Structural brain changes in subacute spinal cord injury: an analysis of diffusion kurtosis imaging and diffusion tensor imaging metrics with clinical correlation

Introduction: Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can quantify indices related to brain structure and their change in pathology. However, only few studies have applied these techniques to spinal cord injury (SCI), and subtle microstructural changes in the brain of SCI individuals are not well understood. Our goal was to investigate structural changes in the brain using DTI (fractional anisotropy, FA; mean diffusivity, MD) and DKI parameters (kurtosis anisotropy, KA; mean kurtosis, MK) in subacute SCI and to study whether these changes were associated with clinical outcomes.

Methods: Twenty-eight individuals with SCI underwent brain MRI 3 months post-injury, alongside 20 healthy controls. Imaging included a multi-shell diffusion protocol, from which DTI and DKI metrics (FA, MD, KA and MK) were derived. Group comparisons were conducted for each metric across 17 brain regions selected based on their relevance to SCI from previous studies. Multiple comparison corrections were applied per metric to account for the number of examined regions. Effect sizes were calculated using Cohen’s d. For regions showing significant group differences, Spearman correlations were performed to assess associations between imaging metrics and clinical outcomes, including neurological status (ISNCSCI) and functional independence (SCIM III), with correction for multiple comparisons.

Results: MD was significantly higher in the right genu of the corpus callosum in the SCI group (adjusted p = 0.021). In this region, MD negatively correlated with SCIM scores (r = −0.51, p = 0.022), whereas MK showed a positive correlation (r = 0.482, p = 0.038).

Discussion: Structural changes in the corpus callosum may reflect impaired interhemispheric communication, linked to reduced functional independence after SCI. DTI and DKI could serve as complementary tools for identifying brain-based biomarkers, potentially informing recovery trajectories.

1 Introduction

Spinal cord injury (SCI) is a serious condition that significantly affects quality of life. In the United States, individuals with SCI have a mortality rate three times higher than those without SCI (DeVivo et al., 2022). In 2021, approximately 15.4 million people were living with SCI worldwide (WHO, 2024). Although advances in the treatment and management of SCI have been made, the effects of SCI on the reorganization of the brain and of the spinal cord remain not well understood. Mapping such changes thus represents an important step to better understand SCI and could aid the development of targeted treatments and rehabilitation strategies.

Previous studies (Freund et al., 2013; Freund et al., 2011; Bammer, 2003) have shown that SCI does not only result in at-level and below-level neurological impairment, but also in changes of the spinal cord above the injury and in the brain. For instance, Freund et al. demonstrated extensive upstream atrophic and microstructural changes of corticospinal axons and sensorimotor cortical areas occurring within the first months after SCI, with faster degenerative changes linked to poorer recovery (Freund et al., 2013; Freund et al., 2011). These changes can be identified using various imaging modalities, including T1-weighted and diffusion-weighted imaging (DWI). Notably, DWI is a promising method for detecting alterations in white matter structures (Bammer, 2003). It measures the random microscopic movement (Brownian motion) of water molecules within a tissue and, in the last decades, has been used for routine clinical applications. An established technique based on DWI is diffusion tensor imaging (DTI). DTI has proven to be sensitive to microstructural changes in several neurological conditions, and has been successfully applied in amyotrophic lateral sclerosis (Zhang et al., 2018; Foerster et al., 2013), multiple sclerosis (Song et al., 2002; Rovaris et al., 2005), Parkinson’s disease (Cochrane and Ebmeier, 2013) and Alzheimer’s dementia (Mayo et al., 2017). Yet, only a few studies in the field of SCI have applied DTI to investigate microstructural brain changes and their clinical correlations. For instance, Guo et al. demonstrated that SCI individuals had decreased fractional anisotropy (FA) and increased mean diffusivity (MD) and radial diffusivity (RD) in the corpus callosum (genu and splenium), superior longitudinal fasciculus, corona radiata, posterior thalamic radiation, right cingulate gyrus and right superior fronto-occipital fasciculus (Guo et al., 2019). Additionally, time since injury was negatively correlated with FA in the right superior fronto-occipital fasciculus, while FA in the left posterior thalamic radiation was positively associated with the American spinal injury association (ASIA) sensory scores. Zheng et al. demonstrated that, compared with non-injured controls, SCI individuals exhibited significant decreases in white matter FA in the left angular gyrus, right cerebellum, left precentral gyrus, left lateral occipital region, left superior longitudinal fasciculus, left supramarginal gyrus, and left postcentral gyrus (Zheng et al., 2017). Furthermore, a significant negative correlation was observed between increased RD in the left angular gyrus and total motor scores. These changes have been linked to demyelination and abnormal sensory perception, as well as motor impairment (Guo et al., 2019). Huynh et al. reported reduced FA in the thalamic radiation and corticospinal tract following SCI, with motor function also showing associations with both FA and MD of the corticospinal tract (Huynh et al., 2021). Sun et al. demonstrated significantly lower FA in the cerebral peduncles in individuals with cervical SCI compared to healthy controls (Sun et al., 2017). Finally, Ilvesmäki et al. reported significant white matter changes across the cerebrum, affecting projection (including the corticospinal tract and thalamocortical tracts), commissural (notably the genu and anterior body of the corpus callosum), and association fibers (including the inferior/superior longitudinal fasciculi, inferior fronto-occipital fasciculus, uncinate fasciculus, and anterior cingulum), with CST alterations extending from the cerebral peduncle to subcortical motor and sensory areas (Ilvesmaki et al., 2017).

These widespread brain abnormalities highlight the need for more advanced diffusion imaging approaches, which have become increasingly feasible with recent advancements in MRI scanner technology and multi-shell acquisition protocols. To leverage the information content of such acquisitions, other diffusion MRI techniques beyond DTI have been introduced, such as diffusion kurtosis imaging (DKI) (Jensen et al., 2005; Jensen and Helpern, 2010). DKI quantifies the deviation of the diffusion process from Gaussianity due to the effect of biological membranes, among others (Kärger, 1985). Several studies (Guglielmetti et al., 2016; Arab et al., 2018) have demonstrated that DKI has an increased sensitivity to pathological changes, as compared to DTI. For instance, microstructural changes, such as cortical demyelination, white and gray matter changes, typically observed in multiple sclerosis, have been studied with DKI in mouse models (Guglielmetti et al., 2016). DKI was also able to detect early microstructural changes in neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s disease (Arab et al., 2018). Furthermore, a previous study demonstrated that DTI-derived metrics based on the kurtosis framework are more accurate compared to DTI (Henriques et al., 2021). However, DKI has so far only been applied in a single study investigating changes in the cervical spinal cord following spinal cord injury (Thygesen et al., 2022).

