Fiber Ball White Matter Modeling Reveals Microstructural Alterations in Healthy Brain Aging.

Age-related white matter degeneration is characterized by myelin breakdown and neuronal fiber loss that preferentially occur in regions that myelinate later in development. Conventional diffusion MRI (dMRI) has demonstrated age-related increases in diffusivity but provide limited information regarding the tissue-specific changes driving these effects. A recently developed dMRI biophysical modeling technique, Fiber Ball White Matter (FBWM) modeling, offers enhanced biological interpretability by estimating microstructural properties specific to the intra-axonal and extra-axonal spaces. We used FBWM to illustrate the biological mechanisms underlying changes throughout white matter in healthy aging using data from 63 cognitively unimpaired adults ages 45-85 with no radiological evidence of neurodegeneration or incipient Alzheimer's disease. Conventional dMRI and FBWM metrics were computed for two late-myelinating (genu of the corpus callosum and association tracts) and two early-myelinating regions (splenium of the corpus callosum and projection tracts). We examined the associations between age and these metrics in each region and tested whether age was differentially associated with these metrics in late- vs. early-myelinating regions. We found that conventional metrics replicated patterns of age-related increases in diffusivity in late-myelinating regions. FBWM additionally revealed specific intra- and extra-axonal changes suggestive of myelin breakdown and preferential loss of smaller-diameter axons, yielding in vivo corroboration of findings from histopathological studies of aged brains. These results demonstrate that advanced biophysical modeling approaches, such as FBWM, offer novel information about the microstructure-specific alterations contributing to white matter changes in healthy aging. These tools hold promise as sensitive indicators of early pathological changes related to neurodegenerative disease.

Greater Diffusion Restriction in White Matter in Preclinical Alzheimer Disease.

OBJECTIVE: The Alzheimer's continuum is biologically defined by beta-amyloid deposition, which at the earliest stages is superimposed upon white matter degeneration in aging. However, the extent to which these co-occurring changes is characterized is relatively underexplored. The goal of this study was to use diffusional kurtosis imaging (DKI) and biophysical modeling to detect and describe amyloid-related white matter changes in preclinical Alzheimer disease. METHODS: Cognitively unimpaired participants ages 45 to 85 years completed brain magnetic resonance imaging, amyloid positron emission tomography (florbetapir), neuropsychological testing, and other clinical measures at baseline in a cohort study. We tested whether beta-amyloid-negative (AB-) and -positive (AB+) participants differed on DKI-based conventional (ie, fractional anisotropy [FA], mean diffusivity [MD], mean kurtosis) and modeling (ie, axonal water fraction [AWF], extra-axonal radial diffusivity [De,⊥ ]) metrics, and whether these metrics were associated with other biomarkers. RESULTS: We found significantly greater diffusion restriction (higher FA/AWF, lower MD/De,⊥ ) in white matter in AB+ than AB- (partial η2 =0.08-0.19), more notably in the extra-axonal space within primarily late myelinating tracts. Diffusion metrics predicted amyloid status incrementally over age (area under the curve = 0.84) with modest yet selective associations, where AWF (a marker of axonal density) correlated with speed/executive functions and neurodegeneration, whereas De,⊥ (a marker of gliosis/myelin repair) correlated with amyloid deposition and white matter hyperintensity volume. INTERPRETATION: These results support prior evidence of a nonmonotonic change in diffusion behavior, where an early increase in diffusion restriction is hypothesized to reflect inflammation and myelin repair prior to an ensuing decrease in diffusion restriction, indicating glial and neuronal degeneration. ANN NEUROL 2022;91:864-877.

Diffusion MRI detects early brain microstructure abnormalities in 2-month-old 3×Tg-AD mice.

