Extra-axonal Diffusion Research Articles

Comparisons of MR and EM inferred tissue microstructure properties using a human autopsy corpus callosum sample

Degeneration of white matter (WM) microstructure in the central nervous system is characteristic of many neurodegenerative conditions. Previous research indicates that axonal degeneration visible in ex vivo electron microscopy (EM) photomicrographs precede the onset of clinical symptoms. Measuring WM microstructural features, such as axon diameter and packing fraction, currently require these highly invasive methods of analysis and it is therefore of great importance to develop methods for in vivo measurements. Diffusion weighted Magnetic Resonance Imaging (MRI) is a non-invasive method which can be used in conjunction with temporal diffusion spectroscopy (TDS) and an oscillating gradient spin echo (OGSE) pulse sequence to probe micron-scale structures within neural tissue. The current experiment aims to compare axon diameter measurements, mean effective axon diameter (AxD¯), and packing fractions calculated from EM histopathological analysis and inferred values from MR images. Mathematical models of axon diameters used for analysis include the ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD) model and the AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) model using ROI (Region of Interest) based analysis (RBA) and voxel-based analysis (VBA), respectively. Overall, it was observed that MRI inferred WM microstructural parameters overestimate those calculated from EM. This may be attributable to tissue shrinkage during EM dehydration, the sensitivity of MR pulse sequences to larger diameter axons, and/or inaccurate model assumptions. The results of the current study provide a means to characterize the precision and accuracy of RBA-ACD and VBA-AAD OGSE-TDS and highlight the need for further research investigating the relationship between ex vivo MRI and EM, with the goal of reaching in vivo MRI.

Modelling white matter microstructure using diffusion OGSE MRI: Model and analysis choices

Alterations in white matter (WM) microstructure of the central nervous system have been shown to be pathophysiological presentations of various neurodegenerative disorders. Current methods for measuring such WM features require ex vivo tissue samples analyzed using electron microscopy. Magnetic Resonance Imaging (MRI) diffusion-weighted pulse sequences provide a non-invasive tool for estimating such microstructural features in vivo. The current project investigated the use of two methods of analysis, including the ROI-based (Region of Interest, RBA) and voxel-based analysis (VBA), as well as four mathematical models of WM microstructure, including the ActiveAx Frequency-Independent Extra-Axonal Diffusion (AAI), ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD), AxCaliber Frequency-Independent Extra-Axonal Diffusion (ACI), and AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) models. Two mice samples imaged at 7 T and 15.2 T were analyzed. Both the AAI and AAD models provide a single value for each of the fit parameters, including mean effective axon diameter AxD¯, packing fraction fin, intra-cellular and Din and extra-cellular Dex diffusion coefficients, as well as the frequency dependence of Dex, βex for the AAD model. The ACI and ACD models provide this, in addition to a distribution of axon diameters for a chosen ROI. VBA extends this, providing a parameter value for each voxel within the selected ROI, at the cost of increased computational load and analysis time. Overall, RBA-ACD and VBA-AAD were found to be optimal for parameter fitting to physically relevant values in a reasonable time frame. A full comparison of each combination of RBA and VBA with AAI, AAD, ACI, and ACD is provided to give the reader sufficient information to make an informed decision of which model is best for their own experiments.

Spinal cord metrics derived from diffusion MRI: improvement in prognostication in cervical spondylotic myelopathy compared with conventional MRI.

A major shortcoming in optimizing care for patients with cervical spondylotic myelopathy (CSM) is the lack of robust quantitative imaging tools offered by conventional MRI. Advanced MRI modalities, such as diffusion MRI (dMRI), including diffusion tensor imaging (DTI) and diffusion basis spectrum imaging (DBSI), may help address this limitation by providing granular evaluations of spinal cord microstructure. Forty-seven patients with CSM underwent comprehensive clinical assessments and dMRI, followed by DTI and DBSI modeling. Conventional MRI metrics included 10 total qualitative and quantitative assessments of spinal cord compression in both the sagittal and axial planes. The dMRI metrics included 12 unique measures including anisotropic tensors, reflecting axonal diffusion, and isotropic tensors, describing extraaxonal diffusion. The primary outcome was the modified Japanese Orthopaedic Association (mJOA) score measured at 2 years postoperatively. Extreme gradient boosting-supervised classification algorithms were used to classify patients into disease groups and to prognosticate surgical outcomes at 2-year follow-up. Forty-seven patients with CSM, including 24 (51%) with a mild mJOA score, 12 (26%) with a moderate mJOA score, and 11 (23%) with a severe mJOA score, as well as 21 control subjects were included. In the classification task, the traditional MRI metrics correctly assigned patients to healthy control versus mild CSM versus moderate/severe CSM cohorts, with an accuracy of 0.647 (95% CI 0.64-0.65). In comparison, the DTI model performed with an accuracy of 0.52 (95% CI 0.51-0.52) and the DBSI model's accuracy was 0.81 (95% CI 0.808-0.814). In the prognostication task, the traditional MRI metrics correctly predicted patients with CSM who improved at 2-year follow-up on the basis of change in mJOA, with an accuracy of 0.58 (95% CI 0.57-0.58). In comparison, the DTI model performed with an accuracy of 0.62 (95% CI 0.61-0.62) and the DBSI model had an accuracy of 0.72 (95% CI 0.718-0.73). Conventional MRI is a powerful tool to assess structural abnormality in CSM but is inherently limited in its ability to characterize spinal cord tissue injury. The results of this study demonstrate that advanced imaging techniques, namely DBSI-derived metrics from dMRI, provide granular assessments of spinal cord microstructure that can offer better diagnostic and prognostic utility.