The previously referenced studies on DTI in SCI were conducted in patients with chronic SCI (Guo et al., 2019; Zheng et al., 2017; Huynh et al., 2021; Sun et al., 2017; Ilvesmaki et al., 2017). However, there is limited knowledge regarding the brain’s structural changes, as evaluated by DTI, in an early phase after SCI. This phase is crucial, as adaptive changes of the central nervous system, known as neuroplasticity, are most prominent during the early period following SCI (Henry et al., 2024). For instance, a study in rats with SCI indicates that implementing an exercise routine in the acute phase of the injury maximizes the potential for recovery of function (Brown et al., 2011). Delaying motor training after SCI on the other hand, had a negative impact on the functional recovery in rats (Norrie et al., 2005). It is therefore believed, that early intervention will show the most significant improvements in functional outcomes (Henry et al., 2024; Brown et al., 2011). Understanding the subacute structural brain changes may therefore benefit therapy strategies resulting in better functional outcome.

The present study is – to the best of our knowledge – the first one to investigate structural white matter changes after subacute SCI using both DTI and DKI technique. The aim of our study is to investigate structural changes in the brain using DTI (fractional anisotropy, FA; mean diffusivity, MD) and DKI parameters (kurtosis anisotropy, KA; mean kurtosis, MK) in subacute SCI, and to study whether these changes are associated with clinical outcomes. We hypothesized that DKI captures different properties of the white matter tissue compared to DTI, and correlates with clinical scores such as ISNCSCI and SCIM, and thus is able to link microstructural brain changes in the subacute phase of SCI with clinically meaningful measures of neurological and functional outcomes.

2 Materials and methods

2.1 Participants and study design

A total of 28 individuals with subacute SCI admitted to the Swiss Paraplegic Centre (Nottwil, Switzerland) for rehabilitation were prospectively recruited between April 1, 2019, and December 31, 2022. Inclusion criteria were as follows: complete or incomplete SCI classified according to the ASIA impairment scale (Asia, Committee ISIS, 2019), lesion level at or below C3 (to exclude ventilator-dependent participants), etiology of the SCI as traumatic or non-traumatic, and age between 18 and 80 years. The MRI measurements and assessments for individuals with subacute SCI were conducted between 70 and 98 days post-injury, as defined by the European Multicenter Study on Spinal Cord Injury (EMSCI) time schedule for acute II (EMSCI, 2018).

The exclusion criteria for individuals with subacute SCI were other known pathologies of the spinal cord/brain (e.g., multiple sclerosis) or progressive neurological disorders, inability to meet the MRI screening requirements (e.g., pacemaker or other electronic devices), severe head trauma as defined by a Glasgow Coma Scale (GCS) of <14, post-traumatic brain injury, and individuals who were ventilator-dependent.

As control group, we included 20 volunteers without SCI, no history of cervical trauma, traumatic brain injury, cervical surgery, no signs of neurological impairment or neurological disease.

2.2 Clinical assessment

All individuals with SCI underwent standardized EMSCI assessments, which included the Spinal Cord Independence Measure III (SCIM III) (Catz et al., 1997; Catz et al., 2001; Catz et al., 2001), ASIA impairment scale (AIS) following the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) assessment (Asia, Committee ISIS, 2019; Rupp et al., 2021). The SCIM, ranging from 0 (full impairment) to 100 (no impairment), is a disability scale specifically developed for individuals with SCI, evaluating functional independence in areas such as self-care, respiration, sphincter management, and social integration. The ISNCSCI assessment is a standardized tool used to assess and classify the severity of SCI, including motor and sensory testing, as well as determining the neurological level of injury (Asia, Committee ISIS, 2019; Rupp et al., 2021).

2.3 Image acquisition

We acquired the data on a 3 T Philips Achieva scanner (Release 5.4.1, Philips Healthcare, Best, The Netherlands) with a 32-channel head coil. The acquisition protocol consisted of (1) a high-resolution sagittal 3D T1-weighted anatomical sequence [repetition time (TR) = 8 ms, echo time (TE) = 3.7 ms, voxel size = 1 × 1 × 1 mm³, acquisition time = 150 s] and (2) a diffusion MRI sequence [TR = 13,000 ms, TE = 80 ms, FOV 240 × 240 × 152 mm³, voxel size = 2.5 × 2.5 × 2.5 mm³, matrix size 96 × 96, 61 slices, SENSE acceleration factor 2, acquisition time = 900 s] resulting in a total acquisition time of 20 min. The diffusion MRI sequence included 63 diffusion weighted volumes (7 at b = 0 s/mm², 6 at b = 500 s/mm², 20 at b = 1,000 s/mm², and 30 at b = 2000 s/mm²).

2.4 Diffusion MRI processing

We processed and analyzed the acquired diffusion data with MATLAB R2020a (Mathworks Inc., version 9.8) using ExploreDTI (Leemans et al., 2009) (version 4.8.6) and MRIToolkit¹ (De Luca, n.d.). The data processing pipeline included signal drift correction (Vos et al., 2017), de-noising with Marchenko-Pastur Principal Component Analysis (MPPCA) (Veraart et al., 2016), Gibbs ringing correction (Perrone et al., 2015), correction for subject motion (Leemans and Jones, 2009), eddy current distortions and EPI deformations. We set the sliding window size used for the MPPCA denoising to 5x5x5 comprising a total of 125 voxels. We estimated the diffusion and kurtosis tensors with linear least square estimator and corrected for unfeasible values with the mean kurtosis (MK)-Curve method (Zhang et al., 2019). In short, the MK-curve method adjusts the measurement at b = 0 s/mm² to ensure that the estimated kurtosis values are plausible (Zhang et al., 2019; Christiaanse et al., 2023). We derived the kurtosis metrics KA and MK and tensor metrics FA and MD as described by Poot et al. (2010) using 1,024 sampling directions. We calculated the DTI metrics from the tensor quantified during the DKI fit, and not with a separate DTI fit.

Outliers outside normal ranges were removed according to the following criteria: valid range for FA and KA was [0:1], for MK [0:3], and for MD [0:0.003] mm/s².

Finally, we extracted regions of interests (ROIs) in the white matter by applying the Statistical Parametric Mapping (SPM) based Cat12 toolbox (Gaser and Dahnke, 2016) to the 3D T1w in order to conduct brain segmentation using the ICBM DTI-81 white matter atlas (Rezende et al., 2019).

