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Background: Deep brain stimulation (DBS) targeting the globus pallidus internus (GPi) and subthalamic nucleus (STN) is employed for the treatment of dystonia. Pallidal low-frequency oscillations have been proposed as a pathophysiological marker for dystonia. However, the role of subthalamic oscillations and STN-GPi coupling in relation to dystonia remains unclear. Objective: We aimed to explore oscillatory activities within the STN-GPi circuit and their correlation with the severity of dystonia and efficacy achieved by DBS treatment. Methods: Local field potentials were recorded simultaneously from the STN and GPi from 13 dystonia patients. Spectral power analysis was conducted for selected frequency bands from both nuclei, while power correlation and the weighted phase lag index were used to evaluate power and phase couplings between these two nuclei, respectively. These features were incorporated into generalized linear models to assess their associations with dystonia severity and DBS efficacy. Results: The results revealed that pallidal theta power, subthalamic beta power and subthalamic-pallidal theta phase coupling and beta power coupling all correlated with clinical severity. The model incorporating all selected features predicts empirical clinical scores and DBS-induced improvements, whereas the model relying solely on pallidal theta power failed to demonstrate significant correlations. Conclusions: Beyond pallidal theta power, subthalamic beta power, STN-GPi couplings in theta and beta bands, play a crucial role in understanding the pathophysiological mechanism of dystonia and developing optimal strategies for DBS.

Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.

Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.

Summary: Pesticides may help safeguard food security but endanger the local ecosystem and farmer health. The globalization of the food trade is masking such impacts by separating production from consumption, and its effects on pesticide use and their related risks remain unclear. Here, we provide a map of the environmental and health risks associated with pesticide footprints along the soybean trade across 197 countries. We find that approximately 64% of soybeans were traded globally, embodying ∼55% of environmental-health risks linked to ∼108 kt of pesticide use. Notably, trade soybean pesticide footprints and their associated environmental-health risks are concentrated in a few hotspot nations, including the USA, Brazil, and Argentina. About 30 kt of future increase in soybean pesticide use and ∼6% of their related environmental-health risks can be offset by reducing 80% of soybean traded from high-pesticide-use-intensity nations to lower ones. Our results highlight the necessity of rethinking the role of agricultural trade in global pesticide management. Science for society: The widespread use of pesticides in food production systems impacts human, animal, and ecosystem health. Food trade complicates the issue, as it can separate consumption from production, shifting pesticide-related burdens from consumers to producers across regions. However, there is a lack of clarity on pesticide footprints and their associated impacts embodied in the global food trade, impeding sustainable pesticide management. Our research develops an integrated framework to assess nation-specific environmental and health burdens linked to pesticide use along the global soybean trade. Our study identifies a concentrated pattern of embodied soybean pesticide footprints and their related risks, as well as a declining contribution of trade to overall pesticide-related risks. Our findings reveal the potential of trade in reducing environmental-health risks associated with soybean pesticides and underscore the need to rethink the role of agricultural trade in global pesticide management.

Abstract The low-carbon power transition, which is key to combatting climate change, has far-reaching effects on achieving the Sustainable Development Goals (SDGs) in terms of issues such as resource use, environmental emissions, employment, and many more. Here, we assess the potential impacts of the power transition on progress toward achieving multiple SDGs (covering 18 targets across the 17 goals) across 49 economies under nine socioeconomic and climate scenarios. We find that the low-carbon power transition under the representative concentration pathway (RCP)2.6 scenarios could lead to an approximately 11% improvement in the global SDG index score from 54.70 in 2015 to 59.89-61.33 in 2100. However, the improvement would be significantly decreased to 4.42%-7.40% and 7.55%-8.93% under the RCP6.0 and RCP4.5 scenarios, respectively. The power transition could improve the overall SDG index in most developed economies under all scenarios while undermining their resource-related SDG scores. Power transition-induced changes in international trade would improve the SDG progress of developed economies but jeopardize that of developing economies, which usually serve as resource hubs for meeting the demand for low-carbon power transition in developed economies.

