Publications

Patent foramen ovale (PFO) is an important cause of embolic cryptogenic stroke (ECS) in young patients. The main mechanism in this case is paradoxical embolism (PE), the basis for which is a right-to-left (R-L) shunt. Objective: to comparatively characterize patients who have undergone ECS, with and without an R-L shunt, as evidenced by transcranial Doppler with the bubble test (TCD-BT).

The most common tools to understand perception of food products are hall-tests, surveys, observations, etc. However to get reliable results these approaches require large samples, making them costly and time-consuming. Furthermore, they are also highly expert-dependent and rely on the assumption that study participants can express their preferences consciously and explicitly. Here we suggested an EEG-based approach to evaluate perceived product similarity in a cross-modal taste-visual task. Two candidate neurometrics measured from Fz electrode were tested: the amplitude of N430-620 from evoked response potentials (ERP) and the power of induced gamma oscillations during 400-600 ms period after visual stimulus presentation. Both suggested metrics showed a strong correlation with the perceived similarity scores at both individual and group levels, however N430-630 had greater inter-subject variability making it less suitable for practical applications. The results based on the power of induced gamma oscillations (N=18) not only reflected the results from traditional hall-tests (N=200) but also allowed better discrimination between different food products.

Processing of mathematical operations and solving numerical tasks implicate a distributed set of brain regions. These regions include the superior and inferior parietal lobules that underlie numerical processing such as size judgments, and additional prefrontal regions that are needed for formal mathematical operations such as addition, subtraction and multiplication [Arsalidou, Taylor, 2011]. Critically, little is known about the connectivity between these regions and the association between math performance and the anatomical structure of white matter tracts. The present study investigates connectivity and white matter tracks associated with networks related to math performance: arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF). Participants performed a computerized task with mathematical operations (addition, subtraction, multiplication, and division) with three levels of difficulty; accuracy and reaction time were recorded. Diffusion tensor imagining (DTI) recordings provided indices on fractional anisotropy (FA) — a measure of the direction of white matter tracks in the brain. The relation between FA and math performance scores is reported.

This paper is an empirical study of the changing nature of the dependence of fundamental factors on the stock market index, which is the trend identified earlier in the Russian stock market. We empirically test the impact of daily values of fundamental factors on the MOEX Russia Index from 2003 to 2018. The analysis of the ARIMA-GARCH (1,1) model with a rolling window reveals that the change in the power and direction of the influence of the fundamental factors on the Russian stock market persists. The Quandt-Andrews breakpoint test and Bai-Perron test identify the number and likely location of structural breaks. We find multiple breaks probably associated with the dramatic falls of the stock market index. The results of the regression models over the different regimes, defined by the structural breaks, can vary markedly over time. This research is of value in macroeconomic forecasting and in the investment strategy development

This paper studies how the influence of the fundamental factors on the Russian stock market changes retrospectively. We empirically test the impact of daily values of fundamental factors (indexes of foreign stock markets, oil price, exchange rate and interest rates in Russia and the USA) on the MOEX Russia Index over long time interval from 2003 to 2018. The analysis of the ARIMA-GARCH (1,1) model with a rolling window reveals the changes in the power and direction of the influence of the fundamental factors which are probably caused by the structural instability revealed earlier in Russia and other stock markets. The Quandt-Andrews breakpoint test and Bai-Perron test identify the number and likely location of the structural breaks. We find multiple breaks probably associated with dramatic falls in the stock market index, for example with the significant falls of the then MICEX index in the spring of 2006 and the global financial crisis of 2008-2009. The results of the regression models over the different regimes, defined by the structural breaks, can vary markedly over time.

This paper describes the results of research project on optimal pricing for LLC "Perm Local Rail Company". In this study we propose a regression tree based approach for estimation of demand function for local rail tickets considering high degree of demand heterogeneity by various trip directions and the goals of travel. Employing detailed data on ticket sales for 5 years we estimate the parameters of demand function and reveal the significant variation in price elasticity of demand. While in average the demand is elastic by price, near a quarter of trips is characterized by weakly elastic demand. Lower elasticity of demand is correlated with lower degree of competition with other transport and inflexible frequency of travel.

