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.

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.

Statistical investigation on the relationship between climate change, food availability, agricultural productivity, and economic expansion.

This study examined the connections between Benin's economic expansion, food production, agricultural productivity, and climate change. Using yearly statistics between 1961 and 2021, and R software version 4.2.2, we aim to: (1) Analyze how agricultural added value affects economic expansion; (2) analyze the effects of food production and temperature lagged values on economic growth; (3) investigate the different causality relationships between food production, temperature variation, agricultural added value and economic growth. To achieve these goals, statistical and econometric techniques such as Autoregressive Distributed Lags (ARDL) and the Toda-Yamamoto Granger causality framework were employed. The ARDL model verifies that there is a positive correlation between economic growth and the added value of agriculture based on empirical data. In addition, the Vector Autoregressive (VAR) model highlights the favorable impact of lagged food production values and the adverse effect of temperature fluctuations on economic growth. Granger causality analysis, employing the Toda-Yamamoto approach, unveils unidirectional links between food production and economic growth, as well as between temperature variation and agricultural added value. Interestingly, the study comes to the conclusion that there are no direct causal links between economic expansion and agricultural growth or between economic growth and temperature variance. Notably, bidirectional causality is established between livestock production and both economic growth and agricultural added value. These insights have significant implications for understanding climate change impacts on agriculture and suggest the need for adapted strategies to mitigate climate effects. Future research could focus on evaluating existing policies, exploring social and economic impacts, investigating market dynamics, and utilizing integrated assessment modeling to inform decision-making and foster sustainable economic growth in Benin's agricultural sector.

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 (ECoG) and subthalamic local field potential (LFP) 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 (EMG). 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.

Visual system structural and functional connections during face viewing in body dysmorphic disorder.

Background: Individuals with body dysmorphic disorder (BDD) perceive distortions in their appearance, which could be due to imbalances in global and local visual processing. The vertical occipital fasciculus connects dorsal and ventral visual stream regions, integrating global and local information, yet the role of this structural connection in BDD has not been explored. Here, we investigated the vertical occipital fasciculus's white matter microstructure in those with BDD and healthy controls and tested associations with psychometric measures and effective connectivity while viewing their face during fMRI. Methods: We analyzed diffusion MRI and fMRI data in 17 unmedicated adults with BDD and 21 healthy controls. For diffusion MRI, bundle-specific analysis was performed, enabling quantitative estimation of neurite density and orientation dispersion of the vertical occipital fasciculus. For task fMRI, participants naturalistically viewed photos of their own face, from which we computed effective connectivity from dorsal to ventral visual regions. Results: In BDD, neurite density was negatively correlated with appearance dissatisfaction and negatively correlated with effective connectivity. Further, those with weaker effective connectivity while viewing their face had worse BDD symptoms and worse insight. In controls, no significant relationships were found between any of the measures. There were no significant group differences in neurite density or orientation dispersion. Conclusion: Those with BDD with worse appearance dissatisfaction have a lower fraction of tissue having axons or dendrites along the vertical occipital fasciculus bundle, possibly reflecting impacting the degree of integration of global and local visual information between the dorsal and ventral visual streams. These results provide early insights into how the vertical occipital fasciculus's microstructure relates to the subjective experience of one's appearance, as well as the possibility of distinct functional-structural relationships in BDD.

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.

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.

On formal limitations of causal ecological networks.

Causal multivariate time-series analysis, combined with network theory, provide a powerful tool for studying complex ecological interactions. However, these methods have limitations often underestimated when used in graphical modelling of ecological systems. In this opinion article, I examine the relationship between formal logic methods used to describe causal networks and their inherent statistical and epistemological limitations. I argue that while these methods offer valuable insights, they are restricted by axiomatic assumptions, statistical constraints and the incompleteness of our knowledge. To prove that, I first consider causal networks as formal systems, define causality and formalize their axioms in terms of modal logic and use ecological counterexamples to question the axioms. I also highlight the statistical limitations when using multivariate time-series analysis and Granger causality to develop ecological networks, including the potential for spurious correlations among other data characteristics. Finally, I draw upon Gödel's incompleteness theorems to highlight the inherent limits of fully understanding complex networks as formal systems and conclude that causal ecological networks are subject to initial rules and data characteristics and, as any formal system, will never fully capture the intricate complexities of the systems they represent. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Regional brain activity and neural network changes in cognitive-motor dual-task interference: A functional near-infrared spectroscopy study.

