Danielle van Westen

Extensive graft-derived dopaminergic innervation is maintained 24 years after transplantation in the degenerating parkinsonian brain

Significance Parkinson’s disease is the most common movement disorder. Here we describe the histopathological analysis of a unique patient with Parkinson’s disease who underwent unilateral cell transplantation in the putamen with human embryonic mesencephalic tissue at 24 y before death. The patient enjoyed major clinical benefits for at least a decade after transplantation. After a quarter of a century, complete graft-derived dopaminergic reinnervation was still evident in the transplanted putamen. α-Synuclein–positive inclusions, some with the appearance of typical Lewy bodies, were present in 11–12% of the grafted dopaminergic neurons, reflecting spread of pathology from the host brain to the transplant. The clinical improvements were gradually lost from 14 y posttransplantation, indicating that even extensive graft-derived dopaminergic reinnervation loses its efficacy in a severely degenerating brain. Clinical trials using cells derived from embryonic ventral mesencephalon have shown that transplanted dopaminergic neurons can survive and function in the long term, as demonstrated by in vivo brain imaging using 18F-fluorodopa and 11C-raclopride positron emission tomography. Here we report the postmortem analysis of a patient with Parkinson’s disease who 24 y earlier underwent unilateral transplantation of embryonic dopaminergic neurons in the putamen and subsequently exhibited major motor improvement and recovery of striatal dopaminergic function. Histopathological analysis showed that a dense, near-normal graft-derived dopaminergic reinnervation of the putamen can be maintained for a quarter of a century despite severe host brain pathology and with no evidence of immune response. In addition, ubiquitin- and α-synuclein–positive inclusions were seen, some with the appearance of typical Lewy bodies, in 11–12% of the grafted dopaminergic neurons, reflecting the spread of pathology from the host brain to the transplants. Because the clinical benefits induced by transplantation in this patient were gradually lost after 14 y posttransplantation, our findings provide the first reported evidence, to our knowledge, that even a viable dopaminergic graft giving rise to extensive striatal reinnervation may lose its efficacy if widespread degenerative changes develop in the host brain.

Noninvasive mapping of water diffusional exchange in the human brain using filter-exchange imaging.

We present the first in vivo application of the filter‐exchange imaging protocol for diffusion MRI. The protocol allows noninvasive mapping of the rate of water exchange between microenvironments with different self‐diffusivities, such as the intracellular and extracellular spaces in tissue. Since diffusional water exchange across the cell membrane is a fundamental process in human physiology and pathophysiology, clinically feasible and noninvasive imaging of the water exchange rate would offer new means to diagnose disease and monitor treatment response in conditions such as cancer and edema. The in vivo use of filter‐exchange imaging was demonstrated by studying the brain of five healthy volunteers and one intracranial tumor (meningioma). Apparent exchange rates in white matter range from 0.8 ± 0.08 s−1 in the internal capsule, to 1.6 ± 0.11 s−1 for frontal white matter, indicating that low values are associated with high myelination. Solid tumor displayed values of up to 2.9 ± 0.8 s−1. In white matter, the apparent exchange rate values suggest intra‐axonal exchange times in the order of seconds, confirming the slow exchange assumption in the analysis of diffusion MRI data. We propose that filter‐exchange imaging could be used clinically to map the water exchange rate in pathologies. Filter‐exchange imaging may also be valuable for evaluating novel therapies targeting the function of aquaporins. Magn Reson Med, 2013.

The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter

Biophysical models that describe the outcome of white matter diffusion MRI experiments have various degrees of complexity. While the simplest models assume equal-sized and parallel axons, more elaborate ones may include distributions of axon diameters and axonal orientation dispersions. These microstructural features can be inferred from diffusion-weighted signal attenuation curves by solving an inverse problem, validated in several Monte Carlo simulation studies. Model development has been paralleled by microscopy studies of the microstructure of excised and fixed nerves, confirming that axon diameter estimates from diffusion measurements agree with those from microscopy. However, results obtained in vivo are less conclusive. For example, the amount of slowly diffusing water is lower than expected, and the diffusion-encoded signal is apparently insensitive to diffusion time variations, contrary to what may be expected. Recent understandings of the resolution limit in diffusion MRI, the rate of water exchange, and the presence of microscopic axonal undulation and axonal orientation dispersions may, however, explain such apparent contradictions. Knowledge of the effects of biophysical mechanisms on water diffusion in tissue can be used to predict the outcome of diffusion tensor imaging (DTI) and of diffusion kurtosis imaging (DKI) studies. Alterations of DTI or DKI parameters found in studies of pathologies such as ischemic stroke can thus be compared with those predicted by modelling. Observations in agreement with the predictions strengthen the credibility of biophysical models; those in disagreement could provide clues of how to improve them. DKI is particularly suited for this purpose; it is performed using higher b-values than DTI, and thus carries more information about the tissue microstructure. The purpose of this review is to provide an update on the current understanding of how various properties of the tissue microstructure and the rate of water exchange between microenvironments are reflected in diffusion MRI measurements. We focus on the use of biophysical models for extracting tissue-specific parameters from data obtained with single PGSE sequences on clinical MRI scanners, but results obtained with animal MRI scanners are also considered. While modelling of white matter is the central theme, experiments on model systems that highlight important aspects of the biophysical models are also reviewed.

