An information-theoretic framework for conditional causality analysis of brain networks.

Identifying directed network models for multivariate time series is a ubiquitous problem in data science. Granger causality measure (GCM) and conditional GCM (cGCM) are widely used methods for identifying directed connections between time series. Both GCM and cGCM have frequency-domain formulations to characterize the dependence of time series in the spectral domain. However, the original methods were developed using a heuristic approach without rigorous theoretical explanations. To overcome the limitation, the minimum-entropy (ME) estimation approach was introduced in our previous work (Ning & Rathi, 2018) to generalize GCM and cGCM with more rigorous frequency-domain formulations. In this work, this information-theoretic framework is further generalized with three formulations for conditional causality analysis using techniques in control theory, such as state-space representations and spectral factorizations. The three conditional causal measures are developed based on different ME estimation procedures that are motivated by equivalent formulations of the classical minimum mean squared error estimation method. The relationship between the three formulations of conditional causality measures is analyzed theoretically. Their performance is evaluated using simulations and real neuroimaging data to analyze brain networks. The results show that the proposed methods provide more accurate network structures than the original approach.

This paper introduces a theoretical framework for causal inference in brain networks using time series measurements based on the principle of minimum-entropy regression. Three types of conditional causality measures are derived based on varying formulations of minimum-entropy regressions. The standard time-domain conditional Granger causality measure is formulated as a special case but with a different expression of the frequency-domain measure. The methods were evaluated using simulations and real resting-state functional MRI data of human brains and compared with standard Granger causality measures and directed transfer functions. Two new formulations of minimum-entropy-based causality measures showed better performance than other methods. The algorithms developed from this work may provide new insights to understand information flow in brain networks.

Delineating Region-Specific contributions and connectivity patterns for semantic association and categorization through ROI and Granger causality analysis.

The neural mechanisms supporting semantic association and categorization are examined in this study. Semantic association involves linking concepts through shared themes, events, or scenes, while semantic categorization organizes meanings hierarchically based on defining features. Twenty-three adults participated in an fMRI study performing categorization and association judgment tasks. Results showed stronger activation in the inferior frontal gyrus during association and marginally weaker activation in the posterior middle temporal gyrus (pMTG) during categorization. Granger causality analysis revealed bottom-up connectivity from the visual cortex to the hippocampus during semantic association, whereas semantic categorization exhibited strong reciprocal connections between the pMTG and frontal semantic control regions, together with information flow from the visual association area and hippocampus to the pars triangularis. We propose that demands on semantic retrieval, precision of semantic representation, perceptual experiences and world knowledge result in observable differences between these two semantic relations.

Cheers to anxiety: Granger causality insights on alcohol consumption patterns across 13 South American countries.

BACKGROUND: The relationship between alcohol consumption and mental health is complex; drinking may exacerbate anxiety, and in turn, anxiety can lead to excessive drinking. This study explores the relationship between alcohol consumption patterns including wine, beer, and spirits, and anxiety prevalence in selected 13 South American nations. METHODS: This study utilises secondary data spanning 29 years from 1991 to 2019 obtained from the Our World in Data database. It investigates the causal link between the prevalence of anxiety and alcohol consumption in the selected countries using the Granger causality test. RESULTS: Anxiety was found to have a unidirectional effect on wine and beer consumption in Chile, Suriname, Uruguay, and Trinidad and Tobago. Additionally, drinking alcohol consumption appears to impact anxiety levels in Brazil. Argentina demonstrates a bidirectional relationship between anxiety and all three types of alcohol consumption, with similar patterns observed in Brazil (wine and beer), Chile (spirits), and Paraguay (spirits). CONCLUSION: No significant causal relationships for alcohol consumption patterns were found in other nations. The identified Granger causal links follow four distinct directions in this study. These findings provide valuable insights for policymakers, governments, and international investors for informed decision-making regarding regulation and policy tools.