  In many practical tasks it is needed to estimates effect of a treatemnt on individual level. For example in medicine it is essential to determine the patients that would benifit from a certain medicament. In marketing knowning the persons that are likely to buy a new product would reduce the amount of spam. In this chapter we review the methods to estimate individulize treatment effect from a randomized trial, i.e., an experiment when a part of individuals recieves a new treatment, while the others does not. Finally, it is shown that new efficient methods are needed in this domain.

In this paper, we analyze a new approach for demand prediction in retail. One of the signicant gaps in demand prediction by machine learning methods is the unaccounted sales data censorship. Econometric approaches to modeling censored demand are used to obtain consistent and unbiased estimates of parameters. These approaches can also be transferred to different classes of machine learning models to reduce the prediction error of sales volume. In this study we build two ensemble models to predict demand with and without demand censorship, aggregating predictions for machine learning methods such as Linear regression, Ridge regression, LASSO and Random forest. Having estimated the predictive properties of both models, we test the best predictive power of the models with accounting for the censored nature of demand.

Background and Purpose Despite the continuing efforts in multimodal assessment of the motor system after stroke, conclusive findings on the complementarity of functional and structural metrics of the corticospinal tract (CST) integrity and the role of the contralesional hemisphere are still missing. The aim of this work was to find the best combination of the motor system parameters, allowing classification of patients into three predefined groups of upper limb motor recovery. Methods 35 chronic ischemic stroke patients (47 [26–66] y.o., 29 [6–58] months post-stroke) with only supratentorial lesion and unilateral upper extremity weakness were enrolled. Patients were divided into three groups depending on the upper limb motor recovery. Non-parametric statistical tests and regression analysis were used to investigate the relationships among structural and functional motor system parameters, probed by diffusion tensor imaging (DTI) and transcranial magnetic stimulation (TMS). In addition, stratification rules were tested, using a decision tree classifier to identify parameters explaining motor recovery. Results Fractional anisotropy (FA) ratio in the internal capsule (IC) and absence/presence of motor evoked potentials (MEPs), were equally discriminative of the worst motor outcome group (96% accuracy). MEP presence diverged for two investigated hand muscles. Concurrently, for the three recovery groups’ classification, the best parameter combination was: IC FA ratio and Fréchet distance between the contralesional and ipsilesional CST FA profiles (91% accuracy). No other metrics had any additional value for patients’ classification. Conclusions This study demonstrates that IC FA ratio and MEPs absence are equally important markers for poor recovery. Importantly, we found that MEPs should be controlled in more than one hand muscle. Finally, we show that better separation between different motor recovery groups may be achieved when considering the whole CST FA profile.

Proceedings of the Fifth Workshop on Experimental Economics and Machine Learning at the National Research Univeristy Higher School of Economics co-located with the Seventh International Conference on Applied Research in Economics (iCare7)

Schizophrenia is a chronic debilitating disease. Sleep disturbances associated with a reduction in spindles are observed as warning signs prior to the first psychotic episode. Every spindle is a short-lasting (~0.5 s) set of bioelectric sinusoidal waves at the frequency of 10-16 Hz generated within the thalamus. Sleep spindles, easily identifiable in a scalp electroencephalogram, occur hundreds of times during sleep and are implicated in cognition like memory processes. For this reason, spindles are seen as an electrobiomarker of the quality of sleep and cognitive performance. In patients at high risk of psychotic transition, the density (number/time unit) of spindles is reduced. The underlying mechanisms of this change are unknown. Glutamate-mediated neurotransmission in the thalamus plays a key role in the generation of spindles and the etiology of schizophrenia. Therefore, we tested the hypothesis that a reduced function of glutamate receptors at the thalamic level is involved in the psychosis-related reduction in spindles. Using cell-to-network neurophysiological methods in sleeping rats, we demonstrate that systemic administration of the NMDA glutamate receptor antagonist, ketamine, significantly decreases spindle density. This effect is consistently prevented by the widely used antipsychotic drug, clozapine. These original findings support the hypothesis of the involvement of a reduced function of NMDA glutamate receptors in the sleep spindle deficit observed in psychosis-related disorders. The present findings lay the foundation for the development of innovative therapies aimed at preventing psychotic, bipolar, and depressive disorders.

