This study addresses the problem of automatic binary classification of experimental participants diagnosed with schizophrenia and a control group using a dataset obtained using a Siemens Magnetom Verio 3T tomograph. The dataset included data from 36 experimental participants undergoing treatment at Clinical Hospital No. 1, Moscow Health Department, and 36 experimental participants from the control group. Machine learning methods were used to address the study task. A separation accuracy of 76

Propionic acid bacteria play an important role in the formation of a pattern on cheese produced using them. In recent years, cheese producers have actively used the rindless technology for its ripening, which has made it difficult to obtain a pre-defined cheese pattern. The influence of the type of packaging material, the ripening temperature in the main chamber, and the salting technology on the process of pattern formation in semi-hard cheese with the involvement of propionic acid bacteria has been studied. The pattern in cheese was analyzed using the non-destructive method of magnetic resonance imaging. Our studies have shown that, the pattern formation during the production of semi-hard cheeses using propionic acid bacteria weighing 5–7 kg and a mass moisture fraction of 44–45

BACKGROUND: Standard magnetic resonance imaging sequences only provide qualitative image assessment, which is rather subjective. However, some quantitative techniques can interpret findings more objectively and expand diagnostic capabilities. Previously, they were mainly used for brain and joint scans; however, current technology allows using them for evaluating peripheral nerve function. AIM: This study aimed to evaluate quantitative parameters of magnetic resonance imaging of the brachial plexus elements in healthy adults, depending on the side and level of spinal nerves and demographic and anthropometric characteristics. METHODS: Ten healthy volunteers were included. Their main demographic and anthropometric characteristics were recorded before they underwent magnetic resonance imaging. A 3T magnetic resonance imaging scanner was used. In addition to standard sequences, the scan protocol included regimens for obtaining T2 relaxation times and magnetization transfer ratios from nerve elements of the brachial plexus. Data were post-processed using the MATLAB software package. Then, regions of interest were manually assigned to in the maps, and numerical values were obtained. Furthermore, thickness of the nerve elements was measured. Data were statistically processed using the SPSS software. RESULTS: In each participant, the numerical values of the quantitative magnetic resonance imaging parameters (measured T2 relaxation time, proton density, magnetization transfer ratio, and thickness) in the anterior rami of the spinal nerves that form the brachial plexus were obtained. The thickness gradient of the normal anterior rami was revealed, with the highest value occurring at the level of the anterior rami of cervical spinal nerve C7. Significant positive correlations between T2 relaxation time and age were determined by analysis of the associations between quantitative magnetic resonance imaging parameters and demographic and anthropometric characteristics. In addition, negative correlations were found between height and measured T2 relaxation time and proton density. CONCLUSION: The study results indicate that future research on T2 relaxation parameters should consider age and height in both healthy volunteers and patients with a brachial plexus condition. Additionally, when measuring the thickness of the anterior rami of the brachial plexus using standard sequences, the size and thickness gradient of the nerve elements should be considered.

Over the past decade magnetic resonance imaging is being increasingly used in revealing pathological changes in peripheral nervous system due to a number of technical innovations and growth of diagnostical strength, and, therefore, due to initiation of research of several magnetic resonance imaging methods which allow to perform quantitative assessment of peripheral nerves. Among them, diffusion tensor magnetic resonance imaging which gives an opportunity to investigate microstructural changes in nerves tissue by water diffusion evaluation should be mentioned first. T2‑relaxometry and magnetization transfer ratio studies allow assessing macromolecular integrity of peripheral nerves elements. Chemical shift‑based fat fraction evaluation in peripheral nerves and corresponding muscles is also of great scientific interest both for diagnostic and therapy effect monitoring purposes. Manuscript presents brief description of above‑ mentioned methods, as well as recent results and perspectives of their application for peripheral nerves evaluation, supplemented with own illustrations of experimental observations.

