Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach

Marquand, Andre F, Filippone, Maurizio, Ashburner, John, Girolami, Mark, Mourao-Miranda, Janaina, Barker, Gareth J, Williams, Steven C R, Leigh, P Nigel and Blain, Camilla R V (2013) Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach. PLoS ONE, 8 (7). e69237. ISSN 1932-6203

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson's disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.

Item Type: Article
Schools and Departments: Brighton and Sussex Medical School > Neuroscience
Subjects: R Medicine
R Medicine > RC Internal medicine > RC0321 Neurosciences. Biological psychiatry. Neuropsychiatry > RC0346 Neurology. Diseases of the nervous system Including speech disorders
Depositing User: Patricia Butler
Date Deposited: 01 Jun 2015 11:53
Last Modified: 08 Aug 2017 05:11
URI: http://sro.sussex.ac.uk/id/eprint/54186

View download statistics for this item

📧 Request an update