674 Diffusion Basis Spectrum Imaging Predicts Comprehensive Clinical Outcomes Following Surgery for Cervical Spondylotic Myelopathy

INTRODUCTION: A major shortcoming in improving care for cervical spondylotic myelopathy (CSM) patients is the lack of robust quantitative imaging tools to guide surgical decision-making. METHODS: A prospective cohort study enrolled fifty CSM patients who underwent cervical decompressive surgery. DBSI metrics assessed white matter tract integrity by fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. To evaluate extra-axonal diffusion, DBSI measures restricted and non-restricted fractions. Neurofunctional status was assessed by the mJOA, MDI, and DASH. Quality of life was measured by the SF-36 PCS and MCS. The NDI was used to measure self-reported neck pain. Patient satisfaction was assessed by the NASS satisfaction index. Support vector machine classification algorithms were used to predict surgical outcomes. Specifically, three models were built for each clinical outcome measure (e.g., mJOA), including clinical, DBSI, and combined models. RESULTS: Twenty-seven mild (mJOA 15-17), 12 moderate (12-14) and 11 severe (0-11) CSM patients were enrolled. Twenty-four (60%) underwent anterior surgery compared to 16 (40%) posterior surgery. The mean (SD) follow-up was 23.2 (5.6, range 6.1-32.8) months. The best performing model was for the prediction of the NASS satisfaction index, with an accuracy [95% CI] of 94.4 [94.3, 94.8]. Conversely, the worst performing model was for the NDI, with an accuracy of 73.8 [73.6, 74.5]. When predicting improvement in the mJOA, the clinically-driven model had an accuracy of 61.9 [61.6, 62.5], compared to 78.6 [78.4, 79.2] in the DBSI model, and 90.5 [90.2, 90.8] in the combined model. CONCLUSIONS: When combined with key clinical covariates, DBSI metrics predicted improvement after surgical decompression with high accuracy. These results suggest that DBSI may serve as a non-invasive imaging biomarker for CSM.

Diffusion basis spectrum imaging predicts long-term clinical outcomes following surgery in cervical spondylotic myelopathy

BACKGROUND CONTEXTA major shortcoming in improving care for cervical spondylotic myelopathy (CSM) patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Diffusion basis spectrum imaging (DBSI), an advanced diffusion-weighted MRI technique, provides objective assessments of white matter tract integrity that may help prognosticate outcomes in patients undergoing surgery for CSM. PURPOSETo examine the ability of DBSI to predict clinically important CSM outcome measures at 2-years follow-up. STUDY DESIGN/SETTINGProspective cohort study. PATIENT SAMPLEPatients undergoing decompressive cervical surgery for CSM. OUTCOME MEASURESNeurofunctional status was assessed by the mJOA, MDI, and DASH. Quality-of-life was measured by the SF-36 PCS and SF-36 MCS. The NDI evaluated self-reported neck pain, and patient satisfaction was assessed by the NASS satisfaction index. METHODSFifty CSM patients who underwent cervical decompressive surgery were enrolled. Preoperative DBSI metrics assessed white matter tract integrity through fractional anisotropy, fiber fraction, axial diffusivity, and radial diffusivity. To evaluate extra-axonal diffusion, DBSI measures restricted and nonrestricted fractions. Patient-reported outcome measures were evaluated preoperatively and up to 2-years follow-up. Support vector machine classification algorithms were used to predict surgical outcomes at 2-years follow-up. Specifically, three feature sets were built for each of the seven clinical outcome measures (eg, mJOA), including clinical only, DBSI only, and combined feature sets. RESULTSTwenty-seven mild (mJOA 15–17), 12 moderate (12–14) and 11 severe (0–11) CSM patients were enrolled. Twenty-four (60%) patients underwent anterior decompressive surgery compared with 16 (40%) posterior approaches. The mean (SD) follow-up was 23.2 (5.6, range 6.1–32.8) months. Feature sets built on combined data (ie, clinical+DBSI metrics) performed significantly better for all outcome measures compared with those only including clinical or DBSI data. When predicting improvement in the mJOA, the clinically driven feature set had an accuracy of 61.9 [61.6, 62.5], compared with 78.6 [78.4, 79.2] in the DBSI feature set, and 90.5 [90.2, 90.8] in the combined feature set. CONCLUSIONSWhen combined with key clinical covariates, preoperative DBSI metrics predicted improvement after surgical decompression for CSM with high accuracy for multiple outcome measures. These results suggest that DBSI may serve as a noninvasive imaging biomarker for CSM valuable in guiding patient selection and informing preoperative counseling. LEVEL OF EVIDENCEII

