Introduction: We previously reported sex differences in the developmental trajectories of contextual learning function and found multiple critical periods for hippocampal function in male rats. Here, we focus on training-induced hippocampal CA1 synaptic plasticity in early childhood to analyze sex differences. Methods: Male (n = 27) and female (n = 18) rats at 16-17 days of postnatal age were subjected to a hippocampal-dependent inhibitory avoidance (IA) task. CA1 neurons received inputs from CA3 and the entorhinal cortex (EC) via different synaptic pathways. Brain slices were then analyzed to assess CA1 synaptic plasticity, focusing on changes in the ratio of AMPA receptor- / NMDA receptor-mediated postsynaptic currents, and single-vesicle-induced miniature excitatory and inhibitory postsynaptic currents (mEPSCs and mIPSCs). Behavioral battery tests evaluated sensory, motor, and emotional functions. Results: IA learning was established in females (P < 0.01) but not in males, indicating sex differences in contextual learning ability without changes in basic sensory/motor functions. In the emotional state, females showed more sociability with others than males (P < 0.05). Frequency of mEPSCs and mIPSCs decreased in males after learning (P < 0.01), whereas mIPSCs frequency increased in females (P < 0.05). Furthermore, AMPA/NMDA ratios increased in the CA3-CA1 and ECIII-CA1 pathways after learning, suggesting a predominance of AMPA receptor-mediated synaptic plasticity after learning (P < 0.01). Unpaired t-tests were used to analyze the results. Conclusion: Female infants showed faster development of hippocampal learning and induced plasticity than male infants, indicating a clear sex difference. These findings provide synaptic evidence for sex-specific development of contextual learning and training-induced plasticity at CA1 synapses.

Introduction: Acquired brain injury (ABI) can significantly impair executive functions (EFs), which are essential for controlling and regulating actions. This impairment makes everyday tasks, such as grocery shopping, particularly challenging for individuals with ABI. This study aims to conduct a pilot investigation to analyze the shopping strategies of control individuals without ABI. The objective is to adapt this ecological assessment method for individuals with ABI and design an EF training program.

Method: For the pilot study, 16 subjects were recruited through word-of-mouth and social networks. The sample included men and women aged 18 to 65 years without brain damage or neurological pathologies. Two tools were implemented to register participant performance. A supermarket plan was uploaded to the Samsung photo editor, where participants' routes were mapped, providing graphical information about their performance. A list of products was created on Notion to record which products were collected and when, facilitating detailed analysis of participant performance during the shopping task.

Results: The mean and standard deviation of section entries (M=1.42, SD=0.5), products acquired (M=12.9, SD=3), and total shopping time (M=16.11, SD=4) were obtained. The use of these tools enabled precise tracking of participant actions, confirming the feasibility of the method.

Conclusions: The method of ecologically evaluating EF and shopping strategies, supported by these tools, is feasible and provides valid information for cognitive skills training in ABI individuals. This pilot study offers promising results for future interventions.

Subject of study: We focused on images of transverse sections of the mammalian spinal cord belonging to its various segments. Aim of study: We selected optimal methods for assessing the similarity of images of transverse sections of the spinal cord obtained earlier in different histological studies. Method: We assessed the similarity of images of transverse sections of the spinal cord with the help of (a) Jaccard index (the size of the contour's intersection divided by the size of the contour's union), (b) metrics of distance between gray matter contours, (c) correlations between gray matter contours, and (d) the difference between gray matter contours based on Hu moments (a set of seven numbers calculated using central moments that are invariant to image transformations). Main results: Jaccard index and metrics of distances between contours allowed us to successfully determine the degree of similarity of sections obtained from the same animal. Hu invariant moments are suitable for the successful recognition of images of spinal cord segments obtained from various sources. Practical significance: The results suggest that the automated identification of spinal cord segments can be based on a comparison of histological or tomographic images of transverse sections of the spinal cord with some databases containing a set of reference images of particular segments. Such comparisons may be performed with the help of the contour similarity methods based on the Hu invariant moments. This work was supported by the Russian Science Foundation, project №21-15-00235.

Terbium halides are interesting compounds that are incorporated inside single-walled carbon nanotubes (SWCNTs). Bioimaging with terbium halide-filled SWCNTs is a very promising field. For applications in bioimaging, it is important to investigate the filling ratio and the yield of the preparation processes of the nanocomposites. The synthesis of filled SWCNTs is a chemical process involving melted substances. The melted terbium chloride (TbCl₃), terbium bromide (TbBr₃), and terbium iodide (TbI₃) are introduced into the SWCNTs in a high-temperature process. This results in the preparation of the compound-containing nanocomposites. The chemical properties of the filled SWCNTs are investigated with transmission electron microscopy (TEM) and spectroscopy. The TEM shows very interesting incorporated compounds. The p-doping of SWCNTs is investigated in the filled SWCNTs. The interesting properties that accompany these nanocomposites may find applications in nanobiotechnology and bioimaging. In this contribution, we synthesize the terbium halide-filled SWCNTs with melted compounds. We incorporate the substances into the SWCNTs in the high-yield process. The filling ratios of the SWCNTs are very large, as revealed with the TEM. The interesting microstructures of the compounds are found in the TEM images. We demonstrate this with micrographs of the filled SWCNTs. In the terbium halide-filled SWCNTs, the p-doping of SWCNTs is revealed with spectroscopy as modifications of Raman modes. Shifts and intensity variations are observed with the spectra. Therefore, the interesting rare-earth compounds inside SWCNTs form a perspective platform for nanobiotechology, and the bioimaging of cells, tissues, and organs.