In this work, we investigate functionally homogeneous regions segmentation method (FHR) to obtain features for binary classification of patients with schizophrenia and healthy controls using support vector machine classifier (SVM) based on resting-state functional magnetic resonance imaging (rs-fMRI) data. For comparison, we used 4 feature-type approaches: functional connectivity maps (FCM), Amplitude of low frequency fluctuations (ALFF) and fractional amplitude of low frequency fluctuations (fALFF), Regional Homogeneity (ReHo). Four different feature selection algorithms were used (χ2, F_test, L1 and L2). SVM classifier was trained and tested on a rs-fMRI dataset of 36 patients with schizophrenia and 36 healthy controls, obtained using Siemens Magnetom Verio MRI 3TL scanner. The best results were achieved by features obtained by the ReHo approach (93

Background: Breakthrough neurotechnologies have allowed for new understanding of some brain disorders; however, identification and differential diagnosis of intracranial stenotic and occlusive lesions remains challenging. Magnetic resonance imaging (MRI) with dynamic contrast enhancement (DCE) is a tool that could be used for the quantitative assessment of endothelial permeability and microvascular volume in atherosclerotic plaques (AP). Aim: To assess quantitative parameters of vascular wall abnormalities in AP area and in obviously unchanged wall of intracranial arteries with MRI DCE and high spatial resolution Т1-weighed images before and after contrast injection, with calculation of the wall enhancement index (WEI) by mathematical modelling. Methods: This was a pilot cross-sectional uncontrolled study with consecutive recruitment of 29 patients with atherosclerotic abnormalities of brachiocephalic arteries, including intracranial. The patients’ median age was 66 [57; 72] years; they were mostly men (75.9%, n = 22). For the assessment of any brain abnormalities, MRI (magnetic induction 3 Tesla, Magnetom Prisma, Siemens) was performed in patients with standard sequence (Т2, T2-FLAIR), as well as MRI DCE for the assessment of intracranial arteries, before and after intravenous contrast injection, with high spatial resolution T1-weighed imaging and suppression of the signal from bloodstream and fat, with the calculation of WEI. Results: There were significant differences in WEI in AP and in unchanged wall (0.962 [0.686; 1.387] vs. 0.111 [0.014; 0.206], p 0.001). No significant differences were found between WEI values in internal carotid arteries APs (0.722 [0.573; 1.580]), middle cerebral arteries (0.921 [0.725; 1.183]), and basilar artery (1.343 [1.002; 1.419]) (p = 0.381). We also found significant difference (p = 0.034) in the extravascular extracellular fraction volumes ve (Tofts) in AP located in the basilar artery (0.171 [0.146; 0.325]), internal carotid arteries (0.579 [0.358; 1.000]), and middle cerebral arteries (0.134 [0.101; 0.269]). Conclusion: This is the first description of quantitative parameters characterizing vascular wall abnormalities in intracranial atherosclerosis. Despite its obviously intact state, vascular walls outside the intracranial AP was shown to be abnormal as well.

This study describes the detection of anatomical changes in brain regions based on morphometric measures and white matter tracts in patients diagnosed with schizophrenia (F20.0 according to ICD-10) compared to a health control group. All data were checked for normality, and significant differences between the health control group and schizophrenia patients divided into groups based on symptomatic severity were tested for the investigated parameters. A correlation analysis was also performed between connectivity strength of brain regions and morphometric parameters. Based on these findings, three tracts were identified as possible biomarkers for this disorder: Paracentral lobule left—Paracentral lobule right; Supplementary motor area left—Anterior cingulate left; Thalamus left—Inferior temporal left.

This work presents the results of studying the evaluation of fMRI data at the group level. Some factors influencing the formation of such estimates are shown. On the basis of experimental data obtained in forensic tests in the information concealment paradigm, attention is drawn to the need for a critical assessment of the results and methodology (design) of research, based on the results of which the outcome of the analysis of fMRI data at the group level are presented.

In this work we solve the problem of automatic binary classification of subjects with a diagnosis of schizophrenia and control groups on a data set obtained on a Siemens 3T tomograph. The data set included 36 subjects undergoing treatment at Psychiatric Hospital no. 1 Named after N.A. Alexeev of the Department of Health of Moscow (GBUZ PKB No. 1 DZM) and 36 subjects from the control group. Machine learning methods were used to solve this problem. As a result, an accuracy of 76% was achieved, which corresponds to the results obtained in other scientific studies. The highest accuracy was obtained for the local homogeneity parameter (regional homogeneity - ReHo), already known in the literature. At the same time, the set of features developed by the authors based on the method for identifying functionally homogeneous regions (FHR) gave a classification accuracy of 74%. But at the same time, the set of FHR features provides higher classification accuracy when using a small number of brain regions. For example, already in 8 regions, the FHR set provided an almost maximum classification accuracy of 72.5% (versus 65% for the ReHo set), which suggests that it is the selected 8 regions that give the highest level of separation.

