Disease-Specific Regions Outperform Whole-Brain Approaches in Identifying Progressive Supranuclear Palsy:A Multicentric MRI Study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F17%3A10338248" target="_blank" >RIV/00064165:_____/17:10338248 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11110/17:10338248 RIV/00023001:_____/17:00060290

Result on the web

<a href="http://dx.doi.org/10.3389/fnins.2017.00100" target="_blank" >http://dx.doi.org/10.3389/fnins.2017.00100</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fnins.2017.00100" target="_blank" >10.3389/fnins.2017.00100</a>

Result language

angličtina

Original language name

Disease-Specific Regions Outperform Whole-Brain Approaches in Identifying Progressive Supranuclear Palsy:A Multicentric MRI Study

Original language description

To identify progressive supranuclear palsy (PSP), we combined voxel-based morphometry (VBM) and support vector machine (SVM) classification using disease-specific features in multicentric magnetic resonance imaging (MRI) data. Structural brain differences were investigated at four centers between 20 patients with PSP and 20 age-matched healthy controls with T1-weighted MRI at 3T. To pave the way for future application in personalized medicine, we applied SVM classification to identify PSP on an individual level besides group analyses based on VBM. We found a major decline in gray matter density in the brainstem, insula, and striatum, and also in frontomedian regions, which is in line with current literature. Moreover, SVM classification yielded high accuracy rates above 80% for disease identification in imaging data. Focusing analyses on disease-specific regions-of-interest (ROI) led to higher accuracy rates compared to a whole-brain approach. Using a polynomial kernel (instead of a linear kernel) led to an increased sensitivity and a higher specificity of disease detection. Our study supports the application of MRI for individual diagnosis of PSP, if combined with SVM approaches. We demonstrate that SVM classification provides high accuracy rates in multicentric dataa prerequisite for potential application in diagnostic routine.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Neuroscience

ISSN

1662-453X

e-ISSN

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Volume of the periodical

11

Issue of the periodical within the volume

March

Country of publishing house

CH - SWITZERLAND

Number of pages

11

Pages from-to

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UT code for WoS article

000395560500001

EID of the result in the Scopus database

2-s2.0-85017168179