Neural Data Analysis Research Articles

A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli

Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data), providing a precise naming convention and data types, as well as providing a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface easily with existing toolboxes, such as EelBrain, NapLib, MNE, and the mTRF-Toolbox. We present guidelines by taking both the user view (how to rapidly re-analyse existing data) and the experimenter view (how to store, analyse, and share), making the process as straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis.

FNIRSNET: A multi-view spatio-temporal convolutional neural network fusion for functional near-infrared spectroscopy-based auditory event classification

Multi-view learning is a rapidly evolving research area focused on developing diverse learning representations. In neural data analysis, this approach holds immense potential by capturing spatial, temporal, and frequency features. Despite its promise, multi-view application to functional near-infrared spectroscopy (fNIRS) has remained largely unexplored. This study addresses this gap by introducing fNIRSNET, a novel framework that generates and fuses multi-view spatio-temporal representations using convolutional neural networks. It investigates the combined informational strength of oxygenated (HbO2) and deoxygenated (HbR) hemoglobin signals, further extending these capabilities by integrating with electroencephalography (EEG) networks to achieve robust multimodal classification. Experiments involved classifying neural responses to auditory stimuli with nine healthy participants. fNIRS signals were decomposed into HbO2/HbR concentration changes, resulting in Parallel and Merged input types. We evaluated four input types across three data compositions: balanced, subject, and complete datasets. Our fNIRSNET's performance was compared with eight baseline classification models and merged it with four common EEG networks to assess the efficacy of combined features for multimodal classification. Compared to baselines, fNIRSNET using the Merged input type achieved the highest accuracy of 83.22%, 81.18%, and 91.58% for balanced, subject, and complete datasets, respectively. In the complete set, the approach effectively mitigated class imbalance issues, achieving sensitivity of 83.58% and specificity of 95.42%. Multimodal fusion of EEG networks and fNIRSNET outperformed single-modality performance with the highest accuracy of 87.15% on balanced data. Overall, this study introduces an innovative fusion approach for decoding fNIRS data and illustrates its integration with established EEG networks to enhance performance.

Individual differences in the consistency of neural and behavioural responses to speech sounds

There are documented individual differences among adults in the consistency of speech sound processing, both at neural and behavioural levels. Some adults show more consistent neural responses to speech sounds than others, as measured by an event-related potential called the frequency-following response (FFR); similarly, some adults show more consistent behavioural responses to native speech sounds than others, as measured by two-alternative forced choice (2AFC) and visual analog scaling (VAS) tasks. Adults also differ in how successfully they can perceive non-native speech sounds. Interestingly, it remains unclear whether these differences are related within individuals. In the current study, native English-speaking adults completed native phonetic perception tasks (2AFC and VAS), a non-native (German) phonetic perception task, and an FFR recording session. From these tasks, we derived measures of the consistency of participants’ neural and behavioural responses to native speech as well as their non-native perception ability. We then examined the relationships among individual differences in these measures. Analysis of the behavioural measures revealed that more consistent responses to native sounds predicted more successful perception of unfamiliar German sounds. Analysis of neural and behavioural data did not reveal clear relationships between FFR consistency and our phonetic perception measures. This multimodal work furthers our understanding of individual differences in speech processing among adults, and may eventually lead to individualized approaches for enhancing non-native language acquisition in adulthood.

Neural representational geometries reflect behavioral differences in monkeys and recurrent neural networks

Animals likely use a variety of strategies to solve laboratory tasks. Traditionally, combined analysis of behavioral and neural recording data across subjects employing different strategies may obscure important signals and give confusing results. Hence, it is essential to develop techniques that can infer strategy at the single-subject level. We analyzed an experiment in which two male monkeys performed a visually cued rule-based task. The analysis of their performance shows no indication that they used a different strategy. However, when we examined the geometry of stimulus representations in the state space of the neural activities recorded in dorsolateral prefrontal cortex, we found striking differences between the two monkeys. Our purely neural results induced us to reanalyze the behavior. The new analysis showed that the differences in representational geometry are associated with differences in the reaction times, revealing behavioral differences we were unaware of. All these analyses suggest that the monkeys are using different strategies. Finally, using recurrent neural network models trained to perform the same task, we show that these strategies correlate with the amount of training, suggesting a possible explanation for the observed neural and behavioral differences.

The Impact of Microwave Annealing on MoS2 Devices Assisted by Neural Network-Based Big Data Analysis.

