Diffusion-MRI in neurodegenerative disorders

Sensitivity of diffusion MRI to perilesional reactive astrogliosis in focal ischemia

Reactive astrogliosis is a response to injury in the central nervous system that plays an essential role in inflammation and tissue repair. It is characterized by hypertrophy of astrocytes, alterations in astrocyte gene expression and astrocyte proliferation. Reactive astrogliosis occurs in multiple neuropathologies, including stroke, traumatic brain injury and Alzheimer's disease, and it has been proposed as a possible source of the changes in diffusion magnetic resonance imaging (dMRI) metrics observed with these diseases. In this study, the sensitivity of dMRI to reactive astrogliosis was tested in an animal model of focal acute and subacute ischemia induced by the vasoconstricting peptide, endothelin-1. Reactive astrogliosis in perilesional cortex was quantified by calculating the astrocyte surface density as determined with a glial fibrillary acidic protein (GFAP) antibody, whereas perilesional diffusion changes were measured in vivo with diffusional kurtosis imaging. We found substantial changes in the surface density of GFAP-positive astrocyte processes and modest changes in dMRI metrics in the perilesional motor cortex following stroke. Although there are time point-specific correlations between dMRI and histological measures, there is no definitive evidence for a causal relationship.

Distribution-aware estimation of the minimum achievable uncertainty in diffusion-tensor imaging (DTI)

Diffusion tensor imaging (DTI) provides exquisite sensitivity to structural and microstructural characteristics of brain tissue, and is routinely employed in advanced neuroimaging applications. DTI is commonly performed using intrinsically noisy echo-planar imaging techniques and poses high demands both on scanner performance and on in-scanner subject time, which in turn is directly related to the number of diffusion-weighting direction one requires. While DTI-derived indices such as fractional anisotropy (FA), diffusion tensor trace and anisotropy mode have proven extremely useful in characterizing disease-related aberrations, their estimation is commonly performed using fitting routines that do not properly take into account MRI noise distribution. In this paper, we present a distribution-aware maximum likelihood tensor estimation framework which also allows, for the first time, separate local noise estimation in both diffusion weighted and reference images. We validate our framework using multiple water phantom diffusion weighted acquisitions, and demonstrate its feasibility in human data. We then employ our framework within Monte Carlo simulations to show how the minimum achievable uncertainty attainable in DTI depends on signal-to-noise ratio (SNR) and number of diffusion gradient directions, demonstrating that these dependencies could be recast into simple power laws which may serve as guidelines for application-specific DTI protocol design.

Analytical performance bounds for multi-tensor diffusion-MRI

PURPOSE

To examine the effects of MR acquisition parameters on brain white matter fiber orientation estimation and parameter of clinical interest in crossing fiber areas based on the Multi-Tensor Model (MTM).

MATERIAL AND METHODS

We compute the Cramér-Rao Bound (CRB) for the MTM and the parameter of clinical interest such as the Fractional Anisotropy (FA) and the dominant fiber orientations, assuming that the diffusion MRI data are recorded by a multi-coil, multi-shell acquisition system. Considering the sum-of-squares method for the reconstructed magnitude image, we introduce an approximate closed-form formula for Fisher Information Matrix that has the simplicity and easy interpretation advantages. In addition, we propose to generalize the FA and the mean diffusivity to the multi-tensor model.

RESULTS

We show the application of the CRB to reduce the scan time while preserving a good estimation precision. We provide results showing how the increase of the number of acquisition coils compensates the decrease of the number of diffusion gradient directions. We analyze the impact of the b-value and the Signal-to-Noise Ratio (SNR). The analysis shows that the estimation error variance decreases with a quadratic rate with the SNR, and that the optimum b-values are not unique but depend on the target parameter, the context, and eventually the target cost function.

CONCLUSION

In this study we highlight the importance of choosing the appropriate acquisition parameters especially when dealing with crossing fiber areas. We also provide a methodology for the optimal tuning of these parameters using the CRB.

Diffusion alterations associated with Parkinson's disease symptomatology: A review of the literature

Parkinson's disease (PD) is a heterogeneous neurological disorder with a variety of motor and non-motor symptoms. The underlying mechanisms of these symptoms are not fully understood. An increased interest in structural connectivity analyses using diffusion tensor imaging (DTI) in PD has led to an expansion of our understanding of the impact of abnormalities in diffusivity on phenotype. This review outlines the contribution of these abnormalities to symptoms of PD including bradykinesia, tremor and non-tremor phenotypes, freezing of gait, cognitive impairment, mood, sleep disturbances, visual hallucinations and olfactory dysfunction. Studies have shown that impairments in cognitive functioning are related to diffusion abnormalities in frontal and parietal regions, as well as in the corpus callosum and major fibres connecting midbrain and subcortical structures with the neocortex. However, the impact of diffusion alterations on motor, mood and other symptoms of PD are less well understood. The findings presented here highlight the challenges faced and the potential areas of future research avenues where DTI may be beneficial. Larger cohort studies and standardized imaging protocols are required to investigate current promising preliminary findings.

