Novel white matter tract integrity metrics sensitive to Alzheimer disease progression

Histological correlation of diffusional kurtosis and white matter modeling metrics in cuprizone-induced corpus callosum demyelination

The cuprizone mouse model is well established for studying the processes of both demyelination and remyelination in the corpus callosum, and it has been utilized together with diffusion tensor imaging (DTI) to investigate myelin and axonal pathology. Although some underlying morphological mechanisms contributing to the changes in diffusion tensor (DT) metrics have been identified, the understanding of specific associations between histology and diffusion measures remains limited. Diffusional kurtosis imaging (DKI) is an extension of DTI that provides metrics of diffusional non-Gaussianity, for which an associated white matter modeling (WMM) method has been developed. The main goal of the present study was to quantitatively assess the relationships between diffusion measures and histological measures in the mouse model of cuprizone-induced corpus callosum demyelination. The diffusional kurtosis (DK) and WMM metrics were found to provide additional information that enhances the sensitivity to detect the morphological heterogeneity in the chronic phase of the disease process in the rostral segment of the corpus callosum. Specifically, in the rostral segment, axonal water fraction (d = 2.6; p < 0.0001), radial kurtosis (d = 2.0; p = 0.001) and mean kurtosis (d = 1.5; p = 0.005) showed the most sensitivity between groups with respect to yielding statistically significant p values and high Cohen's d values. These results demonstrate the ability of DK and WMM metrics to detect white mater changes and inflammatory processes associated with cuprizone-induced demyelination. They also validate, in part, the application of these new WMM metrics for studying neurological diseases, as well as helping to elucidate their biophysical meaning.

Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.

Differential white matter connectivity in early mild cognitive impairment according to CSF biomarkers

Mild cognitive impairment (MCI) is a heterogeneous group and certain MCI subsets eventually convert to dementia. Cerebrospinal fluid (CSF) biomarkers are known to predict this conversion. We sought evidence for the differences in white matter connectivity between early amnestic MCI (EMCI) subgroups according to a CSF phosphorylated tau181p/amyloid beta1-42 ratio of 0.10. From the Alzheimer's Disease Neuroimaging Initiative database, 16 high-ratio, 25 low-ratio EMCI patients, and 20 normal controls with diffusion tensor images and CSF profiles were included. Compared to the high-ratio group, radial diffusivity significantly increased in both sides of the corpus callosum and the superior and inferior longitudinal fasciculus in the low-ratio group. In widespread white matter skeleton regions, the low-ratio group showed significantly increased mean, axial, and radial diffusivity compared to normal controls. However, the high-ratio group showed no differences when compared to the normal group. In conclusion, our study revealed that there were significant differences in white matter connectivity between EMCI subgroups according to CSF phosphorylated tau181p/amyloid beta1-42 ratios.

Axonal deficits in young adults with High Functioning Autism and their impact on processing speed

Microstructural white matter deficits in Autism Spectrum Disorders (ASD) have been suggested by both histological findings and Diffusion Tensor Imaging (DTI) studies, which show reduced fractional anisotropy (FA) and increased mean diffusivity (MD). However, imaging reports are generally not consistent across studies and the underlying physiological causes of the reported differences in FA and MD remain poorly understood. In this study, we sought to further characterize white matter deficits in ASD by employing an advanced diffusion imaging method, the Diffusional Kurtosis Imaging (DKI), and a two-compartment diffusion model of white matter. This model differentially describes intra- and extra-axonal white matter compartments using Axonal Water Fraction (f_axon) a measure reflecting axonal caliber and density, and compartment-specific diffusivity measures. Diagnostic utility of these measures and associations with processing speed performance were also examined. Comparative studies were conducted in 16 young male adults with High Functioning Autism (HFA) and 17 typically developing control participants (TDC). Significantly decreased f_axon was observed in HFA compared to the control group in most of the major white matter tracts, including the corpus callosum, cortico-spinal tracts, and superior longitudinal, inferior longitudinal and inferior fronto-occipital fasciculi. Intra-axonal diffusivity (D_axon) was also found to be reduced in some of these regions. Decreased axial extra-axonal diffusivity (AD_extra) was noted in the genu of the corpus callosum. Reduced processing speed significantly correlated with decreased f_axon and D_axon in several tracts. f_axon of the left cortico-spinal tract and superior longitudinal fasciculi showed good accuracy in discriminating the HFA and TDC groups. In conclusion, these findings suggest altered axonal microstructure in young adults with HFA which is associated with reduced processing speed. Compartment-specific diffusion metrics appear to improve specificity and sensitivity to white matter deficits in this population.

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White matter microstructure is altered in young adults with High Functioning Autism.

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We report decreased Axonal Water Fraction and intra-axonal diffusivity.

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Changes in these DKI metrics are consistent with axonal deficits.

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Alterations in axonal diffusion metrics correlate with reduced processing speed.

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DKI may yield promising biomarkers for autism diagnosis and characterization.

Axonal tract tracing for delineating interacting brain regions: implications for Alzheimer's disease-associated memory

We are studying the projections from the entorhinal cortex to the hippocampal formation in the mouse. The dentate gyrus is innervated by the lateral entorhinal cortex (lateral perforant path) and medial entorhinal cortex (medial perforant path). The entorhinal cortex also projects to hippocampal areas CA3 and CA1, and to the subiculum. In young transgenic Alzheimer's disease mouse models (before amyloid-β pathology), the connections are not different from normal mice. In Alzheimer's disease mice with pathology, two changes occur: first, dystrophic axon endings appear near amyloid-β plaques, and second, there are sparse aberrant axon terminations not in the appropriate area or lamina of the hippocampus. Furthermore, MRI-diffusion tensor imaging analysis indicates a decrease in the quality of the white matter tracts connecting the hippocampus to the brain; in other words, the fimbria/fornix and perforant path. Similar changes in white matter integrity have been found in Alzheimer's disease patients and could potentially be used as early indicators of disease onset.

White matter tract integrity metrics reflect the vulnerability of late-myelinating tracts in Alzheimer's disease

Post-mortem and imaging studies have observed that white matter (WM) degenerates in a pattern inverse to myelin development, suggesting preferential regional vulnerabilities influencing cognitive decline in AD. This study applied novel WM tract integrity (WMTI) metrics derived from diffusional kurtosis imaging (DKI) to examine WM tissue properties in AD within this framework. Using data from amnestic mild cognitive impairment (aMCI, n = 12), AD (n = 14), and normal control (NC; n = 15) subjects, mixed models revealed interaction effects: specific WMTI metrics of axonal density and myelin integrity (i.e. axonal water fraction, radial extra-axonal diffusivity) in late-myelinating tracts (i.e. superior and inferior longitudinal fasciculi) changed in the course of disease, but were stable in the initial stages for early-myelinating tracts (i.e. posterior limb of the internal capsule, cerebral peduncles). WMTI metrics in late-myelinating tracts correlated with semantic verbal fluency, a cognitive function known to decline in AD. These findings corroborate the preferential vulnerability of late-myelinating tracts, and illustrate an application of WMTI metrics to characterizing the regional course of WM changes in AD.

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We investigated the vulnerability of late-myelinating tracts in AD using WMTI metrics.

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WMTI metrics are derived from the biophysical modeling of the DKI signal.

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These metrics indicate pathological features like axonal density and myelin integrity.

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WMTI metrics were largely stable in early-myelinating tracts through the course of AD.

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Axonal density loss & myelin breakdown were observed in late-myelinating tracts.