Basic and Clinical Neuroscience
Volume:14 Issue: 5, Sep-Oct 2023

COVID-19 (coronavirus disease 2019) was first identified in China in December 2019 and is rapidly spreading worldwide as a pandemic. Since COVID-19 causes mild to severe acute respiratory syndrome, most studies in this context have focused on pathogenesis primarily in the respiratory system. However, evidence shows that the central nervous system (CNS) may also be affected by COVID-19. Since COVID-19 is spreading, it is necessary to study its possible cognitive effects on COVID-19 patients and their recovery.

The articles used in this study were searched by keywords, such as cytokine storm and COVID-19, COVID-19 and executive dysfunction, cognitive disorder, and COVID-19, central nervous system (CNS) and COVID-19, coronavirus, neuroinvasion in Science Direct, Scopus, PubMed, Embase, and Web of Science databases based on preferred reporting items for systematic reviews and meta-analysis (PRISMA) checklist. The study evaluates all observational studies published between December 2019 and April 2021 in peer-reviewed journals, including cross-sectional, cohort, case-control studies, case reports, and case series. The search result was 106 articles, of which 73 articles related to COVID-19, the stages of infection by this virus, its effect on the nervous system and neurological symptoms, the cytokine storm caused by this infection, and the possible cognitive consequences caused by this virus in patients, has been reviewed. Other articles were not checked due to their limited relevance to the topic under discussion.

Studies showed that neurons may be directly affected by severe acute respiratory syndrome coronavirus (SARS-CoV)-1 and SARS-CoV-2. Furthermore, various studies indicated that systemic inflammation (so-called “cytokine storm”) is also responsible for brain damage induced by infection with SARS-CoV-1 and SARS-CoV-2. 
In such a way that these patients showed elevated levels of interleukin (IL-), 6, 8, and 10 and of tumor necrosis factor-alpha (TNF-α) in their blood. 

Various cognitive defects have been observed following an increased level of cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-6, 8. Therefore, due to the increased level of these pro-inflammatory factors in the brains of these patients, cognitive deficits can be expected, which need further investigation.

Published data obtained from in vitro and in vivo studies was reviewed systematically and analyzed critically to evaluate the effect of oral cavity-derived stem cells (OCDSCs) on the recovery or therapy of neurodegenerative diseases (NDs), such as Alzheimer disease (AD), amyotrophic lateral sclerosis (ALS), Huntington (HD) diseases, and Parkinson disease (PD). 

An electronic search was accomplished. References of included articles were also manually searched. Studies were critically evaluated for suitability against the inclusion/exclusion criteria and the data was extracted. Bias risk evaluation of the studies and evidence synthesis were conducted. 

A total of 14 in vivo and 10 in vitro studies met the inclusion criteria. PD was induced in 10 in vivo and 7 in vitro studies, while AD was induced in 2 in vivo and 4 in vitro studies. Two studies (1 in vitro and 1 in vivo) evaluated ALS disease and 1 in vivo study evaluated HD. Moderate evidence was found for in vitro studies reporting the positive effect of OCDSCs on PD or AD recovery. Strong evidence was found for in vivo studies in which PD animal models were used; meanwhile, moderate evidence was found for the impact of OCDSCs on AD recovery. Limited evidence was found for in vivo studies evaluating HD and ALS. 

Although studies reported favorable data regarding the OCDSCs on NDs, they presented a considerable risk of bias. Because of heterogeneous study characteristics, the current study recommends improving standardized methods to evaluate the therapeutic effects of OCDSCs on the NDs.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with symptoms appearing from early childhood. Behavioral modifications, special education, and medicines are used to treat ASD; however, the effectiveness of the treatments depends on early diagnosis of the disorder. The primary approach in diagnosing ASD is based on clinical interviews and valid scales. Still, methods based on brain imaging could also be possible diagnostic biomarkers for ASD. 

To identify the amount of information the functional magnetic resonance imaging (fMRI) reveals on ASD, we reviewed 292 task-based fMRI studies on ASD individuals. This study is part of a systematic review with the registration number CRD42017070975.

We observed that face perception, language, attention, and social processing tasks were mainly studied in ASD. In addition, 73 brain regions, nearly 83% of brain grey matter, showed an altered activation between the ASD and normal individuals during these four tasks, either in a lower or a higher activation. 