Evaluation of the diffusion MRI white matter tract integrity model using myelin histology and Monte-Carlo simulations

Quantitative evaluation of brain myelination has drawn considerable attention. Conventional diffusion-based magnetic resonance imaging models, including diffusion tensor imaging and diffusion kurtosis imaging (DKI),11AD: Axial diffusivity; AK: Axial kurtosis; AVF: Axonal volume fraction; AWF: Axonal Water Fraction; DTI: Diffusion Tensor Imaging; DKI: Diffusion Kurtosis Imaging; dMRI: Diffusion magnetic resonance imaging; TE: Echo time; FOV: Field-of-view; FA: Fractional anisotropy; LFB: Luxor fast blue; MD: Mean diffusivity; MK: Mean kurtosis; MRI: Magnetic resonance imaging; MVF: Myelin volume fraction; PLP: Proteolipid protein; RESOLVE: Readout segmentation of long variable echo train; RD: Radial diffusivity; RK: Radial kurtosis; TR: Repetition time; ROI: Region-of-interest; WM: White matter; WMTI: White Matter Tract Integrity. have been used to infer the microstructure and its changes in neurological diseases. White matter tract integrity (WMTI) was proposed as a biophysical model to relate the DKI-derived metrics to the underlying microstructure. Although the model has been validated on ex vivo animal brains, it was not well evaluated with ex vivo human brains. In this study, histological samples (namely corpus callosum) from postmortem human brains have been investigated based on WMTI analyses on a clinical 3T scanner and comparisons with gold standard myelin staining in proteolipid protein and Luxol fast blue. In addition, Monte Carlo simulations were conducted to link changes from ex vivo to in vivo conditions based on the microscale parameters of water diffusivity and permeability. The results show that WMTI metrics, including axonal water fraction AWF, radial extra-axonal diffusivity De⊥, and intra-axonal diffusivity Dawere needed to characterize myelin content alterations. Thus, WMTI model metrics are shown to be promising candidates as sensitive biomarkers of demyelination.

Triple diffusion encoding MRI predicts intra-axonal and extra-axonal diffusion tensors in white matter.

To demonstrate how triple diffusion encoding (TDE) MRI can be applied to separately estimate the intra-axonal and extra-axonal diffusion tensors in white matter (WM). Using a TDE pulse sequence with an axially symmetric b-matrix, diffusion MRI data were acquired at 3T for 3 healthy adults with an axial b-value of 4000 s/mm2 , a radial b-value of 307 s/mm2 , and 64 diffusion encoding directions. This acquisition was then repeated with the radial b-value set to 0. A previously proposed theory was applied to these data in order to estimate the intra-axonal diffusivity and axonal water fraction for each WM voxel. Conventional single diffusion encoding data were also obtained with b-values of 1000 and 2000 s/mm2 , which provided additional information sufficient for determining both the intra-axonal and extra-axonal diffusion tensors. From the TDE data, the average intra-axonal diffusivity in WM was found to be 2.24 ± 0.18 µm2 /ms, and the average axonal water fraction was found to be 0.60 ± 0.11. From the 2 diffusion tensors, average WM values were estimated for several compartment-specific diffusion parameters. In particular, the extra-axonal mean diffusivity was 1.09 ± 0.19 µm2 /ms, the intra-axonal fractional anisotropy was 0.50 ± 0.14, and the extra-axonal fractional anisotropy was 0.23 ± 0.13. By using a simple TDE pulse sequence with an axially symmetric b-matrix, the diffusion tensors for the intra-axonal and extra-axonal spaces can be separately estimated in adult WM. This allows one to determine compartment-specific diffusion properties for these 2 water pools.

