Conventional Diffusion Tensor Imaging Metrics Research Articles

Age-related changes in the healthy adult visual pathway: evidence from diffusion tensor imaging with fixel-based analysis.

Fixel-based analysis (FBA) is anew method that overcomes the technical limitations of diffusion tensor imaging (DTI) by enabling the characterization of multiple fiber populations within avoxel, and provides biologically meaningful indicators. This study aimed to explore age-related changes in the visual pathway in healthy adults and to observe differences in imaging quality between data collected using different b‑values. In this prospective cross-sectional study, brain DTI scans which were collected with more than six uniformly distributed gradient directions and higher b‑values (up to 2000 s/mm2) than traditional DTI were performed in 72healthy adults across the adult lifespan (20-79years). After image preprocessing, FBA was used to process the dataset. At the same time, conventional DTI metrics were also calculated. Pearson's correlation analysis showed that DTI parameters of white matter (optic nerve, optic chiasma, optic tract, and optic radiation) in the optic pathway were correlated with age. FA values were negatively correlated with age, while MD/AD/RD showed apositive correlation (P < 0.05). FBA showed that the index including FD/FC/FDC tended to decline with age (P < 0.05). Linear regression analysis showed alinear relationship between DTI metrics of the dataset collected by b‑values of 1000 and 2000 s/mm2 (P < 0.05). FBA provides auseful method to assess age-related changes in the visual pathway, which is sensitive to diffusion. In addition, the b‑value influences DTI parameters and signal-to-noise ratio of the image.

Effect of sex on the APOE4-aging interaction in the white matter microstructure of cognitively normal older adults using diffusion-tensor MRI with orthogonal-tensor decomposition (DT-DOME).

The influence of the apolipoprotein E ε4 allele (APOE4) on brain microstructure of cognitively normal older adults remains incompletely understood, in part due to heterogeneity within study populations. In this study, we examined white-matter microstructural integrity in cognitively normal older adults as a function of APOE4 carrier status using conventional diffusion-tensor imaging (DTI) and the novel orthogonal-tensor decomposition (DT-DOME), accounting for the effects of age and sex. Age associations with white-matter microstructure did not significantly depend on APOE4 status, but did differ between sexes, emphasizing the importance of accounting for sex differences in APOE research. Moreover, we found the DT-DOME to be more sensitive than conventional DTI metrics to such age-related and sex effects, especially in crossing WM fiber regions, and suggest their use in further investigation of white matter microstructure across the life span in health and disease.

Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity.

Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.

DKI enhances the sensitivity and interpretability of age-related DTI patterns in the white matter of UK biobank participants

Studies of healthy brain aging traditionally report diffusivity patterns associated with white matter degeneration using diffusion tensor imaging (DTI), which assumes that diffusion measured at typical b-values (approximately 1000 s/mm2) is Gaussian. Diffusion kurtosis imaging (DKI) is an extension of DTI that measures non-Gaussian diffusion (kurtosis) to better capture microenvironmental processes by incorporating additional data at a higher b-value. In this study, using diffusion data (b-values of 1000 and 2000 s/mm2) from 700 UK Biobank participants aged 46–80, we investigate (1) the extent of novel information gained from adding diffusional kurtosis to diffusivity observations in aging, and (2) how conventional DTI metrics in aging compare with diffusivity metrics derived from DKI, which are corrected for kurtosis. We establish a pattern of lower kurtosis alongside higher diffusivity among older adults, with kurtosis generally being more sensitive to age than diffusivity. We also find discrepancies between diffusivity metrics derived from DTI and DKI, emphasizing the importance of accounting for non-Gaussian diffusion when interpreting age-related diffusivity patterns.