Association of Exercise with Better Olfactory Performance and Higher Functional Connectivity Between the Olfactory Cortex and the Prefrontal Cortex: A Resting-State Functional Near-Infrared Spectroscopy Study.

Background: Olfactory deterioration is suggested to be a predictor of some neurodegenerative diseases. Recent studies indicate that physical exercise has a positive relationship with olfactory performance, and a subregion in the prefrontal cortex (PFC) may play an important role in olfactory processing. The PFC is not only related to olfactory function but it also engages in complex functions such as cognition and emotional processing. Methodology: Our study compared the functional connectivity between the olfactory cortex and the PFC in healthy individuals who exercised regularly and healthy persons who did not. Those who exercised more than three times/week for at least 30 min each time were considered the exercise group, and those who did not meet this exercise criteria were considered the nonexercise group. We also assessed their odor threshold. Participants were aged 55 years or older, and the two groups were balanced for age, sex, body mass index, and educational level. Results: We found that compared with individuals who did not exercise, exercisers had a significantly lower threshold for detecting odors. In addition, the olfactory cortex had stronger connectivity with the PFC in exercisers than in nonexercisers. More specifically, when the PFC was grouped into three subregions, namely, the ventrolateral prefrontal cortex (VLPFC), dorsolateral prefrontal cortex (DLPFC), and frontopolar cortex (FPA), Pearson correlation analysis revealed stronger connectivity between the VLPFC and the orbitofrontal cortex (OFC), between the OFC and the FPA, and between the left and right OFC hemispheres in the exercisers. In addition, Granger causality indicated higher directional connectivity from the DLPFC to the OFC in exercisers than in nonexercisers. Conclusion: Our findings indicated that the exercise group not only had better olfactory performance but also had stronger functional connectivity between the olfactory cortex and the PFC than nonexercise group.

The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task.

Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding.

Computer vision and statistical insights into cycling near miss dynamics.

Across the globe, many transport bodies are advocating for increased cycling due to its health and environmental benefits. Yet, the real and perceived dangers of urban cycling remain obstacles. While serious injuries and fatalities in cycling are infrequent, "near misses"-events where a person on a bike is forced to avoid a potential crash or is unsettled by a close vehicle-are more prevalent. To understand these occurrences, researchers have turned to naturalistic studies, attaching various sensors like video cameras to bikes or cyclists. This sensor data holds the potential to unravel the risks cyclists face. Still, the sheer amount of video data often demands manual processing, limiting the scope of such studies. In this paper, we unveil a cutting-edge computer vision framework tailored for automated near-miss video analysis and for detecting various associated risk factors. Additionally, the framework can understand the statistical significance of various risk factors, providing a comprehensive understanding of the issues faced by cyclists. We shed light on the pronounced effects of factors like glare, vehicle and pedestrian presence, examining their roles in near misses through Granger causality with varied time lags. This framework enables the automated detection of multiple factors and understanding their significant weight, thus enhancing the efficiency and scope of naturalistic cycling studies. As future work, this research opens the possibility of integrating this AI framework into edge sensors through embedded AI, enabling real-time analysis.

Abnormal inter-hemispheric effective connectivity from left to right auditory regions during Mismatch Negativity (MMN) tasks in psychosis.

Anomalous Mismatch Negativity (MMN) in psychosis could be a consequence of disturbed neural oscillatory activity at sensory/perceptual stages of stimulus processing. This study investigated effective connectivity within and between the auditory regions during auditory odd-ball deviance tasks. The analyses were performed on two magnetoencephalography (MEG) datasets: one on duration MMN in a cohort with various diagnoses within the psychosis spectrum and neurotypical controls, and one on duration and pitch MMN in first-episode psychosis patients and matched neurotypical controls. We applied spectral Granger causality to MEG source-reconstructed signals to compute effective connectivity within and between the left and right auditory regions. Both experiments showed that duration-deviance detection was associated with early increases of effective connectivity in the beta band followed by increases in the alpha and theta bands, with the connectivity strength linked to the laterality of the MMN amplitude. Compared to controls, people with psychosis had overall smaller effective connectivity, particularly from left to right auditory regions, in the pathway where bilateral information converges toward lateralized processing, often rightward. Blunted MMN in psychosis might reflect a deficit in inter-hemispheric communication between auditory regions, highlighting a "dysconnection" already at preattentive stages of stimulus processing as a model system of widespread pathophysiology.