Dynamic changes in hippocampal diffusion and kurtosis metrics following experimental mTBI correlate with glial reactivity.

Diffusion magnetic resonance imaging biomarkers can provide quantifiable information of the brain tissue after a mild traumatic brain injury (mTBI). However, the commonly applied diffusion tensor imaging (DTI) model is not very specific to changes in the underlying cellular structures. To overcome these limitations, other diffusion models have recently emerged to provide a more complete view on the damage profile following TBI. In this study, we investigated longitudinal changes in advanced diffusion metrics following experimental mTBI, utilising three different diffusion models in a rat model of mTBI, including DTI, diffusion kurtosis imaging and a white matter model. Moreover, we investigated the association between the diffusion metrics with histological markers, including glial fibrillary acidic protein (GFAP), neurofilaments and synaptophysin in order to investigate specificity. Our results revealed significant decreases in mean diffusivity in the hippocampus and radial diffusivity and radial extra axonal diffusivity (RadEAD) in the cingulum one week post injury. Furthermore, correlation analysis showed that increased values of fractional anisotropy one day post injury in the hippocampus was highly correlated with GFAP reactivity three months post injury. Additionally, we observed a positive correlation between GFAP on one hand and the kurtosis parameters in the hippocampus on the other hand three months post injury. This result indicated that prolonged glial activation three months post injury is related to higher kurtosis values at later time points. In conclusion, our findings point out to the possible role of kurtosis metrics as well as metrics from the white matter model as prognostic biomarker to monitor prolonged glial reactivity and inflammatory responses after a mTBI not only at early timepoints but also several months after injury.

Diffusion kurtosis imaging in diffuse axonal injury

To evaluate novel neuroimaging biomarkers for monitoring brain injury using diffusion kurtosis imaging (DKI) in patients with severe diffuse axonal injury. DKI data of 12 patients with severe DAI (11 patients with a Glasgow Coma Scale (GCS) score of ≤ 8 and 1 patient with a GCS score of 9) and 8 healthy volunteers (control group) were compared. MRI examination was performed 5 to 19 days after injury; 7 of the 12 patients underwent repeated MRI examinations. We assessed the following parameters: mean, axial, and radial kurtosis (MK, AK, RK, respectively) and kurtosis anisotropy (KA) of the white and gray matter; fractional anisotropy (FA), axonal water fraction (AWF), axial and radial extra-axonal diffusion (AxEAD and RadEAD, respectively), and tortuosity (TORT) of the extra-axonal space) of the white matter. Regions of interest (ROIs) were set bilaterally in the centrum semiovale, genu and splenium of the corpus callosum, anterior and posterior limbs of the internal capsule, putamen, thalamus, midbrain, and pons. A significant reduction in KA (p<0.05) in most of ROIs set on the white matter was revealed. AK was increased (p<0.05) not only in the white matter but also in the putamen and thalamus. A significant reduction in MK with time was observed when the first and second DKI data were compared. AWF was reduced in the centrum semiovale and peduncles. The TORT parameter was decreased (p<0.05) in the majority of ROIs in the white matter, with the most pronounced changes occurring in the genu and splenium of the corpus callosum. DKI provides novel data about microstructural injury in DAI and improves our knowledge of brain trauma pathophysiology. DKI parameters should be considered as potential biomarkers of brain injury and potential predictors of the outcome.