Previous neuroimaging studies have reported dual-task interference (DTi) and deterioration of task performance in a cognitive-motor dual task (DT) compared to that in a single task (ST). Greater frontoparietal activity is a neural signature of DTi; nonetheless, the underlying mechanism of cortical network in DTi still remains unclear. This study aimed to investigate the regional brain activity and neural network changes during DTi induced by highly demanding cognitive-motor DT. Thirty-four right-handed healthy young adults performed the spiral-drawing task. They underwent a paced auditory serial addition test (PASAT) simultaneously or independently while their cortical activity was measured using functional near-infrared spectroscopy. Motor performance was determined using the balanced integration score (BIS), a balanced index of drawing speed and precision. The cognitive task of the PASAT was administered with two difficulty levels defined by 1 s (PASAT-1 s) and 2 s (PASAT-2 s) intervals, allowing for the serial addition of numbers. Cognitive performance was determined using the percentage of correct responses. These motor and cognitive performances were significantly reduced during DT, which combined a drawing and a cognitive task at either difficulty level, compared to those in the corresponding ST conditions. The DT conditions were also characterized by significantly increased activity in the right dorsolateral prefrontal cortex (DLPFC) compared to that in the ST conditions. Multivariate Granger causality (GC) analysis of cortical activity in the selected frontoparietal regions of interest further revealed selective top-down causal connectivity from the right DLPFC to the right inferior parietal cortex during DTs. Furthermore, changes in the frontoparietal GC connectivity strength between the PASAT-2 s DT and ST conditions significantly correlated negatively with changes in the percentage of correct responses. Therefore, DTi can occur even in cognitively proficient young adults, and the right DLPFC and frontoparietal network being crucial neural mechanisms underlying DTi. These findings provide new insights into DTi and its underlying neural mechanisms and have implications for the clinical utility of cognitive-motor DTs applied to clinical populations with cognitive decline, such as those with psychiatric and brain disorders.

Abnormal Functional Connectivity Intra- and Inter-Network in Resting-State Brain Networks of Patients with Toothache.

Objective: To separate the resting-state network of patients with dental pain using independent component analysis (ICA) and analyze abnormal changes in functional connectivity within as well as between the networks. Patients and Methods: Twenty-three patients with dental pain and 30 healthy controls participated in this study. We extracted the resting-state functional network components of both using ICA. Functional connectivity differences within 14 resting-state brain networks were analyzed at the voxel level. Directional interactions between networks were analyzed using Granger causality analysis. Subsequently, functional connectivity values and causal coefficients were assessed for correlations with clinical parameters. Results: Compared to healthy controls, we found enhanced functional connectivity in the left superior temporal gyrus of anterior protrusion network and the right Rolandic operculum of auditory network in patients with dental pain (p<0.01 and cluster-level p<0.05, Gaussian random field corrected). In contrast, functional connectivity of the right precuneus in the precuneus network was reduced, and were significantly as well as negatively correlated to those of the Visual Analogue Scale (r=-4.93, p=0.017), Hamilton Anxiety Scale (r=-0.46, p=0.027), and Hamilton Depression Scale (r=-0.563, p<0.01), using the Spearman correlation analysis. Regarding the causal relationship between resting-state brain networks, we found increased connectivity from the language network to the precuneus in patients with dental pain (p<0.05, false discovery rate corrected). However, the increase in causal coefficients from the verbal network to the precuneus network was independent of clinical parameters. Conclusion: Patients with toothache exhibited abnormal functional changes in cognitive-emotion-related brain networks, such as the salience, auditory, and precuneus networks, thereby offering a new imaging basis for understanding central neural mechanisms in dental pain patients.

Nuclear energy generation's impact on the CO2 emissions and ecological footprint among European Union countries.

This study employs Fully Modified Ordinary Least Squares, Common Correlated Effects and Dumitrescu-Hurlin panel causality techniques to investigate the environmental impacts of nuclear energy generation in European Union countries from 1990 to 2022. The ongoing debate within the European Union and the empirical contradictions in the literature, coupled with the overall singular-dimensionality surrounding the impacts of nuclear energy on the environment, necessitate a broader and comprehensive examination of its effects across various environmental dimensions. These dimensions include the presence of CO2 emissions and the ecological footprint generated. The findings reveal that nuclear energy adoption by countries tends to affect CO2 emissions but this relationship goes from CO2 to nuclear energy consumption as per the causality test, while the ecological footprint variable does not exhibit a causal relationship with nuclear energy consumption. We estimated that a higher presence of air pollutants promotes the generation of nuclear energy as an alternative to fossil fuel energy sources. The study highlights that while nuclear energy generation produces no air pollution, it does impose significant land use requirements, potentially leading to ecosystem degradation. Factors such as uranium extraction, nuclear waste management, disposal, and accidents contribute to this impact. Further research is needed to understand the specific mechanisms and factors contributing to the observed environmental degradation associated with nuclear energy generation.