The 3×Tg-AD mouse is one of the most studied animal models of Alzheimer's disease (AD), and develops both amyloid beta deposits and neurofibrillary tangles in a temporal and spatial pattern that is similar to human AD pathology. Additionally, abnormal myelination patterns with changes in oligodendrocyte and myelin marker expression are reported to be an early pathological feature in this model. Only few diffusion MRI (dMRI) studies have investigated white matter abnormalities in 3×Tg-AD mice, with inconsistent results. Thus, the goal of this study was to investigate the sensitivity of dMRI to capture brain microstructural alterations in 2-month-old 3×Tg-AD mice. In the fimbria, the fractional anisotropy (FA), kurtosis fractional anisotropy (KFA), and radial kurtosis (K┴ ) were found to be significantly lower in 3×Tg-AD mice than in controls, while the mean diffusivity (MD) and radial diffusivity (D┴ ) were found to be elevated. In the fornix, K┴ was lower for 3×Tg-AD mice; in the dorsal hippocampus MD and D┴ were elevated, as were FA, MD, and D┴ in the ventral hippocampus. These results indicate, for the first time, dMRI changes associated with myelin abnormalities in young 3×Tg-AD mice, before they develop AD pathology. Morphological quantification of myelin basic protein immunoreactivity in the fimbria was significantly lower in the 3×Tg-AD mice compared with the age-matched controls. Our results demonstrate that dMRI is able to detect widespread, significant early brain morphological abnormalities in 2-month-old 3×Tg-AD mice.

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

PURPOSE: 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). METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Brain iron levels in attention-deficit/hyperactivity disorder normalize as a function of psychostimulant treatment duration.

Brain iron homeostasis is a dopamine-related mechanism that may be modified with long-term psychostimulant treatment in attention-deficit/hyperactivity disorder (ADHD). We previously reported that while medication-naïve youth with ADHD have reduced brain iron compared to controls and psychostimulant-medicated patients, no differences were detected between the latter groups. In this follow-up study, we examined whether the duration of psychostimulant treatment correlates with the degree of iron normalization. Brain iron was indexed with MRI using an advanced method called magnetic field correlation (MFC) imaging and the conventional R2* proton transverse relaxation rate method. MFC was acquired in 30 psychostimulant-medicated youth with comorbid-free ADHD and 29 age-matched controls (all males). R2* was acquired in a subset of these individuals. Region-of-interest analyses for MFC and R2* group differences and within-group correlations with age and years of psychostimulant treatment were conducted in the globus pallidus (GP), putamen (PUT), caudate nucleus (CN), thalamus (THL) and red nucleus. No significant MFC and R2* group differences were detected. However, while all regional MFC and R2* significantly increased with age in the control group, MFC and R2* increased in the GP, PUT, CN and THL with psychostimulant treatment duration in the ADHD group to a greater degree than with age. Our findings suggest that while youth with ADHD may have less prominent age-related brain iron increases than that seen in typical development, long-term use of psychostimulant medications may compensate through a normalizing effect on basal ganglia iron. Longitudinal studies following ADHD patients before and after long-term psychostimulant treatment are needed to confirm these findings.

Early assessment of recurrent glioblastoma response to bevacizumab treatment by diffusional kurtosis imaging: a preliminary report.

PURPOSE: The purpose of this preliminary study is to apply diffusional kurtosis imaging to assess the early response of recurrent glioblastoma to bevacizumab treatment. METHODS: This prospective cohort study included 10 patients who had been diagnosed with recurrent glioblastoma and scheduled to receive bevacizumab treatment. Diffusional kurtosis images were obtained from all the patients 0-7 days before (pre-bevacizumab) and 28 days after (post-bevacizumab) initiating bevacizumab treatment. The mean, 10th, and 90th percentile values were derived from the histogram of diffusional kurtosis imaging metrics in enhancing and non-enhancing lesions, selected on post-contrast T1-weighted and fluid-attenuated inversion recovery images. Correlations of imaging measures with progression-free survival and overall survival were evaluated using Spearman's rank correlation coefficient. The significance level was set at P < 0.05. RESULTS: Higher pre-bevacizumab non-enhancing lesion volume was correlated with poor overall survival (r = -0.65, P = 0.049). Higher post-bevacizumab mean diffusivity and axial diffusivity (D∥, D∥10% and D∥90%) in non-enhancing lesions were correlated with poor progression-free survival (r = -0.73, -0.83, -0.71 and -0.85; P < 0.05). Lower post-bevacizumab axial kurtosis (K∥10%) in non-enhancing lesions was correlated with poor progression-free survival (r = 0.81, P = 0.008). CONCLUSIONS: This preliminary study demonstrates that diffusional kurtosis imaging metrics allow the detection of tissue changes 28 days after initiating bevacizumab treatment and that they may provide information about tumor progression.

