Early detection of idiopathic Parkinson's disease based on magnetic resonance imaging

The diagnosis of idiopathic Parkinson's disease (IPD) is entirely clinical. The fact that neuronal damage begins 5-10 years before occurrence of sub-clinical signs, underlines the importance of preclinical diagnosis. A new approach for in-vivo pathophysiological assessment of IPD-related neurodegeneration was implemented based on recently developed neuroimaging methods. It is based on non- invasive magnetic resonance data sensitive to brain tissue property changes that precede macroscopic atrophy in the early stages of IPD. This research aims to determine the brain tissue property changes induced by neurodegeneration that can be linked to clinical phenotypes which will allow us to create a predictive model for early diagnosis in IPD. We hypothesized that the degree of disease progression in IPD patients will have a differential and specific impact on brain tissue properties used to create a predictive model of motor and non-motor impairment in IPD. We studied the potential of in-vivo quantitative imaging sensitive to neurodegeneration- related brain tissue characteristics to detect changes in patients with IPD. We carried out methodological work within the well established SPM8 framework to estimate the sensitivity of tissue probability maps for automated tissue classification for detection of early IPD. We performed whole-brain multi parameter mapping at high resolution followed by voxel-based morphometric (VBM) analysis and voxel-based quantification (VBQ) comparing healthy subjects to IPD patients. We found a trend demonstrating non-significant tissue property changes in the olfactory bulb area using the MT and R1 parameter with p<0.001. Comparing to the IPD patients, the healthy group presented a bilateral higher MT and R1 intensity in this specific functional region. These results did not correlate with age, severity or duration of disease. We failed to demonstrate any changes with the R2* parameter. We interpreted our findings as demyelination of the olfactory tract, which is clinically represented as anosmia. However, the lack of correlation with duration or severity complicates its implications in the creation of a predictive model of impairment in IPD

Brain tissue properties differentiate between motor and limbic basal ganglia circuits

Despite advances in understanding basic organizational principles of the human basal ganglia, accurate in vivo assessment of their anatomical properties is essential to improve early diagnosis in disorders with corticosubcortical pathology and optimize target planning in deep brain stimulation. Main goal of this study was the detailed topological characterization of limbic, associative, and motor subdivisions of the subthalamic nucleus (STN) in relation to corresponding corticosubcortical circuits. To this aim, we used magnetic resonance imaging and investigated independently anatomical connectivity via white matter tracts next to brain tissue properties. On the basis of probabilistic diffusion tractography we identified STN subregions with predominantly motor, associative, and limbic connectivity. We then computed for each of the nonoverlapping STN subregions the covariance between local brain tissue properties and the rest of the brain using high-resolution maps of magnetization transfer (MT) saturation and longitudinal (R1) and transverse relaxation rate (R2*). The demonstrated spatial distribution pattern of covariance between brain tissue properties linked to myelin (R1 and MT) and iron (R2*) content clearly segregates between motor and limbic basal ganglia circuits. We interpret the demonstrated covariance pattern as evidence for shared tissue properties within a functional circuit, which is closely linked to its function. Our findings open new possibilities for investigation of changes in the established covariance pattern aiming at accurate diagnosis of basal ganglia disorders and prediction of treatment outcom

Magnetization transfer imaging identifies basal ganglia abnormalities in adult ADHD that are invisible to conventional T1 weighted voxel-based morphometry

In childhood, Attention Deficit Hyperactivity Disorder (ADHD) is reliably associated with reduced volume of the striatum. In contrast, striatal abnormalities are infrequently detected in voxel-based morphometry (VBM) neuroimaging studies of adults with ADHD. This discrepancy has been suggested to reflect normalisation of striatal morphology with age and prolonged treatment of symptoms. If so, this would indicate that while striatal abnormalities are linked to symptom expression in childhood, they cannot explain the persistence of these symptoms in adulthood. However, this may not be case. Instead, we hypothesized that the lack of evidence for striatal abnormalities in adult ADHD may reflect poor sensitivity of typical (T1-weighted) neuroimaging to detect subcortical differences. To address this, we acquired both magnetisation transfer (MT) saturation maps optimised for subcortical contrast, and conventional T1-weighted images in 30 adults with ADHD and 30 age, IQ, gender and handedness-matched controls. Using VBM of both datasets, we demonstrate volumetric reductions within the left ventral striatum on MT that are not observed on identically pre-processed T1-weighted images from the same participants. Nevertheless, both techniques reported similar sensitivity to cortical abnormalities in the right inferior parietal lobe. Additionally, we show that differences in striatal iron may potentially explain this reduced sensitivity of T1-weighted images in adults. Together, these findings indicate that prior VBM studies reporting no abnormalities in striatal volume in adult ADHD might have been compromised by the methodological insensitivity of T1-weighted VBM to subcortical differences, and that structural abnormalities of the striatum in ADHD do indeed persist into adulthood

Creation of new tissue priors for automated delineation of basal ganglia in magnetic resonance imaging