Background and objective: The balance between neural oscillations provides valuable insights into the organisation of neural oscillations related to brain states, which may play important roles in dystonia. We aim to investigate the relationship of the balance in the globus pallidus internus (GPi) with the dystonic severity under different muscular contraction conditions. Methods: Twenty-one patients with dystonia were recruited. All of them underwent bilateral GPi implantation, and local field potentials (LFPs) from the GPi were recorded via simultaneous surface electromyography. The power spectral ratio between neural oscillations was computed as the measure of neural balance. This ratio was calculated under high and low dystonic muscular contraction conditions, and its correlation with the dystonic severity was assessed using clinical scores. Results: The power spectral of the pallidal LFPs peaked in the theta and alpha bands. Within participant comparison showed that the power spectral of the theta oscillations significantly increased during high muscle contraction compared with that during low contraction. The power spectral ratios between the theta and alpha, theta and low beta, and theta and high gamma oscillations were significantly higher during high contraction than during low contraction. The total score and motor score were associated with the power spectral ratio between the low and high beta oscillations, which was correlated with the dystonic severity both during high and low contractions. The power spectral ratios between the low beta and low gamma and between the low beta and high gamma oscillations showed a significantly positive correlation with the total score during both high and low contractions; a correlation with the motor scale score was found only during high contraction. Meanwhile, the power spectral ratio between the theta and alpha oscillations during low contraction showed a significantly negative correlation with the total score. The power spectral ratios between the alpha and high beta, alpha and low gamma, and alpha and high gamma oscillations were significantly correlated with the dystonic severity only during low contraction. Conclusion: The balance between neural oscillations, as quantified by the power ratio between specific frequency bands, differed between the high and low muscular contraction conditions and was correlated with the dystonic severity. The balance between the low and high beta oscillations was correlated with the dystonic severity during both conditions, making this parameter a new possible biomarker for closed-loop deep brain stimulation in patients with dystonia.

Abstract Background Transcranial direct current stimulation (tDCS) has shown mixed results for depression treatment. The efficacies of tDCS combination therapies have not been investigated deliberately. This review aims to evaluate the clinical efficacy of tDCS as a monotherapy and in combination with medication, psychotherapy, and ECT for treating adult patients with major depressive disorder (MDD) and identified the factors influencing treatment outcome measures (i.e. depression score, dropout, response, and remission rates). Methods The systematic review was performed in PubMed/Medline, EMBASE, PsycINFO, Web of Sciences, and OpenGrey. Two authors performed independent literature screening and data extraction. The primary outcomes were the standardized mean difference (SMD) for continuous depression scores after treatment and odds ratio (OR) dropout rate; secondary outcomes included ORs for response and remission rates. Random effects models with 95% confidence intervals were employed in all outcomes. The overall effect of tDCS was investigated by meta-analysis. Sources of heterogeneity were explored via subgroup analyses, meta-regression, sensitivity analyses, and assessment of publication bias. Results Twelve randomised, sham-controlled trials (active group: N = 251, sham group: N = 204) were included. Overall, the integrated depression score of the active group after treatment was significantly lower than that of the sham group (g = − 0.442, p = 0.017), and further analysis showed that only tDCS + medication achieved a significant lower score (g = − 0.855, p

Prolonged periods of cognitive workload will cause mental fatigue, but objective, quantitative, and sensitive measurements that reflect long-term, stress-induced mental fatigue have yet to be elucidated. This study aims to apply a potential marker of Rényi entropy to investigate the mental fatigue changes in a long-term, high-level stress condition and compare three different instruments for assessment of mental fatigue: EEG, the oddball task, and self-scoring. We recruited nine individuals who participated in a 5-day intellectually challenging competition. The participants were assessed for mental fatigue each day of the competition using prefrontal cortex electroencephalogram (EEG). Reaction time in an oddball task and self-rated scoring were used comparatively to evaluate the performance of the EEG. Repeated measures ANOVA was utilized to analyze the differences among score, reaction time, and wavelet Rényi entropy. The results demonstrated that both wavelet Rényi entropy extracted from EEG and self-rated scoring revealed significant increases in mental fatigue during the 5 days of competition (P < 0.001). The reaction time of the oddball task did not show significant changes during the five-day competition (P = 0.066). Moreover, the wavelet Rényi entropy analysis of EEG showed greater sensitivity than the self-rated scoring and reaction time of the oddball task for measuring mental fatigue changes. In conclusion, this study shows that mental fatigue accumulates during long-term, high-level stress situations. The study also indicates that EEG wavelet Rényi entropy is an efficient metric to reflect the change of mental fatigue under a long-term stress condition and that EEG is a better method to assess long-term mental fatigue.