Using imaging methods, such as fMRI, to diagnose and predict ASD is a great objective; research similar to the present study could be the initial step.

It has long been known that Methamphetamine (MA), as a psychostimulant, leads to long-lasting cognitive deficits. Previous studies have shown that lithium, a mood stabilizer, could facilitate cognitive ability in most of brain diseases. In current study the effects of lithium on spatial memory, hippocampal apoptosis and brain edema in METH-exposed rats are investigated.

The present study 32 Wistar rats were used to examine the effects of lithium on spatial memory by the Morris water maze, hippocampal apoptosis using the TUNEL assay, and brain edema following MA administrations. 

The findings indicated that treatment with lithium significantly ameliorated spatial learning and memory impairment in MA-treated rats. In addition, the findings showed that treatment with lithium significantly reduced brain edema and apoptosis in the CA1 neurons in MA -exposed rats. 

The results show that treatment with lithium can partially ameliorate the MA –induced neurocognitive deficits in rats, which may be related to its protective effect in the hippocampus.

About 30% of patients with epilepsy do not respond to anti-epileptic drugs, leading to refractory seizures. The pathogenesis of drug-resistance in mesial temporal lobe epilepsy (MTLE) is not completely understood. Increased activity of drug-efflux transporters might be involved, resulting in subclinical concentrations of the drug at the target site. The major drug-efflux transporters are permeability glycoprotein (P-gp) and multidrug-resistance associated protein-1 (MRP-1). The major drawback so far is the expressional analysis of transporters in equal numbers of drug-resistant epileptic tissue and age-matched non-epileptic tissue.

We have studied P-gp and MRP-1 drug-efflux transporters in the sclerotic hippocampal tissues resected from the epilepsy surgery patients (n=15) and compared their expression profile with the tissues resected from non-epileptic autopsy cases (n=15).

Statistically significant over expression of both P-gp (P<0.0001) and MRP-1 (P=0.01) at gene and protein levels were found in the MTLE cases. The fold change of P-gp was more pronounced than MRP-1. Immunohistochemistry of the patient group showed increased immunoreactivity of P-gp at blood-brain barrier and increased reactivity of MRP-1 in the parenchyma. The results were confirmed by confocal immunofluorescence microscopy.

Our results suggested that P-gp in association with MRP-1 might be responsible for the multi-drug resistance in epilepsy. P-gp and MRP-1 could be important determinants of bio availability and tissue distribution of anti-epileptic drugs in the brain which can pharmacologically inhibited to achieve optimal drug penetration to target site.

Mirror neurons have been suggested as a potential neural mechanism of observational learning. This study aims to investigate the effect of self-modeling, skilled model, and learning model on mu rhythm suppression and golf putting acquisition and retention. 

The study was conducted on 45 male volunteer students (aged 19.4±0.37 years) in three experimental groups, self-modeling, skilled, and learning models with six sessions of physical and observational training in three periods of pre-test, acquisition, and retention. In the pre-test, after the initial familiarity with the skill, participants performed 10 golf putting actions while scores were recorded. Then, electrical brain waves in C3, C4, and Cz regions were recorded during the observation of 10 golf putting actions by their group-related models. The acquisition period consisted of golf putting training during six sessions, each consisting of six blocks of 10 trials. Before each training block, participants observed golf putting related to their group 10 times in the form of a video. Acquisition and delayed retention tests were also performed by recording scores of 10 golf putting actions, as well as recording electrical brain waves while observing the skill performed by the related model.

Mixed analysis of variance (ANOVA) showed that the mu rhythm suppression in the pre-test was more in the self-modeling group compared to the skilled model and learning model groups, but this suppression was not significantly different in all three groups in the acquisition and retention tests. In putting task variables, all three groups that had no significant difference in the pre-test period made considerable progress in learning the desired skill from the pre-test to the acquisition test, and this progress was somewhat stable until the retention test. Also, both in the acquisition and retention periods, the self-modeling group showed better performance than the other two groups; however, no significant difference was observed between these groups.

These results suggest that the model-observer similarity is a crucial factor in modeling interventions and can affect the rate of mu rhythm suppression.