The interconnectedness of energy consumption with economic growth: A granger causality analysis.

In considering today's energy challenges, the link between the usage of renewable and non-renewable energy sources and economic growth has gained substantial policy attention. This research examines the complex relationship between these three variables to understand how non-renewable energy consumption and renewable energy consumption interact and what that means for economic growth. This study uses the Granger causality approach to explore the relationships between non-renewable energy consumption, renewable energy consumption, and economic development. It draws on a comprehensive dataset from the Word Bank database, including 152 nations from 1990 to 2019. The analysis is further disaggregated by four subgroups of countries; least developed, developed, transitional economies and developing countries. The result of this study provides valuable empirical evidence of uni-directional causality running from renewable energy consumption to economic growth and non-renewable energy consumption to economic growth in transitional economies. Furthermore, policymakers should focus on both variables when making decisions because the results show that energy consumption and economic growth are interconnected. Implementing global energy efficiency standards, reducing fossil fuel usage, and adopting regulatory measures are all viable policies for limiting adverse effects on the environment while encouraging economic development.

Multi-dimensional epidemiology and informatics data on COVID-19 wave at the end of zero COVID policy in China.

Background: China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks. Methods: Retrospective observational analyses were conducted at the conclusion of the Zero-COVID policy, integrating internet search data alongside offline records. Methodologies employed were multidimensional, encompassing lagged Spearman correlation analysis, growth rate assessments, independent sample T-tests, Granger causality examinations, and Bayesian structural time series (BSTS) models for comprehensive data scrutiny. Results: Various diseases exhibited a notable upsurge in the BDI after the policy change, consistent with the broader trajectory of the COVID-19 pandemic. Robust connections emerged between COVID-19 and diverse health conditions, predominantly impacting the respiratory, circulatory, ophthalmological, and neurological domains. Notably, 34 diseases displayed a relatively high correlation (r > 0.5) with COVID-19. Among these, 12 exhibited a growth rate exceeding 50% post-policy transition, with myocarditis escalating by 1,708% and pneumonia by 1,332%. In these 34 diseases, causal relationships have been confirmed for 23 of them, while 28 garnered validation from hospital-based evidence. Notably, 19 diseases obtained concurrent validation from both Granger causality and hospital-based data. Finally, the BSTS models approximated approximately 4,332,655 inpatients diagnosed with pneumonia nationwide during the 2 months subsequent to the policy relaxation. Conclusion: This investigation elucidated substantial associations between COVID-19 and respiratory, circulatory, ophthalmological, and neurological disorders. The outcomes from comprehensive multi-dimensional cross-over studies notably augmented the robustness of our comprehension of COVID-19's disease spectrum, advocating for the prospective utility of internet-derived data. Our research highlights the potential of Internet behavior in predicting pandemic-related syndromes, emphasizing its importance for public health strategies, resource allocation, and preparedness for future outbreaks.

Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network.

Unlike other functional integration methods that examine the relationship and correlation between two channels, effective connection reports the direct effect of one channel on another and expresses their causal relationship. In this article, we investigate and classify electroencephalographic (EEG) signals based on effective connectivity. In this study, we leverage the Granger causality (GC) relationship, a method for measuring effective connectivity, to analyze EEG signals from both healthy individuals and those with autism. The EEG signals examined in this article were recorded during the presentation of abstract images. Given the nonstationary nature of EEG signals, a vector autoregression model has been employed to model the relationships between signals across different channels. GC is then used to quantify the influence of these channels on one another. Selecting regions of interest (ROI) is a critical step, as the quality of the time periods under consideration significantly impacts the outcomes of the connectivity analysis among the electrodes. By comparing these effects in the ROI and various areas, we have distinguished healthy subjects from those suffering from autism. Furthermore, through statistical analysis, we have compared the results between healthy individuals and those with autism. It has been observed that the causal relationship between these two hemispheres is significantly weaker in healthy individuals compared to those with autism.