Abstract P59: Peak Width of Skeletonized Mean Diffusivity Outperforms Other Diffusion Tensor Imaging Metrics as Biomarker for Cognition in Memory-Clinic Subjects With Cerebral Amyloid Angiopathy

Introduction: Peak width of skeletonized mean diffusivity (PSMD) is a novel fully automated diffusion tensor imaging (DTI) marker that has been consistently associated with cognition in cerebral small vessel disease (SVD) cohorts, including cerebral amyloid angiopathy (CAA). We hypothesized that PSMD would be more strongly associated with cognitive performance compared to other conventional DTI metrics in our CAA sample. Methods: We recruited non-demented subjects with probable-CAA from a single-center memory-clinic cohort. We analyzed structural MRIs to compute a validated CAA burden score (0-6 points scale, based on the following MRI features: lobar microbleeds, superficial siderosis, perivascular spaces in centrum semiovale, and white matter hyperintensities). PSMD was obtained using a freely available script ( www.psmd-marker.com ). We used the same skeleton-mask to compute: mean of skeletonized mean diffusivity (mean MD) and mean of skeletonized fractional anisotropy (mean FA). We used linear regression analyses to explore relationships with CAA burden score and cognitive composite scores (processing speed, executive function, memory, and language - z-scores adjusted for age, sex and education level). Results: We included 43 subjects (mean age 74.4 ± 5.9 years; 48.8% female; PSMD median [IQR]: 4.05 [3.58 - 4.80] x 10 -4 mm 2 /s). In linear regression models adjusting for age, DTI metrics were significantly associated with CAA burden score (mean FA: β = -0.563, Adj. R 2 : 0.27; p &lt; 0.001; mean MD: β = 0.581; Adj. R 2 : 0.32; p &lt; 0.001; PSMD: β = 0.364, Adj. R 2 : 0.12; p = 0.018). PSMD was significantly associated with cognitive performance, specifically in the domains of executive function ( β = -0.568; Adj. R 2 : 0.25; p &lt; 0.001) and processing speed ( β = -0.447; Adj. R 2 : 0.19; p = 0.004). Other DTI metrics were not significantly associated with cognitive scores. Conclusion: In this CAA sample, all DTI metrics were associated with CAA burden scores, however, only PSMD was significantly associated with cognition, in domains that are commonly affected in vascular cognitive impairment. Our results warrant confirmation in larger samples, but support PSMD as biomarker for cognition in CAA, outperforming other conventional DTI metrics.

White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction

The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk's theorem analysis showed that the ‘FA fine’ metric of the RSI model and ‘orientation dispersion’ (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.

Utility and validity of neurite orientation dispersion and density imaging with diffusion tensor imaging to quantify the severity of cervical spondylotic myelopathy and assess postoperative neurological recovery

BACKGROUND CONTEXTPredicting postoperative prognosis with preoperative diagnostic imaging has clinical importance. Recent studies have indicated the utility of diffusion tensor imaging (DTI) to quantify the severity of cervical spondylotic myelopathy (CSM) and assess the prognosis of surgical outcomes. However, how to apply DTI to evaluate CSM in a clinical setting is not fully elucidated. Neurite orientation dispersion and density imaging (NODDI) is a model-based practical diffusion-weighted magnetic resonance imaging analysis for estimating specific microstructural features related directly to neuronal morphology. In a prior study, we indicated preoperative NODDI parameters are a promising tool with which to predict neuronal recovery after decompression surgery in patients with CSM with 2 years follow-up. However, the correlation between NODDI parameters and postoperative long-term outcomes and change of parameters over time postoperatively has remained largely unknown. STUDY DESIGNRetrospective cohort study. PURPOSETo determine the change of parameters of NODDI and conventional DTI over time, and the relationship between parameters and neurological recovery 2 years after surgery. PATIENT SAMPLEWe included 28 consecutive patients with nontraumatic cervical lesions from CSM who underwent laminoplasty and were followed up for >2 years. Patients underwent magnetic resonance imaging before and approximately 2 weeks, 6 months, and 1 year after surgery. OUTCOME MEASURESIn addition to conventional DTI metrics, we evaluated intracellular volume fraction (ICVF) and orientation dispersion index, which are metrics derived from NODDI. The Japanese Orthopedic Association (JOA) scoring system was used before and 2 years after surgery to assess neurological outcome (JOA recovery rate). METHODSNODDI and conventional DTI values were measured at the C2–C3 intervertebral level (control value) and the most compressed levels (C3–C7 intervertebral levels) were measured by 3 observers. The changes of these values from preoperatively, 2 weeks after surgery, 6 months after surgery, and 1 year after surgery, were determined. The correlations between preoperative neurological severity, postoperative neuronal recovery, and preoperative DTI or NODDI metrics were determined. No financial or material support was obtained for this study. There is no conflict of interest. RESULTSThe preoperative ICVF and fractional anisotropy at the most compressed level were significantly less than the preoperative values at the control C2–C3 intervertebral level and fractional anisotropy at the most compressed level was increased in the immediate postoperative period. By contrast, ICVF at the most compressed level was not increased in the immediate postoperative period and a significant increase was observed at 6 months after surgery. Preoperative ICVF was significantly correlated with JOA recovery rate at 2 years after surgery. CONCLUSIONSNODDI is a reproducible and reliable method for evaluation of CSM. ICVF improved after surgery and recovery of physical findings accompanied this change. ICVF may be applied clinically to predict postoperative recovery.