2.5 Statistical analysis

Statistical analyses were performed in R (version 4.1.3). FA, KA, MD and MK measures in 17 predefined ROIs (Supplementary Table S1) were compared individually between subjects with SCI and without SCI using unpaired t-tests. Given the large number of included regions, we focused on a subset of 17 ROIs, which were selected based on prior studies reporting significant DTI changes following SCI (Guo et al., 2019; Zheng et al., 2017; Huynh et al., 2021; Sun et al., 2017; Ilvesmaki et al., 2017). Benjamini and Hochberg (BH) correction was applied to correct for multiple comparisons across the 17 regions for each diffusion metric separately (Benjamini and Hochberg, 2018). The threshold for statistical significance was set at p_BH = 0.05. Effect sizes were calculated using the absolute value of Cohen’s d. Spearman correlations between clinical variables including total SCIM, total ASIA pinprick, light touch and motor scores were conducted with the DTI/DKI measures of regions with significant results in the group comparison. p-values were corrected for multiple comparisons using Benjamini and Hochberg correction. Subgroup analyses in individuals with SCI were conducted between different lesion levels (cervival, thoracic, lumbar) as well as ASIA impairment scale (AIS) levels across the DTI and DKI measures in the 17 regions using a one-way ANOVA (Supplementary material).

3 Results

A total of 28 individuals (20 men and 8 women, with a mean age of 47.9 ± 15.9 years) with subacute SCI completed the study protocol and underwent an MRI. The clinical characteristics of the SCI group are displayed in Table 1. Twenty healthy controls (9 men, 11 women, mean age 41 ± 12.61 years) underwent one MRI. The two groups did not significantly differ with respect to age (p = 0.202, W = 218) and sex (p = 0.065, ${\chi }^2$=3.41). The distribution of the DTI and DKI measures across the regions of interest in individuals with subacute SCI and non-injured controls is shown in the Supplementary Figures S1–S4.

Table 1

Table 1. Detailed demographics of participants with subacute SCI at timepoint 1.

After multiple comparison correction, the right genu of the corpus callosum showed significant differences in MD (p_BH = 0.021, cohen’s d = 0.955) between SCI and healthy controls (Figures 1, 2; Supplementary Table S2) with higher MD in SCI (Supplementary Figure S3). In contrast, MK in the same region showed a trend to lower values in SCI compared to healthy controls with a significant difference before correction (Supplementary Figure S4; Supplementary Table S2). Other regions also exhibited significant differences before correction, none remained significant after correction for multiple comparisons (Supplementary Tables S2, S3). No significant differences in FA and KA were observed across different lesion levels and AIS grades (Supplementary Tables S4–S7). A significant negative correlation was observed between MD in the right genu of the corpus callosum and the SCIM total score (r = −0.51, p_BH = 0.022) in individuals with SCI (Figure 3A; Supplementary Table S4). In contrast, a significant positive correlation was found between MK and the SCIM total score, with higher MK values in the right genu of the corpus callosum being associated with higher SCIM total scores (r = 0.482, pBH = 0.038) (Figure 3B; Supplementary Table S5). The correlations between MD and MK in the right genu of the corpus callosum with the ASIA pinprick, light touch and motor scores were not significant (Supplementary Tables S8, S9).

Figure 1

Figure 1. Bar plot illustrating the significant difference in mean diffusivity (MD) between subacute SCI and non-injured controls in the right genu of the corpus callosum. Single data points illustrate the MD values in individual subjects. The error bars indicate the standard error of the mean.

Figure 2

Figure 2. Brain image illustrating the location of the ROI for the genu of the corpus callosum.

Figure 3

Figure 3. Scatterplot illustrating the association between MD and MK with the spinal cord independence measure (SCIM). (A) The negative correlation (r = −0.51, p_BH = 0.022) between the SCIM and MD in the right genu of the corpus callosum (rCCGen) in individuals with spinal cord injury. (B) The positive correlation between SCIM and MK in rCCGen (r = 0.482, p_BH = 0.038).

4 Discussion

We present a study analyzing structural brain changes after SCI with a comparison between DTI and DKI measurements of the brain and correlation with clinical variables in individuals with subacute SCI. We observed a significant difference in MD in the right genu of the corpus callosum between individuals with subacute SCI and non-injured controls, with SCI individuals showing higher MD compared to healthy controls. Additionally, in the right genu of the corpus callosum, a negative correlation was found between MD and the SCIM total score, as well as a positive correlation between MK and the total SCIM score. Our findings are consistent with previous studies that reported increased MD in the corpus callosum in individuals with SCI (Guo et al., 2019; Ilvesmaki et al., 2017).

MD is a scalar metric that reflects the average diffusivity of water molecules within a voxel (Le Bihan et al., 1986). At the microstructural level, MD is influenced by several biological factors, including cell membrane integrity, intracellular and extracellular volume fractions, and the density and organization of cellular and subcellular components (Le Bihan et al., 2001), but also perfusion (De Luca et al., 2018) and partial volume effects. In healthy white matter, the presence of intact axonal membranes and myelin sheaths restricts the free diffusion of water molecules, hindering MD to much lower values than those of freely diffusing water. When tissue structural integrity is impaired by processes such as axon degeneration, myelin breakdown, edema, or cell death, the barriers that typically hinder and restrict diffusion are reduced (Song et al., 2003). This results in an increase in the extracellular space and a corresponding rise in water mobility, which is reflected by an increase in MD (Stebbins, 2010; Schmierer et al., 2007). Mean kurtosis (MK), on the other hand, is derived from DKI, an extension of DTI that quantifies the non-Gaussianity of water diffusion (Jensen et al., 2005; Jensen and Helpern, 2010). The assumption of Gaussian diffusion relies on a uniform medium, yet biological tissues are structurally heterogeneous, containing numerous microstructural barriers, such as cell membranes and organelles, that influence water motion in anisotropic and non-linear ways (Tuch et al., 2003). MK quantifies the degree to which this diffusion deviates from a Gaussian profile, making it a sensitive marker for microstructural complexity and tissue heterogeneity. High MK values are typically observed in healthy, well-organized white matter due to the abundance of microstructural obstacles that restrict diffusion. A decrease in MK therefore suggests a loss of tissue complexity, which may result from the breakdown of structural barriers due to demyelination, reduction in axonal diameter, or loss of cellular organization due to gliotic scarring or neuroinflammation.

In the context of SCI, secondary changes extend beyond the primary lesion site and can impact distant supraspinal structures through Wallerian degeneration and systemic inflammatory responses (Ziegler et al., 2018; Bloom et al., 2020; Anjum et al., 2020). These processes can lead to white matter atrophy and demyelination, including major commissural pathways such as the corpus callosum (Freund et al., 2013; Ziegler et al., 2018; Buss and Schwab, 2003). Elevated MD in this region likely reflects the cumulative effects of axonal loss and myelin degradation, consistent with prior histopathological and neuroimaging findings in SCI and other neurodegenerative conditions (Le Bihan et al., 1986; Le Bihan et al., 2001; Schmierer et al., 2007; Garg et al., 2015).