Diffusion Tensor Imaging to Map Brain Microstructural Changes in CADASIL

BACKGROUND AND PURPOSE

Diffusion tensor imaging (DTI) is sensitive to brain microstructural changes. The aims of this DTI study were to map voxelwise the spatial distribution of brain microstructural changes in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) and to investigate any correlation between DTI-derived indices and extension of T2 hyperintensity.

METHODS

Eighteen patients with CADASIL and 18 age-, sex-, and education-level-matched healthy controls underwent magnetic resonance imaging at 3 T. Differences in DTI-derived indices (mean diffusivity [MD], fractional anisotropy [FA], axial [AD] and radial [RD] diffusivities, and mode of anisotropy [MO]) of brain white matter (WM) between CADASIL patients and healthy subjects were assessed through tract-based spatial statistics. Then, DTI-derived indices were correlated with the patient's score on the extended Fazekas visual scale of the T2 hyperintensity.

RESULTS

When compared to healthy controls, CADASIL patients showed extensive symmetric areas of increased MD/RD and decreased AD/FA/MO that involved almost the entire hemispheric cerebral WM (internal and external capsule, WM of the temporal poles, superior and inferior longitudinal fasciculus, inferior frontal-occipital fasciculus, uncinate fasciculus, cingulum, forceps major and minor, corticospinal tracts, and thalamic radiations), thalami, and corpus callosum. Additional areas of increased RD were observed in pons, midbrain, cerebellar peduncles, and cerebellar WM. Only FA was negatively correlated with extended Fazekas visual score.

CONCLUSIONS

Our results indicate that brain damage in CADASIL is associated with extensive microstructural changes implying impairment of intra- and inter-hemispheric cerebral, thalamocortical, and cerebrocerebellar connections. Severity of microstructural changes correlates with extension of T2 hyperintensity.

Potential Therapies by Stem Cell-Derived Exosomes in CNS Diseases: Focusing on the Neurogenic Niche

Neurodegenerative disorders are one of the leading causes of death and disability and one of the biggest burdens on health care systems. Novel approaches using various types of stem cells have been proposed to treat common neurodegenerative disorders such as Alzheimer's Disease, Parkinson's Disease, or stroke. Moreover, as the secretome of these cells appears to be of greater benefit compared to the cells themselves, the extracellular components responsible for its therapeutic benefit have been explored. Stem cells, as well as most cells, release extracellular vesicles such as exosomes, which are nanovesicles able to target specific cell types and thus to modify their function by delivering proteins, lipids, and nucleic acids. Exosomes have recently been tested in vivo and in vitro as therapeutic conveyors for the treatment of diseases. As such, they could be engineered to target specific populations of cells within the CNS. Considering the fact that many degenerative brain diseases have an impact on adult neurogenesis, we discuss how the modulation of the adult neurogenic niches may be a therapeutic target of stem cell-derived exosomes. These novel approaches should be examined in cellular and animal models to provide better, more effective, and specific therapeutic tools in the future.

Computer-based magnetic resonance imaging as a tool in clinical diagnosis in neurodegenerative diseases

Magnetic resonance imaging (MRI) is one of the core elements within the differential diagnostic work-up of patients with neurodegenerative diseases such as dementia syndromes, Parkinsonian syndromes, and motor neuron diseases. Currently, computerized MRI analyses are not routinely used for individual diagnosis; however, they have improved the anatomical understanding of pathomorphological alterations in various neurodegenerative diseases by quantitative comparisons between patients and controls at the group level. For multiparametric MRI protocols, including T1-weighted MRI, diffusion-weighted imaging, and intrinsic functional connectivity MRI, the potential as a surrogate marker is a subject of investigation. The additional value of MRI with respect to diagnosis at the individual level and for future disease-modifying multicentre trials remains to be defined. Here, we give an overview of recent applications of multiparametric MRI to patients with various neurodegenerative diseases. Starting from applications at the group level, continuous progress of a transfer to individual diagnostic classification is ongoing.