Optimization of data acquisition and analysis for fiber ball imaging.

The inverse Funk transform of high angular resolution diffusion imaging (HARDI) data provides an estimate for the fiber orientation density function (fODF) in white matter (WM). Since the inverse Funk transform is a straightforward linear transformation, this technique, referred to as fiber ball imaging (FBI), offers a practical means of calculating the fODF that avoids the need for a response function or nonlinear numerical fitting. Nevertheless, the accuracy of FBI depends on both the choice of b-value and the number of diffusion-encoding directions used to acquire the HARDI data. To inform the design of optimal scan protocols for its implementation, FBI predictions are investigated here with in vivo data from healthy adult volunteers acquired at 3 T for b-values spanning 1000 to 10,000 s/mm2, for diffusion-encoding directions varying in number from 30 to 256 and for TE ranging from 90 to 120 ms. Our results suggest b-values above 4000 s/mm2 with at least 64 diffusion-encoding directions are adequate to achieve reasonable accuracy with FBI for calculating axon-specific diffusion measures and for performing WM fiber tractography (WMFT).

Diffusion MRI detects longitudinal white matter changes in the 3xTg-AD mouse model of Alzheimer's disease.

The sensitivity of multiple diffusion MRI (dMRI) parameters to longitudinal changes in white matter microstructure was investigated for the 3xTg-AD transgenic mouse model of Alzheimer's disease, which manifests both amyloid beta plaques and neurofibrillary tangles. By employing a specific dMRI method known as diffusional kurtosis imaging, eight different diffusion parameters were quantified to characterize distinct aspects of water diffusion. Four female 3xTg-AD mice were imaged at five time points, ranging from 4.5 to 18 months of age, and the diffusion parameters were investigated in four white matter regions (fimbria, external capsule, internal capsule and corpus callosum). Significant changes were observed in several diffusion parameters, particularly in the fimbria and in the external capsule, with a statistically significant decrease in diffusivity and a statistically significant increase in kurtosis. Our preliminary results demonstrate that dMRI can detect microstructural changes in white matter for the 3xTg-AD mouse model due to aging and/or progression of pathology, depending strongly on the diffusion parameter and anatomical region.

Elevated Brain Iron in Cocaine Use Disorder as Indexed by Magnetic Field Correlation Imaging.

BACKGROUND: Iron homeostasis is a critical biological process that may be disrupted in cocaine use disorder (CUD). In the brain, iron is required for neural processes involved in addiction and can be lethal to cells if unbound, especially in excess. Moreover, recent studies have implicated elevated brain iron in conditions of prolonged psychostimulant exposure. Thus, the purpose of this study was to examine iron in basal ganglia reward regions of individuals with CUD using an advanced imaging method called magnetic field correlation (MFC) imaging. METHODS: MFC imaging was acquired in 19 non-treatment-seeking individuals with CUD and 19 healthy control individuals (both male and female). Region-of-interest analyses for MFC group differences and within-group correlations with age and years of cocaine use were conducted in the globus pallidus internal segment (GPi), globus pallidus external segment, putamen, caudate nucleus, thalamus, and red nucleus. RESULTS: Individuals with CUD had significantly elevated MFC compared with control individuals within the GPi. In control individuals, MFC significantly increased with age in the GPi, globus pallidus external segment, putamen, and caudate nucleus. Conversely, there were no significant MFC within-group correlations in the CUD group. CONCLUSIONS: Individuals with CUD have excess iron in the GPi, as indexed by MFC, and lack the age-related gradual iron deposition seen in normal aging. Because the globus pallidus is critical for the transition of goal-directed behavior to compulsive behavior, significantly elevated iron in the GPi may contribute to the persistence of CUD. These findings implicate dysregulation of brain iron homeostasis in CUD and support pursuing this new line of research.

