Path ahead: Tackling the Challenge of Computationally Estimating Lithium Diffusion in Cathode Materials.

In the roadmap toward designing new and improved materials for Lithium ion batteries, the ability to estimate the diffusion coefficient of Li atoms in electrodes, and eventually solid-state electrolytes, is key. Nevertheless, as of today, accurate prediction through computational tools remains challenging. Its experimental measurement does not appear to be much easier. In this work, we devise a computational protocol for the determination of the Li-migration energy barrier and diffusion coefficient, focusing on a common cathode material such as LiNiO2, which represents a prototype of the widely adopted NMC (LiNi1-x-y Mn x Co y O2) class of materials. Different methodologies are exploited, combining ab initio metadynamics, path sampling, and density functional theory. Furthermore, we propose a novel, fast, and simple 1D approximation for the estimation of the effective frequency. The outlined computational protocol aims to be generally applicable to Lithium diffusion in other materials and components for batteries, including anodes and solid electrolytes.

Phenotyping in clinical laterality research: a comparison of commonly used methods to determine mixed-handedness and ambidexterity.

An increased prevalence of mixed-handedness has been reported in several neurodevelopmental and psychiatric disorders. Unfortunately, there is high between-study variability in the definition of mixed-handedness, leading to a major methodological problem in clinical laterality research and endangering replicability and comparability of research findings. Adding to this challenge is the fact that sometimes researchers use the concepts of mixed-handedness and ambidexterity interchangeably. Therefore, having a consensus on how to determine mixed-handedness and how to distinguish it from ambidexterity is crucial for clinical laterality research. To this end, hand preference and hand performance data from more than 600 participants from the Dortmund Vital Study (Trial registration: ClinicalTrials.gov NCT05155397), a population-based study in Germany, was analyzed to ascertain an optimal classification to determine mixed-handedness and ambidexterity. Using a combination of latent class analyses, effect size determination, and comparisons with the existing literature, we establish that an LQ cut-off criterion of +/-60 for mixed-handedness is optimal for future clinical laterality studies. Moreover, we show that mixed-handedness and ambidexterity are not identical and that the terms should not be used interchangeably. We further highlight the need for a consensus on how to mathematically determine ambidexterity as results of existing categorization schemes largely differ.Trial registration: ClinicalTrials.gov NCT05155397; https://clinicaltrials.gov/ct2/show/NCT05155397.

Addressing data limitations in seizure prediction through transfer learning.

According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research.

Stress from early life to adulthood: Is there a protective role of cognitive control?

Early life events can have long-lasting effects that may impact the quality of life into adulthood. The link between childhood adversities and adult mental and physical health is well documented, however, the underlying mechanisms remain poorly understood. Executive functions are assumed to be a key factor in successfully dealing with cognitive-emotional challenges thereby contributing to stress resilience and mental health across the lifespan. Here, we examined whether cognitive control moderates the link between early life adversity and depression. Data was available from a sample of 424 participants aged 20-70 years (Clinicaltrials.gov: NCT05155397). They performed in the Stroop task and behavior as well as frontal theta power were recorded. Negative childhood experiences were assessed with the Childhood Trauma Questionnaire (CTQ), chronic stress was measured with the Trier Inventory for Chronic Stress (TICS) and depression symptoms with Beck's Depression Inventory (BDI). The CTQ predicted symptoms of chronic stress and depression. Regression models pointed to the TICS as a crucial mediator in the relationship between CTQ and BDI. However, parameters of cognitive control demonstrated a rather weak effect as moderators. These results indicate that chronic stress is an important mediator linking childhood trauma to depression but suggest only a limited role for cognitive control.

Effects of Different Weekly Set Volumes on Strength and Perceptual Responses in Athletes.

This study investigated the effects of different resistance training (RT) volumes quantified by weekly sets at high intensity (load and effort) on dynamic strength adaptations and psychophysiological responses in trained individuals. Twenty-four athletes were randomly allocated to three groups that performed three (3 S, n=8), six (6 S, n=8), and nine (9 S, n=8) weekly sets, respectively, three times a week on the barbell back squat and bench press during an 8-week period. While all groups showcased strength gains (p<0.05), post hoc comparisons revealed that 6 S and 9 S elicited greater strength adaptations than 3 S in barbell back squat (p=0.027 and p=0.004, respectively) and bench press (p=0.001 and p=0.044, respectively). There were no differences between 6 S and 9 S conditions for back squat (p=0.999) and bench press (p=0.378). Although a time effect was observed for Session-RPE (p=0.014) and Total Quality Recovery scale (p=0.020), psychophysiological responses were similar among groups. Our findings suggest that performing six and nine weekly sets at high intensities led to greater strength gains compared to three weekly sets in strength-trained individuals, despite similar psychophysiological responses.

