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

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 bioma...

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Autores principales:	Andre F Marquand, Maurizio Filippone, John Ashburner, Mark Girolami, Janaina Mourao-Miranda, Gareth J Barker, Steven C R Williams, P Nigel Leigh, Camilla R V Blain
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/48557835fe5446fb9747a82f89161e74
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spelling	oai:doaj.org-article:48557835fe5446fb9747a82f89161e742021-11-18T07:37:38ZAutomated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.1932-620310.1371/journal.pone.0069237https://doaj.org/article/48557835fe5446fb9747a82f89161e742013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23869237/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Progressive 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.Andre F MarquandMaurizio FilipponeJohn AshburnerMark GirolamiJanaina Mourao-MirandaGareth J BarkerSteven C R WilliamsP Nigel LeighCamilla R V BlainPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e69237 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andre F Marquand Maurizio Filippone John Ashburner Mark Girolami Janaina Mourao-Miranda Gareth J Barker Steven C R Williams P Nigel Leigh Camilla R V Blain Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.
description	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.
format	article
author	Andre F Marquand Maurizio Filippone John Ashburner Mark Girolami Janaina Mourao-Miranda Gareth J Barker Steven C R Williams P Nigel Leigh Camilla R V Blain
author_facet	Andre F Marquand Maurizio Filippone John Ashburner Mark Girolami Janaina Mourao-Miranda Gareth J Barker Steven C R Williams P Nigel Leigh Camilla R V Blain
author_sort	Andre F Marquand
title	Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.
title_short	Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.
title_full	Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.
title_fullStr	Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.
title_full_unstemmed	Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.
title_sort	automated, high accuracy classification of parkinsonian disorders: a pattern recognition approach.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/48557835fe5446fb9747a82f89161e74
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Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.

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