In our study, the simultaneous increase in MD and a trend toward decreased MK in the right genu of the corpus callosum among individuals with SCI is consistent with microstructural changes after SCI. From a neurobiological perspective, the observed diffusion changes can presumably be attributed to a combination of anterograde degeneration of corticospinal tracts, cortical reorganization, and inflammation-mediated demyelination, all of which contribute to alterations in white matter microstructure. The pattern of increased MD and reduced MK may therefore serve as a sensitive imaging marker for tracking disease progression, assessing treatment effects, or predicting functional outcomes in individuals with SCI.

In previous literature (Cunningham et al., 2019; Friedrich et al., 2020; Dong et al., 2023), differences in DTI measures have been observed after SCI in similar brain regions as in our study. For instance, the genu of the corpus callosum has been associated with functional motor ability of the upper limb, with higher FA indicating better ability (Cunningham et al., 2019). Beyond motor function, however, changes in the corpus callosum have also been linked to sensory and pain-related processes. For example, a previous study observed changes in the corpus callosum in individuals with subacute SCI and neuropathic pain, which may be associated with disrupted somatosensory information processing or interhemispheric communication. These alterations could trigger excitatory activity, potentially leading to cortical hyperexcitability linked to the perception of neuropathic pain (Dong et al., 2023). Alterations in parallel and perpendicular diffusivities in the corpus callosum and corticospinal tract were also associated with interhemispheric reorganization in motor regions and motor impairments in stroke patients (Wang et al., 2012). The corpus callosum has been widely recognized as a critical structure for interhemispheric integration, facilitating the exchange of motor, sensory, and cognitive information between the brain’s hemispheres (Friedrich et al., 2020). This integrative function is particularly important for bimanual coordination, where the timing and execution of movements by both hands must be precisely aligned. Evidence from both clinical and neuroimaging studies across various neurological and developmental conditions highlights the critical role of the corpus callosum in planning and synchronizing bimanual actions (Pauwels and Gooijers, 2023). Structural alterations in the genu of the right corpus callosum after SCI, reflected by increased MD, were linked to poorer functional outcomes, as evidenced by a negative correlation between MD and SCIM scores. In contrast, the positive correlation between SCIM and mean kurtosis MK indicates that higher microstructural complexity is associated with better recovery, underscoring the critical role of the corpus callosum in interhemispheric communication and the coordination of sensory and motor functions essential for functional independence, as captured by the SCIM.

The changes in the corpus callosum in our study may therefore reflect structural and functional plasticity in the nervous system in response to the injury (Ding et al., 2005; Gatto, 2020).

The total SCIM score helps predict how likely and how difficult it will be for a person to become independent in specific daily activities. Hence, the current total SCIM score can be used as a guide for assessing progress in daily living skills. Additionally, higher total scores—and the chances of success with each individual activity—can help healthcare providers plan more effective rehabilitation strategies, based on how challenging each task is for the person (Unai et al., 2019). Our findings may have a clinical implication for prognostic value, for instance, they may support the identification of imaging-based biomarkers of SCI, to be monitored throughout the subacute phase. With regards to prognosis, brain imaging has already shown functional and structural changes following SCI (Freund et al., 2019), and it has been highlighted that, while clinical recovery stops improving after 2 years post-injury, both macroscopic and microstructural changes in the brain continue, with the highest predictive validity in relation to clinical outcome at the level of the spinal cord, brainstem, and cortex over the first 6 months (Ziegler et al., 2018; Seif et al., 2018). Our results suggests that early structural brain changes after SCI, as depicted by the elevated MD in SCI and by the correlation between MD and MK with the SCIM score, take place already during the subacute phase. With regards to therapy, treatment of SCI-induced comorbidities remains difficult due to the incomplete understanding of its underlying mechanisms (Shiao and Lee-Kubli, 2018). Detecting changes in the brain after SCI requires the use of more advanced and sensitive techniques such as DKI. Our study is, to the best of our knowledge, among the first ones to apply DKI to the brain of individuals with SCI, and therefore only limited literature is available in the field. For instance, in a study comparing patients with early-clinical-stage cervical spondylotic myelopathy (CSM) to healthy controls, decreased MK values were found in white matter of the cervical spinal cord in the CSM group (Liu et al., 2020). Another study showed significant differences in MK and radial kurtosis in the spinal cord of pediatric individuals with chronic SCI compared to healthy controls (Conklin et al., 2016). The latter study suggested that DKI is a promising technique to characterize microstructural changes in SCI at the level of the spinal cord. One novel aspect of our study is that, while previous research has focused on chronic SCI, we included subjects in the subacute phase, and demonstrated that there is a positive correlation between MK and SCIM between 70 and 98 days after injury.

In our study, significant changes between the two groups were detected for MD with a trend toward decreased MK. This may be because these techniques measure different properties. While DKI measures are generally more sensitive to structural changes, these changes may take longer to develop to reach a certain threshold, making them detectable by MK only after a certain period. Previous studies in SCI (Guo et al., 2019; Zheng et al., 2017; Huynh et al., 2021) mostly included individuals with chronic injuries which may have made it easier to detect broader changes in the brain. Other studies have demonstrated that DKI is highly sensitive in detecting pathology in both gray matter (Zhuo et al., 2012) and white matter (Guglielmetti et al., 2016), and is able to capture both anisotropic and isotropic diffusion, suggesting that DKI has the potential to serve as an early-stage biomarker for various neurodegenerative disorders (Arab et al., 2018). Moreover, DKI allows for the assessment of isotropic structures, including the cortex and basal ganglia, which is an important limitation of DTI (Steven et al., 2014). It also enables the detection of crossing fibers, which results in a substantial improvement relative to DTI, as up to 90% of white matter contains two or more fiber populations crossing at different levels (Jeurissen et al., 2013), and this information is crucial when investigating tractography throughout regions with complex fiber bundle geometries (Glenn et al., 2016).

As this is the first study to investigate brain alterations with DKI measures in subacute SCI, further studies need to be conducted to investigate potential correlations of white matter microstructural properties with other clinical variables to understand their relationship with symptoms present in SCI. DKI shows potential for monitoring structural brain changes, assessing therapeutic response, and detecting early signs of clinical deterioration. Additionally, it may support the development of personalized treatment strategies by identifying region-specific changes, particularly in motor and sensory areas, that reflect adaptive plasticity. These insights could help clinicians determine the most effective timing and intensity of interventions to optimize functional outcomes.

