Patterns of gray matter atrophy in atypical parkinsonism syndromes: A VBM meta-analysis

Fang Yu, Daniel S. Barron, Bundhit Tantiwongkosi, Peter Fox

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


Background and Purpose: Accurate diagnosis of Atypical Parkinsonian Syndromes (APS) is important due to differences in prognosis and management, but remains a challenge in the clinical setting. The purpose of our meta-analysis was to identify characteristic patterns of gray matter atrophy in Corticobasal Degeneration (CBD), Progressive Supranuclear Palsy (PSP), Multisystem-Atrophy Parkinsonian type (MSA-P), and Idiopathic Parkinson's Disease (IPD). Materials and Methods: Whole-brain meta-analysis was performed on 39 published voxel-based morphometry (VBM) articles (consisting of 404 IPD, 87 MSA-P, 165 CBD, and 176 PSP subjects) using the modified Anatomic Likelihood Estimation method. Based on these results, contrast analyses were then utilized to determine areas of atrophy shared by as well as unique to each disorder. Results: CBD was characterized by asymmetric gray matter atrophy in multiple cortical regions, while the thalamus-midbrain and insula were predominantly involved in PSP. The striatum and superior cerebellum were affected in MSA-P, while IPD demonstrated an anterior cerebral pattern. Although there was a mild overlap among PSP, CBD, and MSA-P, significant regions of atrophy unique to each disorder were identified, including (1) the superior parietal lobule in CBD (2) putamen in MSA-P (3) insula and medial dorsal nucleus in PSP. Conclusion: Our results suggest that there are characteristic patterns of atrophy in APS. Guided by these findings, future studies on the individual subject level may lead to the development of robust imaging biomarkers.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalBrain and Behavior
Issue number6
StatePublished - Jun 1 2015


  • Neurodegenerative diseases
  • Neuroimaging
  • Parkinson's disease

ASJC Scopus subject areas

  • Behavioral Neuroscience


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