Microwave annealing, an emerging annealing method known for its efficiency and low thermal budget, has established a foundational research base in the annealing of molybdenum disulfide (MoS2) devices. Typically, to obtain high-quality MoS2 devices, mechanical exfoliation is commonly employed. This method's challenge lies in achieving uniform film thickness, which limits the use of extensive data for studying the effects of microwave annealing on the MoS2 devices. In this experiment, we utilized a neural network approach based on the HSV (hue, saturation, value) color space to assist in distinguishing film thickness for the fabrication of numerous MoS2 devices with enhanced uniformity and consistency. This method allowed us to precisely assess the impact of microwave annealing on device performance. We discovered a relationship between the device's electrical performance and the annealing power. By analyzing the statistical data of these electrical parameters, we identified the optimal annealing power for MoS2 devices as 700 W, providing insights and guidance for the microwave annealing process of two-dimensional materials.

FORECASTING THE SUCCESS OF EDUCATION SEEKERS FROM A SEPARATE EDUCATIONAL COMPONENT BASED ON THE RESULTS OF THE PRELIMINARY MASTERY OF SUBJECT COMPETENCIES

The paper examines the main concepts related to the quality of education in general and the assimilation of educational material by higher education seekers. The task of predicting a seeker's grade in any discipline is formulated with data on his assimilation of program learning outcomes that also correspond to this discipline. The available specialized information system of own development is described which applies a number of methods (multivariate linear regression, artificial neural networks, k-nearest neighbors) and determines which method will be the most effective for the analysis of specific data. It is noted that during the further improvement of the quality system of knowledge assessment, it is important to determine at what level the student of education possesses the acquired competences, i.e. to calculate the success of seekers in terms of general and professional competences and program learning outcomes, determined by the standards of higher education and educational programs developed on their basis. The developed algorithm for calculating the success rate of higher education applicants in terms of program learning outcomes is presented; according to this algorithm, data were prepared on the acquisition of software creation competencies by 78 seekers of the first level of higher education of the educational and professional program Intelligent Decision Support Systems specialty 124, Systems analysis, of the DSEA. To solve the problem of forecasting by the method of artificial neural networks, the programming and data analysis language R is proposed. A script for finding the optimal neural network architecture is created. It was found that the best result (correlation is 0.9599, average absolute reduced error is 0.1132, percentage of correctly predicted points on the Ukrainian scale is 79.2) provides a perceptron with two hidden layers and five neurons in each one. This network was applied to predict the success of the new academic group: correlation is 0.923, the average absolute reduced error is 0.0654, the percentage of correctly predicted points on the Ukrainian scale is 82.4. The obtained results can be used to assess the quality of the structural and logical scheme of the EPP and in the work of the department during the analysis of seekers' success, etc.

An artificial intelligence-assisted microfluidic colorimetric wearable sensor system for monitoring of key tear biomarkers

The precise, simultaneous, and rapid detection of essential biomarkers in human tears is imperative for monitoring both ocular and systemic health. The utilization of a wearable colorimetric biochemical sensor exhibits potential in achieving swift and concurrent detection of pivotal biomarkers in tears. Nevertheless, challenges arise in the collection, interpretation, and sharing of data from the colorimetric sensor, thereby restricting the practical implementation of this technology. To overcome these challenges, this research introduces an artificial intelligence-assisted wearable microfluidic colorimetric sensor system (AI-WMCS) for rapid, non-invasive, and simultaneous detection of key biomarkers in human tears, including vitamin C, H+(pH), Ca2+, and proteins. The sensor consists of a flexible microfluidic epidermal patch that collects tears and facilitates the colorimetric reaction, and a deep-learning neural network-based cloud server data analysis system (CSDAS) embedded in a smartphone enabling color data acquisition, interpretation, auto-correction, and display. To enhance accuracy, a well-trained multichannel convolutional recurrent neural network (CNN-GRU) corrects errors in the interpreted concentration data caused by varying pH and color temperature in different measurements. The test set determination coefficients (R2) of 1D-CNN-GRU for predicting pH and 3D-CNN-GRU for predicting the other three biomarkers were as high as 0.998 and 0.994, respectively. This correction significantly improves the accuracy of the predicted concentration, enabling accurate, simultaneous, and quick detection of four critical tear biomarkers using only minute amounts of tears ( ~ 20 μL). This research demonstrates the powerful integration of a flexible microfluidic colorimetric biosensor and deep-learning algorithm, which holds immense potential to revolutionize the fields of health monitoring.