A Comparative Study of Causality Detection Methods in Root Cause Diagnosis: From Industrial Processes to Brain Networks.

Abstracting causal knowledge from process measurements has become an appealing topic for decades, especially for fault root cause analysis (RCA) based on signals recorded by multiple sensors in a complex system. Although many causality detection methods have been developed and applied in different fields, some research communities may have an idiosyncratic implementation of their preferred methods, with limited accessibility to the wider community. Targeting interested experimental researchers and engineers, this paper provides a comprehensive comparison of data-based causality detection methods in root cause diagnosis across two distinct domains. We provide a possible taxonomy of those methods followed by descriptions of the main motivations of those concepts. Of the two cases we investigated, one is a root cause diagnosis of plant-wide oscillations in an industrial process, while the other is the localization of the epileptogenic focus in a human brain network where the connectivity pattern is transient and even more complex. Considering the differences in various causality detection methods, we designed several sets of experiments so that for each case, a total of 11 methods could be appropriately compared under a unified and reasonable evaluation framework. In each case, these methods were implemented separately and in a standard way to infer causal interactions among multiple variables to thus establish the causal network for RCA. From the cross-domain investigation, several findings are presented along with insights into them, including an interpretative pitfall that warrants caution.

The contribution of granger causality analysis to our understanding of cardiovascular homeostasis: from cardiovascular and respiratory interactions to central autonomic network control.

Homeostatic regulation plays a fundamental role in maintenance of multicellular life. At different scales and in different biological systems, this principle allows a better understanding of biological organization. Consequently, a growing interest in studying cause-effect relations between physiological systems has emerged, such as in the fields of cardiovascular and cardiorespiratory regulations. For this, mathematical approaches such as Granger causality (GC) were applied to the field of cardiovascular physiology in the last 20 years, overcoming the limitations of previous approaches and offering new perspectives in understanding cardiac, vascular and respiratory homeostatic interactions. In clinical practice, continuous recording of clinical data of hospitalized patients or by telemetry has opened new applicability for these approaches with potential early diagnostic and prognostic information. In this review, we describe a theoretical background of approaches based on linear GC in time and frequency domains applied to detect couplings between time series of RR intervals, blood pressure and respiration. Interestingly, these tools help in understanding the contribution of homeostatic negative feedback and the anticipatory feedforward mechanisms in homeostatic cardiovascular and cardiorespiratory controls. We also describe experimental and clinical results based on these mathematical tools, consolidating previous experimental and clinical evidence on the coupling in cardiovascular and cardiorespiratory studies. Finally, we propose perspectives allowing to complete the understanding of these interactions between cardiovascular and cardiorespiratory systems, as well as the interplay between brain and cardiac, and vascular and respiratory systems, offering a high integrative view of cardiovascular and cardiorespiratory homeostatic regulation.

Intracranial Directed Connectivity Links Subregions of the Prefrontal Cortex to Major Depression.