Although DKI provides additional sensitivity to microstructural changes, it also presents certain limitations compared to DTI. DKI generally employs higher b-values (up to 3,000 s/mm²) than DTI, requiring stronger diffusion gradients. Because clinical MRI systems are limited in their maximum gradient amplitude, the desired diffusion weighting is often achieved by prolonging the gradient duration. This can lead to an increased echo time, which may reduce the signal-to-noise ratio, extend the total scan time, and consequently increase sensitivity to motion artifacts and the presence of outliers (Li et al., 2014). In clinical settings, image quality may also be compromised by spasticity, which is common after SCI and may be more likely to affect scans with longer acquisition times. The higher model complexity of DKI inherently leads to higher sensitivity to signal noise (Glenn et al., 2015). Additionally, DKI involves greater post-processing complexity due to the nonlinear fitting of higher-order diffusion models, which demands extensive computational resources and rigorous preprocessing steps such as denoising and artifact correction. This complexity further elevates the risk of fitting instabilities and model overfitting, necessitating careful optimization to achieve robust and reliable parameter estimation. DKI metrics can also be more difficult to interpret in heterogeneous tissues because the diffusion signal reflects a complex combination of microstructural environments within each voxel. This complexity leads to overlapping contributions from different tissue types and structures, making it challenging to attribute changes in kurtosis measures to specific biological processes. Another limitation of the study is the limited number of SCI subjects and heterogeneous SCI sample in terms of lesion and AIS level. We conducted subgroup analyses to investigate potential differences in DTI and DKI measures, but we did not find any significant results, which could also be attributed to the small sample size within each subgroup. Further research should examine the effect of lesion and AIS level on diffusion metrics in individuals with SCI. Moreover, there was a trend toward significance in the sex ratio between the SCI group and the controls. However, due to the small sample size, the study may be underpowered for including sex as an additional covariate in the model, and therefore future studies should include more participants and better matched groups.

5 Conclusion

Our study demonstrated that DTI and DKI can assess structural brain changes during the subacute phase following SCI and show meaningful correlations with clinical outcome measures. The genu of the corpus callosum showed significant changes in MD between individuals with SCI and non-injured controls, which may be related to phenomena of maladaptive plasticity following the injury. Structural tissue properties, indicated by MD and MK, were correlated with functional independence, as reflected by the SCIM scores. With the appropriate transfer from research to clinical applications, DKI may be used in clinical practize to monitor plastic changes in the brain after SCI, as well as to develop future treatment techniques.

Data availability statement

The datasets presented in this article are not readily available because the data that support the findings of this study are available from the corresponding author upon reasonable request. Requests to access the datasets should be directed to ZXJuc3QuY2hyaXN0aWFhbnNlQHBhcmFwbGVnaWUuY2g=.

Ethics statement

The studies involving humans were approved by Ethikkommission Nordwest- und Zentralschweiz (EKNZ). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

EC: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. JS: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. PW: Funding acquisition, Investigation, Writing – review & editing. AS-S: Formal analysis, Investigation, Methodology, Resources, Writing – review & editing. ALe: Investigation, Writing – review & editing. RV: Formal analysis, Writing – review & editing. ALu: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. GZ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was funded by the Swiss Paraplegic Foundation (Research Program Radiology) and the Swiss Paraplegic Research.

Acknowledgments

We thank all people who participated in this study. Special thanks go to Mihael Abramovic and Peter Zweers for their help in data acquisition and patient positioning and to Nadine Hunkeler for recruiting volunteers.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2025.1652416/full#supplementary-material

Footnotes

1. ^www.mritoolkit.org

References

Anjum, A., Yazid, M. D., Fauzi Daud, M., Idris, J., Ng, A. M. H., Selvi Naicker, A., et al. (2020). Spinal cord injury: pathophysiology, multimolecular interactions, and underlying recovery mechanisms. Int. J. Mol. Sci. 21:7533. doi: 10.3390/ijms21207533

PubMed Abstract | Crossref Full Text | Google Scholar

Arab, A., Wojna-Pelczar, A., Khairnar, A., Szabo, N., and Ruda-Kucerova, J. (2018). Principles of diffusion kurtosis imaging and its role in early diagnosis of neurodegenerative disorders. Brain Res. Bull. 139, 91–98. doi: 10.1016/j.brainresbull.2018.01.015

PubMed Abstract | Crossref Full Text | Google Scholar

Asia, Committee ISIS (2019). The 2019 revision of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI)-what's new? Spinal Cord 57, 815–817. doi: 10.1038/s41393-019-0350-9

PubMed Abstract | Crossref Full Text | Google Scholar

Bammer, R. (2003). Basic principles of diffusion-weighted imaging. Eur. J. Radiol. 45, 169–184. doi: 10.1016/s0720-048x(02)00303-0

Crossref Full Text | Google Scholar

Benjamini, Y., and Hochberg, Y. (2018). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat Methodol. 57, 289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x

Crossref Full Text | Google Scholar

Bloom, O., Herman, P. E., and Spungen, A. M. (2020). Systemic inflammation in traumatic spinal cord injury. Exp. Neurol. 325:113143. doi: 10.1016/j.expneurol.2019.113143

PubMed Abstract | Crossref Full Text | Google Scholar

Brown, A. K., Woller, S. A., Moreno, G., Grau, J. W., and Hook, M. A. (2011). Exercise therapy and recovery after SCI: evidence that shows early intervention improves recovery of function. Spinal Cord 49, 623–628. doi: 10.1038/sc.2010.167

PubMed Abstract | Crossref Full Text | Google Scholar

Buss, A., and Schwab, M. E. (2003). Sequential loss of myelin proteins during Wallerian degeneration in the rat spinal cord. Glia 42, 424–432. doi: 10.1002/glia.10220

PubMed Abstract | Crossref Full Text | Google Scholar

Catz, A., Itzkovich, M., Agranov, E., Ring, H., and Tamir, A. (1997). SCIM--spinal cord independence measure: a new disability scale for patients with spinal cord lesions. Spinal Cord 35, 850–856. doi: 10.1038/sj.sc.3100504

PubMed Abstract | Crossref Full Text | Google Scholar

Catz, A., Itzkovich, M., Agranov, E., Ring, H., and Tamir, A. (2001). The spinal cord independence measure (SCIM): sensitivity to functional changes in subgroups of spinal cord lesion patients. Spinal Cord 39, 97–100. doi: 10.1038/sj.sc.3101118

PubMed Abstract | Crossref Full Text | Google Scholar

Catz, A., Itzkovich, M., Steinberg, F., Philo, O., Ring, H., Ronen, J., et al. (2001). The Catz-Itzkovich SCIM: a revised version of the Spinal Cord Independence Measure. Disabil. Rehabil. 23, 263–268. doi: 10.1080/096382801750110919