The advent of magnetic resonance (MR) imaging has brought to the field of neurosciences the stupendous ability of in-vivo study of the human brain's tissular properties. More recent developments in the field of computational anatomy have led to automated approaches of volumetric assessment of the brain in voxel-based morphometry (VBM). VBM has provided significant understanding about physiopathology of brain diseases, including psychiatric and neurodegenerative diseases (NDDs). VBM performs tissue classification using algorithms that rely on contrast between tissues and probabilistic maps, termed tissue priors. These algorithms have provided accurate and satisfying study of the human cortex. However, tissue classification of deep gray matter such as the basal ganglia has been found to be largely unreliable. Conventional T1-weighted MR imaging provides lower contrast for deep gray matter than the cortical gray matter. The main reason for this contrast bias is the higher iron concentration in those structures. Moreover, iron deposits increase in the normal ageing adult and reach pathological concentrations in a wide number of neurological disorders including NDDs. Accurate assessment is thus challenged for subcortical structures in both health and disease. Recently, quantitative MR imaging (qMRI) has been developed to allow quantitative assessment of tissular microstructure of the brain. Those new sequences, such as magnetization transfer saturation (MT) and effective transverse relaxation rate (R2*) parameter maps, provide better contrast by displaying quantitative surrogates for myelin and iron respectively. MT parameter maps have shown to overcome high iron content sensitivity and to be highly suitable for automated delineation of the basal ganglia. Although MT parameter maps provide sufficient contrast, current tissue priors remain insufficient to provide satisfying tissue classification. In this work, we created robust and accurate tissue priors for deep gray matter

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates and , proton density and magnetisation transfer saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research

Parcellation of the human substantia nigra based on anatomical connectivity to the striatum

Substantia nigra/ventral tegmental area (SN/VTA) subregions, defined by dopaminergic projections to the striatum, are differentially affected by health (e.g. normal aging) and disease (e.g. Parkinson's disease). This may have an impact on reward processing which relies on dopaminergic regions and circuits. We acquired diffusion tensor imaging (DTI) with probabilistic tractography in 30 healthy older adults to determine whether subregions of the SN/VTA could be delineated based on anatomical connectivity to the striatum. We found that a dorsomedial region of the SN/VTA preferentially connected to the ventral striatum whereas a more ventrolateral region connected to the dorsal striatum. These SN/VTA subregions could be characterised by differences in quantitative structural imaging parameters, suggesting different underlying tissue properties. We also observed that these connectivity patterns differentially mapped onto reward dependence personality trait. We show that tractography can be used to parcellate the SN/VTA into anatomically plausible and behaviourally meaningful compartments, an approach that may help future studies to provide a more fine-grained synopsis of pathological changes in the dopaminergic midbrain and their functional impact

Prevalence of Grey Matter Pathology in Early Multiple Sclerosis Assessed by Magnetization Transfer Ratio Imaging

The aim of the study was to assess the prevalence, the distribution and the impact on disability of grey matter (GM) pathology in early multiple sclerosis. Eighty-eight patients with a clinically isolated syndrome with a high risk developing multiple sclerosis were included in the study. Forty-four healthy controls constituted the normative population. An optimized statistical mapping analysis was performed to compare each subject's GM Magnetization Transfer Ratio (MTR) imaging maps with those of the whole group of controls. The statistical threshold of significant GM MTR decrease was determined as the maximum p value (p<0.05 FDR) for which no significant cluster survived when comparing each control to the whole control population. Using this threshold, 51% of patients showed GM abnormalities compared to controls. Locally, 37% of patients presented abnormalities inside the limbic cortex, 34% in the temporal cortex, 32% in the deep grey matter, 30% in the cerebellum, 30% in the frontal cortex, 26% in the occipital cortex and 19% in the parietal cortex. Stepwise regression analysis evidenced significant association (p = 0.002) between EDSS and both GM pathology (p = 0.028) and T2 white matter lesions load (p = 0.019). In the present study, we evidenced that individual analysis of GM MTR map allowed demonstrating that GM pathology is highly heterogeneous across patients at the early stage of MS and partly underlies irreversible disability

Parkinson's disease may disrupt overlapping subthalamic nucleus and pallidal motor networks.

There is an ongoing debate about differential clinical outcome and associated adverse effects of deep brain stimulation (DBS) in Parkinson's disease (PD) targeting the subthalamic nucleus (STN) or the globus pallidus pars interna (GPi). Given that functional connectivity profiles suggest beneficial DBS effects within a common network, the empirical evidence about the underlying anatomical circuitry is still scarce. Therefore, we investigate the STN and GPi-associated structural covariance brain patterns in PD patients and healthy controls. We estimate GPi's and STN's whole-brain structural covariance from magnetic resonance imaging (MRI) in a normative mid- to old-age community-dwelling cohort (n = 1184) across maps of grey matter volume, magnetization transfer (MT) saturation, longitudinal relaxation rate (R1), effective transversal relaxation rate (R2*) and effective proton density (PD*). We compare these with the structural covariance estimates in patients with idiopathic PD (n = 32) followed by validation using a reduced size controls' cohort (n = 32). In the normative data set, we observed overlapping spatially distributed cortical and subcortical covariance patterns across maps confined to basal ganglia, thalamus, motor, and premotor cortical areas. Only the subcortical and midline motor cortical areas were confirmed in the reduced size cohort. These findings contrasted with the absence of structural covariance with cortical areas in the PD cohort. We interpret with caution the differential covariance maps of overlapping STN and GPi networks in patients with PD and healthy controls as correlates of motor network disruption. Our study provides face validity to the proposed extension of the currently existing structural covariance methods based on morphometry features to multiparameter MRI sensitive to brain tissue microstructure