The paper describes application of the co-activation patterns analysis (CAPA) method for analyzing resting-state fMRI data obtained in order to detect stable substates. The research involved 25 healthy volunteers. The analysis revealed that inside a resting-state we could distinguish 8 alternating stable substates. Their average duration was estimated at about 20–25 s.

This paper is devoted to the analysis of the temporal stability of the independent components obtained by analyzing data of resting state functional Magnetic Resonance Imaging (fMRI). We analyzed 25 datasets of healthy volunteers, consisting of 1000 time samples each. The fMRI data recording time was 33.3 min for each volunteer. This approach made it possible to divide the experimental session into several time ranges to assess the temporal stability of the results obtained with the independent component analysis (ICA). During the analysis, the property of additivity of independent components was discovered: the dynamics of the independent components obtained in the analysis of individual time ranges have a high level of Pearson correlation (at least 0.9) with the dynamics of the independent components obtained in the analysis of the full experimental session, i.e., the result of ICA is robust to the choice of window size when analyzing a representative data sample. It was also shown that the time series of independent components, which topology corresponds to resting state networks, have a correlation with the global signal at the level of 0.4–0.5.

Background: catatonia is the focus of neurophysiological research as a syndrome with unspecified pathogenesis. Modern neuroimaging techniques contribute to the understanding of the pathophysiological mechanisms of this disorder. The aim was to conduct a systematic review of the scientific literature to confirm that catatonia is associated with structural and functional changes in the brain. The analysis made up researches using diffusion MRI for judgement on indirect measure of changes in white or gray matter density using a fractional anisotropy (FA) and resting state functional MRI for assessment a measure of connectivity. Materials and methods: PubMed, ScienceDirect and Mendeley databases were searched using the search terms (and their derivatives) for: “catatonia”, “resting state functional magnetic resonance imaging” and “catatonia”, “diffusion weighted magnetic resonance imaging”. The search yielded 147 publications for preliminary screening, of which 96 were on fMRI of catatonia and 51 on dMRI. During the screening stage, duplicates and articles that could not be accessed were removed. This left 21 fMRI articles and 18 dMRI articles. After which the articles were checked for compliance with the inclusion criteria: 1) original full-text articles; 2) identification of catatonia not caused by a somatic disease and verified using the Bush–Francis and/or Northoff psychometric scales; 3) age of the examined 18 years and over. 3 fMRI and 3 dMRI articles were included. Conclusions: aberrations of FA indicators were found in catatonia, which may be associated with the density of the white matter of the brain. Changes in connectivity in the somatosensory network have been identified, which allows to consider these disorders as potential markers of catatonia. To confirm the hypothesis and results obtained, further research is required due to the small number of publications on this topic.

The article explores the dependence of the accuracy of the binary classification of subjects based on the presence of schizophrenia pathology using fMRI data on two parameters: scanning duration and spatial smoothing. The data set was acquired on a Siemens Magnetom Verio 3 T MRI scanner and included 36 subjects undergoing treatment at the State budgetary institution of health care “Psychiatric Hospital no. 1 Named after N.A. Alexeev” as well as 36 subjects from the control group. The total scanning duration for each subject was 900 time points (648 s) with a resolution of 2 × 2 × 2 mm3. To simulate different scan durations, the data was divided into 3 sets of 300 time points, each of which was independently used for classification. To analyze the effect of spatial smoothing, Gaussian blur with convolution kernels of 4, 6, 8, 10, 12 mm was used. For classification 38 machine learning methods from the scikit-learn software library were used. To generate sets of feature vectors for classification, well-known regional homogeneity (ReHo) methods and correlation matrices calculated using the structural and functional atlas of the CONN software package were used.