DIFFUSION-KURTOSIS IMAGING IN ASSESMENT OF BRAIN MICROSTRUCTURE. HEALTHY VOLUNTEERS MEASURMENTS

Aim:discover quantitative and qualitative variance of diffusion parameters in white and gray matter of healthyvolunteers brain. Discover correlation between diffusion and kurtosis parameters, find out if there is correlation between the parameters and aging microstructural changes.Materials and methods.14 healthy volunteers were investigated (9 men, 5 women; age from 21 to 55 years, mean 34). The volunteers were classified into two groups by age: 7 subjects who younger 35 (6 men and 1 woman, mean age 25) and 7 subjects who older 35 years (3 men and 4 women, mean age 44). We used 3.0 Tesla MRI (3.0T SignaHDxt, General Electric, USA) with 8 channel head coil, gradient strength 50 mT/m, slew rate 150 T/m/s. Diffusion imaging was based on echo planar “spin echo” sequence (SE EPI), TR = 10000 ms, TEmin = 102 ms, FOV = 240 mm, voxel size 3 × 3 × 3 mm3, 60 non-coplanar diffusion directions and three b-values: 0, 1000, 2500 s/mm2. Acquisition time of diffusion kurtosis imaging was 22 minutes. We excluded extracerebral areas on diffusion and kurtosis parametric maps using semi-automatic approach. After that, brain images were transformed to MNI152 space using affine method. Masks of 9 anatomical structures were applied to the transformed images. Diffusion and kurtosis values were measured in these structures.Results.Fractional anisotropy (FA) changed from 0.06 in lateral occipital cortex to 0.25 in cerebral white matter, kurtosis anisotropy (KA) changed from 0.03 to 0.14 in the same cerebral structures. Axial (AK), radial (RK) and mean kurtosis (MK) were minimal in superior frontal gyrus and maximal in cerebral white matter. AK changed from 0.55 to 0.72, RK changed from 0.62 to 1.05, MK from 0.59 to 0.88. Axial(AxEAD) and radial extra axonal water diffusion (RadEAD) were minimal in putamen and maximal in superior frontal gyrus. AxEAD was changing from 1.38 • 10–3 to 2.57 • 10–3, RadEAD from 1.03 • 10–3 to 2.34 • 10–3. Axonal water fraction (AWF) had minimal value 0,18 in superior frontal gyrus and maximal value 0.29 in cerebral white matter. Tortuosity (TORT) changed from 1.06 in lateral occipital cortex to 1.43 in cerebral white matter. There was significant difference between age groups in AWF, RK, RadEAD in putamen and in KA in superior temporal gyrus. Maximal correlation with age was in MK in superior temporal gyrus, anterior division. It was equal to 0.562.Conclusions:Diffusion kurtosis imaging is highly sensitive method of brain tissue microstructure assessment, which detects age-related changes.

Modeling white matter tract integrity in aging with diffusional kurtosis imaging.

Myelin breakdown and neural fiber loss occur in aging. This study used white matter tract integrity metrics derived from biophysical modeling using Diffusional Kurtosis Imaging to assess loss of myelin (i.e., extraaxonal diffusivity, radial direction, De,⊥) and axonal density (i.e., axonal water fraction) in cognitively unimpaired older adults. Tract-based spatial statistics and region of interest analyses sought to identify ontogenic differences and age-related changes in white matter tracts using cross-sectional and longitudinal data analyzed with general linear and mixed-effects models. In addition to pure diffusion parameters (i.e., fractional anisotropy, mean diffusivity, mean kurtosis), we found that white matter tract integrity metrics significantly differentiated early- from late-myelinating tracts, correlated with age in spatially distinct regions, and identified primarily extraaxonal changes over time. Percent metric changes were |0.3-0.9|% and |0.0-1.9|% per year using cross-sectional data and longitudinal data, respectively. There was accelerated decline in some late- versus early-myelinating tracts in older age. These results demonstrate that these metrics may inform further study of the transition from age-related changes to neurodegenerative decline.

Inferring diameters of spheres and cylinders using interstitial water.

Most early methods to infer axon diameter distributions using magnetic resonance imaging (MRI) used single diffusion encoding sequences such as pulsed gradient spin echo (SE) and are thus sensitive to axons of diameters > 5 μm. We previously simulated oscillating gradient (OG) SE sequences for diffusion spectroscopy to study smaller axons including the majority constituting cortical connections. That study suggested the model of constant extra-axonal diffusion breaks down at OG accessible frequencies. In this study we present data from phantoms to test a time-varying interstitial apparent diffusion coefficient. Diffusion spectra were measured in four samples from water packed around beads of diameters 3, 6 and 10 μm; and 151 μm diameter tubes. Surface-to-volume ratios, and diameters were inferred. The bead pore radii estimates were 0.60±0.08 μm, 0.54±0.06 μm and 1.0±0.1 μm corresponding to bead diameters ranging from 2.9±0.4 μm to 5.3±0.7 μm, 2.6±0.3 μm to 4.8±0.6 μm, and 4.9±0.7 μm to 9±1 μm. The tube surface-to-volume ratio estimate was 0.06±0.02 μm-1 corresponding to a tube diameter of 180±70 μm. Interstitial models with OG inferred 3-10 μm bead diameters from 0.54±0.06 μm to 1.0±0.1 μm pore radii and 151 μm tube diameters from 0.06±0.02 μm-1 surface-to-volume ratios.