Today, treatment effect estimation at the individual level is a vital problem in many areas of science and business. For example, in marketing, estimates of the treatment effect are used to select the most efficient promo-mechanics; in medicine, individual treatment effects are used to determine the optimal dose of medication for each patient and so on. At the same time, the question on choosing the best method, i.e., the method that ensures the smallest predictive error (for instance, RMSE) or the highest total (average) value of the effect, remains open. Accordingly, in this paper we compare the effectiveness of machine learning methods for estimation of individual treatment effects. The comparison is performed on the Criteo Uplift Modeling Dataset. In this paper we show that the combination of the Logistic Regression method and the Difference Score method as well as Uplift Random Forest method provide the best correctness of Individual Treatment Effect prediction on the top 30% observations of the test dataset.

Today, treatment effect estimation at the individual level is a vital problem in many areas of science and business. For example, in marketing, estimates of the treatment effect are used to select the most efficient promo-mechanics; in medicine, individual treatment effects are used to determine the optimal dose of medication for each patient and so on. At the same time, the question on choosing the best method, i.e., the method that ensures the smallest predictive error (for instance, RMSE) or the highest total (average) value of the effect, remains open. Accordingly, in this paper we compare the effectiveness of machine learning methods for estimation of individual treatment effects. The comparison is performed on the Criteo Uplift Modeling Dataset. In this paper we show that the combination of the Logistic Regression method and the Difference Score method as well as Uplift Random Forest method provide the best correctness of Individual Treatment Effect prediction on the top 30% observations of the test dataset.

This paper investigates the change in the behavior of prices and volumes on the Russian electricity market, caused by changes in the electrical grid. The analysis is performed on two previously unconnected macro regions, which currently have a “fictional” interregional transmission line. We use a dataset with economic variables together with the flow frequency as a technical variable of the electrical grid and prove that the latter matters. Our estimates indicate that given the existence of the interregional link, prices in the regions converge to some extent and generation volumes in the regions are shaped by its regions' and the adjacent regions' load. In the future research, the whole electrical grid of Russia should be taken into account.

Trial-to-trial variability of the motor evoked potentials (MEP) to transcranial magnetic stimulation (TMS) is a well-known phenomenon. However, the relationship between the fluctuations of the different types of the motor output and other motor system parameters such as corticospinal excitability, interhemispheric inhibition (IHI) and their interhemispheric asymmetry have not yet been fully investigated. We studied 20 young healthy right-handed volunteers. Four TMS sessions were performed (two single-pulse TMS and two paired-coil TMS with IHI paradigm sessions for each hemisphere), 70 stimuli were delivered during every session. Coefficient of quartile variation (CQV) was used to quantify trail-to-trail variability of MEPs amplitude. Resting motor threshold values were correlated between hemispheres (r = .842, p < .001). IHI phenomenon from the left hemisphere was obtained in 18 out of 20 volunteers, while IHI phenomena from the right hemisphere was shown in 16 out of 20. A strong correlation between the variability of MEP‘s amplitudes during IHI paradigm and the degree of IHI was found for the left hand (r = −.718, p < .001). We also observed a strong correlation between CQV of MEPs from both hands to single-pulse TMS (r = .632, p = .004). A side-specific correlation between the variability of the responses to single-pulse and paired-coil TMS was found for the dominant hemisphere (r = .524, p = .021). Our preliminary results demonstrate the importance of the trial-to-trial variability of the MEPs and its interhemispheric specificity as a defining characteristics of the motor system. This study was partially supported by ofi-m RFBR grant 17-29-02518, by HSE Basic Research Program and Russian Academic Excellence Project ‘5-100’.

Russian energy system is one of the largest among centralized ones in the world. For electricity consumption planning it is essential to consider heterogeneity of energy system areas in terms of both consumption structure and climate conditions. In this paper, the relationship of electricity consumption and air temperature is investigated on the data for 64 Russian regions. Hierarchical and non-hierarchical cluster analysis is employed to form homogeneous groups of regions: three temperature clusters are retrieved. Bearing in mind different electricity needs in regions, piecewise regression with endogenous reference temperature is estimated for each temperature cluster. In all clusters both cooling and heating effects are clearly observable but reference temperature differs. For the clusters of hot and middle climate regions cooling effect prevails, while heating effect dominates in the cold regions cluster. These effects consideration in energy consumption planning may result in a higher quality of forecasting. This is of a great importance for wholesale electricity market agents and functioning of Russian energy system as a whole.