Evidence for Progressive Microstructural Damage in Early Multiple Sclerosis by Multi-Shell Diffusion Magnetic Resonance Imaging

In multiple sclerosis (MS), it would be of clinical value to be able to track the progression of axonal pathology, especially before the manifestation of clinical disability. However, non-invasive evaluation of short-term longitudinal progression of white matter integrity is challenging. This study aims at assessing longitudinal changes in the restricted (i.e. intracellular) diffusion signal fraction (FR) in early-stage MS by using ultra-high gradient strength multi-shell diffusion magnetic resonance imaging. In 11 early MS subjects (disease duration ≤5 years), FR was obtained at two timepoints (one year apart) through the Composite Hindered and Restricted Model of Diffusion, along with conventional Diffusion Tensor Imaging metrics. At follow-up, no statistically significant change was detected in clinical variables, while all imaging metrics showed statistically significant longitudinal changes (p < 0.01, corrected for multiple comparisons) in widespread regions in normal-appearing white matter (NAWM). The most extensive longitudinal changes were observed in FR, including areas known to include a large fraction of crossing fibers. Furthermore, FR was also the only metric showing significant longitudinal changes in lesions that were present at both time points (p = 0.007), with no significant differences found for conventional diffusion metrics. Finally, FR was the only diffusion metric (as compared to Diffusion Tensor Imaging) that revealed pre-lesional changes already present at baseline. Taken together, our data provide evidence for progressive microstructural damage in the NAWM of early MS cases detectable already at 1-year follow-up. Our study highlights the value of multi-shell diffusion imaging for sensitive tracking of disease evolution in MS before any clinical changes are observed.This article is part of a Special Issue entitled: SI: MRI and Neuroinflammation.

Autosomal dominant cerebellar ataxias: Imaging biomarkers with high effect sizes.

ObjectiveAs gene-based therapies may soon arise for patients with spinocerebellar ataxia (SCA), there is a critical need to identify biomarkers of disease progression with effect sizes greater than clinical scores, enabling trials with smaller sample sizes.MethodsWe enrolled a unique cohort of patients with SCA1 (n = 15), SCA2 (n = 12), SCA3 (n = 20) and SCA7 (n = 10) and 24 healthy controls of similar age, sex and body mass index. We collected longitudinal clinical and imaging data at baseline and follow-up (mean interval of 24 months). We performed both manual and automated volumetric analyses. Diffusion tensor imaging (DTI) and a novel tractography method, called fixel-based analysis (FBA), were assessed at follow-up. Effect sizes were calculated for clinical scores and imaging parameters.ResultsClinical scores worsened as atrophy increased over time (p < 0.05). However, atrophy of cerebellum and pons showed very large effect sizes (>1.2) compared to clinical scores (<0.8). FBA, applied for the first time to SCA, was sensitive to microstructural cross-sectional differences that were not captured by conventional DTI metrics, especially in the less studied SCA7 group. FBA also showed larger effect sizes than DTI metrics.ConclusionThis study showed that volumetry outperformed clinical scores to measure disease progression in SCA1, SCA2, SCA3 and SCA7. Therefore, we advocate the use of volumetric biomarkers in therapeutic trials of autosomal dominant ataxias. In addition, FBA showed larger effect size than DTI to detect cross-sectional microstructural alterations in patients relative to controls.