Types of naming errors in chronic post-stroke aphasia are dissociated by dual stream axonal loss.

The types of errors during speech production can vary across individuals with chronic post-stroke aphasia, possibly due to the location and extent of brain damage. In this study, we evaluated the relationship between semantic vs. phonemic errors during confrontational naming, and their relationship with the degree of damage to ventral and dorsal white matter pathways extending beyond the necrotic stroke lesion. Based on the dual stream model of language processing, we tested the hypothesis that semantic errors would be associated with ventral stream damage, whereas phonemic errors would be associated with dorsal stream damage, but not vice-versa. Multi-shell diffusion MRI was used to obtain kurtosis-based white matter tractography from 32 chronic stroke survivors. Using diffusion microstructural tissue modeling, we estimated axonal loss along the length of the inferior and superior longitudinal fasciculi (ILF and SLF), representing the main pathways in the ventral and dorsal streams, respectively. The frequency of semantic paraphasias was strongly associated with ILF axonal loss, whereas phonemic paraphasias were strongly associated with SLF axonal loss, but not vice versa. This dissociation between semantic and phonological processing is in agreement with the dual stream model of language processing and corroborates the concept that, during speech production, knowledge association (semantics) depends on the integrity of ventral, whereas form encoding (phonological encoding) is more localized to dorsal pathways. These findings also demonstrate the importance of the residual integrity of specific white matter pathways beyond regional gray matter damage for speech production.

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.

Characterizing intra-axonal water diffusion with direction-averaged triple diffusion encoding MRI.

For large diffusion weightings, the direction-averaged diffusion MRI (dMRI) signal from white matter is typically dominated by the contribution of water confined to axons. This fact can be exploited to characterize intra-axonal diffusion properties, which may be valuable for interpreting the biophysical meaning of diffusion changes associated with pathology. However, using just the classic Stejskal-Tanner pulse sequence, it has proven challenging to obtain reliable estimates for both the intrinsic intra-axonal diffusivity and the intra-axonal water fraction. Here we propose to apply a modification of the Stejskal-Tanner sequence designed for achieving such estimates. The key feature of the sequence is the addition of a set of extra diffusion encoding gradients that are orthogonal to the direction of the primary gradients, which corresponds to a specific type of triple diffusion encoding (TDE) MRI sequence. Given direction-averaged dMRI data for this TDE sequence, it is shown how the intra-axonal diffusivity and the intra-axonal water fraction can be determined by applying simple, analytic formulae. The method is illustrated with numerical simulations, which suggest that it should be accurate for b-values of about 4000 s/mm2 or higher.

Modeling white matter microstructure with fiber ball imaging.

Fiber ball imaging (FBI) provides a means of calculating the fiber orientation density function (fODF) in white matter from diffusion MRI (dMRI) data obtained over a spherical shell with a b-value of about 4000 s/mm2 or higher. By supplementing this FBI-derived fODF with dMRI data acquired for two lower b-value shells, it is shown that several microstructural parameters may be estimated, including the axonal water fraction (AWF) and the intrinsic intra-axonal diffusivity. This fiber ball white matter (FBWM) modeling method is demonstrated for dMRI data acquired from healthy volunteers, and the results are compared with those of the white matter tract integrity (WMTI) method. Both the AWF and the intra-axonal diffusivity obtained with FBWM are found to be significantly larger than for WMTI, with the FBWM values for the intra-axonal diffusivity being more consistent with recent results obtained using isotropic diffusion weighting. An important practical advantage of FBWM is that the only nonlinear fitting required is the minimization of a cost function with just a single free parameter, which facilitates the implementation of efficient and robust numerical routines.