To avoid catastrophic climate change, the world is promoting a fast and unprecedented transition from fuels to renewables. However, the infrastructures of renewables, such as wind turbines and solar cells, rely heavily on critical minerals like rare earths, indium, etc. Such interactions between climate targets, energy transitions, and critical minerals were widely overlooked in the present climate scenario analysis. This study aims to fill this gap through an introduction of metal–energy–climate nexus framework with its application on global energy transition towards a carbon-neutral (or below 1.5 °C) target, in which six state-of-the-art integrated assessment models (IAMs) under different shared socioeconomic pathways were applied. Our analysis revealed that climate mitigation is expected to boost significantly the critical mineral demand by 2.6–267-fold, which varies greatly by IAM models. Solar power development may be constrained by tellurium (Te) and selenium (Se) shortage, while wind power will be jeopardized by the limited scalability of rare earth production. Moreover, a more sustainable pathway may come at higher demand for critical minerals along with higher renewable ratios. Consequently, a holistic investigation of the interaction of mineral, energy, and climate systems is highly recommended for future scenario designing.

Objectives: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been successfully used to treat both Parkinson's disease (PD) and dystonia. Local field potentials (LFPs) recorded from the STN of PD patients demonstrate prominent beta frequency band activity. It is unclear whether such activity occurs in the STN in dystonia, and, if not, whether dystonia has another distinctive neural population activity in the STN. Methods: Twelve patients with PD, and eight patients with dystonia underwent DBS electrode implantation targeting the STN. Seven dystonia patients were off medication and one was on aripiprazole and clonazepam. LFPs were recorded from the DBS electrodes in PD in the on/off medication states and in dystonia. Power spectra and temporal dynamics measured by the with Lempel-Ziv complexity of the LFPs were compared among these states. Results: Normalised power spectra and Lempel-Ziv complexity of subthalamic LFPs differed between dystonia off and PD on/off, and between PD off and on over the low frequency, beta and high gamma bands. Patients with dystonia and off medication had lower beta power but higher low frequency and high gamma power than PD. Spectral power in the low beta frequency (11–20 Hz) range was attenuated in medicated PD. Conclusion: The results suggest that dystonia and PD are characterized by different patterns of oscillatory activities even within the same nucleus, and exaggerated beta activity may relate to hypo-dopaminergic status.

Background: Dystonia and Huntington's disease (HD) are both hyperkinetic movement disorders but exhibit distinct clinical characteristics. Aberrant output from the globus pallidus internus (GPi) is involved in the pathophysiology of both HD and dystonia, and deep brain stimulation (DBS) of the GPi shows good clinical efficacy in both disorders. The electrode externalized period provides an opportunity to record local field potentials (LFPs) from the GPi to examine if activity patterns differ between hyperkinetic disorders and are associated with specific clinical characteristics.Methods: LFPs were recorded from 7 chorea-dominant HD and nine cervical dystonia patients. Differences in oscillatory activities were compared by power spectrum and Lempel-Ziv complexity (LZC). The discrepancy band power ratio was used to control for the influence of absolute power differences between groups. We further identified discrepant frequency bands and frequency band ratios for each subject and examined the correlations with clinical scores.Results: Dystonia patients exhibited greater low frequency power (6–14 Hz) while HD patients demonstrated greater high-beta and low-gamma power (26–43 Hz) (p < 0.0298, corrected). United Huntington Disease Rating Scale chorea sub-score was positively correlated with 26–43 Hz frequency band power and negatively correlated with the 6–14 Hz/26–43 Hz band power ratio.Conclusion: Dystonia and HD are characterized by distinct oscillatory activity patterns, which may relate to distinct clinical characteristics. Specifically, chorea may be related to elevated high-beta and low-gamma band power, while dystonia may be related to elevated low frequency band power. These LFPs may be useful biomarkers for adaptive DBS to treat hyperkinetic diseases.