Particularities of Electrophysiological and Psychoneurological Assessment of Mild Traumatic Brain Injury

Neuropsychological functioning after mTBI is individualized and dynamic, with no currently known predictors and usually having a trajectory of gradual improvement. It is still a challenge to identify specific cognitive profiles associated with mTBI. One of the causes is the transient character of TBI symptoms as they are not appearing immediately after the injury. Another explanation resides in the individual and group variability of cognitive impairements following mTBI, which also affects the standardisation of the neuropsychological tests to use in mTBI assessment batteries (Iverson et al., 2013; Prince & Bruhns, 2017; Tulsky et al., 2017). Presently concussion has no accepted definition or diagnostic criteria. Also, there is no standard (or gold standard) for screening or properly identifying and diagnosing all population with concussion. (Borg et al., 2004). Patients with mTBI could evolve in a bunch of physical, cognitive, and emotional symptoms (Permenter et al., 2021) that are usually known as post-concussion syndrome (PCS). In terms of symptoms, we target neuropsychological evaluation of four key domains (“higher-order attention”, “executive function”, “episodic memory”, and “speed of information processing”) implicated in chronic impairment after mTBI. Alternatively, studies on the EEG frequency domain shed new light on the possibility to have a diagnostic marker based on QEEG patterns identified in the mTBI population and some prognostic factors for the PCS syndrome.(Rapp et al., 2015; Thornton & Carmody, 2009). Given the particularities of neuropsychological functioning after mTBI we emphasize the need of a mixed methodology, using both electrophysiological and psychoneurological tools, to provide the best sensitivity and specificity in assessing cognitive and functional deficits and in predicting further PCS.

Mild respiratory SARS-CoV-2 infection can cause multi-lineage cellular dysregulation and myelin loss in the brain

Survivors of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection frequently experience lingering neurological symptoms, including impairment in attention, concentration, speed of information processing and memory. This long-COVID cognitive syndrome shares many features with the syndrome of cancer therapy-related cognitive impairment (CRCI). Neuroinflammation, particularly microglial reactivity and consequent dysregulation of hippocampal neurogenesis and oligodendrocyte lineage cells, is central to CRCI. We hypothesized that similar cellular mechanisms may contribute to the persistent neurological symptoms associated with even mild SARS-CoV-2 respiratory infection. Here, we explored neuroinflammation caused by mild respiratory SARS-CoV-2 infection, without neuroinvasion, and effects on hippocampal neurogenesis and the oligodendroglial lineage. Using a mouse model of mild respiratory SARS-CoV-2 infection induced by intranasal SARS-CoV-2 delivery, we found white matter-selective microglial reactivity, a pattern observed in CRCI. Human brain tissue from 9 individuals with COVID-19 or SARS-CoV-2 infection exhibits the same pattern of prominent white matter-selective microglial reactivity. In mice, pro-inflammatory CSF cytokines/chemokines were elevated for at least 7-weeks post-infection; among the chemokines demonstrating persistent elevation is CCL11, which is associated with impairments in neurogenesis and cognitive function. Humans experiencing long-COVID with cognitive symptoms (48 subjects) similarly demonstrate elevated CCL11 levels compared to those with long-COVID who lack cognitive symptoms (15 subjects). Impaired hippocampal neurogenesis, decreased oligodendrocytes and myelin loss in subcortical white matter were evident at 1 week, and persisted until at least 7 weeks, following mild respiratory SARS-CoV-2 infection in mice. Taken together, the findings presented here illustrate striking similarities between neuropathophysiology after cancer therapy and after SARS-CoV-2 infection, and elucidate cellular deficits that may contribute to lasting neurological symptoms following even mild SARS-CoV-2 infection.

Gut Microbiome and Plasma Metabolome Signatures in Middle-Aged Mice With Cognitive Dysfunction Induced by Chronic Neuropathic Pain

Patients with chronic neuropathic pain (CNP) often complain about their terrible memory, especially the speed of information processing. Accumulating evidence suggests a possible link between gut microbiota and pain processing as well as cognitive function via the microbiota-gut-brain axis. This study aimed at exploring the fecal microbiome and plasma metabolite profiles in middle-aged spared nerve injury (SNI) mice model with cognitive dysfunction (CD) induced by CNP. The hierarchical cluster analysis of performance in the Morris water maze test was used to classify SNI mice with CD or without CD [i.e., non-CD (NCD)] phenotype. 16S rRNA sequencing revealed a lower diversity of gut bacteria in SNI mice, and the increase of Actinobacteria, Proteus, and Bifidobacterium might contribute to the cognitive impairment in the CNP condition. The plasma metabolome analysis showed that the endocannabinoid (eCB) system, disturbances of lipids, and amino acid metabolism might be the dominant signatures of CD mice. The fecal microbiota transplantation of the Sham (not CD) group improved allodynia and cognitive performance in pseudo-germ-free mice via normalizing the mRNA expression of eCB receptors, such as cn1r, cn2r, and htr1a, reflecting the effects of gut bacteria on metabolic activity. Collectively, the findings of this study suggest that the modulation of gut microbiota and eCB signaling may serve as therapeutic targets for cognitive deficits in patients with CNP.