The effect of aluminium (Al) exposure and silicon (Si) intake on the levels of chromium (Cr) and vanadium (V) in mouse brains was studied. Six-week-old male NMRI mice were divided into four groups. Three groups received Al(NO₃)₃ at a dose of 450 μg/ml for three months; meanwhile, the fourth group only received deionised water. The first group received aluminium nitrate (Al group); the second group aluminium nitrate and silicic acid (50 mg/ml); and the third group aluminium nitrate and commercial beer. Metals were monitored by ICP-OES in the right hemibrain, faeces, urine and blood. V was only detected in the faecal samples, being significantly higher in the Al group (4.132 vs. 3.383, 3.100 and 3.315; groups 4, 2 and 3, respectively; all in μg/g; p-value=0.038). Conversely, lower and significantly lower levels of Cr were detected in the faeces (2.867 vs. 3.155, 2.270 and 2.550 μg/g; p-value=0.296) and blood (0.187 vs. 0.158, 0.197 and 0.211 μg/l; p-value=0.013) in the Al group, respectively, as well as in the urine (0.00047 vs. 0.00069, 0.00060, 0.00065 μg/μmol creatinine; p-value=0.311), suggesting a potential effect of Al intoxication on the metabolism of Cr. These unknown effects might explain the lower levels of Cr that were detected in the intoxicated animals’ brains (0.346 μg/g). Thus, the intoxicated animals that were provided with Si showed Cr brain levels slightly higher than those in the Al group (0.360 and 0.352 vs. 0.346 μg/g; p-value=0.552). The consumption of beer/silicic acid appears to partially block the negative effects of aluminium ingestion on the normal metabolism of chromium.

Title: Advancements in Neuroimaging Techniques: A Window into the Brain's Complexity

Authors: [Your Name], [Your Affiliation]

Session: Neurotechnology and Neuroimaging (S3)

Keywords: magnetic resonance imaging; positron emission tomography; neurodegeneration; nanotechnology; optical coherence tomography

Abstract:

Introduction: Neurological disorders present a significant healthcare challenge globally, necessitating continuous advancements in diagnostic and therapeutic approaches. Neuroimaging techniques have emerged as indispensable tools for unraveling the complexities of the brain and understanding the pathophysiology of various neurological conditions.

Methods: This research conducted a systematic review of recent literature studies to explore the latest advancements in neuroimaging techniques and their applications in neurological disorders. Key methodologies included in the review were magnetic resonance imaging (MRI), positron emission tomography (PET), nanotechnology, and optical coherence tomography (OCT).

Results: Our analysis revealed significant progress in neuroimaging technologies, particularly in MRI and PET, enabling earlier and more accurate diagnoses of neurological disorders. Nanotechnology has facilitated targeted drug delivery systems, offering promising therapeutic interventions for neurodegenerative diseases. Additionally, OCT has provided insights into neurodegeneration mechanisms, aiding in the development of novel diagnostic and monitoring strategies.

Conclusions: The findings underscore the critical role of neuroimaging techniques in advancing our understanding of neurological disorders. These advancements hold immense potential for improving patient care through early detection, precise diagnosis, and targeted interventions. Continued research and innovation in neuroimaging are essential for addressing the complex challenges posed by neurological diseases and enhancing clinical outcomes.

Understanding the intricate and dynamic nature of brain disorders, such as epilepsy, Parkinson's disease, and schizophrenia, presents a formidable challenge due to their inherent chaotic properties, which defy conventional analytical approaches. In response to this challenge, our research introduces a groundbreaking methodology aimed at simulating the chaotic behavior characteristic of these neurological conditions using advanced electrical circuit models. By conceptualizing the interactions among neurons and synapses as electrical components within our model, we endeavor to unravel the complex underlying mechanisms driving these disorders. Leveraging insights from chaos theory and drawing upon the rich toolkit of electrical engineering, our simulation framework offers a novel perspective on the ways in which disruptions within neural circuits manifest as pathological states, shedding light on the intricate dynamics of brain diseases. Through rigorous numerical simulations and thorough analysis, we illustrate the efficacy of our approach in deciphering the chaotic dynamics inherent in these disorders, thus laying the foundation for the development of innovative therapeutic interventions. Furthermore, our research underscores the paramount importance of fostering interdisciplinary collaboration between the fields of neuroscience and electrical engineering; as such, synergistic partnerships hold the key to unlocking new frontiers in understanding and effectively treating complex neurological disorders, thus paving the way for improved patient outcomes and enhanced quality of life.

