Connectivity-based segmentation of the Substantia Nigra in human and its implications in Parkinson's disease

FMRIB Centre, John Radcliffe Hospital, University of Oxford, Oxford, UK.
NeuroImage (Impact Factor: 6.36). 06/2010; 52(4). DOI: 10.1016/j.neuroimage.2010.05.086
Source: PubMed


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.

1 Follower
14 Reads
  • Source
    • "As mesocortical networks have been involved in adaptive behaviors and survival-oriented responses (Alcaro et al., 2007; Floresco and Magyar, 2006), it may also be that the reduced mortality risk reported in open people has a specific neurobiological basis (Iwasa et al., 2008; Taylor et al., 2009; Turiano et al., 2012). The more positive SN/VTA–DLPFC functional connectivity change in open people may have an underlying structural correlate (Menke et al., 2010). In particular, a previous voxel-based-morphometry (VBM) study found increased gray-matter volume in the SN/VTA and DLPFC in people with high levels of creativity and divergent thinking, two mental processes strongly associated with openness (Takeuchi et al., 2010). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Openness is a personality trait reflecting absorption in sensory experience, preference for novelty, and creativity, and is thus considered a driving force of human evolution. At the brain level, a relation between openness and dopaminergic circuits has been proposed, although evidence to support this hypothesis is lacking. Recent behavioral research has also found that people with mania, a psychopathological condition linked to dopaminergic dysfunctions, may display high levels of openness. However, whether openness is related to dopaminergic circuits has not been determined thus far.
    Full-text · Article · Sep 2014 · NeuroImage
  • Source
    • "To understand the function of SN DA and GABA neurons, we sought to extract the activity of these neuronal populations from microelectrode recordings. Because pars compacta and pars reticulata are largely interspersed in the primate SN (Poirier et al., 1983), the location of the microelectrode relative to any anatomical landmarks is typically not used to isolate activity from these neuronal populations (also, see Menke et al., 2010). Instead, non-human primate electrophysiology studies usually identify putative DA and GABA units based on the properties of extracellular spike waveforms recorded on the microelectrode (Fiorillo et al., 2013). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The human substantia nigra (SN) is thought to consist of two functionally distinct neuronal populations-dopaminergic (DA) neurons in the pars compacta subregion and GABA-ergic neurons in the pars reticulata subregion. However, a functional dissociation between these neuronal populations has not previously been demonstrated in the awake human. Here we obtained microelectrode recordings from the SN of patients undergoing deep brain stimulation (DBS) surgery for Parkinson's disease as they performed a two-alternative reinforcement learning task. Following positive feedback presentation, we found that putative DA and GABA neurons demonstrated distinct temporal dynamics. DA neurons demonstrated phasic increases in activity (250-500 ms post-feedback) whereas putative GABA neurons demonstrated more delayed and sustained increases in activity (500-1000 ms post-feedback). These results provide the first electrophysiological evidence for a functional dissociation between DA and GABA neurons in the human SN. We discuss possible functions for these neuronal responses based on previous findings in human and animal studies.
    Full-text · Article · Sep 2014 · Frontiers in Human Neuroscience
  • Source
    • "From left to right and top to bottom Refs. 85, 84, 102, 81, 87, 77, 103, 104, 83, 74, 105, 106, with permission. "
    [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.
    Full-text · Article · Dec 2013 · Annals of the New York Academy of Sciences
Show more