Application of neurite orientation dispersion and density imaging or diffusion tensor imaging to quantify the severity of cervical spondylotic myelopathy and to assess postoperative neurologic recovery

Background ContextSurgical outcome and the severity of cervical spondylotic myelopathy (CSM) are unpredictable and cannot be estimated by conventional anatomical magnetic resonance imaging (MRI). The utility of diffusion tensor imaging (DTI) to quantify the severity of CSM and to assess postoperative neurologic recovery has been investigated. However, whether conventional DTI should be applied in a clinical setting remains controversial. Neurite orientation dispersion and density imaging (NODDI) is a recently introduced model-based diffusion-weighted MRI technique that quantifies specific microstructural features related directly to neuronal morphology. However, there are as yet few clinical applications of NODDI reported. Indeed, there are no reports to indicate NODDI is useful for diagnosing CSM. Study DesignThis is a retrospective cohort study using consecutive patients. PurposeThe objective of this study was to evaluate the utility of NODDI and conventional DTI for detecting changes in the spinal cord microstructure. In particular, this study aimed to quantify the preoperative severity of CSM and to assess postoperative neurologic recovery from this myelopathy. Patient SampleWe included 27 consecutive patients with a nontraumatic cervical lesion from CSM who underwent laminoplasty at a single institution between April 2012 and April 2015. The patients underwent MRI before and approximately 2 weeks after surgery. Outcome MeasuresIn addition to conventional DTI metrics, we evaluated the intracellular volume fraction (ICVF) and the orientation dispersion index (ODI), which are metrics derived from NODDI. The 10-second grip and release test and the Japanese Orthopaedic Association scoring system were used before and 1 year after surgery to assess neurologic outcome. Materials and MethodsNeurite orientation dispersion and density imaging and conventional DTI values were measured at the C2–C3 intervertebral level (control value) and at the most compressed levels (C3–C7 intervertebral levels) were measured. The changes in these values pre- and postoperative were demonstrated. Correlations between NODDI and conventional DTI values and clinical outcome were determined. ResultsPreoperative fractional anisotropy was significantly correlated with the severity of neural damage, but not with postoperative neurologic recovery. No significant correlation could be found between the preoperative ICVF, the ODI, the apparent diffusion coefficient, and the severity of the preoperative neurologic dysfunction. Preoperative ICVF was most strongly correlated with the severity of neurologic dysfunction and postoperative neurologic recovery. ConclusionsConventional DTI may be applied clinically to assess the severity of myelopathy. Neurite orientation dispersion and density imaging may be more valuable than conventional DTI to predict outcome following surgery in patients with CSM.

Detection of acute nervous system injury with advanced diffusion-weighted MRI: a simulation and sensitivity analysis.

