Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F11%3A00052818" target="_blank" >RIV/00216224:14110/11:00052818 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/65269705:_____/11:#0001309

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.pscychresns.2010.09.016" target="_blank" >http://dx.doi.org/10.1016/j.pscychresns.2010.09.016</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.pscychresns.2010.09.016" target="_blank" >10.1016/j.pscychresns.2010.09.016</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects

Popis výsledku v původním jazyce

Recent techniques of image analysis brought the possibility to recognize subjects based on discriminative image features. We performed a magnetic resonance imaging (MRI)-based classification study to assess its usefulness for outcome prediction of first-episode schizophrenia patients (FES). We included 39 FES patients and 39 healthy controls (HC) and performed the maximum-uncertainty linear discrimination analysis (MLDA) of MRI brain intensity images. The classification accuracy index (CA) was correlated with the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning scale (GAF) at 1-year follow-up. The rate of correct classifications of patients with poor and good outcomes was analyzed using chi-square tests. MLDA classification was significantly better than classification by chance. Leave-oneout accuracy was 72%. CA correlated significantly with PANSS and GAF scores at the 1-year follow-up.

Název v anglickém jazyce

Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects

Popis výsledku anglicky

Recent techniques of image analysis brought the possibility to recognize subjects based on discriminative image features. We performed a magnetic resonance imaging (MRI)-based classification study to assess its usefulness for outcome prediction of first-episode schizophrenia patients (FES). We included 39 FES patients and 39 healthy controls (HC) and performed the maximum-uncertainty linear discrimination analysis (MLDA) of MRI brain intensity images. The classification accuracy index (CA) was correlated with the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning scale (GAF) at 1-year follow-up. The rate of correct classifications of patients with poor and good outcomes was analyzed using chi-square tests. MLDA classification was significantly better than classification by chance. Leave-oneout accuracy was 72%. CA correlated significantly with PANSS and GAF scores at the 1-year follow-up.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

FH - Neurologie, neurochirurgie, neurovědy

OECD FORD obor

—

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)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

Psychiatry Research: Neuroimaging

ISSN

0925-4927

e-ISSN

—

Svazek periodika

191

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

IE - Irsko

Počet stran výsledku

8

Strana od-do

174-181

Kód UT WoS článku

000288728500004

EID výsledku v databázi Scopus

—