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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Frontiers in Neuroscience

ISSN

1662-453X

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

000395560500001

EID výsledku v databázi Scopus

2-s2.0-85017168179