PubMed Abstract | Crossref Full Text | Google Scholar

Christiaanse, E., Wyss, P. O., Scheel-Sailer, A., Frotzler, A., Lehnick, D., Verma, R. K., et al. (2023). Mean kurtosis-Curve (MK-Curve) correction improves the test-retest reproducibility of diffusion kurtosis imaging at 3 T. NMR Biomed. 36:e4856. doi: 10.1002/nbm.4856

PubMed Abstract | Crossref Full Text | Google Scholar

Cochrane, C. J., and Ebmeier, K. P. (2013). Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis. Neurology 80, 857–864. doi: 10.1212/WNL.0b013e318284070c

PubMed Abstract | Crossref Full Text | Google Scholar

Conklin, C. J., Middleton, D. M., Alizadeh, M., Finsterbusch, J., Raunig, D. L., Faro, S. H., et al. (2016). Spatially selective 2D RF inner field of view (iFOV) diffusion kurtosis imaging (DKI) of the pediatric spinal cord. Neuroimage Clin. 11, 61–67. doi: 10.1016/j.nicl.2016.01.009

PubMed Abstract | Crossref Full Text | Google Scholar

Cunningham, E. E., Noble, J. W., Krassioukov, A., Boyd, L. A., and Eng, J. J. (2019). Decreased white matter fractional anisotropy is associated with poorer functional motor skills following spinal cord injury: a pilot study. Spinal Cord 57, 206–213. doi: 10.1038/s41393-018-0191-y

PubMed Abstract | Crossref Full Text | Google Scholar

De Luca, A. (n.d.). MRIToolkit. Available online at: http://www.mritoolkit.org.

Google Scholar

De Luca, A., Leemans, A., Bertoldo, A., Arrigoni, F., and Froeling, M. (2018). A robust deconvolution method to disentangle multiple water pools in diffusion MRI. NMR Biomed. 31:e3965. doi: 10.1002/nbm.3965

PubMed Abstract | Crossref Full Text | Google Scholar

DeVivo, M. J., Chen, Y., and Wen, H. (2022). Cause of death trends among persons with spinal cord injury in the United States: 1960-2017. Arch. Phys. Med. Rehabil. 103, 634–641. doi: 10.1016/j.apmr.2021.09.019

Crossref Full Text | Google Scholar

Ding, Y., Kastin, A. J., and Pan, W. (2005). Neural plasticity after spinal cord injury. Curr. Pharm. Des. 11, 1441–1450. doi: 10.2174/1381612053507855

PubMed Abstract | Crossref Full Text | Google Scholar

Dong, D., Hosomi, K., Mori, N., Kamijo, Y. I., Furotani, Y., Yamagami, D., et al. (2023). White matter microstructural alterations in patients with neuropathic pain after spinal cord injury: a diffusion tensor imaging study. Front. Neurol. 14:1241658. doi: 10.3389/fneur.2023.1241658

PubMed Abstract | Crossref Full Text | Google Scholar

EMSCI. (2018). European multicenter study about spinal cord injury. Available online at: https://emsci.org/ (Accessed June 09, 2018).

Google Scholar

Foerster, B. R., Dwamena, B. A., Petrou, M., Carlos, R. C., Callaghan, B. C., Churchill, C. L., et al. (2013). Diagnostic accuracy of diffusion tensor imaging in amyotrophic lateral sclerosis: a systematic review and individual patient data meta-analysis. Acad. Radiol. 20, 1099–1106. doi: 10.1016/j.acra.2013.03.017

PubMed Abstract | Crossref Full Text | Google Scholar

Freund, P., Curt, A., Friston, K., and Thompson, A. (2013). Tracking changes following spinal cord injury: insights from neuroimaging. Neuroscientist 19, 116–128. doi: 10.1177/1073858412449192

PubMed Abstract | Crossref Full Text | Google Scholar

Freund, P., Seif, M., Weiskopf, N., Friston, K., Fehlings, M. G., Thompson, A. J., et al. (2019). MRI in traumatic spinal cord injury: from clinical assessment to neuroimaging biomarkers. Lancet Neurol. 18, 1123–1135. doi: 10.1016/S1474-4422(19)30138-3

PubMed Abstract | Crossref Full Text | Google Scholar

Freund, P., Weiskopf, N., Ward, N. S., Hutton, C., Gall, A., Ciccarelli, O., et al. (2011). Disability, atrophy and cortical reorganization following spinal cord injury. Brain 134, 1610–1622. doi: 10.1093/brain/awr093

PubMed Abstract | Crossref Full Text | Google Scholar

Friedrich, P., Forkel, S. J., and Thiebaut de Schotten, M. (2020). Mapping the principal gradient onto the corpus callosum. NeuroImage 223:117317. doi: 10.1016/j.neuroimage.2020.117317

PubMed Abstract | Crossref Full Text | Google Scholar

Garg, N., Reddel, S. W., Miller, D. H., Chataway, J., Riminton, D. S., Barnett, Y., et al. (2015). The corpus callosum in the diagnosis of multiple sclerosis and other CNS demyelinating and inflammatory diseases. J. Neurol. Neurosurg. Psychiatry 86, 1374–1382. doi: 10.1136/jnnp-2014-309649

PubMed Abstract | Crossref Full Text | Google Scholar

Gaser, C, and Dahnke, R, editors. CAT-A computational anatomy toolbox for the analysis of structural MRI data (2016). doi: 10.1016/j.neuroimage.2020.116948

Crossref Full Text | Google Scholar

Gatto, R. G. (2020). Molecular and microstructural biomarkers of neuroplasticity in neurodegenerative disorders through preclinical and diffusion magnetic resonance imaging studies. J. Integr. Neurosci. 19, 571–592. doi: 10.31083/j.jin.2020.03.165

PubMed Abstract | Crossref Full Text | Google Scholar

Glenn, G. R., Kuo, L. W., Chao, Y. P., Lee, C. Y., Helpern, J. A., and Jensen, J. H. (2016). Mapping the Orientation of White Matter Fiber Bundles: A Comparative Study of Diffusion Tensor Imaging, Diffusional Kurtosis Imaging, and Diffusion Spectrum Imaging. AJNR Am. J. Neuroradiol. 37, 1216–1222. doi: 10.3174/ajnr.A4714

PubMed Abstract | Crossref Full Text | Google Scholar

Glenn, G. R., Tabesh, A., and Jensen, J. H. (2015). A simple noise correction scheme for diffusional kurtosis imaging. Magn. Reson. Imaging 33, 124–133. doi: 10.1016/j.mri.2014.08.028

PubMed Abstract | Crossref Full Text | Google Scholar

Guglielmetti, C., Veraart, J., Roelant, E., Mai, Z., Daans, J., Van Audekerke, J., et al. (2016). Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination. NeuroImage 125, 363–377. doi: 10.1016/j.neuroimage.2015.10.052

