectors (GA). In the DoS results, various colors indicate the Kendall's τ coefficient and the numbers in the boxes represent the significance (p-values). For GA-based analysis, the number in the boxes represents the total number of length-scales (out of 10) that showed significant (p b 0.05) associations.

interpret. We attempted to satisfy this constraint by restricting analysis to a small 3 × 3 voxel grid of MR data and sub-voxel-sized histology sections. The picture from the perspective of cerebral changes that continue to manifest into chronic stages after stroke is not a simple one either. Processes like neuronal degeneration, neuronal plasticity, reactive astrogliosis, extracellular matrix modification, glial scar formation, etc., occurring in concert or independently, dynamically alter the (micro) structural status of remodeling brain tissue during recovery from stroke. However their effect on MR diffusion signals can be different, or sometimes even similar. Given the difficulty in disentangling the sources of diffusion-based signals, associating specific diffusion parameter changes to particular biological processes is even more fraught with difficulties, unless at most a few dominant biological processes are at play. Thus, the myriad pathological and plastic processes in post-stroke brain, combined with the non-specific nature of MR diffusion signal formation, should be carefully taken into account when interpreting changes on DTI/DKI-based tissue maps. Our proposed approach at quantifying the relationship between MRI and histology data based on length-scale decomposition does seem to elicit several correlations which would otherwise go undetected. Eventually, approaches such as the one proposed here, or recently proposed by Budde and Frank (2012), may improve our understanding of the interaction between diffusion parameter changes and the underlying tissue microstructure.

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The process of brain reorganization continues for weeks after stroke, 603 leading to structural changes in the ipsilateral as well as contralateral 604 hemispheres (Overman and Carmichael, 2014). Discrete structural 605 Q9


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Q10627 Q14 Uncited references

Galtrey and Fawcett, 2007 Gherardini et al., 2013 Lahiri and Maloney, 2010 Oermann et al., 2004 Rosenstein et al., 1992 Siman et al., 1989 Smith-Swintosky et al., 1994 Sofroniew, 2009 Ueno et al., 2012

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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.

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Appendix A. Immunohistochemical staining procedures

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Paraformaldehyde-fixed, free floating brain sections were rinsed three times in phosphate buffered saline (PBS). Endogenous peroxide was quenched with 10% hydrogen peroxide and 10% methanol in PBS for 15 min. After blocking in PBS containing 0.25% Triton X-100 and 5% normal donkey serum (NDS; Jackson ImmunoResearch, Suffolk, UK), sections were incubated at 4 °C overnight with primary antibodies listed in Table A1 diluted in PBS containing 5% NDS and 0.25% Triton X-100 (PBS/Tx). On the following day, sections were washed three times in PBS with 2% NDS and 0.25% Triton X-100, and incubated with

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Appendix B. Supplementary data

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a biotinylated donkey anti-mouse IgG (Jackson ImmunoResearch, dilution 1:400) at room temperature for 90 min. Signals were amplified using the Vectastain ABC Kit (Vector Labs, Burlingame, CA, USA). For Luxol Fast Blue (LFB) staining, 1% Solvent Blue solution was prepared by dissolving 0.2 g Solvent Blue 38 in 200 mL 95% alcohol and then 1 mL of 10% acetic acid was added. Free floating sections were rinsed in PBS and thereafter mounted to positively charged glass slides and dried. After dehydration, sections were placed in the Solvent Blue solution and incubated at 56 °C for 16 h. Slides were then rinsed in 95% alcohol and distilled water followed by differentiation in lithium carbonate solution for 25 min and 70% alcohol for 20 s. Thereafter, the slides were dehydrated and mounted with Pertex Mounting Medium. For Hematoxylin (HTX) staining, paraformaldehyde fixed, free floating sections were washed in PBS and then put on positively charged glass slides to dry. Slides were then rinsed in distilled water for 2 × 5 min and then put in Mayer's Hematoxylin Solution for 5 min. After a short rinse in distilled water, slides were put in running tap water for 5 min followed by 5 min in Scott's Tap Water Substitute. After another 5 min in tap water the slides were rinsed in 2 × 2 min distilled water. The slides were then dehydrated in increasing concentrations of alcohol (95% and 99.5%) and after a final 2 × 5 min rinse in Xylene mounted with Pertex Mounting Medium (Histolab, Gothenburg, Sweden).