The Early Motor Repertoire in Preterm Infancy and Cognition in Young Adulthood: Preliminary Findings

Objective: Preterm birth poses a risk to cognition during childhood. The resulting cognitive problems may persist into young adulthood. The early motor repertoire in infancy is predictive of neurocognitive development in childhood. Our present aim was to investigate whether it also predicts neurocognitive status in young adulthood. Method: We conducted an explorative observational follow-up study in 37 young adults born at a gestational age of less than 35 weeks and/or with a birth weight below 1200 g. Between 1992 and 1997, these individuals were videotaped up until 3 months’ corrected age to assess the quality of their early motor repertoire according to Prechtl. The assessment includes general movements, fidgety movements (FMs), and a motor optimality score (MOS). In young adulthood, the following cognitive domains were assessed: memory, speed of information processing, language, attention, and executive function. Results: Participants in whom FMs were absent in infancy obtained lower scores on memory, speed of information processing, and attention than those with normal FMs. Participants with aberrant FMs, that is, absent or abnormal, obtained poorer scores on memory, speed of information processing speed, attention, and executive function compared to peers who had normal FMs. A higher MOS was associated with better executive function. Conclusions: The quality of the early motor repertoire is associated with performance in various cognitive domains in young adulthood. This knowledge may be applied to enable the timely recognition of preterm-born individuals at risk of cognitive dysfunctions.

Application of an artificial neural network for determination of the cylinder-piston group wear level of the automobile-tractor engine

The article proposes reducing the redundancy of the neural network and the need to reduce the number of neurons in the hidden layer for a given level of network learning error. The minimum number of neurons of the hidden layer for the case of 11 monitoring standard sensors, the parameters of the automobile and tractor engine (ATE) and five classes of typical defects of the ATE nodes can be reduced to 5-7 with a high quality of recognition of the state of the ATE engine. The goal is to provide an expanded reliable knowledge base, the speed of information processing, the accuracy of the resulting technical diagnosis and the ability to quickly determine the technical state of an automotive engine in the mode real time. The basis of the proposed method is to ensure obtaining an extended reliable knowledge base, the speed of information processing, the accuracy of the obtained technical diagnosis and the ability to quickly determine the technical state of an ATE engine in real time. A feature of the proposed method is the use of voltages obtained in an artificial neural network from sensors that are standard in an ATE engine as input signals, and additionally indicate the output signal of the fuel cut-off device, provided for one step, containing a winding of a normally closed electromagnetic valve, which redirects fuel to the drain line. The use of the algorithm for identifying the values of the indicators of operating modes and malfunctions of the cylinder-piston group is the result of the analysis of an artificial neural network, which receive the results of the diagnostic parameters of the automotive engine. Having studied the artificial neural network 1 with different volumes of training data, we obtained the dependence of the change in the reliability of the result on the size of the training data and the reliability of the recognition result is 91.2%, the optimal amount of training data is 1200. Having examined the artificial neural network 2 with different volumes of training data, we obtained the dependence of the change in the reliability the result from the size of the training data and the reliability of the recognition result is 86.5%, the optimal amount of training data is from 10 to 15. The results obtained show the fundamental possibility of creating predictive models of units and assemblies of the tested automotive engines. The model can be created using the apparatus of artificial neural networks and using a fairly large database of tests performed.

Relationships Between University Students’ Gender and Physical Activity (PA) and Their Exercise Self-Schemas, Processing of PA-Related Information, and PA Decision-Making