Transcranial direct current stimulation (tDCS) has been studied as an adjunctive treatment option for substance use disorders (SUDs). Alterations in brain structure following SUD may change tDCS-induced electric field (EF) and subsequent responses; however, group-level differences between healthy controls (HC) and participants with SUDs in terms of EF and its association with cortical architecture have not yet been modeled quantitatively. This study provides a methodology for group-level analysis of computational head models to investigate the influence of cortical morphology metrics on EFs. 

Whole-brain surface-based morphology was conducted, and cortical thickness, volume, and surface area were compared between participants with cannabis use disorders (CUD) (n=20) and age-matched HC (n=22). Meanwhile, EFs were simulated for bilateral tDCS over the dorsolateral prefrontal cortex. The effects of structural alterations on EF distribution were investigated based on individualized computational head models.

Regarding EF, no significant difference was found within the prefrontal cortex; however, EFs were significantly different in left-postcentral and right-superior temporal gyrus (P<0.05) with higher levels of variance in CUD compared to HC [F(39, 43)=5.31, P<0.0001, C=0.95]. Significant differences were observed in cortical area (caudal anterior cingulate and rostral middle frontal), thickness (lateral orbitofrontal), and volume (paracentral and fusiform) between the two groups.

Brain morphology and tDCS-induced EFs may be changed following CUD; however, differences between CUD and HCs in EFs do not always overlap with brain areas that show structural alterations. To sufficiently modulate stimulation targets, whether individuals with CUD need different stimulation doses based on tDCS target location should be checked.

Peroxisomes are essential organelles in lipid metabolism. They contain enzymes for β-oxidation of very long-chain fatty acids (VLCFA) that cannot be broken down in mitochondria. Reduced expression in hepatic acyl-CoA oxidase 1 (ACOX1), a peroxisome β-oxidation enzyme, followed by modification of the brain fatty acid profile has been observed in aged rodents. These studies have suggested a potential role for peroxisome β-oxidation in brain aging. This study was designed to examine the effect of hepatic ACOX1 inhibition on brain fatty acid composition and neuronal cell activities of young rats (200-250 g). 

A specific ACOX1 inhibitor, 10, 12- tricosadiynoic acid (TDYA), 100 μg/kg (in olive oil) was administered by daily gavage for 25 days in male Wistar rats. The brain fatty acid composition and electrophysiological properties of dentate gyrus granule cells were determined using gas chromatography and whole-cell patch-clamp, respectively. 

A significant increase in C20, C22, C18:1, C20:1, and a decrease of C18, C24, C20:3n6, and C22:6n3 were found in 10, 12- tricosadiynoic acid (TDYA) treated rats compared to the control group. The results showed that ACOX1 inhibition changes fatty acid composition similar to old rats. ACOX1 inhibition caused hyperpolarization of resting membrane potential, and also reduction of input resistance, action potential duration, and spike firing. Moreover, ACOX1 inhibition increased rheobase current and afterhyperpolarization amplitude in granule cells. 

The results indicated that systemic inhibition of ACOX1 causes hypo-excitability of neuronal cells. These results provide new evidence on the involvement of peroxisome function and hepatic ACOX1 activity in brain fatty acid profile and the electrophysiological properties of dentate gyrus cells.

Muscle biopsy is commonly used to diagnose inflammatory myopathies. We evaluated the ability of muscle ultrasound, a non-invasive and simple tool, to distinguish between healthy subjects and patients with inflammatory myopathy.

This study was conducted on 17 patients recently diagnosed with biopsy inflammatory myopathies (12 dermatomyositis, 5 polymyositis) compared with 17 age- and gender-matched healthy control adults. All patients underwent clinical assessments, including manual muscle testing, hand-held dynamometry, and muscle ultrasound evaluations, including thickness and echo intensity in predefined muscle groups. 

The disease duration was seven months (interquartile range: 3 to 11 months). Except for the biceps and gastrocnemius, patients’ muscles had significantly higher echo intensity and lower thickness than the control group. The echo intensity sum-score manifested the highest area under the curve compared to the sum-scores of other variables (echo intensity vs manual muscle testing: Area under curves-difference=0.18, P<0.01; echo intensity vs dynamometry: Area under curves-difference=0.14, P=0.02; echo intensity vs thickness: Area under curves-differences-difference=0.25, P<0.01). 