Estimating high-dimensional Hawkes process with time-dependent covariates

The Hawkes process models have been recently become a popular tool for modeling and analysis of neural spike train data. Despite this popularity, existing methodological and theoretical work has mainly focused on models without assumption of time-varying covariates, such as stimulus drive. In this article, motivated by neuronal spike trains study in the presence of stimuli, we propose a new Hawkes process model, where covariates are included in the intensity function. We consider the problem of simultaneous variable selection and estimation for the Hawkes process in the high-dimensional regime. Estimation of the intensity function of the high-dimensional point process is considered within a nonparametric framework, applying B-splines and the SCAD penalty for matters of sparsity. We apply the Doob-Kolmogorov inequality to establish the consistency of the resulting estimators. Finally, we illustrate the performance of our proposal through simulation and demonstrate its utility by applying it to the neuron spike train data set.

ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ИНКЛЮЗИВНОМ ХУДОЖЕСТВЕННОМ ОБРАЗОВАНИИ

The use of artificial intelligence in inclusive arts education opens up new opportunities for learning and engaging all students, including those with special needs or disabilities. At the moment, adaptive tools are the most relevant. AI can offer adaptive tools and technologies to help students with various special needs participate in the artistic process. This article discusses the use of a tool such as Kandinsky for training the visually impaired and checking the level of elaboration and use of this software in image visualisation. In addition, the possibilities of the MidJourney software in implementing this software product in the educational process for visually impaired students are presented. The issue of “unambiguity” of visual images is considered, i.e. whether neural network can produce an authentic image obtained at the first request if a similar request is received several times and at different times? A comparative analysis of neural network data is carried out according to two criteria: technical excellence (to what extent the program contributes to the image visualization required by students) and “interface friendliness” (how easy and understandable it is for the student to work with this program).

An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action.

A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions-including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings-were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.

Circumstantial evidence and explanatory models for synapses in large-scale spike recordings

Whether, when, and how causal interactions between neurons can be meaningfully studied from observations of neural activity alone are vital questions in neural data analysis. Here we aim to better outline the concept of functional connectivity for the specific situation where systems neuroscientists aim to study synapses using spike train recordings. In some cases, cross-correlations between the spikes of two neurons are such that, although we may not be able to say that a relationship is causal without experimental manipulations, models based on synaptic connections provide precise explanations of the data. Additionally, there is often strong circumstantial evidence that pairs of neurons are monosynaptically connected. Here we illustrate how circumstantial evidence for or against synapses can be systematically assessed and show how models of synaptic effects can provide testable predictions for pair-wise spike statistics. We use case studies from large-scale multi-electrode spike recordings to illustrate key points and to demonstrate how modeling synaptic effects using large-scale spike recordings opens a wide range of data analytic questions.

Study of the effectiveness of diagnostic method for respiratory system diseases by analyzing the exhaled air using a gas analytical complex

Aim: To study in patients the dependence of the exhaled air composition on pathological processes occurring in the respiratory system, including: lung cancer, community-acquired pneumonia and COVID-19.Material and Methods. The studies were carried out on the basis of a gas analytical complex using the method of neural network data analysis. The gas analytical complex includes semiconductor sensors that measure the concentrations of gas components in exhaled air with an average sensitivity of 1 ppm. Based on signals from sensors, the neural network classifies and identifies patients with certain pathological processes.Results. The statistical data set for training the neural network and testing the method included samples from 173 patients. Our study collected exhaled air samples from groups of patients with lung cancer, pneumonia, and COVID-19. In the case of lung cancer, the parameters of the diagnostic device have been determined at the level of sensitivity – 95.24%, specificity – 76.19%. For pneumonia and COVID-19, these parameters were 97.36% and 98.63, respectively.Conclusion. Taking into account the known value of diagnostic methods such as computed tomography (CT) and magnetic resonance imaging (MRI), the sensitivity and specificity indicators of the gas analytical complex achieved during the study reflect the promise of the proposed technique in the diagnosis of tumor processes in patients with lung cancer, COVID-19 and community-acquired pneumonia.

Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies.

