In patients with psychotic disorders, sleep spindles are reduced, supporting the hypothesis that the thalamus and glutamate receptors play a crucial etio-pathophysiological role, whose underlying mechanisms remain unknown. We hypothesized that a reduced function of NMDA receptors is involved in the spindle deficit observed in schizophrenia.
An electrophysiological multisite cell-to-network exploration was used to investigate, in pentobarbital-sedated rats, the effects of a single psychotomimetic dose of the NMDA glutamate receptor antagonist ketamine in the sensorimotor and associative/cognitive thalamocortical (TC) systems.
Under the control condition, spontaneously-occurring spindles (intra-frequency: 10–16 waves/s) and delta-frequency (1–4 Hz) oscillations were recorded in the frontoparietal cortical EEG, in thalamic extracellular recordings, in dual juxtacellularly recorded GABAergic thalamic reticular nucleus (TRN) and glutamatergic TC neurons, and in intracellularly recorded TC neurons. The TRN cells rhythmically exhibited robust high-frequency bursts of action potentials (7 to 15 APs at 200–700 Hz). A single administration of low-dose ketamine fleetingly reduced TC spindles and delta oscillations, amplified ongoing gamma-(30–80 Hz) and higher-frequency oscillations, and switched the firing pattern of both TC and TRN neurons from a burst mode to a single AP mode. Furthermore, ketamine strengthened the gamma-frequency band TRN-TC connectivity. The antipsychotic clozapine consistently prevented the ketamine effects on spindles, delta- and gamma−/higher-frequency TC oscillations.
The present findings support the hypothesis that NMDA receptor hypofunction is involved in the reduction in sleep spindles and delta oscillations. The ketamine-induced swift conversion of ongoing TC-TRN activities may have involved at least both the ascending reticular activating system and the corticothalamic pathway.
The most common tools to understand perception of food products are hall tests, surveys and observations. However, these approaches require large samples to get reliable results and they are rather 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. In our paper, we suggest an electroencephalography- based (EEG) approach to evaluate perceived product similarity in a cross-modal taste-visual task. We tested two potential neurometrics measured from Fz electrode: the amplitude of the N400-like evoked response potentials (ERP) and the power of induced gamma oscillations during 400-600 ms period after visual stimulus presentation. Both metrics showed a strong correlation with the perceived similarity scores at both individual and group levels; however, N400-like amplitude had greater inter-subject variability making it less suitable for practical applications. The results based on the power of induced gamma oscillations (N=18) could be compared to traditional hall-tests (N=200) and may potentially reveal subtle differences in food perception that can not be captured in the hall-tests.
In this paper, we address several aspects of applying classical machine learning algorithms to a regression problem. We compare the predictive power to validate our approach on a data about revenue of a large Russian restaurant chain. We pay special attention to solve two problems: data heterogeneity and a high number of correlated features. We describe methods for considering heterogeneity — observations weighting and estimating models on subsamples. We define a weighting function via Mahalanobis distance in the space of features and show its predictive properties on following methods: ordinary least squares regression, elastic net, support vector regression, and random forest.
Understanding neurological mechanisms of motor recovery after stroke is important for selecting appropriate therapeutic and rehabilitation strategies. One of the most widely-used but yet rather controversial MRI predictors is a co-called lesion load on the cortico-spinal tract (CST). This metric corresponds to the overlap between the volumes of the lesion and the cortico-spinal tract which is responsible for conducting neuronal signals that lead to motion generation. In this study we evaluated the potential of the lesion load to explain the motor outcome in a cohort of patients with chronic ischemic stroke. Lesions were automatically identified on structural T1-weighted images using LINDA package. Once lesions are identified, lesion loads on CST were calculated automatically using PALS software package (Ito et al., 2018). Finally, the obtained results were used to classify patients according to their motor outcome using decision tree classifier J48 implemented in WEKA software. However, the classification accuracy was much lower compared to the classification results based on another widely accepted MRI parameter: asymmetry of the fractional anisotropy in the internal capsule of the CST.
The degree of mental attention in childhood and adolescence determines in the future the effectiveness of working memory (ability to store and manipulate information). Attention has been previously found to be related to the prefrontal and parietal areas of the human cortex. But the relationship between attention and white matter properties are still largely unknown. The goal of this study was to identify the relationships between attention and fractional anisotropy (FA) of diffusion MRI in bilateral superior longitudinal fasciculus (in three subdivisions SLF 1- 3), arcuate fasciculus (AF), and corpus callosum (CC) in children and adolescents. Subjects: 14 children (9-11 years) and 13 teenagers (12-15 years). During the experiments participants had to establish a match between the colors on the screen and the colors on the previous slide. The task had six difficulty levels and both performance accuracy (m-score) and reaction time (RT) were measured. There was a positive correlation for m-score and a negative correlation for RT with FA in СС (levels 1-3) in the children's group (p<0.05). On the contrary, when FA increases in the right SLF 3 (level 6), there is a decrease in m-score, and when FA increases in the left SLF 3 and AF, there is an increase in RT at 2,3,4 and 6 levels. In contrast, a decrease in RT with an increase FA of bilateral SLF 3 (level 6) and left AF (level 4) was observed for adolescents, which reflects the redistribution of the roles between fiber tracts with age. FA values of the left (level 2) and right (level 1) SLF 2 negatively correlated with mscore (p <0.05) in the same group. For females (n=13) (regardless the age), there was only a negative correlation for m-score (2,3,5 levels) and the only positive correlation for RT (level 2) with FA of the right SLF 1, left and right SLF 2, in the left SLF 3 and СС (p<0.05). For males (n=13), on the contrary, there were positive correlations between m-score and FA of the СС (1,3,4 levels) and the left SLF 1 (5 level), and inverse correlations between RT and FA for the same fibers of the white matter (1 level) (p<0,05). Interestingly, an increase in FA with age was found in males in all the components of the white matter (p<0.01), except for the СС, and in females, on thecontrary – only in the СС. Further research is needed, taking into account gender, to fully understand the influence of white matter on the development of mental attention.
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).
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
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.
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)
In this research we analyze a new approach for prediction of demand. In the studied market of performing arts the observed demand is limited by capacity of the house. Then one needs to account for demand censorship to obtain unbiased estimates of demand function parameters. The presence of consumer segments with different purposes of going to the theater and willingness‐to‐pay for performance and ticket characteristics causes a heterogeneity in theater demand. We propose an estimator for prediction of demand that accounts for both demand censorship and preferences heterogeneity. The estimator is based on the idea of classification and regression trees and bagging prediction aggregation extended for prediction of censored data. Our algorithm predicts and combines predictions for both discrete and continuous parts of censored data. We show that our estimator performs better in terms of prediction accuracy compared with estimators which account either for censorship or heterogeneity only. The proposed approach is helpful for finding product segments and optimal price setting.