Comparison of cumulant expansion and q-space imaging estimates for diffusional kurtosis in brain.

PURPOSE: To compare estimates for the diffusional kurtosis in brain as obtained from a cumulant expansion (CE) of the diffusion MRI (dMRI) signal and from q-space (QS) imaging. THEORY AND METHODS: For the CE estimates of the kurtosis, the CE was truncated to quadratic order in the b-value and fit to the dMRI signal for b-values from 0 up to 2000s/mm2. For the QS estimates, b-values ranging from 0 up to 10,000s/mm2 were used to determine the diffusion displacement probability density function (dPDF) via Stejskal's formula. The kurtosis was then calculated directly from the second and fourth order moments of the dPDF. These two approximations were studied for in vivo human data obtained on a 3T MRI scanner using three orthogonal diffusion encoding directions. RESULTS: The whole brain mean values for the CE and QS kurtosis estimates differed by 16% or less in each of the considered diffusion encoding directions, and the Pearson correlation coefficients all exceeded 0.85. Nonetheless, there were large discrepancies in many voxels, particularly those with either very high or very low kurtoses relative to the mean values. CONCLUSION: Estimates of the diffusional kurtosis in brain obtained using CE and QS approximations are strongly correlated, suggesting that they encode similar information. However, for the choice of b-values employed here, there may be substantial differences, depending on the properties of the diffusion microenvironment in each voxel.

Structural plasticity of the ventral stream and aphasia recovery.

Restrengthening of the residual language network is likely to be crucial for speech recovery in poststroke aphasia. Eight participants with chronic aphasia received intensive speech therapy for 3 weeks, with standardized naming tests and brain magnetic resonance imaging before and after therapy. Kurtosis-based diffusion tensor tractography was used to measure mean kurtosis (MK) along a segment of the inferior longitudinal fasciculus (ILF). Therapy-related reduction in the number of semantic but not phonemic errors was associated with strengthening (renormalization) of ILF MK (r = -0.90, p < 0.05 corrected), suggesting that speech recovery is related to structural plasticity of language-specific components of the residual language network. Ann Neurol 2017;82:147-151.

Tensor estimation for double-pulsed diffusional kurtosis imaging.

Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.

Evaluating kurtosis-based diffusion MRI tissue models for white matter with fiber ball imaging.

In order to quantify well-defined microstructural properties of brain tissue from diffusion MRI (dMRI) data, tissue models are typically employed that relate biological features, such as cell morphology and cell membrane permeability, to the diffusion dynamics. A variety of such models have been proposed for white matter, and their validation is a topic of active interest. In this paper, three different tissue models are tested by comparing their predictions for a specific microstructural parameter to a value measured independently with a recently proposed dMRI method known as fiber ball imaging (FBI). The three tissue models are all constructed with the diffusion and kurtosis tensors, and they are hence compatible with diffusional kurtosis imaging. Nevertheless, the models differ significantly in their details and predictions. For voxels with fractional anisotropies (FAs) exceeding 0.5, all three are reasonably consistent with FBI. However, for lower FA values, one of these, called the white matter tract integrity (WMTI) model, is found to be in much better accord with FBI than the other two, suggesting that the WMTI model has a broader range of applicability.

Functional deficits induced by cortical microinfarcts.