In the current study, we investigated the effects of gender and regular physical activity (PA) on PA decision-making and speed of information processing. We enrolled 110 university students ( Mage = 20.91, SD =2.28 years) in an experiment involving two tasks and a questionnaire. One of the two tasks assessed how much participants agreed with presented PA words and phrases and the other task predicted behavior and responses to future situations. We collected and measured the participants’ choices and the time they took to make them. The questionnaire, the International Physical Activity Questionnaire (IPAQ), consisted of exercise self-schema and PA questions. We conducted a 2 (gender: male or female) ×2 (regular PA or not) multivariate analysis of variance (MANOVA) and found statistically significant differences between variables as a function of participants’ gender (λ = .66, p < .001) and regular PA engagement (λ = .51, p < .001). In a regression analysis, we also found gender differences [males showed relationships between agreement with PA information and information processing speed for decisions on future behavior ( R 2 = .31, F = 12.50); females showed relationships between their exercise self-schema ( R 2 = .26, F = 18.18) and regular PA such that, in the non-regular PA group, exercise self-schema was related to reaction time in making decisions on future behavior ( R 2 = .29, F = 11.23), and in the regular PA group, agreement with PA information was related to reaction time for PA-related words, and agreement with non-PA information ( R 2 = .29, F = 8.91)]. These results highlight the need to consider participant characteristics when designing exercise interventions, and we present supplementary data regarding exercise self-schemas, decision-making, and the speed of processing PA information.

MODELING THE DEVELOPMENT OF ECONOMIC SYSTEMS TAKING INTO ACCOUNT CLIMATIC CHANGE

The strategic directions of Russia's economic development presuppose the existence of state programs, the transition from the expert-raw material type to the innovative type. A qualitatively new level of information processing opens up new development opportunities as a result of an increase in the speed of information processing throughout the entire life cycle of agricultural production. Sustainable development is measured by production on the territory and its efficiency in relation to the country's food security. Since the territories are not only producers of products, but also a complex socio-economic structure that functions according to certain laws, rules and traditions, a kind of habitat for residents. To a large extent, the sustainable development of territories depends on the level of development of production, implements the sustainable development of the economy. The state regulates the sustainable development of territories in order to support production as the main element of processes, controlling its development, supporting domestic producers, and performs one of the most important state functions, which is a macroeconomic problem of research of the territorial complex. Consequently, there is a dialectical relationship between the process of economic stabilization and the sustainable development of territories, which requires the development of research approaches. The search for new approaches to the development of territories is a particularly important circumstance at the present stage. A full-fledged system of economic, mathematical, structural modeling allows us to obtain predictive estimates in the context of food security indicators, based on current trends, global challenges and changes.

Speed of Information Processing and Working Memory in Children with Cochlear Implants

Background: Cochlear implants (CIs) restore partial hearing to deaf children, promoting the development of spoken language skills. However, because of reduced auditory and language experience, children who receive CIs are at risk for delays not only in language skills but also in language-related neurocognitive skills such as verbal working memory (VWM - the ability to retain language information in immediate memory concurrently with other cognitive processing). Although VWM delays in children with CIs are well-documented, the foundational processes underlying these delays are less clear. This study investigated the hypotheses that slower speed of information processing during VWM tasks contributes to VWM delays in CI users and that this slower information processing speed is associated with spoken language outcomes. Methods: 25 early-implanted, prelingually-deaf children with CIs and 25 normal-hearing (NH) peers completed tests of VWM, neurocognitive, and speech-language functioning. Speed of information processing during the VWM test was assessed by measuring response latency and average pause duration. Results: Children with CIs showed poorer VWM scores than NH peers, but the groups did not differ on response latencies or pause durations. Response latencies were significantly correlated with VWM capacity, speech, and language outcomes in both groups. Conclusion: Speed of information processing in VWM was similar for children with CIs and NH. In both groups, shorter response latencies (faster speed of execution of the cognitive operations of working memory) were associated with better neurocognitive and spoken language outcomes. In the CI sample, pause durations were inconsistently associated with VWM and language outcomes. Clinical Policy Impact and Implications: Speed of information processing for VWM is associated with core neurocognitive and spoken language outcomes for children with CIs and should be a routine target of assessment and intervention post-implantation.

Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task

Background: The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed. Methods: Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced. Results: The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes. Conclusion: The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.

The impact of pandemic-related worry on cognitive functioning and risk-taking

Here, we sought to quantify the effects of experienced fear and worry, engendered by the COVID-19 pandemic, on both cognitive abilities—speed of information processing, task-set shifting, and proactive control—as well as economic risk-taking. Leveraging a repeated-measures cross-sectional design, we examined the performance of 1517 participants, collected during the early phase of the pandemic in the US (April–June 2020), finding that self-reported pandemic-related worry predicted deficits in information processing speed and maintenance of goal-related contextual information. In a classic economic risk-taking task, we observed that worried individuals’ choices were more sensitive to the described outcome probabilities of risky actions. Overall, these results elucidate the cognitive consequences of a large-scale, unpredictable, and uncontrollable stressor, which may in turn play an important role in individuals’ understanding of, and adherence to safety directives both in the current crisis and future public health emergencies.