Morphological alterations in the caudate, putamen, pallidum, and thalamus in Parkinson's disease

Like many neurodegenerative diseases, the clinical symptoms of Parkinsons disease (PD) do not manifest until significant progression of the disease has already taken place, motivating the need for sensitive biomarkers of the disease. While structural imaging is a potentially attractive method due to its widespread availability and non-invasive nature, global morphometric measures (e.g., volume) have proven insensitive to subtle disease change. Here we use individual surface displacements from deformations of an average surface model to capture disease related changes in shape of the subcortical structures in PD. Data were obtained from both the University of British Columbia (UBC) [n = 54 healthy controls (HC) and n = 55 Parkinsons disease (PD) patients] and the publicly available Parkinsons Progression Markers Initiative (PPMI) [n = 137 (HC) and n = 189 (PD)] database. A high dimensional non-rigid registration algorithm was used to register target segmentation labels (caudate, putamen, pallidum, and thalamus) to a set of segmentation labels defined on the average-template. The vertex-wise surface displacements were significantly different between PD and HC in thalamic and caudate structures. However, overall displacements did not correlate with disease severity, as assessed by the Unified Parkinson's Disease Rating Scale (UPDRS). The results from this study suggest disease-relevant shape abnormalities can be robustly detected in subcortical structures in PD. Future studies will be required to determine if shape changes in subcortical structures are seen in the prodromal phases of the disease.

[1]  O. Paulson,et al.  Increased intracranial volume in Parkinson's disease , 2005, Journal of the Neurological Sciences.

[2]  Amity E. Green,et al.  Hippocampal, caudate, and ventricular changes in Parkinson's disease with and without dementia , 2010, Movement disorders : official journal of the Movement Disorder Society.

[3]  L. Astrakas,et al.  Voxel‐Based Morphometry and Voxel‐Based Relaxometry in Parkinsonian Variant of Multiple System Atrophy , 2009, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[4]  James C. Grotta,et al.  Stroke: new horizons in treatment , 2014, The Lancet Neurology.

[5]  Christos Davatzikos,et al.  Why voxel-based morphometric analysis should be used with great caution when characterizing group differences , 2004, NeuroImage.

[6]  Mirza Faisal Beg,et al.  Multi-structure whole brain registration and population average , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  W. M. van der Flier,et al.  Frontal lobe damage and thalamic volume changes , 2000, Neuroreport.

[8]  M. Skalej,et al.  Magnetic resonance imaging–based volumetry differentiates idiopathic Parkinson's syndrome from multiple system atrophy and progressive supranuclear palsy , 1999, Annals of neurology.

[9]  Ali R. Khan,et al.  FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping , 2008, NeuroImage.

[10]  Mirza Faisal Beg,et al.  Manually segmented template library for 8-year-old pediatric brain MRI data with 16 subcortical structures , 2014, Journal of medical imaging.

[11]  H. Lilliefors On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[12]  F. Skidmore,et al.  White Matter Microstructure Changes in the Thalamus in Parkinson Disease with Depression: A Diffusion Tensor MR Imaging Study , 2010, American Journal of Neuroradiology.

[13]  Michael Schocke,et al.  Progression of brain atrophy in multiple system atrophy , 2007, Journal of Neurology.

[14]  Simon Duchesne,et al.  Automated computer differential classification in Parkinsonian Syndromes via pattern analysis on MRI. , 2009, Academic radiology.

[15]  Karl J. Friston,et al.  A unified statistical approach for determining significant signals in images of cerebral activation , 1996, Human brain mapping.

[16]  I. McKeith,et al.  Brain atrophy rates in Parkinson's disease with and without dementia using serial magnetic resonance imaging , 2005, Movement disorders : official journal of the Movement Disorder Society.

[17]  Reduced striatal volumes in Parkinson’s disease: a magnetic resonance imaging study , 2012, Translational Neurodegeneration.

[18]  W. Heiss,et al.  Differentiating multiple system atrophy from Parkinson’s disease: contribution of striatal and midbrain MRI volumetry and multi-tracer PET imaging , 2002, Journal of neurology, neurosurgery, and psychiatry.

[19]  Karl J. Friston,et al.  Voxel-Based Morphometry , 2015 .

[20]  M. Styner,et al.  Striatal shape in Parkinson's disease , 2013, Neurobiology of Aging.

[21]  Karsten Specht,et al.  Voxel-based analysis of multiple-system atrophy of cerebellar type: complementary results by combining voxel-based morphometry and voxel-based relaxometry , 2005, NeuroImage.

[22]  Marie Chupin,et al.  Can voxel based morphometry, manual segmentation and automated segmentation equally detect hippocampal volume differences in acute depression? , 2009, NeuroImage.

[23]  E. Katunina,et al.  [Epidemiology of Parkinson's disease]. , 2013, Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova.

[24]  H. Bergman,et al.  Goal-directed and habitual control in the basal ganglia: implications for Parkinson's disease , 2010, Nature Reviews Neuroscience.

[25]  K. Worsley,et al.  Unified univariate and multivariate random field theory , 2004, NeuroImage.

[26]  Kailash P Bhatia,et al.  The expanding universe of disorders of the basal ganglia , 2014, The Lancet.

[27]  Nick C Fox,et al.  Longitudinal MRI in progressive supranuclear palsy and multiple system atrophy: rates and regions of atrophy. , 2006, Brain : a journal of neurology.

[28]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[29]  Alexander Hammers,et al.  Three‐dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe , 2003, Human brain mapping.

[30]  Rafeef Abugharbieh,et al.  Shape (but not volume) changes in the thalami in Parkinson disease , 2008, BMC neurology.

[31]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[32]  Alan C. Evans,et al.  Cerebral atrophy and its relation to cognitive impairment in Parkinson disease , 2005, Neurology.

[33]  João Ricardo Sato,et al.  Depression in Parkinson's disease: Convergence from voxel-based morphometry and functional magnetic resonance imaging in the limbic thalamus , 2009, NeuroImage.