Premorbid physical activity and prognosis after incident myocardial infarction: The atherosclerosis risk in communities study.

BACKGROUND: High to moderate levels of physical activity (PA) are associated with low risk of incident cardiovascular disease. However, it is unclear whether the benefits of PA in midlife extend to cardiovascular health following myocardial infarction (MI) in later life. METHODS: Among 1,111 Atherosclerosis Risk in Communities study participants with incident MI during Atherosclerosis Risk in Communities follow-up (mean age 73 [SD 9] years at MI, 54% men, 21% Black), PA on average 11.9 (SD 6.9) years prior to incident MI (premorbid PA) was evaluated as the average score of PA between visit 1 (1987-1989) and visit 3 (1993-1995) using a modified Baecke questionnaire. Total and domain-specific PA (sport, nonsport leisure, and work PA) was analyzed for associations with composite and individual outcomes of mortality, recurrent MI, and stroke after index MI using multivariable Cox models. RESULTS: During a median follow-up of 4.6 (IQI 1.0-10.5) years after incident MI, 823 participants (74%) developed a composite outcome. The 10-year cumulative incidence of the composite outcome was lower in the highest, as compared to the lowest tertile of premorbid total PA (56% vs. 70%, respectively). This association remained statistically significant even after adjusting for potential confounders (adjusted hazard ratio [aHR] 0.80 [0.67-0.96] for the highest vs. lowest tertile). For individual outcomes, high premorbid total PA was associated with a low risk of recurrent MI (corresponding aHR 0.64 [0.44, 0.93]). When domain-specific PA was analyzed, similar results were seen for sport and work PA. The association was strongest in the first year following MI (e.g., aHR of composite outcome 0.66 [95% CI 0.47, 0.91] for the highest vs. lowest tertile of total PA). CONCLUSIONS: Premorbid PA was associated positively with post-MI cardiovascular health. Our results demonstrate the additional prognostic advantages of PA beyond reducing the risk of incident MI.

Comparison between epileptic seizure prediction and forecasting based on machine learning.

Epilepsy affects around 1% of the population worldwide. Anti-epileptic drugs are an excellent option for controlling seizure occurrence but do not work for around one-third of patients. Warning devices employing seizure prediction or forecasting algorithms could bring patients new-found comfort and quality of life. These algorithms would attempt to detect a seizure's preictal period, a transitional moment between regular brain activity and the seizure, and relay this information to the user. Over the years, many seizure prediction studies using Electroencephalogram-based methodologies have been developed, triggering an alarm when detecting the preictal period. Recent studies have suggested a shift in view from prediction to forecasting. Seizure forecasting takes a probabilistic approach to the problem in question instead of the crisp approach of seizure prediction. In this field of study, the triggered alarm to symbolize the detection of a preictal period is substituted by a constant risk assessment analysis. The present work aims to explore methodologies capable of seizure forecasting and establish a comparison with seizure prediction results. Using 40 patients from the EPILEPSIAE database, we developed several patient-specific prediction and forecasting algorithms with different classifiers (a Logistic Regression, a 15 Support Vector Machines ensemble, and a 15 Shallow Neural Networks ensemble). Results show an increase of the seizure sensitivity in forecasting relative to prediction of up to 146% and in the number of patients that displayed an improvement over chance of up to 300%. These results suggest that a seizure forecasting methodology may be more suitable for seizure warning devices than a seizure prediction one.

Triggered by your heart: Effects of cardioafferent traffic and stress on automatic responses in a Simon task.