Diffusion-weighted imaging (DWI) is a powerful tool to investigate the microscopic structure of the central nervous system (CNS). Diffusion tensor imaging (DTI), a common model of the DWI signal, has a demonstrated sensitivity to detect microscopic changes as a result of injury or disease. However, DTI and other similar models have inherent limitations that reduce their specificity for certain pathological features, particularly in tissues with complex fiber arrangements. Methods such as double pulsed field gradient (dPFG) and q-vector magic angle spinning (qMAS) have been proposed to specifically probe the underlying microscopic anisotropy without interference from the macroscopic tissue organization. This is particularly important for the study of acute injury, where abrupt changes in the microscopic morphology of axons and dendrites manifest as focal enlargements known as beading. The purpose of this work was to assess the relative sensitivity of DWI measures to beading in the context of macroscopic fiber organization and edema. Computational simulations of DWI experiments in normal and beaded axons demonstrated that, although DWI models can be highly specific for the simulated pathologies of beading and volume fraction changes in coherent fiber pathways, their sensitivity to a single idealized pathology is considerably reduced in crossing and dispersed fibers. However, dPFG and qMAS have a high sensitivity for beading, even in complex fiber tracts. Moreover, in tissues with coherent arrangements, such as the spinal cord or nerve fibers in which tract orientation is known a priori, a specific dPFG sequence variant decreases the effects of edema and improves specificity for beading. Collectively, the simulation results demonstrate that advanced DWI methods, particularly those which sample diffusion along multiple directions within a single acquisition, have improved sensitivity to acute axonal injury over conventional DTI metrics and hold promise for more informative clinical diagnostic use in CNS injury evaluation.

Evaluation of diffusion kurtosis imaging in ex vivo hypomyelinated mouse brains

Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and DKI-derived white matter tract integrity metrics (WMTI) were experimentally evaluated ex vivo through comparisons to histological measurements and established magnetic resonance imaging (MRI) measures of myelin in two knockout mouse models with varying degrees of hypomyelination. DKI metrics of mean and radial kurtosis were found to be better indicators of myelin content than conventional DTI metrics. The biophysical WMTI model based on the DKI framework reported on axon water fraction with good accuracy in cases with near normal axon density, but did not provide additional specificity to myelination. Overall, DKI provided additional information regarding white matter microstructure compared with DTI, making it an attractive method for future assessments of white matter development and pathology.

Robust MR-based approaches to quantifying white matter structure and structure/function alterations in Huntington's disease

BackgroundHuge advances have been made in understanding and addressing confounds in diffusion MRI data to quantify white matter microstructure. However, there has been a lag in applying these advances in clinical research. Some confounds are more pronounced in HD which impedes data quality and interpretability of patient-control differences. This study presents an optimised analysis pipeline and addresses specific confounds in a HD patient cohort. Method15 HD gene-positive and 13 matched control participants were scanned on a 3T MRI system with two diffusion MRI sequences. An optimised post processing pipeline included motion, eddy current and EPI correction, rotation of the B matrix, free water elimination (FWE) and tractography analysis using an algorithm capable of reconstructing crossing fibres. The corpus callosum was examined using both a region-of-interest and a deterministic tractography approach, using both conventional diffusion tensor imaging (DTI)-based and spherical deconvolution analyses. ResultsCorrecting for CSF contamination significantly altered microstructural metrics and the detection of group differences. Reconstructing the corpus callosum using spherical deconvolution produced a more complete reconstruction with greater sensitivity to group differences, compared to DTI-based tractography. Tissue volume fraction (TVF) was reduced in HD participants and was more sensitive to disease burden compared to DTI metrics. ConclusionAddressing confounds in diffusion MR data results in more valid, anatomically faithful white matter tract reconstructions with reduced within-group variance. TVF is recommended as a complementary metric, providing insight into the relationship with clinical symptoms in HD not fully captured by conventional DTI metrics.

A reply to the letter to the editor regarding “microstructural changes of the corticospinal tract in idiopathic normal pressure hydrocephalus: a comparison of diffusion tensor and diffusional kurtosis imaging”