Introduction

The plotting of neuron-coordinated ‘economic’ choice direction has witnessed remarkable progress since the turn of the century. Gaps in the understanding of neuron-centered substitutions introduce a behavioral examination of business ‘actors’, ‘representations’ and ‘maxims’ that are at the epicenter of the ‘perceptive mosaic’, with a focus on the neurotrajectory. The field of cognito-administration examines this via ‘cognito - strategic monikers’ (CTMs) to test how the brain (‘Cognitive Miser’) performs in higher cognitive capacities.

Hypothesis

Studies generally overlook the impact of aAmygdala function on entrepreneurs' well-being and economic decision-making quality.

Aim

This paper explores the amygdala's influence beyond its well-known emotional processing and stress response. The size of the population is 50.

Methods

We utilize a combination of stress tests and controlled lab studies (the electrocardiogram stress test, stress echocardiogram, echocardiogram stress test) with cardio tracking to explore the ‘tripod’ concept. Results suggest a link between high amygdala activity, stress, and negative outcomes like mental health issues, communication breakdowns, and hindered economic decision-making.

Results

Results indicate that the amygdala plays the role of a ‘tripod’ in economic decision-making and high levels of stress, and that amygdala activation contributes to mental health disorders, interpersonal conflicts and communication breakdowns, hindering economic decision-making.

Conclusions

The expanding paradigm of the brain's wiring graph (‘Cognitive Miser’) calls for establishing a probable cause–consequence’ linkage proof between neurobiology and business decisions. Researchers ought to recognize ‘drivers’ (the frontal cortex, orbito-frontal cortex, front cingulate cortex and ventro-medial prefrontal cortex) that make an ‘economic’ choice mosaic’. Unaddressed issues show how ‘economic’ decisional progressions negate brain hallways (‘Cognitive Miser’) and how the brain considers information to make an ‘economic’ choice.

Alzheimer’s disease (AD) is a rising pandemic that is estimated to affect up to 139 million people in 2050 along with other dementias. The failure of using mono-target therapy against Alzheimer’s has shifted the focus towards multi-target approaches. Phytocompounds have complex chemical structures that enable them to target multiple pathways of disease. Our previous studies and studies from several other groups have emphasized the use of individual phytocompounds like Quercetin, Betanin, and Betaine for the treatment and management of AD. So, we hypothesized about a combinatorial therapy approach by making dual combinations of Quercetin, Betanin, and Betaine. We tested the antioxidant, anti-inflammatory, and anti-acetylcholinesterase potential of the individual compounds, as well as dual combinations, by performing in vitro assays. We then performed an enzyme (AChE) kinetics analysis to identify the mechanism of inhibition employed by each compound against AChE. From the results obtained, we calculated the drug–drug interaction-like synergy between these drug combinations by using mathematical models of their synergy. In this way, we were able to find combinations with higher antioxidant, anti-inflammatory, and anti-AChE activity, even when the dose was reduced. We then performed network pharmacology-based identification of multiple gene and pathway targets for our compounds in humans against AD. We conclude that these combinations have excellent multi-target potential and should be further tested in AD models to better understand their effects on AD pathology.

Electroencephalographic (EEG) techniques are widely used in cognitive science, neuroscience, psychophysiology, and brain–computer Interface (BCI) research due to their non-invasive nature, portability, and high temporal resolution. However, EEG signals often suffer from contamination by non-brain electrical activities such as those from eye movements (EOG), muscles (EMG), and the heart (ECG), necessitating preprocessing to maintain a high signal-to-noise ratio (SNR) for accurate analysis. This research evaluates techniques for mitigating artifacts from oculomotor activities, particularly saccades, which are more challenging to remove than eye blinks. The primary methods for correcting these artifacts are regression-based techniques and Independent Component Analysis (ICA). Regression methods like the Gratton algorithm use EOG channels but can introduce contamination, while ICA methods such as AMICA require substantial computational resources and the careful selection of EEG channels. Moreover, recent advancements in algorithms have focused on identifying and correcting ocular artifacts in out-of-lab applications, using data from a low number of channels. Notably, EEGANet, based on Generative Adversarial Networks (GANs), stands out as a promising approach. It requires an initial training and optimization process using EOG channels. EEGANet’s performance was compared to Gratton, AMICA, SGEYESUB, REBLINCA, and MWF using publicly available datasets, with evaluation metrics including Pearson's correlation, mutual information, and frequency correlation. The results revealed that EEGANet showed a superior correction performance over frontal EEG channels, effectively identifying and correcting both horizontal and vertical eye saccade artifacts. It preserved the EEG signal's spectral characteristics across theta, alpha, and beta frequency bands, indicating minimal impact on the signal's neurophysiological content.