The obtained results suggest that in the case of schizophrenia problems with pathological imitative behavior more likely occurred in executive rather than in the preparatory stage of response. Our findings can help to detect a latent echopraxia in schizophrenia patients that cannot be revealed by direct observation.

Background. It is important to prepare response in advance to increase the efficiency of its execution. The process of response preparation is usually studied using the precueing paradigm. In this paradigm subjects have to employ the preceding information about further imperative stimulus to perform proper response preparation, which shortens the reaction time of subsequent response execution. Previous studies detected the impairment of response preparation in schizophrenia only with the help of electroencephalographic parameters, but not with the assessing of reaction time. Therefore, in this study we attempted to find a behavioural parameter that could detect impairment in response preparation of schizophrenia patients. It was recently found that appropriate response preparation not only shortens the reaction time but also increases its stability, which is measured with the intra-individual reaction time variability. It was also revealed that response stability could better find cognitive dysfunction in some studies of schizophrenia disorder than classical behavioural parameters. Hence, the main goal of this study was to verify if intra-individual reaction time variability could detect the impairment of response preparation in schizophrenia patients.Materials and methods. In order to achieve the main purpose, we carried out a study with 14 schizophrenia patients and 14 control group subjects. We used precueing paradigm in our research, in which participants had to employ information about stimulus probability for the proper response preparation.Results. Our main result showed that despite the responses of schizophrenia patients were faster to the high-probability stimulus than to the low-probability one (F (1, 13) = 30.9, p < 0.001), intra-individual reaction time variability did not differ in this group between the responses to more and less probable stimuli (F (1, 13) = 0.64, p = 0.44).Conclusions. Results of the study suggest that people with schizophrenia were able to use precueing probabilistic information only to shorten their reaction time, but not to increase response stability. Therefore, it was found that intra-individual reaction time variability parameter could detect response preparation impairment in schizophrenia, and could be used in clinical purposes.

Machine learning (ML) represents a set of artificial in­telligence techniques that can assist in recognition of schizophrenia by classifying a person as belonging to either clinical or healthy subjects group. In the current study, we employed cognitive assessments of frontal lobe functions (the deficit of which is one of the prominent features of schizophrenia) for the training of ML models. Dataset for this research was engaged from our previous studies of two frontal lobe functions (response prepara­tion and inhibition of imitation) in case of schizophrenia. According to our knowledge, all previous cognitive ML schizophrenia studies used only the data from standard neuropsychological test batteries. Nevertheless, we em­ployed the cognitive data assessed with special experi­mental techniques that allowed us to engage Intra-indi­vidual reaction time variability (IIV) together with the classical reaction time (RT) assessment. It is important to emphasize that IIV is a cognitive measurement parameter that received vast attention of neuroscientists during the two last two decades and showed higher results in distin­guishing of schizophrenia patients from healthy subjects than standard RT in the number of studies. The result revealed statistically significant accuracy for all ML models in current study. Moreover, ML classifier with the highest accuracy outperformed the accuracy of a number of best models previously trained with standard neuropsychological test batteries datasets. Thus, cogni­tive experimental assessments of frontal lobe functions (response preparation and inhibition of imitation) can be effectively employed in developing of ML classifiers for distinguishing schizophrenia patients from healthy subjects.

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