Understanding the neural basis of major depressive disorder (MDD) is vital to guiding neuromodulatory treatments. The available evidence supports the hypothesis that MDD is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neural systems lead to diminished emotion regulation and intrusive ruminations. Recent research also points towards network changes in the brain, especially within the prefrontal cortex (PFC), as primary sources of MDD etiology. However, due to limitations in spatiotemporal resolution and clinical opportunities for intracranial recordings, this hypothesis has not been directly tested. We recorded intracranial EEG from the dorsolateral (dlPFC), orbitofrontal (OFC), and anterior cingulate cortices (ACC) in neurosurgical patients with MDD. We measured daily fluctuations in self-reported depression severity alongside directed connectivity between these PFC subregions. We focused primarily on delta oscillations (1-3 Hz), which have been linked to GABAergic inhibitory control and intracortical communication. Depression symptoms worsened when connectivity within the left vs. right PFC became imbalanced. In the left hemisphere, all directed connectivity towards the ACC, from the dlPFC and OFC, was positively correlated with depression severity. In the right hemisphere, directed connectivity between the OFC and dlPFC increased with depression severity as well. This is the first evidence that delta oscillations flowing between prefrontal subregions transiently increase intensity when people are experiencing more negative mood. These findings support the overarching hypothesis that MDD worsens with prefrontal disinhibition.

Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy.

Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures.

Energy dynamics affecting the US economy; natural energy investments, carbon emissions, production, exports and GDP per Capita.

This study investigates how carbon dioxide emissions, natural gas, energy consumption, energy investment, coal and crude oil, and per capita exports affected the economic growth of the United States from 1993 to 2023 using the Vector Error Correction (VEC) model. The findings highlight the importance of exports and energy investment in driving both short- and long-term economic growth, while also highlighting interactions between carbon emissions, coal use and crude oil. It was determined that changes in natural gas and exports affected energy investment in the short term, while coal and exports affected natural gas. These results provide valuable information about the dynamics of the American economy and contribute to our understanding of the complex interactions between various factors and their effects on economic growth, offering implications for further research and policy development to promote sustainable economic development.

Altered Effective Connectivity of the Pain Matrix in Herpes Zoster and Postherpetic Neuralgia Patients: Granger Causality Analysis of Resting-State fMRI.

BACKGROUND: Shingles can cause long-term pain and negative emotions, along with changes in brain function. In this study, Granger Causality Analysis (GCA) was used to compare herpes zoster (HZ) and postherpetic neuralgia (PHN) differences in effective connections within the "pain matrix" between patients and healthy controls to further understand patterns of interaction between brain regions and explore the relationship between changes in effective connections and clinical features. METHODS: Resting-state functional magnetic resonance imaging (fMRI) scans were performed on 55 HZ; 55 PHN; and 50 age-, sex- matched healthy controls (HCs). The brain regions associated with the pain matrix are used as the seeds of effective connectivity. GCA was used to analyze effective connections in brain regions that differed significantly between groups. Then the correlation between GCA values and clinical indicators was studied. RESULTS: Compared with HC, GCA values between the thalamus and the amygdala, between the thalamus and the precentral gyrus, from the thalamus to the postcentral gyrus, and from the parahippocampal gyrus to the amygdala, anterior cingulate gyrus were significantly reduced in HZ patients. Compared with HC, GCA values between the insular and the postcentral gyrus, from the insular to the inferior parietal lobe, and from the postcentral gyrus to the amygdala were significantly reduced in PHN patients. Compared with HZ, GCA values between the inferior parietal lobe and the parahippocampal gyrus, between the inferior parietal lobe and the anterior cingulate gyrus, and from the anterior cingulate gyrus to the amygdala were significantly increased in PHN patients. The visual analogue scale (VAS) score of PHN patients was positively correlated with the GCA value from the central posterior lobe to the insula. CONCLUSIONS: PHN and HZ patients showed a broad reduction in effective connections, mainly reflected in abnormal pain pathway regulation, pain perception, negative emotion and memory production, providing new perspectives to understand the neuroimaging mechanisms of shingles.

From Behavioral Genetics to Idiographic Science: Methodological Developments and Applications Inspired by the Work of Peter C. M. Molenaar.

This special issue is a collection of papers inspired by Dr. Molenaar's work and innovations - a tribute to his passion for advancing science and his ability to ignite a spark of creativity and innovation in multiple generations of scientists. Following Dr. Molenaar's creative breadth, the papers address a wide variety of topics - sharing of new methodological developments, ideas, and findings in idiographic science, study of intraindividual variation, behavioral genetics, model inference/identification/selection, and more.