Variations in cardioafferent traffic are relayed to the brain via arterial baroreceptors and have been shown to modulate perceptual processing. However, less is known about the cognitive-behavioral consequences of these effects and their role during stress. Here, we investigated in how far automatic responses during the Simon task were modulated by exposure to a laboratory stressor and the different phases of the cardiac cycle. In this study, 30 participants performed three blocks of a combined horizontal and vertical Simon task, which is characterized by either sensorimotor or cognitive response conflicts, respectively. Before each block, subjects were exposed to both the cold pressor test (CPT) and a control condition according to a within-subjects design. Target stimuli were presented during either systole or diastole. Behavioral and EEG-correlates of task processing were assessed along with subjective, cardiovascular, and endocrine measures of stress. The stress induction was successful yielding significant increases in all these measures compared to control. Moreover, we found the expected Simon effects: in incompatible compared to compatible trials performance was decreased and LRP latency as well as anterior N2 area increased. Importantly, accuracy was improved in compatible but reduced in incompatible trials during systole vs. diastole but only in the horizontal Simon condition. Stress dampened N2 area, however, no interactions with cardiac cycle were evident. These results indicate a faciliatory effect of cardioafferent traffic on automated sensorimotor processes.

Classifier Combination Supported by the Sleep-Wake Cycle Improves EEG Seizure Prediction Performance.

OBJECTIVE: Seizure prediction is a promising solution to improve the quality of life for drug-resistant patients, which concerns nearly 30% of patients with epilepsy. The present study aimed to ascertain the impact of incorporating sleep-wake information in seizure prediction. METHODS: We developed five patient-specific prediction approaches that use vigilance state information differently: i) using it as an input feature, ii) building a pool of two classifiers, each with different weights to sleep/wake training samples, iii) building a pool of two classifiers, each with only sleep/wake samples, iv) changing the alarm-threshold concerning each sleep/wake state, and v) adjusting the alarm-threshold after a sleep-wake transition. We compared these approaches with a control method that did not integrate sleep-wake information. Our models were tested with data (43 seizures and 482 hours) acquired during presurgical monitoring of 17 patients from the EPILEPSIAE database. As EPILEPSIAE does not contain vigilance state annotations, we developed a sleep-wake classifier using 33 patients diagnosed with nocturnal frontal lobe epilepsy from the CAP Sleep database. RESULTS: Although different patients may require different strategies, our best approach, the pool of weighted predictors, obtained 65% of patients performing above chance level with a surrogate analysis (against 41% in the control method). CONCLUSION: The inclusion of vigilance state information improves seizure prediction. Higher results and testing with long-term recordings from daily-life conditions are necessary to ensure clinical acceptance. SIGNIFICANCE: As automated sleep-wake detection is possible, it would be feasible to incorporate these algorithms into future devices for seizure prediction.

Caffeine Does Not Alter Performance, Perceptual Responses, and Oxidative Stress After Short Sprint Interval Training.

Despite the abundance of research investigating the efficacy of caffeine supplementation on exercise performance, the physiological and biochemical responses to caffeine supplementation during intermittent activities are less evident. This study investigated the acute effects of caffeine supplementation on measures of exercise performance, ratings of perceived exertion, and biomarkers of oxidative stress induced by an acute bout of sprint interval training. In a randomized crossover design, 12 healthy males (age: 26 ± 4 years, height: 177.5 ± 6 cm, body mass: 80.7 ± 7.6 kg) ingested 6 mg/kg of caffeine or placebo 60 min prior to performing sprint interval training (12 × 6 s "all-out sprints" interspersed by 60 s of rest). Performance scores and ratings of perceived exertion were assessed after every sprint. Blood samples were collected before supplementation, prior to and following each sprint, and 5 and 60 min after the last sprint. Caffeine had no effect on any performance measures, ratings of perceived exertion, or biomarkers of oxidative stress (p > .05). In conclusion, caffeine supplementation does not improve performance or decrease oxidative stress after an acute bout of sprint interval training.

EEG epilepsy seizure prediction: the post-processing stage as a chronology.