PubMed Abstract | Crossref Full Text | Google Scholar

Guo, Y., Gao, F., Liu, Y., Guo, H., Yu, W., Chen, Z., et al. (2019). White Matter Microstructure Alterations in Patients With Spinal Cord Injury Assessed by Diffusion Tensor Imaging. Front. Hum. Neurosci. 13:11. doi: 10.3389/fnhum.2019.00011

PubMed Abstract | Crossref Full Text | Google Scholar

Henriques, R. N., Jespersen, S. N., Jones, D. K., and Veraart, J. (2021). Toward more robust and reproducible diffusion kurtosis imaging. Magn. Reson. Med. 86, 1600–1613. doi: 10.1002/mrm.28730

PubMed Abstract | Crossref Full Text | Google Scholar

Henry, M., Coxe, R. C., Barry, A., O'Donnell, A., Kessler, A., Shan, G., et al. (2024). A research protocol to study the critical time window for rehabilitation after incomplete spinal cord injury: early vs. late locomotor training. BMC Neurol. 24:482. doi: 10.1186/s12883-024-03980-x

PubMed Abstract | Crossref Full Text | Google Scholar

Huynh, V., Staempfli, P., Luetolf, R., Luechinger, R., Curt, A., Kollias, S., et al. (2021). Investigation of Cerebral White Matter Changes After Spinal Cord Injury With a Measure of Fiber Density. Front. Neurol. 12:598336. doi: 10.3389/fneur.2021.598336

PubMed Abstract | Crossref Full Text | Google Scholar

Ilvesmaki, T., Koskinen, E., Brander, A., Luoto, T., Ohman, J., and Eskola, H. (2017). Spinal cord injury induces widespread chronic changes in cerebral white matter. Hum. Brain Mapp. 38, 3637–3647. doi: 10.1002/hbm.23619

PubMed Abstract | Crossref Full Text | Google Scholar

Jensen, J. H., and Helpern, J. A. (2010). MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed. 23, 698–710. doi: 10.1002/nbm.1518

PubMed Abstract | Crossref Full Text | Google Scholar

Jensen, J. H., Helpern, J. A., Ramani, A., Lu, H., and Kaczynski, K. (2005). Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn. Reson. Med. 53, 1432–1440. doi: 10.1002/mrm.20508

PubMed Abstract | Crossref Full Text | Google Scholar

Jeurissen, B., Leemans, A., Tournier, J. D., Jones, D. K., and Sijbers, J. (2013). Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum. Brain Mapp. 34, 2747–2766. doi: 10.1002/hbm.22099

PubMed Abstract | Crossref Full Text | Google Scholar

Kärger, J. (1985). NMR self-diffusion studies in heterogeneous systems. Adv. Colloid Interf. Sci. 23, 129–148. doi: 10.1016/0001-8686(85)80018-X

Crossref Full Text | Google Scholar

Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., and Laval-Jeantet, M. (1986). MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161, 401–407. doi: 10.1148/radiology.161.2.3763909

PubMed Abstract | Crossref Full Text | Google Scholar

Le Bihan, D., Mangin, J. F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., et al. (2001). Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13, 534–546. doi: 10.1002/jmri.1076

PubMed Abstract | Crossref Full Text | Google Scholar

Leemans, A., and Jones, D. K. (2009). The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 61, 1336–1349. doi: 10.1002/mrm.21890

PubMed Abstract | Crossref Full Text | Google Scholar

Leemans, A. J. B., Sijbers, J., and Jones, D. K. (2009). ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Proc Intl Soc Mag Reson Med 17:3536.

Google Scholar

Li, X., Yang, J., Gao, J., Luo, X., Zhou, Z., Hu, Y., et al. (2014). A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging. PLoS One 9:e94592. doi: 10.1371/journal.pone.0094592

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, Z., Bian, B., Wang, G., Tian, C., Lv, Z., Shao, Z., et al. (2020). Evaluation of microstructural changes in spinal cord of patients with degenerative cervical myelopathy by diffusion kurtosis imaging and investigate the correlation with JOA score. BMC Neurol. 20:185. doi: 10.1186/s12883-020-01752-x

PubMed Abstract | Crossref Full Text | Google Scholar

Mayo, C. D., Mazerolle, E. L., Ritchie, L., Fisk, J. D., and Gawryluk, J. R.Alzheimer's Disease Neuroimaging I (2017). Longitudinal changes in microstructural white matter metrics in Alzheimer's disease. NeuroImage Clin. 13, 330–338. doi: 10.1016/j.nicl.2016.12.012

PubMed Abstract | Crossref Full Text | Google Scholar

Norrie, B. A., Nevett-Duchcherer, J. M., and Gorassini, M. A. (2005). Reduced functional recovery by delaying motor training after spinal cord injury. J. Neurophysiol. 94, 255–264. doi: 10.1152/jn.00970.2004

PubMed Abstract | Crossref Full Text | Google Scholar

Pauwels, L., and Gooijers, J. (2023). The Role of the Corpus Callosum (Micro)Structure in Bimanual Coordination: A Literature Review Update. J. Mot. Behav. 55, 525–537. doi: 10.1080/00222895.2023.2221985

PubMed Abstract | Crossref Full Text | Google Scholar

Perrone, D., Aelterman, J., Pižurica, A., Jeurissen, B., Philips, W., and Leemans, A. (2015). The effect of Gibbs ringing artifacts on measures derived from diffusion MRI. NeuroImage 120, 441–455. doi: 10.1016/j.neuroimage.2015.06.068

PubMed Abstract | Crossref Full Text | Google Scholar

Poot, D. H., den Dekker, A. J., Achten, E., Verhoye, M., and Sijbers, J. (2010). Optimal experimental design for diffusion kurtosis imaging. IEEE Trans. Med. Imaging 29, 819–829. doi: 10.1109/TMI.2009.2037915

PubMed Abstract | Crossref Full Text | Google Scholar

Rezende, T. J. R., Campos, B. M., Hsu, J., Li, Y., Ceritoglu, C., Kutten, K., et al. (2019). Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI. Brain Behav. 9:e01363. doi: 10.1002/brb3.1363

PubMed Abstract | Crossref Full Text | Google Scholar

Rovaris, M., Gass, A., Bammer, R., Hickman, S. J., Ciccarelli, O., Miller, D. H., et al. (2005). Diffusion MRI in multiple sclerosis. Neurology 65, 1526–1532. doi: 10.1212/01.wnl.0000184471.83948.e0

PubMed Abstract | Crossref Full Text | Google Scholar

Rupp, R., Biering-Sorensen, F., Burns, S. P., Graves, D. E., Guest, J., Jones, L., et al. (2021). International Standards for Neurological Classification of Spinal Cord Injury: Revised 2019. Top Spinal Cord Inj Rehabil. 27, 1–22. doi: 10.46292/sci2702-1