Diffusion time dependence of microstructural parameters in fixed spinal cord

Biophysical modelling of diffusion MRI is necessary to provide specific microstructural tissue properties. However, estimating model parameters from data with limited diffusion gradient strength, such as clinical scanners, has proven unreliable due to a shallow optimization landscape. On the other hand, estimation of diffusion kurtosis (DKI) parameters is more robust, and its parameters may be connected to microstructural parameters, given an appropriate biophysical model. However, it was previously shown that this procedure still does not provide sufficient information to uniquely determine all model parameters. In particular, a parameter degeneracy related to the relative magnitude of intra-axonal and extra-axonal diffusivities remains. Here we develop a model of diffusion in white matter including axonal dispersion and demonstrate stable estimation of all model parameters from DKI in fixed pig spinal cord. By employing the recently developed fast axisymmetric DKI, we use stimulated echo acquisition mode to collect data over a two orders of magnitude diffusion time range with very narrow diffusion gradient pulses, enabling finely resolved measurements of diffusion time dependence of both net diffusion and kurtosis metrics, as well as model intra- and extra-axonal diffusivities, and axonal dispersion. Our results demonstrate substantial time dependence of all parameters except volume fractions, and the additional time dimension provides support for intra-axonal diffusivity to be larger than extra-axonal diffusivity in spinal cord white matter, although not unambiguously. We compare our findings for the time-dependent compartmental diffusivities to predictions from effective medium theory with reasonable agreement.

AXONAL DENSITY AND MYELIN INTEGRITY IN COGNITIVE DECLINE: A DIFFUSIONAL KURTOSIS IMAGING STUDY

Age-related changes in brain white matter have been associated with cognitive decline and Alzheimer's disease. Here, with advanced biophysical modeling of Diffusional Kurtosis Imaging (DKI) data (Fig. 1), we test the hypothesis that cognitively intact older adults who demonstrate cognitive decline over 15 months (i.e. “Decliners”) already demonstrate a more pronounced loss of axonal density and myelin integrity at baseline compared to matched controls (i.e. “Controls”). 69 cognitively intact older adults underwent neuropsychological testing and MRI (Siemens 3T TIM Trio), with a subset (n=39) completing the same procedures at follow-up 14.8 ± 3.3 months later. Of these subjects, 6 demonstrated declines on cognitive testing, i.e. “Decliners” had ≦-0.5 z-score decline in memory and at least 1 other UDS test normed for age). 12 “Controls” were selected to match these “Decliners;” these groups did not differ on baseline neuropsychological performance or several other health variables (Table 1). DKI protocol: 2.5 mm3 voxels, 3 b-values (0, 1000, 2000 s/mm2), 64 directions, 1 average. DKI data were processed using Diffusional Kurtosis Estimator to derive metrics of axonal density (i.e. axonal water fraction [AWF]) and myelin integrity (i.e. extra-axonal diffusivity in the radial direction [De_radial]). Voxelwise analysis of AWF and De_radial were performed with tract-based spatial statistics in FSL (5,000 permutations and threshold-free cluster enhancement to correct for familywise error). Using FSL's randomise, we tested whether these groups differed in AWF and De_radial, covarying for age. Contrary to the expected “accelerated aging” effect of pronounced loss of axonal density (↓ AWF) and myelin integrity (↑ De_radial) in Decliners, we found higher AWF in Decliners versus Controls along the corpus callosum and in the right temporal lobe (p<0.05), and lower De_radial in Decliners versus Controls (p=0.07) in the genu of the corpus callosum (Fig. 2). These results suggest that individuals at greatest risk for cognitive decline may demonstrate greater axonal density for enhanced inter-hemispheric processing and compensatory scaffolding in the right temporal lobe. This finding is one of the first diffusion MRI-based studies to corroborate the well-established compensation theories of cognitive aging described in the fMRI and cognitive neuroscience literature. Panel A: The WM model assumes that in highly aligned WM tracts, diffusion occurs in non- exchanging compartments: the intra-axonal space (red) and the extra-axonal (blue) space. Panel B: Diffusion parallel to the direction of axons is measured by the metrics extra-axonal diffusivity, axial (De,||) and intra-axonal diffusivity (Daxon). Panel C: A cross-sectional image of a fiber bundle with myelinated axons. Panel D: Diffusion perpendicular to the axons is measured by the metric extra-axonal diffusivity, radial (De,⊥).

Abnormalities in Diffusional Kurtosis Metrics Related to Head Impact Exposure in a Season of High School Varsity Football.