Clinical studies have revealed a strong link between increased burden of cerebral microinfarcts and risk for cognitive impairment. Since the sum of tissue damage incurred by microinfarcts is a miniscule percentage of total brain volume, we hypothesized that microinfarcts disrupt brain function beyond the injury site visible to histological or radiological examination. We tested this idea using a mouse model of microinfarcts, where single penetrating vessels that supply mouse cortex were occluded by targeted photothrombosis. We found that in vivo structural and diffusion MRI reliably reported the acute microinfarct core, based on spatial co-registrations with post-mortem stains of neuronal viability. Consistent with our hypothesis, c-Fos assays for neuronal activity and in vivo imaging of single vessel hemodynamics both reported functional deficits in viable peri-lesional tissues beyond the microinfarct core. We estimated that the volume of tissue with functional deficit in cortex was at least 12-fold greater than the volume of the microinfarct core. Impaired hemodynamic responses in peri-lesional tissues persisted at least 14 days, and were attributed to lasting deficits in neuronal circuitry or neurovascular coupling. These data show how individually miniscule microinfarcts could contribute to broader brain dysfunction during vascular cognitive impairment and dementia.

Comparison of Diffusion Metrics Obtained at 1.5T and 3T in Human Brain With Diffusional Kurtosis Imaging.

PURPOSE: To quantitatively compare diffusion metrics for human brain estimated with diffusional kurtosis imaging (DKI) at applied field strengths of 1.5 and 3T. MATERIALS AND METHODS: DKI data for brain were acquired at both 1.5 and 3T from each of six healthy volunteers using a twice-refocused diffusion-weighted imaging sequence. From these data, parametric maps of mean diffusivity (MD), axial diffusivity (D‖ ), radial diffusivity (D⊥ ), fractional anisotropy (FA), mean diffusional kurtosis (MK), axial kurtosis (K‖ ), radial kurtosis (K⊥ ), and kurtosis fractional anisotropy (KFA) were estimated. Comparisons of the results from the two field strengths were made for each metric using both Bland-Altman plots and linear regression to calculate coefficients of determination (R2 ) and best fit lines. RESULTS: Diffusion metrics measured at 1.5 and 3T were observed to be similar. Linear regression of the full datasets had coefficients of determination varying from a low of R2 = 0.86 for KFA to a high of R2 = 0.97 for FA. The slopes of the 3T vs. 1.5T best linear fits varied from 0.881 ± 0.009 for KFA to 1.038 ± 0.010 for D‖ . From a Bland-Altman analysis of selected regions of interest, the mean differences of the metrics for the two field strengths were all found to be less than 6%, except for KFA, which showed the largest relative discrepancy of 10%. CONCLUSION: Diffusion metrics measured with DKI at 1.5 and 3T are strongly correlated and typically differ by only a few percent. The somewhat higher discrepancy for the KFA is argued to mainly reflect the effects of signal noise. This supports the robustness DKI results with respect to field strength. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2017;45:673-680.

Dependence on b-value of the direction-averaged diffusion-weighted imaging signal in brain.

PURPOSE: The dependence of the direction-averaged diffusion-weighted imaging (DWI) signal in brain was studied as a function of b-value in order to help elucidate the relationship between diffusion weighting and brain microstructure. METHODS: High angular resolution diffusion imaging (HARDI) data were acquired from two human volunteers with 128 diffusion-encoding directions and six b-value shells ranging from 1000 to 6000s/mm2 in increments of 1000s/mm2. The direction-averaged signal was calculated for each shell by averaging over all diffusion-encoding directions, and the signal was plotted as a function of b-value for selected regions of interest. As a supplementary analysis, similar methods were also applied to retrospective DWI data obtained from the human connectome project (HCP), which includes b-values up to 10,000s/mm2. RESULTS: For all regions of interest, a simple power law relationship accurately described the observed dependence of the direction-averaged signal as a function of the diffusion weighting. In white matter, the characteristic exponent was 0.56±0.05, while in gray matter it was 0.88±0.11. Comparable results were found with the HCP data. CONCLUSION: The direction-averaged DWI signal varies, to a good approximation, as a power of the b-value, for b-values between 1000 and 6000s/mm2. The exponents characterizing this power law behavior were markedly different for white and gray matter, indicative of sharply contrasting microstructural environments. These results may inform the construction of microstructural models used to interpret the DWI signal.