PubMed Abstract | Crossref Full Text | Google Scholar

Schmierer, K., Wheeler-Kingshott, C. A., Boulby, P. A., Scaravilli, F., Altmann, D. R., Barker, G. J., et al. (2007). Diffusion tensor imaging of post mortem multiple sclerosis brain. NeuroImage 35, 467–477. doi: 10.1016/j.neuroimage.2006.12.010

PubMed Abstract | Crossref Full Text | Google Scholar

Seif, M., Curt, A., Thompson, A. J., Grabher, P., Weiskopf, N., and Freund, P. (2018). Quantitative MRI of rostral spinal cord and brain regions is predictive of functional recovery in acute spinal cord injury. Neuroimage Clin. 20, 556–563. doi: 10.1016/j.nicl.2018.08.026

PubMed Abstract | Crossref Full Text | Google Scholar

Shiao, R., and Lee-Kubli, C. A. (2018). Neuropathic Pain After Spinal Cord Injury: Challenges and Research Perspectives. Neurotherapeutics 15, 635–653. doi: 10.1007/s13311-018-0633-4

PubMed Abstract | Crossref Full Text | Google Scholar

Song, S. K., Sun, S. W., Ju, W. K., Lin, S. J., Cross, A. H., and Neufeld, A. H. (2003). Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. NeuroImage 20, 1714–1722. doi: 10.1016/j.neuroimage.2003.07.005

PubMed Abstract | Crossref Full Text | Google Scholar

Song, S. K., Sun, S. W., Ramsbottom, M. J., Chang, C., Russell, J., and Cross, A. H. (2002). Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage 17, 1429–1436. doi: 10.1006/nimg.2002.1267

PubMed Abstract | Crossref Full Text | Google Scholar

Stebbins, G. T. (2010). “Diffusion tensor imaging in Parkinson’s disease” in Encyclopedia of movement disorders. eds. K. Kompoliti and L. V. Metman (Oxford: Academic Press), 308–310.

Google Scholar

Steven, A. J., Zhuo, J., and Melhem, E. R. (2014). Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain. AJR Am. J. Roentgenol. 202, W26–W33. doi: 10.2214/AJR.13.11365

PubMed Abstract | Crossref Full Text | Google Scholar

Sun, P., Murphy, R. K., Gamble, P., George, A., Song, S. K., and Ray, W. Z. (2017). Diffusion Assessment of Cortical Changes, Induced by Traumatic Spinal Cord Injury. Brain Sci. 7:21. doi: 10.3390/brainsci7020021

PubMed Abstract | Crossref Full Text | Google Scholar

Thygesen, M. M., Lund, T. E., Mikkelsen, I. K., Kasch, H., Dalby, R. B., Dyrskog, S. E., et al. (2022). Diffusional kurtosis imaging as a possible prognostic marker of cervical incomplete spinal cord injury outcome: a prospective pilot study. Acta Neurochir. 164, 25–32. doi: 10.1007/s00701-021-05018-4

Crossref Full Text | Google Scholar

Tuch, D. S., Reese, T. G., Wiegell, M. R., and Wedeen, V. J. (2003). Diffusion MRI of complex neural architecture. Neuron 40, 885–895. doi: 10.1016/s0896-6273(03)00758-x

PubMed Abstract | Crossref Full Text | Google Scholar

Unai, K., Uemura, O., Takemura, R., Kawakami, M., and Liu, M. (2019). Association Between SCIM III Total Scores and Individual Item Scores to Predict Independence With ADLs in Persons With Spinal Cord Injury. Arch Rehabil Res Clin Transl. 1:100029. doi: 10.1016/j.arrct.2019.100029

PubMed Abstract | Crossref Full Text | Google Scholar

Veraart, J., Novikov, D. S., Christiaens, D., Ades-Aron, B., Sijbers, J., and Fieremans, E. (2016). Denoising of diffusion MRI using random matrix theory. NeuroImage 142, 394–406. doi: 10.1016/j.neuroimage.2016.08.016

PubMed Abstract | Crossref Full Text | Google Scholar

Vos, S. B., Tax, C. M., Luijten, P. R., Ourselin, S., Leemans, A., and Froeling, M. (2017). The importance of correcting for signal drift in diffusion MRI. Magn. Reson. Med. 77, 285–299. doi: 10.1002/mrm.26124

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, L. E., Tittgemeyer, M., Imperati, D., Diekhoff, S., Ameli, M., Fink, G. R., et al. (2012). Degeneration of corpus callosum and recovery of motor function after stroke: a multimodal magnetic resonance imaging study. Hum. Brain Mapp. 33, 2941–2956. doi: 10.1002/hbm.21417

PubMed Abstract | Crossref Full Text | Google Scholar

WHO. Factsheet spinal cord injury. (2024). Available online at: https://www.who.int/news-room/fact-sheets/detail/spinal-cord-injury (Accessed May 17, 2024).

Google Scholar

Zhang, F., Chen, G., He, M., Dai, J., Shang, H., Gong, Q., et al. (2018). Altered white matter microarchitecture in amyotrophic lateral sclerosis: a voxel-based meta-analysis of diffusion tensor imaging. Neuroimage Clin. 19, 122–129. doi: 10.1016/j.nicl.2018.04.005

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, F., Ning, L., O'Donnell, L. J., and Pasternak, O. (2019). MK-curve – Characterizing the relation between mean kurtosis and alterations in the diffusion MRI signal. NeuroImage 196, 68–80. doi: 10.1016/j.neuroimage.2019.04.015

PubMed Abstract | Crossref Full Text | Google Scholar

Zheng, W., Chen, Q., Chen, X., Wan, L., Qin, W., Qi, Z., et al. (2017). Brain White Matter Impairment in Patients with Spinal Cord Injury. Neural Plast. 2017, 1–8. doi: 10.1155/2017/4671607

PubMed Abstract | Crossref Full Text | Google Scholar

Zhuo, J., Xu, S., Proctor, J. L., Mullins, R. J., Simon, J. Z., Fiskum, G., et al. (2012). Diffusion kurtosis as an in vivo imaging marker for reactive astrogliosis in traumatic brain injury. NeuroImage 59, 467–477. doi: 10.1016/j.neuroimage.2011.07.050

PubMed Abstract | Crossref Full Text | Google Scholar

Ziegler, G., Grabher, P., Thompson, A., Altmann, D., Hupp, M., Ashburner, J., et al. (2018). Progressive neurodegeneration following spinal cord injury: Implications for clinical trials. Neurology 90, e1257–e1266. doi: 10.1212/WNL.0000000000005258

PubMed Abstract | Crossref Full Text | Google Scholar