Connectivity-based segmentation of the substantia Nigra in human and its implications in Parkinson's disease.
ABSTRACT The aim of this study was to i) identify substantia nigra subregions i.e. pars reticulata (SNr) and pars compacta (SNc), in human, and ii) to assess volumetric changes in these subregions in the diagnosis of Parkinson's disease. Current MR imaging techniques are unable to distinguish SNr and SNc. Segmentation of these regions may be clinically useful in Parkinson's disease (PD) as substantia nigra is invariably affected in PD. We acquired quantitative T1 as well as diffusion tensor imaging (DTI) data from ten healthy subjects and ten PD patients. For each subject, the left and right SN were manually outlined on T1 images and then classified into two discrete regions based on the characteristics of their connectivity with the rest of the brain using an automated clustering method on the DTI data. We identified two regions in each subjects' SN: an internal region that is likely to correspond with SNc because it was mainly connected with posterior striatum, pallidum, anterior thalamus, and prefrontal cortex; and an external region that correspond with SNr because it was chiefly connected with posterior thalamus, ventral thalamus, and motor cortex. Volumetric study of these regions in PD patients showed a general atrophy in PD particularly in the right SNr. This pilot study showed that automated DTI-based parcellation of SN subregions may provide a useful tool for in-vivo identification of SNc and SNr and might therefore assist to detect changes that occur in patients with PD.
- SourceAvailable from: Stefan T Schwarz[show abstract] [hide abstract]
ABSTRACT: There is increasing interest in developing a reliable, affordable and accessible disease biomarker of Parkinson's disease (PD) to facilitate disease modifying PD-trials. Imaging biomarkers using magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can describe parameters such as fractional anisotropy (FA), mean diffusivity (MD) or apparent diffusion coefficient (ADC). These parameters, when measured in the substantia nigra (SN), have not only shown promising but also varying and controversial results.To clarify the potential diagnostic value of nigral DTI in PD and its dependency on selection of region-of-interest, we undertook a high resolution DTI study at 3T. 59 subjects (32 PD patients, 27 age and sex matched healthy controls) were analysed using manual outlining of SN and substructures, and voxel-based analysis (VBA). We also performed a systematic literature review and meta-analysis to estimate the effect size (DES) of disease related nigral DTI changes.We found a regional increase in nigral mean diffusivity in PD (mean±SD, PD 0.80±0.10 vs. controls 0.73±0.06·10−3mm2/s, p=0.002), but no difference using a voxel based approach. No significant disease effect was seen using meta-analysis of nigral MD changes (10 studies, DES=+0.26, p=0.17, I2=30%). None of the nigral regional or voxel based analyses of this study showed altered fractional anisotropy. Meta-analysis of 11 studies on nigral FA changes revealed a significant PD induced FA decrease. There was, however, a very large variation in results (I2=86%) comparing all studies. After exclusion of five studies with unusual high values of nigral FA in the control group, an acceptable heterogeneity was reached, but there was non-significant disease effect (DES=−0.5, p=0.22, I2=28%).The small PD related nigral MD changes in conjunction with the negative findings on VBA and meta-analysis limit the usefulness of nigral MD measures as biomarker of Parkinson's disease. The negative results of nigral FA measurements at regional, sub-regional and voxel level in conjunction with the results of the meta-analysis of nigral FA changes question the stability and validity of this measure as a PD biomarker.NeuroImage: Clinical. 10/2013; 3:481-488.
Article: Long-range connectomics.[show abstract] [hide abstract]
ABSTRACT: Decoding neural algorithms is one of the major goals of neuroscience. It is generally accepted that brain computations rely on the orchestration of neural activity at local scales, as well as across the brain through long-range connections. Understanding the relationship between brain activity and connectivity is therefore a prerequisite to cracking the neural code. In the past few decades, tremendous technological advances have been achieved in connectivity measurement techniques. We now possess a battery of tools to measure brain activity and connections at all available scales. A great source of excitement are the new in vivo tools that allow us to measure structural and functional connections noninvasively. Here, we discuss how these new technologies may contribute to deciphering the neural code.Annals of the New York Academy of Sciences 12/2013; 1305(1):83-93. · 4.38 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Differentiating Parkinson's disease (PD) from other types of neurodegenerative atypical parkinsonism (AP) can be challenging, especially in early disease stages. Routine brain magnetic resonance imaging (MRI) can show atrophy or signal changes in several parts of the brain with fairly high specificity for particular forms of AP, but the overall diagnostic value of routine brain MRI is limited. In recent years, various advanced MRI sequences have become available, including diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Here, we review available literature on the value of diffusion MRI for identifying and quantifying different patterns of neurodegeneration in PD and AP, in relation to what is known of underlying histopathologic changes and clinical presentation of these diseases. Next, we evaluate the value of diffusion MRI to differentiate between PD and AP and the potential value of serial diffusion MRI to monitor disease progression. We conclude that diffusion MRI may quantify patterns of neurodegeneration which could be of additional value in clinical use. Future prospective clinical cohort studies are warranted to assess the added diagnostic value of diffusion MRI.Journal of the neurological sciences 07/2013; · 2.32 Impact Factor