Studies investigating neural information processing often implicitly ask both, which processing strategy out of several alternatives is used and how this strategy is implemented in neural dynamics. A prime example are studies on predictive coding. These often ask whether confirmed predictions about inputs or prediction errors between internal predictions and inputs are passed on in a hierarchical neural system-while at the same time looking for the neural correlates of coding for errors and predictions. If we do not know exactly what a neural system predicts at any given moment, this results in a circular analysis-as has been criticized correctly. To circumvent such circular analysis, we propose to express information processing strategies (such as predictive coding) by local information-theoretic quantities, such that they can be estimated directly from neural data. We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy. We define testable hypotheses on the relationship of both quantities, allowing us to identify which of the assumed strategies was used. We demonstrate our approach on spiking data collected from the retinogeniculate synapse of the cat (N = 16). Applying our local information dynamics framework, we are able to show that the synapse codes for predictable rather than surprising input. To support our findings, we estimate quantities applied in the partial information decomposition framework, which allow to differentiate whether the transferred information is primarily bottom-up sensory input or information transferred conditionally on the current state of the synapse. Supporting our local information-theoretic results, we find that the synapse preferentially transfers bottom-up information.

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub.

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possible, and to turn the model into a useful framework for making predictions based on the principles governing the nature of neural cells. In such a context, the access to existing neural models and data outstandingly facilitates the work of computational neuroscientists and fosters its novelty, as the scientific community grows wider and neural models progressively increase in type, size, and number. Nonetheless, even when accessibility is guaranteed, data and models are rarely reused since it is difficult to retrieve, extract and/or understand relevant information and scientists are often required to download and modify individual files, perform neural data analysis, optimize model parameters, and run simulations, on their own and with their own resources. While focusing on the construction of biophysically and morphologically accurate models of hippocampal cells, we have created an online resource, the Build section of the Hippocampus Hub -a scientific portal for research on the hippocampus- that gathers data and models from different online open repositories and allows their collection as the first step of a single cell model building workflow. Interoperability of tools and data is the key feature of the work we are presenting. Through a simple click-and-collect procedure, like filling the shopping cart of an online store, researchers can intuitively select the files of interest (i.e., electrophysiological recordings, neural morphology, and model components), and get started with the construction of a data-driven hippocampal neuron model. Such a workflow importantly includes a model optimization process, which leverages high performance computing resources transparently granted to the users, and a framework for running simulations of the optimized model, both available through the EBRAINS Hodgkin-Huxley Neuron Builder online tool.

Automated system for training and assessing reaching and grasping behaviors in rodents

BackgroundReaching, grasping, and pulling behaviors are studied across species to investigate motor control and problem solving. String pulling is a distinct reaching and grasping behavior that is rapidly learned, requires bimanual coordination, is ethologically grounded, and has been applied across species and disease conditions. New MethodHere we describe the PANDA system (Pulling And Neural Data Analysis), a hardware and software system that integrates a continuous string loop connected to a rotary encoder, feeder, microcontroller, high-speed camera, and analysis software for the assessment and training of reaching, grasping, and pulling behaviors and synchronization with neural data. ResultsWe demonstrate this system in rats implanted with electrodes in motor cortex and hippocampus and show how it can be used to assess relationships between reaching, pulling, and grasping movements and single-unit and local-field activity. Furthermore, we found that automating the shaping procedure significantly improved performance over manual training, with rats pulling > 100 m during a 15-minute session. Comparison with Existing MethodsString-pulling is typically shaped by tying food reward to the string and visually scoring behavior. The system described here automates training, streamlines video assessment with deep learning, and automatically segments reaching movements into distinct reach/pull phases. No system, to our knowledge, exists for the automated shaping and assessment of this behavior. ConclusionsThis system will be of general use to researchers investigating motor control, motivation, sensorimotor integration, and motor disorders such as Parkinson's disease and stroke.

Attractor dynamics reflect decision confidence in macaque prefrontal cortex.

Decisions are made with different degrees of consistency, and this consistency can be linked to the confidence that the best choice has been made. Theoretical work suggests that attractor dynamics in networks can account for choice consistency, but how this is implemented in the brain remains unclear. Here we provide evidence that the energy landscape around attractor basins in population neural activity in the prefrontal cortex reflects choice consistency. We trained two rhesus monkeys to make accept/reject decisions based on pretrained visual cues that signaled reward offers with different magnitudes and delays to reward. Monkeys made consistent decisions for very good and very bad offers, but decisions were less consistent for intermediate offers. Analysis of neural data showed that the attractor basins around patterns of activity reflecting decisions had steeper landscapes for offers that led to consistent decisions. Therefore, we provide neural evidence that energy landscapes predict decision consistency, which reflects decision confidence.