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changes of neurons and astrocytes have been observed in the contralateral hemisphere, accompanied by changes in the morphology of dendritic spines (Takatsuru et al., 2014), at least until the early chronic phase. Pathological changes can also result from secondary neurodegeneration as a consequence of axonal and trans synaptic degeneration (Zhang et al., 2012), even at chronic time points. Thus, the assertion that the contralateral brain tissue would be near normal at 56 days after large unilateral stroke in rats may need more careful considerations. However, that assumption is not critical for the conclusions drawn in this study, since we were mostly interested in eliciting hemispheric differences, rather than their actual causes and changes from baseline. Another limitation is the fact that the diffusion properties were derived from fixed brain samples, which may differ from in-vivo brains. Nevertheless, comparable values of diffusion anisotropy measures have been reported for fixed and in-vivo brain tissue (Guilfoyle et al., 2003; Sun et al., 2003). Lastly, among all the possible sets of DTI and DKI parameters that could have been considered for the study, we have used those that are commonly reported in applied diffusion studies. Whether the observations on these sets of parameters can be generalized to other parameters, remains to be shown.

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Table A1 Monoclonal antibodies used for immunohistochemical stainings.

t1:3

Antibody

Source

Clone

Dilution

t1:4 t1:5 t1:6 t1:7 t1:8 t1:9 t1:10 t1:11

Amyloid precursor protein (APP) Beta amyloid (Aβ) Chondroitin sulfate proteoglycan (CSPG) (mouse) Glial fibrillary acidic protein (GFAP) Neuronal nuclei (NeuN) Anti-CD11b (OX42) (mouse) Neurofilament H non-phosphorylated (NFH) Endothelial barrier antigen (EBA)

Merck Millipore Covance Merck Millipore Sigma Aldrich Merck Millipore AbD Serotec Covance Covance

22C11 6E10 Cat-315 G-A-5 A60

1:1500 1:3000 1:3000 1:5000 1:1500 1:800 1:10,000 1:1000

SMI 32 SMI 71


681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708


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Performances of diffusion kurtosis imaging and diffusion tensor imaging in detecting white matter abnormality in schizophrenia.

Diffusional kurtosis and diffusion tensor imaging reveal different time-sensitive stroke-induced microstructural changes.

Diffusion Kurtosis Imaging Detects Microstructural Changes in the Brain after Acute Alcohol Intoxication in Rats.

Cerebral Microstructural Alterations after Radiation Therapy in High-Grade Glioma: A Diffusion Tensor Imaging-Based Study.

Diffusion tensor imaging in hemorrhagic stroke.

Diffusion tensor imaging and histology of developing hearts.

Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain.

Test-retest reliability of high spatial resolution diffusion tensor and diffusion kurtosis imaging.

Hippocampal diffusion tensor imaging microstructural changes in vascular dementia.

Magnetic Resonance Diffusion Tensor Imaging Provides New Insights Into the Microstructural Alterations in Dilated Cardiomyopathy.

Diffusion tensor imaging of pyramidal tract reorganization after pediatric stroke.

Non-Gaussian diffusion alterations on diffusion kurtosis imaging in patients with early Alzheimer's disease.

A preliminary diffusional kurtosis imaging study of Parkinson disease: comparison with conventional diffusion tensor imaging.

Diffusion Kurtosis Imaging of Microstructural Alterations in the Brains of Paediatric Patients with Congenital Sensorineural Hearing Loss.

Diffusion tensor imaging and neuropsychologic assessment in aphasic stroke.

Microstructural effects of Ramadan fasting on the brain: a diffusion tensor imaging study.

Diffusion tensor imaging of the anterior cruciate ligament graft after reconstruction: repeatability and diffusion tensor imaging metrics.

Diurnal microstructural variations in healthy adult brain revealed by diffusion tensor imaging.

Microstructural white matter tissue characteristics are modulated by homocysteine: a diffusion tensor imaging study.

Anisotropic fractional diffusion tensor imaging.

Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging.

In vivo 3T and ex vivo 7T diffusion tensor imaging of prostate cancer: Correlation with histology.

Microstructural brain abnormalities and symptom dimensions in child and adolescent patients with obsessive-compulsive disorder: a diffusion tensor imaging study.

Peripheral neuropathy: detection with diffusion-tensor imaging.