Dear Sir, We read with interest the letter concerning our article “Microstructural changes of the corticospinal tract in idiopathic normal pressure hydrocephalus: a comparison of diffusion tensor and diffusional kurtosis imaging.” The correspondents noted that diffusion metrics such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC) depend on the combination of data (i.e., different b values) and theory (Gaussian or non-Gaussian). We quite agree with their opinion that FA of diffusion tensor imaging (DTI) should be calculated from tensor model usually from b = 0 and 1,000. Our description might lead the readers to misunderstanding. We think that our description of the section “Tractography and tract-specific analysis of CST” was unclear because we indeed calculated ADC and FA from a portion of the diffusional kurtosis imaging (DKI) data based on b values of 0 and 1,000 s/mm2 using the conventional model. These calculated values were then used as the estimates for ADC and FA. We also recognize that the conventional DTI metrics for clinical studies should be estimated by using the same b values of 0 and 1,000 s/mm2, as in the many reports in the clinical DTI literatures. In other studies concerning DKI, we explicitly stated that the DTI metrics used for comparison with DKI are calculated using the data obtained from b values of 0 and 1,000 s/mm2 and a mono-exponential fit [1–3]. Moreover, although we did not provide the details in this paper, our software for DTI and DKI calculation (dTV.II.FZRx; Image Computing and Analysis Laboratory, Department of Radiology, The University of Tokyo Hospital, Japan) [4] has the ability to calculate DTI metrics such as FA and ADC on the basis of the mono-exponential model by using user-specified b value data when performing DKI calculations. The letter, which suggests that differences between the diffusion metrics derive from differences in parameters or analysis theories, is still of great value. We agree that in DKI or other diffusion-related studies that employ non-conventional signal-fitting procedures, the authors should clarify the type of method and data used for calculating the diffusion metrics.

Effect of cerebral spinal fluid suppression for diffusional kurtosis imaging

To evaluate the cerebral spinal fluid (CSF) partial volume effect on diffusional kurtosis imaging (DKI) metrics in white matter and cortical gray matter. Four healthy volunteers participated in this study. Standard DKI and fluid-attenuated inversion recovery (FLAIR) DKI experiments were performed using a twice-refocused-spin-echo diffusion sequence. The conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, D[symbol in text], D[symbol in text] together with DKI metrics of mean, axial, and radial kurtosis (MK, K[symbol in text], K[symbol in text], were measured and compared. Single image slices located above the lateral ventricles, with similar anatomical features for each subject, were selected to minimize the effect of CSF from the ventricles. In white matter, differences of less than 10% were observed between diffusion metrics measured with standard DKI and FLAIR-DKI sequences, suggesting minimal CSF contamination. For gray matter, conventional DTI metrics differed by 19% to 52%, reflecting significant CSF partial volume effects. Kurtosis metrics, however, changed by 11% or less, indicating greater robustness with respect to CSF contamination. Kurtosis metrics are less sensitive to CSF partial voluming in cortical gray matter than conventional diffusion metrics. The kurtosis metrics may then be more specific indicators of changes in tissue microstructure, provided the effect sizes for the changes are comparable.

Improved sensitivity to cerebral white matter abnormalities in Alzheimer's disease with spherical deconvolution based tractography.

Diffusion tensor imaging (DTI) based fiber tractography (FT) is the most popular approach for investigating white matter tracts in vivo, despite its inability to reconstruct fiber pathways in regions with “crossing fibers.” Recently, constrained spherical deconvolution (CSD) has been developed to mitigate the adverse effects of “crossing fibers” on DTI based FT. Notwithstanding the methodological benefit, the clinical relevance of CSD based FT for the assessment of white matter abnormalities remains unclear. In this work, we evaluated the applicability of a hybrid framework, in which CSD based FT is combined with conventional DTI metrics to assess white matter abnormalities in 25 patients with early Alzheimer’s disease. Both CSD and DTI based FT were used to reconstruct two white matter tracts: one with regions of “crossing fibers,” i.e., the superior longitudinal fasciculus (SLF) and one which contains only one fiber orientation, i.e. the midsagittal section of the corpus callosum (CC). The DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD), obtained from these tracts were related to memory function. Our results show that in the tract with “crossing fibers” the relation between FA/MD and memory was stronger with CSD than with DTI based FT. By contrast, in the fiber bundle where one fiber population predominates, the relation between FA/MD and memory was comparable between both tractography methods. Importantly, these associations were most pronounced after adjustment for the planar diffusion coefficient, a measure reflecting the degree of fiber organization complexity. These findings indicate that compared to conventionally applied DTI based FT, CSD based FT combined with DTI metrics can increase the sensitivity to detect functionally significant white matter abnormalities in tracts with complex white matter architecture.