MRS Communications (1)
Standardization of Czech version of beck depression inventory (BDI II)
R. Ptacek, J. Raboch, M. Vnukova, J. Hlinka, M. Cervenkova
Journal: European Psychiatry / Volume 41 / Issue S1 / April 2017
Published online by Cambridge University Press: 23 March 2020, p. S539
Depression is now the fourth most common cause of invalidity. World Health Organization (WHO) predicts that by 2020 it will be the second most common cause (WHO, 2001). Beck Depression Inventory (BDI II) is highly reliable tool for measuring the intensity of depression.
The aim of this study was to assess the validity and reliability of the Czech version of BDI II. This was done on a representative sample of working population.
Results from 1027 participants were obtained. The sample was equally distributed among males and females. T-test showed that on average women suffered from higher depressive symptoms than males. Cronbach alpha showed high items consistency of 0,92 and confirmatory factor analysis found, as predicted, 3 factors: cognitive, somatic and affective.
Cronbach alpha and factor analysis showed high internal consistency and reliability of Czech version of BDI II. Czech version of BDI II is thus not only a translation but can be considered a psychometric tool that is comparable with the original version. The results of this study are therefore comparable with other available results.
The authors have not supplied their declaration of competing interest.
Classification of first-episode schizophrenia spectrum disorders and controls from whole brain white matter fractional anisotropy using machine learning
P. Mikolas, J. Hlinka, Z. Pitra, A. Skoch, T. Frodl, F. Spaniel, T. Hajek
Schizophrenia is a chronic disorder with an early onset and high disease burden in terms of life disability. Its early recognition may delay the resulting brain structural/functional alterations and improve treatment outcomes. Unlike conventional group-statistics, machine-learning techniques made it possible to classify patients and controls based on the disease patterns on an individual level. Diagnostic classification in first-episode schizophrenia to date was mostly performed on sMRI or fMRI data. DTI modalities have not gained comparable attention.
We performed the classification of 77 FES patients and 77 healthy controls matched by age and sex from fractional anisotropy data from using linear support-vector machine (SVM). We further analyzed the effect of medication and symptoms on the classification performance using standard statistical measures (t-test, linear regression) and machine learning (Kernel–Ridge regression).
The SVM distinguished between patients and controls with significant accuracy of 62.34% (P = 0.005). There was no association between the classification performance and medication nor symptoms. Group level statistical analysis yielded brain-wide significant differences in FA.
The SVM in combination with brain white-matter fractional anisotropy might help differentiate FES from HC. The performance of our classification model was not associated with symptoms or medications and therefore reflects trait markers in the early course of the disease.
Emergence of central mode in the paraelectric phase of ferroelectric perovskites
Jeevaka Weerasinghe, L. Bellaiche, T. Ostapchuk, P. Kužel, C. Kadlec, S. Lisenkov, I. Ponomareva, J. Hlinka
Journal: MRS Communications / Volume 3 / Issue 1 / March 2013
Published online by Cambridge University Press: 12 February 2013, pp. 41-45
Print publication: March 2013
THz-range dielectric spectroscopy and first-principle-based effective-Hamiltonian molecular dynamics simulations were used to elucidate the dielectric response in the paraelectric phase of (Ba, Sr)TiO3 solid solutions. Our analysis suggests a crossover between two regimes: a higher-temperature regime governed by the soft mode only versus a lower-temperature regime exhibiting a coupled soft mode/central mode dynamics. Interestingly, a single model can be used to adjust the THz dielectric response in the entire range of the paraelectric phase. The central peak cannot be discerned anymore in the dielectric spectra when the rate of underlying thermally activated processes exceeds certain characteristic frequency of the system.