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Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects
T. Kasparek, C. E. Thomaz, J. R. Sato, D. Schwarz, E. Janousova, R. Marecek, R. Prikryl, J. Vanicek, A. Fujita, E. Ceskova
Jazyk angličtina Země Irsko
Typ dokumentu časopisecké články
Grantová podpora
NS9893
MZ0
CEP - Centrální evidence projektů
NS10347
MZ0
CEP - Centrální evidence projektů
- MeSH
- diskriminační analýza MeSH
- dospělí MeSH
- lidé MeSH
- lineární modely MeSH
- magnetická rezonanční tomografie metody MeSH
- mapování mozku MeSH
- mladý dospělý MeSH
- mozek patologie MeSH
- počítačové zpracování obrazu metody MeSH
- psychiatrické posuzovací škály MeSH
- schizofrenie diagnóza MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
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-one-out 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. Several methodological issues need to be addressed to increase the usefulness of this classification approach.
Citace poskytuje Crossref.org
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- $a Kašpárek, Tomáš, $d 1975- $7 xx0031812 $u Department of Psychiatry, Masaryk University, Faculty of Medicine and Faculty Hospital Brno-Bohunice, Jihlavska 20, 625 00, Brno, Czech Republic. tomas.kasparek@centrum.cz
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- $a 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-one-out 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. Several methodological issues need to be addressed to increase the usefulness of this classification approach.
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