Normative data for the Slovak version of the Frontal Assessment Battery (FAB).

The Frontal Assessment Battery (FAB) is a well-established screening measure of frontal lobe pathology. The aim of this study is the development of normative data for healthy Slovak adults. The final sample consisted of 487 healthy adults (54% of them female). The mean age in our sample was M = 55.29 (SD = 19.96). For the whole sample, the mean score on the FAB was 16.46 and the SD was 1.64. The mean score on the MMSE for the whole sample was 28.39 and the SD was 1.43. All participants underwent a complex neuropsychological examination spanning the relevant cognitive domains. FAB scores were found to be negatively associated with age (rs = -0.464, p < 0.001) and positively associated with years of education (rs = 0.199, p < 0.001). FAB scores positively correlated with the performance in MMSE (rs = 0.266, p < 0.001). Statistically significant and theoretically meaningful associations to other neuropsychological tests used in this study suggested the adequate convergent validity of the Slovak version of the FAB. The present study provided accurate normative FAB data, which can be used for clinical and research purposes.

An Analysis of Eye-Tracking Features and Modelling Methods for Free-Viewed Standard Stimulus: Application for Schizophrenia Detection.

Currently psychiatry is a medical field lacking an automated diagnostic process. The presence of a mental disorder is established by observing its typical symptoms. Eye-movement specifics have already been established as an "endophenotype" for schizophrenia, but an automated diagnostic process of eye-movement analysis is still lacking. This article presents several novel approaches for the automatic detection of a schizophrenic disorder based on a free-view image test using a Rorschach inkblot and an eye tracker. Several features that enabled us to analyse the eye-tracker signal as a whole as well as its specific parts were tested. The variety of features spans global (heat maps, gaze plots), sequences of features (means, variances, and spectra), static (x and y signals as 2D images), dynamic (velocities), and model-based (limiting probabilities and transition matrices) categories. For each set of features, a proper modelling and classification method was designed (convolutional, recurrent, fully connected and combined neural networks; Hidden Markov models). By doing so, it was possible to find the importance of each feature and its physical representation using k-fold cross validation and a paired t-test. The dataset was sampled on 22 people with schizophrenia and 22 healthy individuals. The most successful approach was based on heat maps using all data and convolutional networks, reaching a 78.8% accuracy, which is a 10.5% improvement over the reference method. From all tested methods, there are two in an 85% accuracy range and over fifteen others in a 75% accuracy range at a 10% significance level.