Neural effects of dopaminergic compounds revealed by multi-site electrophysiology and interpretable machine-learning.

Background: Neuropsychopharmacological compounds may exert complex brain-wide effects due to an anatomically and genetically broad expression of their molecular targets and indirect effects via interconnected brain circuits. Electrophysiological measurements in multiple brain regions using electroencephalography (EEG) or local field potential (LFP) depth-electrodes may record fingerprints of such pharmacologically-induced changes in local activity and interregional connectivity (pEEG/pLFP). However, in order to reveal such patterns comprehensively and potentially derive mechanisms of therapeutic pharmacological effects, both activity and connectivity have to be estimated for many brain regions. This entails the problem that hundreds of electrophysiological parameters are derived from a typically small number of subjects, making frequentist statistics ill-suited for their analysis. Methods: We here present an optimized interpretable machine-learning (ML) approach which relies on predictive power in individual recording sequences to extract and quantify the robustness of compound-induced neural changes from multi-site recordings using Shapley additive explanations (SHAP) values. To evaluate this approach, we recorded LFPs in mediodorsal thalamus (MD), prefrontal cortex (PFC), dorsal hippocampus (CA1 and CA3), and ventral hippocampus (vHC) of mice after application of amphetamine or of the dopaminergic antagonists clozapine, raclopride, or SCH23390, for which effects on directed neural communication between those brain structures were so far unknown. Results: Our approach identified complex patterns of neurophysiological changes induced by each of these compounds, which were reproducible across time intervals, doses (where tested), and ML algorithms. We found, for example, that the action of clozapine in the analysed cortico-thalamo-hippocampal network entails a larger share of D1-as opposed to D2-receptor induced effects, and that the D2-antagonist raclopride reconfigures connectivity in the delta-frequency band. Furthermore, the effects of amphetamine and clozapine were surprisingly similar in terms of decreasing thalamic input to PFC and vHC, and vHC activity, whereas an increase of dorsal-hippocampal communication and of thalamic activity distinguished amphetamine from all tested anti-dopaminergic drugs. Conclusion: Our study suggests that communication from the dorsal hippocampus scales proportionally with dopamine receptor activation and demonstrates, more generally, the high complexity of neuropharmacological effects on the circuit level. We envision that the presented approach can aid in the standardization and improved data extraction in pEEG/pLFP-studies.

Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

Dynamics of Noctiluca scintillans blooms: A 20-year study in Jangmok Bay, Korea.

This 20-year study (2001-2020) conducted in Jangmok Bay, Korea, assessed the intricate relationships between environmental factors and Noctiluca scintillans blooms. Granger causality tests and PCA analysis were used to assess the impact of sea surface temperature (SST), salinity, dissolved oxygen (DO) concentration, wind patterns, rainfall, and chlorophyll-a (Chl-a) concentration on bloom dynamics. The results revealed significant, albeit delayed, influences of these variables on bloom occurrence, with SST exhibiting a notable 2-month lag and salinity a 1-month lag in their impact. Additionally, the analysis highlighted the significant roles of phosphate, ammonium, and silicate, which influenced N. scintillans blooms with lags of 1 to 3 months. The PCA demonstrates how SST and wind speed during spring and summer, along with wind direction and salinity in winter, significantly impact N. scintillans blooms. We noted not only an increase in large-scale N. scintillans blooms but also a cyclical pattern of occurrence every 3 years. These findings underscore the synergistic effects of environmental factors, highlighting the complex interplay between SST, salinity, DO concentration, and weather conditions to influence bloom patterns. This research enhances our understanding of harmful algal blooms (HABs), emphasizing the importance of a comprehensive approach that considers multiple interconnected environmental variables for predicting and managing N. scintillans blooms.