Almost one-third of epileptic patients fail to achieve seizure control through anti-epileptic drug administration. In the scarcity of completely controlling a patient's epilepsy, seizure prediction plays a significant role in clinical management and providing new therapeutic options such as warning or intervention devices. Seizure prediction algorithms aim to identify the preictal period that Electroencephalogram (EEG) signals can capture. However, this period is associated with substantial heterogeneity, varying among patients or even between seizures from the same patient. The present work proposes a patient-specific seizure prediction algorithm using post-processing techniques to explore the existence of a set of chronological events of brain activity that precedes epileptic seizures. The study was conducted with 37 patients with Temporal Lobe Epilepsy (TLE) from the EPILEPSIAE database. The designed methodology combines univariate linear features with a classifier based on Support Vector Machines (SVM) and two post-processing techniques to handle pre-seizure temporality in an easily explainable way, employing knowledge from network theory. In the Chronological Firing Power approach, we considered the preictal as a sequence of three brain activity events separated in time. In the Cumulative Firing Power approach, we assumed the preictal period as a sequence of three overlapping events. These methodologies were compared with a control approach based on the typical machine learning pipeline. We considered a Seizure Prediction horizon (SPH) of 5 mins and analyzed several values for the Seizure Occurrence Period (SOP) duration, between 10 and 55 mins. Our results showed that the Cumulative Firing Power approach may improve the seizure prediction performance. This new strategy performed above chance for 62% of patients, whereas the control approach only validated 49% of its models.

Corrigendum: Home-based exercise program in the indeterminate form of Chagas disease (PEDI-CHAGAS study): a study protocol for a randomized clinical trial.

[This corrects the article DOI: 10.3389/fmed.2022.1087188.].

Toward invasive mussel genetic biocontrol: Approaches, challenges, and perspectives.

Invasive freshwater mussels, such as the zebra (Dreissena polymorpha), quagga (Dreissena rostriformis bugensis), and golden (Limnoperna fortunei) mussel have spread outside their native ranges throughout many regions of the North American, South American, and European continents in recent decades, damaging infrastructure and the environment. This review describes ongoing efforts by multiple groups to develop genetic biocontrol methods for invasive mussels. First, we provide an overview of genetic biocontrol strategies that have been applied in other invasive or pest species. Next, we summarize physical and chemical methods that are currently in use for invasive mussel control. We then describe the multidisciplinary approaches our groups are employing to develop genetic biocontrol tools for invasive mussels. Finally, we discuss the challenges and limitations of applying genetic biocontrol tools to invasive mussels. Collectively, we aim to openly share information and combine expertise to develop practical tools to enable the management of invasive freshwater mussels.

The relationship between self-reported chronic stress, physiological stress axis dysregulation and medically-unexplained symptoms.

The positive feedback model of medically-unexplained symptoms posits that chronic stress affects the activity of the physiological stress axes, which in turn generates medically-unexplained symptoms. As a first step to empirically test its model assumptions, we investigated potential associations between chronic stress, physiological stress axis activity and medically-unexplained in a cross-sectional study. One hundred-ninety-nine healthy individuals provided self-reports on chronic stress and medically-unexplained symptoms, resting heart rate/variability (HR/HRV; e.g., root mean square of successive differences/RMSSD, low frequency/LF power), cortisol awakening response (CAR) and diurnal cortisol. Significant positive contributors to medically-unexplained symptoms were the chronic stress scales 'lack of social appreciation' and 'chronic worries', as well as CAR and LF HRV; diurnal cortisol was a negative contributor. Mediation analyses showed that the impact of neural indicators associated with physiological stress axis activity (HR/HRV) related to medically-unexplained symptoms, which was mediated by chronic stress, whereas the mediation effect as suggested by the positive feedback model was not significant. These cross-sectional findings do not support the positive feedback model. Longitudinal studies are required to conclude about potential mechanistic and causal relationships in the model. Nevertheless, our mediation analyses give first indication that the constitution of physiological stress axes may play a major role in how stressors are perceived and which kind of health-consequences (e.g., medically-unexplained symptoms) this may have.

Conditional expression of NanoLuc luciferase through a multimodular system offers rapid detection of antimalarial drug activity.