Unveiling the underlying drivers of Phanerozoic marine diversification.

In investigating global patterns of biodiversity through deep time, many large-scale drivers of diversification have been proposed, both biotic and abiotic. However, few robust conclusions about these hypothesized effectors or their roles have been drawn. Here, we use a linear stochastic differential equation (SDE) framework to test for the presence of underlying drivers of diversification patterns before examining specific hypothesized drivers. Using a global dataset of observations of skeletonized marine fossils, we infer origination, extinction and sampling rates (collectively called fossil time series) throughout the Phanerozoic using a capture-mark-recapture approach. Using linear SDEs, we then compare models including and excluding hidden (i.e. unmeasured) drivers of these fossil time series. We find evidence of large-scale underlying drivers of marine Phanerozoic diversification rates and present quantitative characterizations of these. We then test whether changing global temperature, sea-level, marine sediment area or continental fragmentation could act as drivers of the fossil time series. We show that it is unlikely any of these four abiotic factors are the hidden drivers we identified, though there is evidence for correlative links between sediment area and origination/extinction rates. Our characterization of the hidden drivers of Phanerozoic diversification and sampling will aid in the search for their ultimate identities.

Spatiotemporal dynamics of cortical somatosensory network in typically developing children.

Sense of touch is essential for our interactions with external objects and fine control of hand actions. Despite extensive research on human somatosensory processing, it is still elusive how involved brain regions interact as a dynamic network in processing tactile information. Few studies probed temporal dynamics of somatosensory information flow and reported inconsistent results. Here, we examined cortical somatosensory processing through magnetic source imaging and cortico-cortical coupling dynamics. We recorded magnetoencephalography signals from typically developing children during unilateral pneumatic stimulation. Neural activities underlying somatosensory evoked fields were mapped with dynamic statistical parametric mapping, assessed with spatiotemporal activation analysis, and modeled by Granger causality. Unilateral pneumatic stimulation evoked prominent and consistent activations in the contralateral primary and secondary somatosensory areas but weaker and less consistent activations in the ipsilateral primary and secondary somatosensory areas. Activations in the contralateral primary motor cortex and supramarginal gyrus were also consistently observed. Spatiotemporal activation and Granger causality analysis revealed initial serial information flow from contralateral primary to supramarginal gyrus, contralateral primary motor cortex, and contralateral secondary and later dynamic and parallel information flows between the consistently activated contralateral cortical areas. Our study reveals the spatiotemporal dynamics of cortical somatosensory processing in the normal developing brain.

Green economic growth in BRIC and CIVETS countries: The effects of trade openness and sustainable development goals.

This study aims to analyze the impact of trade openness and Sustainable Development Goals, Financial Development, and Technology on the economic growth of Brazil, Russia, India, China and Colombia, Indonesia, Vietnam, Egypt, Turkey, South Africa countries. The present analysis employs a balanced panel data set from 1996 to 2022. This study also uses various tests, such as the Johansen-Fisher cointegration and Granger causality test. The study's findings suggest that economic growth, trade openness, Sustainable Development Goals, financial development, inflation, technology, labor forces, and financial openness have a long-term relationship among them. In the long run, a positive relationship exists between economic growth, trade openness, and the sustainable development goals index in (BRIC) and (CIVETS) countries. Based on the heterogeneous panel non-causality tests, the findings demonstrate that trade openness and Sustainable Development Goals are a unidirectional causality between trade openness, Sustainable Development Goals, and economic growth.

Today's positive affect predicts tomorrow's experience of meaningful coincidences: a cross-lagged multilevel analysis.

The perception of meaningful patterns in random arrangements and unrelated events takes place in our everyday lives, coined apophenia, synchronicity, or the experience of meaningful coincidences. However, we do not know yet what predicts this phenomenon. To investigate this, we re-analyzed a combined data set of two daily diary studies with a total of N = 169 participants (mean age 29.95 years; 54 men). We investigated if positive or negative affect (PA, NA) predicts the number of meaningful coincidences on the following day (or vice versa). By means of a cross-lagged multilevel modelling approach (Bayesian estimation) we evaluated with which of two theoretical assumptions the data are more in line. First, if meaningful coincidences are facilitated by a broader and more flexible thinking style, PA should positively predict meaningful coincidences at the following day. However, if the experience of meaningful coincidences signifies a strategy to cope with negative feeling states, NA should predict the experience of meaningful coincidences during the following day. In favour of a more flexible thinking style, we found that PA predicted the number of perceived coincidences the following day. We did not find any effect for NA, and therefore, no evidence arguing for the coping mechanism hypothesis of meaningful coincidences.