The echo intensity of muscles differed significantly between healthy individuals and patients with inflammatory myopathies and may serve as a useful diagnostic biomarker.

The study explores the use of Electroencephalograph (EEG) signals as a means to uncover various states of the human brain, with a specific focus on emotion classification. Despite the potential of EEG signals in this domain, existing methods face challenges. Features extracted from EEG signals may not accurately represent an individual's emotional patterns due to interference from time-varying factors and noise. Additionally, higher-level cognitive factors, such as personality, mood, and past experiences, further complicate emotion recognition. The dynamic nature of EEG data in terms of time series introduces variability in feature distribution and interclass discrimination across different time stages.

To address these challenges, the paper proposes a novel adaptive ensemble classification method. The study introduces a new method for providing emotional stimuli, categorizing them into three groups (sadness, neutral, and happiness) based on their valence-arousal (VA) scores. The experiment involved 60 participants aged 19–30 years, and the proposed method aimed to mitigate the limitations associated with conventional classifiers.

The results demonstrate a significant improvement in the performance of emotion classifiers compared to conventional methods. The classification accuracy achieved by the proposed adaptive ensemble classification method is reported at 87.96%. This suggests a promising advancement in the ability to accurately classify emotions using EEG signals, overcoming the limitations outlined in the introduction.

In conclusion, the paper introduces an innovative approach to emotion classification based on EEG signals, addressing key challenges associated with existing methods. By employing a new adaptive ensemble classification method and refining the process of providing emotional stimuli, the study achieves a noteworthy improvement in classification accuracy. This advancement is crucial for enhancing our understanding of the complexities of emotion recognition through EEG signals, paving the way for more effective applications in fields such as neuroinformatics and affective computing.

Drug-resistant epilepsy is an unmet medical condition that impacts 30% of epileptic patients. Numerous antiseizure drugs have already been developed but they provide only symptomatic relief and do not target the underlying pathogenesis. Preclinical models provide opportunities to gain insights into obscure mechanisms of drug-resistant epilepsy. Current animal models possess lacunae that need rectification and validation to discover novel antiepileptic drugs. The present study aims to validate 3 different doses of phenobarbital at 2 different periods.

Pentylenetetrazole was given at a sub-convulsive dose (30 mg/kg/day/intraperitoneal [IP]) for 28 days to develop kindling in male Wistar rats. Further, kindled rats were divided into the following four groups: Pentylenetetrazole control, pentylenetetrazole and phenobarbital (20 mg/kg), pentylenetetrazole and phenobarbital 40 mg/kg, and pentylenetetrazole and phenobarbital (60 mg/kg). They were assessed on days 14 and 28 post-kindling. Seizure scoring, oxidative stress, phenobarbital plasma levels, and histopathology of hippocampal neurons were analyzed.

The results showed that the combination of pentylenetetrazole and phenobarbital (40 and 60 mg/kg) remarkably decreased seizure score, elucidated higher antioxidant effect, and prevented neuronal injury on day 14, whereas increased seizure score, oxidative stress, and neuronal death was observed with chronic administration of pentylenetetrazole and phenobarbital in kindled rats at day 28. Moreover, phenobarbital levels in blood were significantly increased at day 28 of phenobarbital treatment compared to day 14.

The adapted protocol with phenobarbital 40 mg/kg dose could be of great potential in screening antiseizure drugs in refractory epilepsy.

The aim of this study was to compare the brain wave pattern of two groups of dyslexic students with perceptual and linguistic types with normal students in reading.

In this study, 27 students (24 boys and 3 girls) from first to fifth grade with an Mean±SD of age 8.16±10.09 years participated. Eight students with perceptual type dyslexia, ten students with linguistic type dyslexia, and nine normal students with reading were selected by purposive sampling method.

After removing noise and artifacts, the data were converted into quantitative digits using Neuroguide software and analyzed using multivariate analysis of variance (MANOVA) and univariate analysis of variance (ANOVA). Based on the results, the linguistic group and the normal group differed in the relative power of the alpha wave in the two channels Fp1 and Fp2, but there was no difference between the three linguistic, perceptual, and normal groups in the absolute power of the four waves of the delta, theta, alpha, and beta.

The relative power spectrum of the alpha band in the forehead can be significantly related to dyslexia problems as seen in the linguistic type.