Conditional gene expression is a powerful tool to investigate putative vaccine and drug targets, especially in a haploid organism such as Plasmodium falciparum. Inducible systems based on regulation of either transcription, translation, protein or mRNA stability, among others, allow switching on an off the expression of any desired gene causing specific gain or loss of function phenotypes. However, those systems can be cumbersome involving the construction of large plasmids and generation of multiple transgenic parasite lines. In addition, the dynamic range of regulation achieved is not predictable for each individual gene and can be insufficient to generate detectable phenotypes when the genes of interest are silenced. Here, we combined up to three distinct inducible systems to regulate the expression of a single gene. Expression of the reporter NanoLuc luciferase was regulated over 40-fold, which correlates to the regulation achieved by each individual system multiplied by each other. We applied the conditionally expressed NanoLuc to evaluate the effect of fast-acting antimalarials such as chloroquine and artesunate as well as of slower-acting ones such as atovaquone. The conditionally expressed reporter allowed faster and more reliable detection of toxicity to the parasite, which correlated to the expected action of each compound. Bioluminescence achieved by the expression of this inducible highly sensitive reporter is therefore a promising tool to investigate the temporal effect of potential new antimalarials. This single plasmid combination system might also prove useful to achieve sufficient regulation of genes of interest to produce loss-of-function phenotypes.

Biomarkers and Echocardiographic Predictors of Cardiovascular Outcome in Patients With Chronic Chagas Disease.

Background Chagas disease (CD) presents an ominous prognosis. The predictive value of biomarkers and new echocardiogram parameters in adjusted models have not been well studied. Methods and Results There were 361 patients with chronic CD (57.6% men, 61±11 years of age, clinical forms: indeterminate 27.1%, cardiac 56.6%, digestive 3.6%, cardiodigestive 12.7%) included in this single-center, observational, prospective longitudinal study. Echocardiographic evaluation included strain analyses of left atrial, left ventricular (LV), and right ventricular and 3-dimensional analyses of left atrial and LV volumes. Biomarkers included cardiac troponin I, brain natriuretic peptide, transforming growth factor ß1, tumor necrosis factor, matrix metalloproteinases, and Trypanosoma cruzi polymerase chain reaction. The studied end point was a composite of CD-related mortality, heart transplant, hospital admission due to worsening heart failure, or new cardiac device insertion. Event-free survival was analyzed by multivariable regression analyses adjusted for competing risks. P values <0.05 were considered significant. The composite event occurred in 79 patients after 4.9±2.0 years follow-up. LV end-diastolic volume (hazard ratio [HR], 1.01 [95% CI, 1.00-1.02]; P=0.02), peak negative global atrial strain (HR, 1.08 [95% CI, 1.00-1.17]; P=0.04), LV global circumferential strain (HR, 1.12 [95% CI, 1.04-1.21]; P=0.003), LV torsion (HR, 0.55 [95% CI, 0.35-0.81]; P=0.003), brain natriuretic peptide (HR, 2.03 [95% CI, 1.23-3.34]; P=0.005), and positive T cruzi polymerase chain reaction (HR, 1.80 [95% CI, 1.12-2.91]; P=0.01) were end point predictors independent from age, sex, 2-dimensional echocardiographic indexes, hypertension, previous cardiac device, and CD cardiac form. Conclusions Two-dimensional strain- and 3-dimensional-derived parameters, brain natriuretic peptide, and positive T cruzi polymerase chain reaction can be useful for prediction of CD cardiovascular events.

Cytogenetic characterization of the golden mussel (Limnoperna fortunei) reveals the absence of sex heteromorphic chromosomes.

The golden mussel (Limnoperna fortunei) is an aggressive invasive species in South America, where it endangers native species and freshwater ecosystems, in addition to causing extensive economic losses, mainly to the hydroelectric sector. Currently, there's no efficient control method available and the invasion has progressed across the continent. Its high reproduction rate is one of the key factors of the golden mussel's high invasive potential and, recently, efforts have been done in order to understand the reproduction and the sexual features of this species. However, its cytogenetics characterization is incipient and the possible occurrence of sex-specific cytogenetic features was never investigated. In this study, we aimed to characterize the chromosomal morphometry, the distribution profile of heterochromatin, and to detect possible sex-related epigenetic marks in the golden mussel. Results revealed that the karyotypic structure is similar in both sexes and no chromosome heteromorphism was observed between males and females specimens. The data increment the cytogenetic characterization of Limnoperna fortunei and contribute for future studies that aim to further investigate its reproduction and underlying sex determination processes.

Removing the cardiac field artifact from the EEG using neural network regression.