The purpose of this study was to determine whether the effects of cumulative head impacts during a season of high school football produce changes in diffusional kurtosis imaging (DKI) metrics in the absence of clinically diagnosed concussion. Subjects were recruited from a high school football team and were outfitted with the Head Impact Telemetry System (HITS) during all practices and games. Biomechanical head impact exposure metrics were calculated, including: total impacts, summed acceleration, and Risk Weighted Cumulative Exposure (RWE). Twenty-four players completed pre- and post-season magnetic resonance imaging, including DKI; players who experienced clinical concussion were excluded. Fourteen subjects completed pre- and post-season Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT). DKI-derived metrics included mean kurtosis (MK), axial kurtosis (K axial), and radial kurtosis (K radial), and white matter modeling (WMM) parameters included axonal water fraction, tortuosity of the extra-axonal space, extra-axonal diffusivity (De axial and radial), and intra-axonal diffusivity (Da). These metrics were used to determine the total number of abnormal voxels, defined as 2 standard deviations above or below the group mean. Linear regression analysis revealed a statistically significant relationship between RWE combined probability (RWECP) and MK. Secondary analysis of other DKI-derived and WMM metrics demonstrated statistically significant linear relationships with RWECP after covariate adjustment. These results were compared with the results of DTI-derived metrics from the same imaging sessions in this exact same cohort. Several of the DKI-derived scalars (Da, MK, K axial, and K radial) explained more variance, compared with RWECP, suggesting that DKI may be more sensitive to subconcussive head impacts. No significant relationships between DKI-derived metrics and ImPACT measures were found. It is important to note that the pathological implications of these metrics are not well understood. In summary, we demonstrate a single season of high school football can produce DKI measurable changes in the absence of clinically diagnosed concussion.

Quantification of normal-appearing white matter tract integrity in multiple sclerosis: a diffusion kurtosis imaging study.

Our aim was to characterize the nature and extent of pathological changes in the normal-appearing white matter (NAWM) of patients with multiple sclerosis (MS) using novel diffusion kurtosis imaging-derived white matter tract integrity (WMTI) metrics and to investigate the association between these WMTI metrics and clinical parameters. Thirty-two patients with relapsing-remitting MS and 19 age- and gender-matched healthy controls underwent MRI and neurological examination. Maps of mean diffusivity, fractional anisotropy and WMTI metrics (intra-axonal diffusivity, axonal water fraction, tortuosity and axial and radial extra-axonal diffusivity) were created. Tract-based spatial statistics analysis was performed to assess for differences in the NAWM between patients and controls. A region of interest analysis of the corpus callosum was also performed to assess for group differences and to evaluate correlations between WMTI metrics and measures of disease severity. Mean diffusivity and radial extra-axonal diffusivity were significantly increased while fractional anisotropy, axonal water fraction, intra-axonal diffusivity and tortuosity were decreased in MS patients compared with controls (p values ranging from <0.001 to <0.05). Axonal water fraction in the corpus callosum was significantly associated with the expanded disability status scale score (ρ=-0.39, p=0.035). With the exception of the axial extra-axonal diffusivity, all metrics were correlated with the symbol digits modality test score (p values ranging from 0.001 to <0.05). WMTI metrics are thus sensitive to changes in the NAWM of MS patients and might provide a more pathologically specific, clinically meaningful and practical complement to standard diffusion tensor imaging-derived metrics.

Differentiating T2 hyperintensity in neonatal white matter by two-compartment model of diffusional kurtosis imaging.

In conventional neonatal MRI, the T2 hyperintensity (T2h) in cerebral white matter (WM) at term-equivalent age due to immaturity or impairment is still difficult to identify. To clarify such issue, this study used the metrics derived from a two-compartment WM model of diffusional kurtosis imaging (WM-DKI), including intra-axonal, extra-axonal axial and radial diffusivities (Da, De,// and De,⊥), to compare WM differences between the simple T2h and normal control for both preterm and full-term neonates, and between simple T2h and complex T2h with hypoxic-ischemic encephalopathy (HIE). Results indicated that compared with control, the simple T2h showed significantly increased De,// and De,⊥, but no significant change in Da in multiple premyelination regions, indicative of expanding extra-axonal diffusion microenvironment; while myelinated regions showed no changes. However, compared with simple T2h, the complex T2h with HIE had decreased Da, increased De,⊥ in both premyelination and myelinated regions, indicative of both intra- and extra-axonal diffusion alterations. While diffusion tensor imaging (DTI) failed to distinguish simple T2h from complex T2h with HIE. In conclusion, superior to DTI-metrics, WM-DKI metrics showed more specificity for WM microstructural changes to distinguish simple T2h from complex T2h with HIE.