CSF Aβ42/Aβ40 and Aβ42/Aβ38 ratios : better diagnostic markers of Alzheimer disease

The diagnostic accuracy of cerebrospinal fluid (CSF) biomarkers for Alzheimers disease (AD) must be improved before widespread clinical use. This study aimed to determine whether CSF Aβ42/Aβ40 and Aβ42/Aβ38 ratios are better diagnostic biomarkers of AD during both predementia and dementia stages in comparison to CSF Aβ42 alone.

The importance of axonal undulation in diffusion MR measurements: a Monte Carlo simulation study

Many axons follow wave‐like undulating courses. This is a general feature of extracranial nerve segments, but is also found in some intracranial nervous tissue. The importance of axonal undulation has previously been considered, for example, in the context of biomechanics, where it has been shown that posture affects undulation properties. However, the importance of axonal undulation in the context of diffusion MR measurements has not been investigated. Using an analytical model and Monte Carlo simulations of water diffusion, this study compared undulating and straight axons in terms of diffusion propagators, diffusion‐weighted signal intensities and parameters derived from diffusion tensor imaging, such as the mean diffusivity (MD), the eigenvalues and the fractional anisotropy (FA). All parameters were strongly affected by the presence of undulation. The diffusivity perpendicular to the undulating axons increased with the undulation amplitude, thus resembling that of straight axons with larger diameters. Consequently, models assuming straight axons for the estimation of the axon diameter from diffusion MR measurements might overestimate the diameter if undulation is present. FA decreased from approximately 0.7 to 0.5 when axonal undulation was introduced into the simulation model structure. Our results indicate that axonal undulation may play a role in diffusion measurements when investigating, for example, the optic and sciatic nerves and the spinal cord. The simulations also demonstrate that the stretching or compression of neuronal tissue comprising undulating axons alters the observed water diffusivity, suggesting that posture may be of importance for the outcome of diffusion MRI measurements. Copyright

A Swedish family with de novo α-synuclein A53T mutation: Evidence for early cortical dysfunction

A de novo alpha-synuclein A53T (p.Ala53 Th; c.209G > A) mutation has been identified in a Swedish family with autosomal dominant Parkinsons disease (PD). Two affected individuals had early-onset (before 31 and 40 years), severe levodopa-responsive PD with prominent dysphasia, dysarthria, and cognitive decline. Longitudinal clinical follow-up, EEG, SPECT and CSF biomarker examinations suggested an underlying encephalopathy with cortical involvement. The mutated allele (c.209A) was present within a haplotype different from that shared among mutation carriers in the Italian (Contursi) and the Greek-American Family H kindreds. One unaffected family member carried the mutation haplotype without the c.209A mutation, strongly suggesting its de novo occurrence within this family. Furthermore, a novel mutation c.488G > A (p.Arg163His; R163H) in the presenilin-2 (PSEN2) gene was detected, but was not associated with disease state.

Plasma β-amyloid in Alzheimer’s disease and vascular disease

Implementation of amyloid biomarkers in clinical practice would be accelerated if such biomarkers could be measured in blood. We analyzed plasma levels of Aβ42 and Aβ40 in a cohort of 719 individuals (the Swedish BioFINDER study), including patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI), Alzheimer’s disease (AD) dementia and cognitively healthy elderly, using a ultrasensitive immunoassay (Simoa platform). There were weak positive correlations between plasma and cerebrospinal fluid (CSF) levels for both Aβ42 and Aβ40, and negative correlations between plasma Aβ42 and neocortical amyloid deposition (measured with PET). Plasma levels of Aβ42 and Aβ40 were reduced in AD dementia compared with all other diagnostic groups. However, during the preclinical or prodromal AD stages (i.e. in amyloid positive controls, SCD and MCI) plasma concentration of Aβ42 was just moderately decreased whereas Aβ40 levels were unchanged. Higher plasma (but not CSF) levels of Aβ were associated with white matter lesions, cerebral microbleeds, hypertension, diabetes and ischemic heart disease. In summary, plasma Aβ is overtly decreased during the dementia stage of AD indicating that prominent changes in Aβ metabolism occur later in the periphery compared to the brain. Further, increased levels of Aβ in plasma are associated with vascular disease.

Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI : A model comparison using spherical tensor encoding

ABSTRACT In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersion. Separation of these properties is of high importance, since it could allow dMRI to non‐invasively probe elongated structures such as neurites (axons and dendrites). However, conventional dMRI, based on single diffusion encoding (SDE), entangles microscopic anisotropy and orientation dispersion with intra‐voxel variance in isotropic diffusivity. SDE‐based methods for estimating microscopic anisotropy, such as the neurite orientation dispersion and density imaging (NODDI) method, must thus rely on model assumptions to disentangle these features. An alternative approach is to directly quantify microscopic anisotropy by the use of variable shape of the b‐tensor. Along those lines, we here present the ‘constrained diffusional variance decomposition’ (CODIVIDE) method, which jointly analyzes data acquired with diffusion encoding applied in a single direction at a time (linear tensor encoding, LTE) and in all directions (spherical tensor encoding, STE). We then contrast the two approaches by comparing neurite density estimated using NODDI with microscopic anisotropy estimated using CODIVIDE. Data were acquired in healthy volunteers and in glioma patients. NODDI and CODIVIDE differed the most in gray matter and in gliomas, where NODDI detected a neurite fraction higher than expected from the level of microscopic diffusion anisotropy found with CODIVIDE. The discrepancies could be explained by the NODDI tortuosity assumption, which enforces a connection between the neurite density and the mean diffusivity of tissue. Our results suggest that this assumption is invalid, which leads to a NODDI neurite density that is inconsistent between LTE and STE data. Using simulations, we demonstrate that the NODDI assumptions result in parameter bias that precludes the use of NODDI to map neurite density. With CODIVIDE, we found high levels of microscopic anisotropy in white matter, intermediate levels in structures such as the thalamus and the putamen, and low levels in the cortex and in gliomas. We conclude that accurate mapping of microscopic anisotropy requires data acquired with variable shape of the b‐tensor. HIGHLIGHTSNeuroimaging was performed with linear and spherical tensor encoding (LTE and STE) at 3 T and 7 T.Microscopic anisotropy was quantified by two methods: NODDI and CODIVIDE.NODDI predictions of microscopic anisotropy were not supported by STE data.Levels of microscopic anisotropy were low in the cortex and high in the white matter.

Q-space trajectory imaging for multidimensional diffusion MRI of the human brain.

This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture.

Absolute quantification of cerebral blood flow: correlation between dynamic susceptibility contrast MRI and model-free arterial spin labeling

PURPOSE To compare absolute cerebral blood flow (CBF) estimates obtained by model-free arterial spin labeling (ASL) and dynamic susceptibility contrast MRI (DSC-MRI), corrected for partial volume effects (PVEs). METHODS CBF was measured using DSC-MRI and model-free ASL (quantitative signal targeting with alternating radiofrequency labeling of arterial regions) at 3 T in 15 subjects with brain tumor, and the two modalities were compared with regard to CBF estimates in normal gray matter (GM) and DSC-to-ASL CBF ratios in selected tumor regions. The DSC-MRI CBF maps were calculated using a global arterial input function (AIF) from the sylvian-fissure region, but, in order to minimize PVEs, the AIF time integral was rescaled by a venous output function time integral obtained from the sagittal sinus. RESULTS In GM, the average DSC-MRI CBF estimate was 150+/-45 ml/(min 100 g) (mean+/-SD) while the corresponding ASL CBF was 44+/-10 ml/(min 100 g). The linear correlation between GM CBF estimates obtained by DSC-MRI and ASL was r=.89, and observed DSC-to-ASL CBF ratios differed by less than 3% between GM and tumor regions. CONCLUSIONS A satisfactory positive linear correlation between the CBF estimates obtained by model-free ASL and DSC-MRI was observed, and DSC-to-ASL CBF ratios showed no obvious tissue dependence.