A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke

Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients’ diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction.

Naplib-python: Neural acoustic data processing and analysis tools in python.

Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli, as well as extensive preprocessing, feature extraction, and analysis tools which operate on that data structure. The package removes many of the complications associated with this domain, such as varying trial durations and multi-modal stimuli, and provides a general-purpose analysis framework that interfaces easily with existing toolboxes used in the field.

Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding

Objective. Neural decoding, an important area of neural engineering, helps to link neural activity to behavior. Deep neural networks (DNNs), which are becoming increasingly popular in many application fields of machine learning, show promising performance in neural decoding compared to traditional neural decoding methods. Various neural decoding applications, such as brain computer interface applications, require both high decoding accuracy and real-time decoding speed. Pruning methods are used to produce compact DNN models for faster computational speed. Greedy inter-layer order with Random Selection (GRS) is a recently-designed structured pruning method that derives compact DNN models for calcium-imaging-based neural decoding. Although GRS has advantages in terms of detailed structure analysis and consideration of both learned information and model structure during the pruning process, the method is very computationally intensive, and is not feasible when large-scale DNN models need to be pruned within typical constraints on time and computational resources. Large-scale DNN models arise in neural decoding when large numbers of neurons are involved. In this paper, we build on GRS to develop a new structured pruning algorithm called jump GRS (JGRS) that is designed to efficiently compress large-scale DNN models. Approach. On top of GRS, JGRS implements a ‘jump mechanism’, which bypasses retraining intermediate models when model accuracy is relatively less sensitive to pruning operations. Design of the jump mechanism is motivated by identifying different phases of the structured pruning process, where retraining can be done infrequently in earlier phases without sacrificing accuracy. The jump mechanism helps to significantly speed up execution of the pruning process and greatly enhance its scalability. We compare the pruning performance and speed of JGRS and GRS with extensive experiments in the context of neural decoding. Main results. Our results demonstrate that JGRS provides significantly faster pruning speed compared to GRS, and at the same time, JGRS provides pruned models that are similarly compact as those generated by GRS. Significance. In our experiments, we demonstrate that JGRS achieves on average 9%–20% more compressed models compared to GRS with 2–8 times faster speed (less time required for pruning) across four different initial models on a relevant dataset for neural data analysis.

Predicting a clinically narrow pelvis using neural network data analysis

Aim: to improve the efficiency of predicting a clinically narrow pelvis (СNP) using neural network data analysis and to evaluate its prognostic characteristics.Materials and Мethods. The study was designed as a retrospective non-randomized clinical trial. An analysis of 184 born neonates was carried out: group 1 included 135 female patients whose delivery occurred through the natural birth canal, group 2 – 49 patients whose delivery was complicated by СNP development and ended up with emergency caesarean section. Examination of patients was carried out on the eve of childbirth (1–2 days) and included anamnesis, general and special obstetric examination, including pelvimetry, a clinical assessment of cephalopelvic disproportion was carried out during childbirth. The condition of newborns was assessed using the Apgar scale, height and body weight were measured. Neural network analysis was performed using the built-in Neural Networks module of SPSS Statistics Version 25.0 (IBM, USA).Results. Despite hypothetically important role of anatomically narrowed pelvis in development of cephalopelvic disproportion, no significant inter-group differences were found. Significant parameters (abdominal circumference, uterine fundus height and woman’s weight, fetal head circumference, as well as data on the presence or absence of oligohydramnios and fetal macrosomia) were determined, which were included in the test database to create the basis for training the multilayer perceptron. Out of 135 patients of group 1, the prognosis was negative in 131 (97.0 %), positive in 4 (3.0 %); out of 49 patients in group 2, negative in 0 (0.0 %), positive in 49 (100.0 %). The forecast accuracy of the developed model was 98 % (sensitivity – 100 %, specificity –97 %). The information content of neural network data analysis in СNP predicting is presented in ROC analysis: area under the curve (AUC) = 0.99 (95 % confidence interval = 0.97–1.00). Neonatal anthropometric parameters were significantly higher in group 2 vs. group 1, and the Apgar score at 1 minute was correspondingly lower.Conclusion. The use of neural network analysis of clinical data obtained on the eve of childbirth allows to predict СNP development at sufficient degree of accuracy (98.0 %), which, in the future, after being introduced into clinical practice, will optimize a choice of delivery method in patients at risk (anatomically narrow pelvis, large fetus), reduce emergency caesarean sections and improve birth outcomes.