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IntroductionMotor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.MethodsOur main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.ResultsAmong all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications.DiscussionOur research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation.

Objective: To determine the optimum miniscrew head design in orthodontic treatments for primary stability and compare stress distribution on a representative bone structure. Methods: Miniscrews with cross heads, mushroom-shaped heads, button heads, bracket heads, and through-hole heads were compared using finite element analysis. Miniscrews, whose three-dimensional drawings were completed using the SolidWorks computer-aided software package, were inserted in the bone block. Orthodontic force was applied to the head, and stress distributions, strains, and total deformations were investigated. Results: The lowest von Mises stress of 5.67 MPa was obtained using the bracket head. On the other hand, the highest von Mises stress of 22.4 MPa was found with the button head. Through mesh convergence analysis, the most appropriate mesh size was determined to be 0.5 mm; approximately 230,000 elements were formed for each model. Conclusion: Because the need for low stress is substantial for the primary stability of the miniscrew, this study demonstrated that the bracket head miniscrew is the optimal head design. In addition, it is posited that the success rate of orthodontic anchorage treatments will increase when bracket head miniscrews are used.

IntroductionBrain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.MethodsIn this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.ResultsAs a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.DiscussionWhen compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).

Sports anxiety is an important obstacle for athletes' performance, negatively affecting their life satisfaction levels. Positive thinking skills can contribute to overcoming such negative conditions. This study explored the relationships between sport anxiety, positive thinking skills, and life satisfaction in male athletes. A total of 338 male athletes participated voluntarily, using convenience sampling. The study employed a relational survey model, and data were collected through the Sports Anxiety Scale-2, Positive Thinking Skills Scale, and Life Satisfaction Scale. Analyses, including Pearson's correlation, were performed using the JAMOVI program, with mediation analysis verified through bootstrapping. Results indicated a negative correlation between sport anxiety and life satisfaction, and a positive correlation between positive thinking skills and life satisfaction. Moreover, positive thinking skills were found to moderate the relationship between sport anxiety and life satisfaction. These insights underscore the value of developing positive thinking skills to help athletes reduce anxiety and enhance their life satisfaction. Therefore, incorporating strategies to foster these skills in training programs could be crucial for improving athletes' overall wellbeing., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tolukan, Yildiz, Yenel, Yalcin, Stoica, Iordan and Ilie.)

Increased levels of lactate, an end-product of glycolysis, have been proposed as a potential surrogate marker for metabolic changes during neuronal excitation. These changes in lactate levels can result in decreased brain pH, which has been implicated in patients with various neuropsychiatric disorders. We previously demonstrated that such alterations are commonly observed in five mouse models of schizophrenia, bipolar disorder, and autism, suggesting a shared endophenotype among these disorders rather than mere artifacts due to medications or agonal state. However, there is still limited research on this phenomenon in animal models, leaving its generality across other disease animal models uncertain. Moreover, the association between changes in brain lactate levels and specific behavioral abnormalities remains unclear. To address these gaps, the International Brain pH Project Consortium investigated brain pH and lactate levels in 109 strains/conditions of 2294 animals with genetic and other experimental manipulations relevant to neuropsychiatric disorders. Systematic analysis revealed that decreased brain pH and increased lactate levels were common features observed in multiple models of depression, epilepsy, Alzheimer's disease, and some additional schizophrenia models. While certain autism models also exhibited decreased pH and increased lactate levels, others showed the opposite pattern, potentially reflecting subpopulations within the autism spectrum. Furthermore, utilizing large-scale behavioral test battery, a multivariate cross-validated prediction analysis demonstrated that poor working memory performance was predominantly associated with increased brain lactate levels. Importantly, this association was confirmed in an independent cohort of animal models. Collectively, these findings suggest that altered brain pH and lactate levels, which could be attributed to dysregulated excitation/inhibition balance, may serve as transdiagnostic endophenotypes of debilitating neuropsychiatric disorders characterized by cognitive impairment, irrespective of their beneficial or detrimental nature., Competing Interests: HH, HS, SH, GS, YT, MT, MI, MS, IH, KH, MH, AN, YM, SO, MM, AU, YK, AM, KN, SK, TS, TI, HN, YG, MR, TT, KY, NT, HK, JI, IN, TK, TT, MD, HN, KT, KS, KN, TT, HF, YS, KY, LR, NH, HM, TY, SW, TA, KT, TN, HK, FH, AH, HH, KT, TT, TK, TK, IG, GC, NA, HH, SK, TK, MH, YH, RM, KO, AH, LB, IY, YH, HK, YM, KI, HK, BK, HO, YY, AT, NK, SG, MI, MN, KM, EO, IO, TS, KY, HA, KT, KI, TS, AK, MF, NU, TK, TT, YY, MI, TH, KI, KN, SH, KE, KH, YT, SN, NS, TY, NU, TY, YK, RO, NS, KS, SH, KO, HM, TF, MT, HS, DS, YI, TI, YK, TY, KS, TM No competing interests declared, SY, NN Employee of Takeda Pharmaceutical Company, Ltd