Chinese stock market integration with developed world: A portfolio diversification analysis.

This study investigates integration dynamics between the Chinese stock market and major developed counterparts-Australia, Germany, Japan, the UK, and the US-focusing on portfolio diversification. Using a comprehensive analytical approach from 2012 to 2022, encompassing events like the Belt and Road Initiative, the Shanghai market crash, US-China trade tensions, and the COVID-19 pandemic, the research employs descriptive statistics, unit root tests, cointegration analysis, and VECM-based Granger Causality Tests. Findings indicate modest integration, endorsing diversified portfolios for developed country investors due to higher returns in China with acceptable risk. Unit root analysis confirms cointegration with developed indices, indicating relatively low integration. Granger Causality Tests reveal bidirectional causality, emphasizing mutual influence. Notably, no causal link exists between the US and China, possibly due to regulatory disparities and the trade war. The study enhances understanding of Chinese stock market dynamics, supporting global economic intertwining and urging further openness of China's domestic shares for economic growth.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors.

Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.

Aberrant brain topological organization and granger causality connectivity in Parkinson's disease with impulse control disorders.

Introduction: Impulse control disorders (ICDs) refer to the common neuropsychiatric complication of Parkinson's disease (PD). The white matter (WM) topological organization and its impact on brain networks remain to be established. Methods: A total of 17 PD patients with ICD (PD-ICD), 17 without ICD (PD-NICD), and 18 healthy controls (HCs) were recruited. Graph theoretic analyses and Granger causality analyses were combined to investigate WM topological organization and the directional connection patterns of key regions. Results: Compared to PD-NICD, ICD patients showed abnormal global properties, including decreased shortest path length (Lp) and increased global efficiency (Eg). Locally, the ICD group manifested abnormal nodal topological parameters predominantly in the left middle cingulate gyrus (MCG) and left superior cerebellum. Decreased directional connectivity from the left MCG to the right medial superior frontal gyrus was observed in the PD-ICD group. ICD severity was significantly correlated with Lp and Eg. Discussion: Our findings reflected that ICD patients had excessively optimized WM topological organization, abnormally strengthened nodal structure connections within the reward network, and aberrant causal connectivity in specific cortical- limbic circuits. We hypothesized that the aberrant reward and motor inhibition circuit could play a crucial role in the emergence of ICDs.

FNIRS based study of brain network characteristics in children with cerebral palsy during bilateral lower limb movement.

BACKGROUND: Motor dysfunctions in children with cerebral palsy (CP) are caused by nonprogressive brain damage. Understanding the functional characteristics of the brain is important for rehabilitation. PURPOSE: This paper aimed to study the brain networks of children with CP during bilateral lower limb movement using functional near-infrared spectroscopy (fNIRS) and to explore effective fNIRS indices for reflecting functional brain activity. METHODS: Using fNIRS, cerebral oxygenation signals in the bilateral prefrontal cortex (LPFC/RPFC) and motor cortex (LMC/RMC) were recorded from fifteen children with spastic CP and seventeen children with typical development (CTDs) in the resting state and during bilateral lower limb movement. Functional connectivity matrices based on phase-locking values (PLVs) were calculated using Hilbert transformation, and binary networks were constructed at different sparsity levels. Network metrics such as the clustering coefficient, global efficiency, local efficiency, and transitivity were calculated. Furthermore, the time-varying curves of network metrics during movement were obtained by dividing the time window and using sparse inverse covariance matrices. Finally, conditional Granger causality (GC) was used to explore the causal relationships between different brain regions. RESULTS: Compared to CTDs, the connectivity between RMC-RPFC (p = 0.017) and RMC-LMC (p = 0.002) in the brain network was decreased in children with CP, and the clustering coefficient (p = 0.003), global efficiency (p = 0.034), local efficiency (p = 0.015), and transitivity (p = 0.009) were significantly lower. The standard deviation of the changes in global efficiency of children with CP during motion was also greater than that of CTDs. Using GC, it was found that there was a significant increase in causal strength from the RMC to the RPFC (p = 0.04) and from the RMC to the LMC (p = 0.042) in children with CP during motion. Additionally, there were significant negative correlations between the PLV of LMC-RMC (p = 0.002) and the Gross Motor Function Classification System (GMFCS) and between the GMFCS and the clustering coefficient (p = 0.01). CONCLUSIONS: During rehabilitation training of the lower limbs, there were significant differences in brain network indices between children with CP and CTDs. The indicators proposed in this paper are effective at evaluating motor function and the real-time impact of rehabilitation training on the brain network and have great potential for application in guiding clinical motor function assessment and planning rehabilitation strategies.