Chaos in schizophrenia associations, reality or metaphor?
Jazyk angličtina Země Nizozemsko Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
19166884
DOI
10.1016/j.ijpsycho.2008.12.013
PII: S0167-8760(08)00870-2
Knihovny.cz E-zdroje
- MeSH
- asociace (psychologie) * MeSH
- časové faktory MeSH
- dospělí MeSH
- galvanická kožní odpověď fyziologie MeSH
- lidé MeSH
- metafora * MeSH
- mladý dospělý MeSH
- nelineární dynamika * MeSH
- neparametrická statistika MeSH
- neuropsychologické testy MeSH
- ověřování skutečnosti * MeSH
- psychologické modely MeSH
- schizofrenie (psychologie) * MeSH
- schizofrenie patofyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
There is evidence that schizophrenic associations display "chaotic", random-like behavior and decreased predictability. The evidence suggests a hypothesis that the "chaotic" mental disorganization could be explained within the concept of nonlinear dynamics and complexity in the brain that may cause chaotic neural organization. Testing of the hypothesis in the present study was performed using nonlinear analysis of bilateral electrodermal activity (EDA) during resting state and an association test in 56 schizophrenic patients and 44 healthy participants. EDA is a suitable measure of brain and autonomic activity reflecting neurobiological changes in schizophrenia that may indicate changes in nonlinear neural dynamics related to associative process. The results show that quantitative indices of chaotic dynamics (the largest Lyapunov exponents) calculated from EDA signals recorded during rest and the association test are significantly higher in schizophrenia patients than in the control group and increase during the test in comparison to the resting state. The difference was confirmed by statistical methods and using surrogate data testing that rejected an explanation within the linear statistical framework. The results provide supportive evidence that pseudo-randomness of schizophrenic associations and less predictability could be linked to increased complexity of nonlinear neural dynamics, although certain limitations in data interpretation must be taken into account.
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