When analyzing physiological signals, the problem of setting data processing parameters arises due to the blurring of the boundary between signal and noise properties, as well as the fundamental lack of objective criteria for the quality of data processing in psychophysiology. This paper describes an approach to optimiz-ing processing parameters on the example of galvanic skin response (GSR) and photoplethysmogram (PPG), based on the use of stimuli that are significant for a person, selected on the basis of biographical data, which can be considered as criteria validation. As a metric for the optimization, we used the frequency of coincidence of the stimuli identified as a result of the analysis with the a priori given ones (human names, including the name of the volunteer, and also visit cards selected by the volunteer). GSR and PPG signals were recorded using an MRI-compatible polygraph under conditions of functional magnetic resonance imaging (N=46 volunteers). In the first part of the work, optimization of frequency filters and analysis intervals (epochs) was performed. It has been established that the following processing parameters are optimal for analyzing the amplitude properties of the GSR signal: first-order Butterworth filters, frequency range is 0.025-0.25 Hz, interval of analysisis 1-7 s from a stimulus. To analyze the PPG signal using the length of the curve, the following processing parameters are optimal: second-order Butterworth filters, frequency range is 1.25-12.5 Hz, interval of analysis is 3-10 s from a stimulus. Using the same criterion, several alternative signal processing methods were tested: change in the amplitude of the GSR signal over the analysis interval compared to the classical method by the amplitude maximum relative to the baseline; several types of ranking of reactions within a block of stimuli compared to simple averaging of all responses. The parameters and methods of processing of the GSR and PPG signals obtained in the work demonstrate universality in relation to the variety of initial data and could be applicable in applied and fundamental research. The general approach described in the work can also be used to optimize the processing parameters of other physiological signals including fMRI.

Impact of the variation of the parameters of the RF pulse sequence scenario of an MR tomograph is studied using a 3D model of the human brain. The model developed is based on mathematical processing of the output experimental data of nuclear magnetic resonance imaging. An algorithm for comparative analysis of 3D models obtained for the same tested person using different modes of the MR tomograph operation has been developed. The software implementation of the algorithm has been implemented as a service of the “Neuroimaging” system of the “Digital Laboratory” of the National Research Center “Kurchatov Institute”.

The paper deals with the problem of differentiation of human speech and language systems. Based on the modern ideas in the field of speech psychology, speech study, intonology, the concept of units (forms) of speech as non-linguistic phenomena is formed. These phenomena function as translators of illocutionary meanings in human communication, which mostly are communicative and social goals, as well as the quality of social relations. To support the concept of “Speech vs. Language”, the results of an fMRI study conducted on a sample collection of adults are presented. The differences between the neural networks of the human brain that provide the perception of natural forms of oral speech having the same pragmasemantic content – the actual speech and the minimal structure of speech-language signals are established. Due to the data obtained, the prospects of research related to the analysis of functional connectivity in the structures of two types of networks, as well as with the sequential complication of the speech and language components of the statement and the presentation of multimodal multilevel natural speech-communicative stimuli are discussed. The analysis of the differences between the processes and neural networks of speech and language is important for the development of speech synthesis technologies, diagnostic and communicative artificial intelligence.

— This paper explores the problem of differentiation of human speech and language systems. On the basis of contemporary perspectives in speech psychology, speech studies, and intonology, the concept of speech units (forms) as non-linguistic phenomena is formed. In human communication, the units of speech have the function of translating the illocutionary meanings, i.e., first of all, communicative and social goals, as well as the quality of social relations. In support of the “Speech vs Language” concept, the results from functional magnetic resonance imaging (fMRI) conducted on a sample of adults are presented. The differences in neural networks of the human brain, which provide the perception of natural oral speech forms having the same pragma-semantic content, i.e., pure speech signal and the minimal structure of the speech-language signal, were demonstrated. In light of the data obtained, the directions for future studies associated with the analysis of functional connectivity in the structures of the two types of networks, gradual complication of speech and language components of the utterance, and the presentation of multimodal natural speech-communicative stimuli are discussed. Analysis of the differences in the speech and language processes and neural networks is important for the development of speech synthesis technologies, as well as diagnostic and communicative artificial intelligence.