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

J 2011

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

KAŠPÁREK, Tomáš, Carlos Eduardo THOMAZ, Joao Ricardo SATO, Daniel SCHWARZ, Eva JANOUŠOVÁ et. al.

Základní údaje

Originální název

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

Autoři

KAŠPÁREK, Tomáš (203 Česká republika, garant, domácí), Carlos Eduardo THOMAZ (76 Brazílie), Joao Ricardo SATO (76 Brazílie), Daniel SCHWARZ (203 Česká republika, domácí), Eva JANOUŠOVÁ (203 Česká republika, domácí), Radek MAREČEK (203 Česká republika, domácí), Radovan PŘIKRYL (203 Česká republika, domácí), Jiří VANÍČEK (203 Česká republika, domácí), Andre FUJITA (392 Japonsko) a Eva ČEŠKOVÁ (203 Česká republika, domácí)

Vydání

Psychiatry Research: Neuroimaging, 2011, 0925-4927

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30000 3. Medical and Health Sciences

Stát vydavatele

Irsko

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 2.964

Kód RIV

RIV/00216224:14110/11:00052818

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.1016/j.pscychresns.2010.09.016

UT WoS

000288728500004

Klíčová slova anglicky

Schizophrenia; First episode; Classification; Brain morphology

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 31. 1. 2014 12:42, RNDr. Eva Koriťáková, Ph.D.

Anotace

V originále

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. Moreover, significantly more patients with poor outcome than those with good outcome were classified correctly.MLDA of brain MR intensity features is, therefore, able to correctly classify a significant number of FES patients, and the discriminative features are clinically relevant for clinical presentation 1 year after the first episode of schizophrenia. The accuracy of the current approach is, however, insufficient to be used in clinical practice immediately. Severalmethodological issues need to be addressed to increase the usefulness of this classification approach.

Návaznosti

MSM0021622404, záměr

Název: Vnitřní organizace a neurobiologické mechanismy funkčních systémů CNS

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Vnitřní organizace a neurobiologické mechanismy funkčních systémů CNS

NS10347, projekt VaV

Název: Moderní metody rozpoznávání pro analýzu obrazových dat v neuropsychiatrickém výzkumu

Investor: Ministerstvo zdravotnictví ČR, Moderní metody rozpoznávání pro analýzu obrazových dat v neuropsychiatrickém výzkumu

NS9893, projekt VaV

Název: Predikce průběhu iniciálních fází schizofrenie pomocí morfologie mozku

Investor: Ministerstvo zdravotnictví ČR, Predikace průběhu iniciálních fází schizofrenie pomocí morfologie mozku

Citovat

KAŠPÁREK, Tomáš, Carlos Eduardo THOMAZ, Joao Ricardo SATO, Daniel SCHWARZ, Eva JANOUŠOVÁ, Radek MAREČEK, Radovan PŘIKRYL, Jiří VANÍČEK, Andre FUJITA a Eva ČEŠKOVÁ. Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects. Psychiatry Research: Neuroimaging. 2011, roč. 191, č. 3, s. 174-181. ISSN 0925-4927. Dostupné z: https://dx.doi.org/10.1016/j.pscychresns.2010.09.016.

@article{947134,
   author = {Kašpárek, Tomáš and Thomaz, Carlos Eduardo and Sato, Joao Ricardo and Schwarz, Daniel and Janoušová, Eva and Mareček, Radek and Přikryl, Radovan and Vaníček, Jiří and Fujita, Andre and Češková, Eva},
   article_number = {3},
   doi = {http://dx.doi.org/10.1016/j.pscychresns.2010.09.016},
   keywords = {Schizophrenia; First episode; Classification; Brain morphology},
   language = {eng},
   issn = {0925-4927},
   journal = {Psychiatry Research: Neuroimaging},
   title = {Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects},
   volume = {191},
   year = {2011}
}

TY  - JOUR
ID  - 947134
AU  - Kašpárek, Tomáš - Thomaz, Carlos Eduardo - Sato, Joao Ricardo - Schwarz, Daniel - Janoušová, Eva - Mareček, Radek - Přikryl, Radovan - Vaníček, Jiří - Fujita, Andre - Češková, Eva
PY  - 2011
TI  - Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects
JF  - Psychiatry Research: Neuroimaging
VL  - 191
IS  - 3
SP  - 174-181
EP  - 174-181
SN  - 09254927
KW  - Schizophrenia
KW  - First episode
KW  - Classification
KW  - Brain morphology
N2  - 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. Moreover, significantly more patients with poor outcome than those with good outcome were classified correctly.MLDA of brain MR intensity features is, therefore, able to correctly classify a significant number of FES patients, and the discriminative features are clinically relevant for clinical presentation 1 year after the first episode of schizophrenia. The accuracy of the current approach is, however, insufficient to be used in clinical practice immediately. Severalmethodological issues need to be addressed to increase the usefulness of this classification approach.
ER  -

KAŠPÁREK, Tomáš, Carlos Eduardo THOMAZ, Joao Ricardo SATO, Daniel SCHWARZ, Eva JANOUŠOVÁ, Radek MAREČEK, Radovan PŘIKRYL, Jiří VANÍČEK, Andre FUJITA a Eva ČEŠKOVÁ. Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects. \textit{Psychiatry Research: Neuroimaging}. 2011, roč.~191, č.~3, s.~174-181. ISSN~0925-4927. Dostupné z: https://dx.doi.org/10.1016/j.pscychresns.2010.09.016.

Podrobný výpis o publikaci