When EEG recordings are used to reveal interactions between central-nervous and cardiovascular processes, the cardiac field artifact (CFA) poses a major challenge. Because the electric field generated by cardiac activity is also captured by scalp electrodes, the CFA arises as a heavy contaminant whenever EEG data are analyzed time-locked to cardio-electric events. A typical example is measuring stimulus-evoked potentials elicited at different phases of the cardiac cycle. Here, we present a nonlinear regression method deploying neural networks that allows to remove the CFA from the EEG signal in such scenarios. We train neural network models to predict R-peak centered EEG episodes based on the ECG and additional CFA-related information. In a second step, these trained models are used to predict and consequently remove the CFA in EEG episodes containing visual stimulation occurring time-locked to the ECG. We show that removing these predictions from the signal effectively removes the CFA without affecting the intertrial phase coherence of stimulus-evoked activity. In addition, we provide the results of an extensive grid search suggesting a set of appropriate model hyperparameters. The proposed method offers a replicable way of removing the CFA on the single-trial level, without affecting stimulus-related variance occurring time-locked to cardiac events. Disentangling the cardiac field artifact (CFA) from the EEG signal is a major challenge when investigating the neurocognitive impact of cardioafferent traffic by means of the EEG. When stimuli are presented time-locked to the cardiac cycle, both sources of variance are systematically confounded. Here, we propose a regression-based approach deploying neural network models to remove the CFA from the EEG. This approach effectively removes the CFA on a single-trial level and is purely data-driven, providing replicable results.

Drop-Set Resistance Training versus Pyramidal and Traditional Sets Elicits Greater Psychophysiological Responses in Men.

We compared the effects of resistance training (ResisT) to pyramidal and traditional weightlifting sets on men's psychophysiological responses. In a randomized crossover design, 24 resistance-trained males performed drop-set, descending pyramid, and traditional ResisT in the barbell back squat, 45° leg press, and seated knee extension. We assessed participants' rating of perceived exertion (RPE) and feelings of pleasure/displeasure (FPD) at the end of each set and at 10, 15, 20, and 30 minutes post-session. No differences were detected across ResisT Methods in total training volume (p = 0.180). Post hoc comparisons revealed that drop-set training elicited higher RPE (M 8.8 SD 0.7 arbitrary units) and lower FPD (M -1.4 SD 1.5 arbitrary units) values compared to descending pyramid (M Set RPE 8.0 SD 0.9 arbitrary units and M Set FPD 0.4 SD 1.6 arbitrary units) and traditional set (M Set RPE 7.5 SD 1.1 arbitrary units and M Set FPD 1.3 SD 1.2 arbitrary units) schemes (p < 0.05). In addition, drop-set training elicited higher session RPE (M 8.1 SD 0.8 arbitrary units) and lower session FPD (M 0.2 SD 1.4 arbitrary units) values than descending pyramid and traditional ResisT (p < 0.001). Similarly, descending pyramid training elicited higher session RPE (M 6.6 SD 0.9 arbitrary units) and lower session FPD (M 1.2 SD 1.4 arbitrary units) than traditional set (M Session RPE 5.9 SD 0.8 arbitrary units and M Session FPD 1.5 SD 1.2 arbitrary units) training (p = 0.015). No differences were found in the temporality of post-session metrics, suggesting that testing 10 and 15 minutes post-ResisT was sufficient to assess session RPE (p = 0.480) and session FPD (p = 0.855), respectively. In conclusion, even with similar total training volume, drop-set training elicited more pronounced psychophysiological responses than either pyramidal or traditional ResisT in resistance-trained males.

Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models.

The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, they suffer from major shortcomings. First, long-term EEG is usually highly contaminated with artefacts. Second, changes in the EEG signal over long intervals, known as concept drift, are often neglected. We evaluate the influence of these problems on deep neural networks using EEG time series and on shallow neural networks using widely-used EEG features. Our patient-specific prediction models were tested in 1577 hours of continuous EEG, containing 91 seizures from 41 patients with temporal lobe epilepsy who were undergoing pre-surgical monitoring. Our results showed that cleaning EEG data, using a previously developed artefact removal method based on deep convolutional neural networks, improved prediction performance. We also found that retraining the models over time reduced false predictions. Furthermore, the results show that although deep neural networks processing EEG time series are less susceptible to false alarms, they may need more data to surpass feature-based methods. These findings highlight the importance of robust data denoising and periodic adaptation of seizure prediction models.