The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter

Diffusion magnetic resonance imaging (MRI) methods for axon diameter mapping benefit from higher maximum gradient strengths than are currently available on commercial human scanners. Using a dedicated high-gradient 3T human MRI scanner with a maximum gradient strength of 300mT/m, we systematically studied the effect of gradient strength on in vivo axon diameter and density estimates in the human corpus callosum. Pulsed gradient spin echo experiments were performed in a single scan session lasting approximately 2h on each of three human subjects. The data were then divided into subsets with maximum gradient strengths of 77, 145, 212, and 293mT/m and diffusion times encompassing short (16 and 25ms) and long (60 and 94ms) diffusion time regimes. A three-compartment model of intra-axonal diffusion, extra-axonal diffusion, and free diffusion in cerebrospinal fluid was fitted to the data using a Markov chain Monte Carlo approach. For the acquisition parameters, model, and fitting routine used in our study, it was found that higher maximum gradient strengths decreased the mean axon diameter estimates by two to three fold and decreased the uncertainty in axon diameter estimates by more than half across the corpus callosum. The exclusive use of longer diffusion times resulted in axon diameter estimates that were up to two times larger than those obtained with shorter diffusion times. Axon diameter and density maps appeared less noisy and showed improved contrast between different regions of the corpus callosum with higher maximum gradient strength. Known differences in axon diameter and density between the genu, body, and splenium of the corpus callosum were preserved and became more reproducible at higher maximum gradient strengths. Our results suggest that an optimal q-space sampling scheme for estimating in vivo axon diameters should incorporate the highest possible gradient strength. The improvement in axon diameter and density estimates that we demonstrate from increasing maximum gradient strength will inform protocol development and encourage the adoption of higher maximum gradient strengths for use in commercial human scanners.

Mapping mean axon diameter and axonal volume fraction by MRI using temporal diffusion spectroscopy

Mapping mean axon diameter and intra-axonal volume fraction may have significant clinical potential because nerve conduction velocity is directly dependent on axon diameter, and several neurodegenerative diseases affect axons of specific sizes and alter axon counts. Diffusion-weighted MRI methods based on the pulsed gradient spin echo (PGSE) sequence have been reported to be able to assess axon diameter and volume fraction non-invasively. However, due to the relatively long diffusion times used, e.g. >20ms, the sensitivity to small axons (diameter<2μm) is low, and the derived mean axon diameter has been reported to be overestimated. In the current study, oscillating gradient spin echo (OGSE) diffusion sequences with variable frequency gradients were used to assess rat spinal white matter tracts with relatively short effective diffusion times (1–5ms). In contrast to previous PGSE-based methods, the extra-axonal diffusion cannot be modeled as hindered (Gaussian) diffusion when short diffusion times are used. Appropriate frequency-dependent rates are therefore incorporated into our analysis and validated by histology-based computer simulation of water diffusion. OGSE data were analyzed to derive mean axon diameters and intra-axonal volume fractions of rat spinal white matter tracts (mean axon diameter of ~1.27–5.54μm). The estimated values were in good agreement with histology, including the small axon diameters (<2.5μm). This study establishes a framework for the quantification of nerve morphology using the OGSE method with high sensitivity to small axons.

A model for extra-axonal diffusion spectra with frequency-dependent restriction.

PurposeIn the brain, there is growing interest in using the temporal diffusion spectrum to characterize axonal geometry in white matter because of the potential to be more sensitive to small pores compared to conventional time-dependent diffusion. However, analytical expressions for the diffusion spectrum of particles have only been derived for simple, restricting geometries such as cylinders, which are often used as a model for intra-axonal diffusion. The extra-axonal space is more complex, but the diffusion spectrum has largely not been modeled. We propose a model for the extra-axonal space, which can be used for interpretation of experimental data.Theory and MethodsAn empirical model describing the extra-axonal space diffusion spectrum was compared with simulated spectra. Spectra were simulated using Monte Carlo methods for idealized, regularly and randomly packed axons over a wide range of packing densities and spatial scales. The model parameters are related to the microstructural properties of tortuosity, axonal radius, and separation for regularly packed axons and pore size for randomly packed axons.ResultsForward model predictions closely matched simulations. The model fitted the simulated spectra well and accurately estimated microstructural properties.ConclusionsThis simple model provides expressions that relate the diffusion spectrum to biologically relevant microstructural properties. Magn Reson Med 73:2306–2320, 2015. © 2014 The authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance.