Establishing or Exaggerating Causality for the Gut Microbiome: Lessons from Human Microbiota-Associated Rodents.

Human diseases are increasingly linked with an altered or "dysbiotic" gut microbiota, but whether such changes are causal, consequential, or bystanders to disease is, for the most part, unresolved. Human microbiota-associated (HMA) rodents have become a cornerstone of microbiome science for addressing causal relationships between altered microbiomes and host pathology. In a systematic review, we found that 95% of published studies (36/38) on HMA rodents reported a transfer of pathological phenotypes to recipient animals, and many extrapolated the findings to make causal inferences to human diseases. We posit that this exceedingly high rate of inter-species transferable pathologies is implausible and overstates the role of the gut microbiome in human disease. We advocate for a more rigorous and critical approach for inferring causality to avoid false concepts and prevent unrealistic expectations that may undermine the credibility of microbiome science and delay its translation.

Causal connectivity measures for pulse-output network reconstruction: Analysis and applications.

The causal connectivity of a network is often inferred to understand network function. It is arguably acknowledged that the inferred causal connectivity relies on the causality measure one applies, and it may differ from the network's underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, e.g., neural networks with spike output, and address the above issues based on four commonly utilized causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking a simulated Hodgkin-Huxley network and a real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore illustrating a direct link between the causal and structural connectivity. We stress that the structural connectivity of pulse-output networks can be reconstructed pairwise without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction.

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks.

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.

Genetic correlation between circulating cytokines and risk of three ophthalmic diseases: a bidirectional two-sample Mendelian randomization study.

PURPOSE: Pathogenesis and the associated risk factors of cataracts, glaucoma, and age-related macular degeneration (AMD) remain unclear. We aimed to investigate causal relationships between circulating cytokine levels and the development of these diseases. PATIENTS AND METHODS: Genetic instrumental variables for circulating cytokines were derived from a genome-wide association study of 8293 European participants. Summary-level data for AMD, glaucoma, and senile cataract were obtained from the FinnGen database. The inverse variance weighted (IVW) was the main Mendelian randomization (MR) analysis method. The Cochran's Q, MR-Egger regression, and MR pleiotropy residual sum and outlier test were used for sensitivity analysis. RESULTS: Based on the IVW method, MR analysis demonstrated five circulating cytokines suggestively associated with AMD (SCGF-ß, 1.099 [95%CI, 1.037-1.166], P = 0.002; SCF, 1.155 [95%CI, 1.015-1.315], P = 0.029; MCP-1, 1.103 [95%CI, 1.012-1.202], P = 0.026; IL-10, 1.102 [95%CI, 1.012-1.200], P = 0.025; eotaxin, 1.086 [95%CI, 1.002-1.176], P = 0.044), five suggestively linked with glaucoma (MCP-1, 0.945 [95%CI, 0.894-0.999], P = 0.047; IL1ra, 0.886 [95%CI, 0.809-0.969], P = 0.008; IL-1ß, 0.866 [95%CI, 0.762-0.983], P = 0.027; IL-9, 0.908 [95%CI, 0.841-0.980], P = 0.014; IL2ra, 1.065 [95%CI, 1.004-1.130], P = 0.035), and four suggestively associated with senile cataract (TRAIL, 1.043 [95%CI, 1.009-1.077], P = 0.011; IL-16, 1.032 [95%CI, 1.001-1.064], P = 0.046; IL1ra, 0.942 [95%CI, 0.887-0.999], P = 0.047; FGF-basic, 1.144 [95%CI, 1.052-1.244], P = 0.002). Furthermore, sensitivity analysis results supported the above associations. CONCLUSION: This study highlights the involvement of several circulating cytokines in the development ophthalmic diseases and holds potential as viable pharmacological targets for these diseases.

Infants' understanding of the causal power of agents and tools.

Tools are objects that are manipulated by agents with the intention to cause an effect in the world. We show that the cognitive capacity to understand tools is present in young infants, even if these tools produce arbitrary, causally opaque effects. In experiments 1-2, we used pupillometry to show that 8-mo-old infants infer an invisible causal contact to account for the-otherwise unexplained-motion of a ball. In experiments 3, we probed 8-mo-old infants' account of a state change event (flickering of a cube) that lies outside of the explanatory power of intuitive physics. Infants repeatedly watched an intentional agent launch a ball behind an occluder. After a short delay, a cube, positioned at the other end of the occluder began flickering. Rare unoccluded events served to probe infants' representation of what happened behind the occluder. Infants exhibited larger pupil dilation, signaling more surprise, when the ball stopped before touching the cube, than when it contacted the cube, suggesting that infants inferred that the cause of the state change was contact between the ball and the cube. This effect was canceled in experiment 4, when an inanimate sphere replaced the intentional agent. Altogether, results suggest that, in the infants' eyes, a ball (an inanimate object) has the power to cause an arbitrary state change, but only if it inherits this power from an intentional agent. Eight-month-olds are thus capable of representing complex event structures, involving an intentional agent causing a change with a tool.

Machine-guided discovery of a real-world rogue wave model.

Big data and large-scale machine learning have had a profound impact on science and engineering, particularly in fields focused on forecasting and prediction. Yet, it is still not clear how we can use the superior pattern-matching abilities of machine learning models for scientific discovery. This is because the goals of machine learning and science are generally not aligned. In addition to being accurate, scientific theories must also be causally consistent with the underlying physical process and allow for human analysis, reasoning, and manipulation to advance the field. In this paper, we present a case study on discovering a symbolic model for oceanic rogue waves from data using causal analysis, deep learning, parsimony-guided model selection, and symbolic regression. We train an artificial neural network on causal features from an extensive dataset of observations from wave buoys, while selecting for predictive performance and causal invariance. We apply symbolic regression to distill this black-box model into a mathematical equation that retains the neural network's predictive capabilities, while allowing for interpretation in the context of existing wave theory. The resulting model reproduces known behavior, generates well-calibrated probabilities, and achieves better predictive scores on unseen data than current theory. This showcases how machine learning can facilitate inductive scientific discovery and paves the way for more accurate rogue wave forecasting.

Single-cell causal network inferred by cross-mapping entropy.

Gene regulatory networks (GRNs) reveal the complex molecular interactions that govern cell state. However, it is challenging for identifying causal relations among genes due to noisy data and molecular nonlinearity. Here, we propose a novel causal criterion, neighbor cross-mapping entropy (NME), for inferring GRNs from both steady data and time-series data. NME is designed to quantify 'continuous causality' or functional dependency from one variable to another based on their function continuity with varying neighbor sizes. NME shows superior performance on benchmark datasets, comparing with existing methods. By applying to scRNA-seq datasets, NME not only reliably inferred GRNs for cell types but also identified cell states. Based on the inferred GRNs and further their activity matrices, NME showed better performance in single-cell clustering and downstream analyses. In summary, based on continuous causality, NME provides a powerful tool in inferring causal regulations of GRNs between genes from scRNA-seq data, which is further exploited to identify novel cell types/states and predict cell type-specific network modules.

Molecular causality in the advent of foundation models.

Correlation is not causation: this simple and uncontroversial statement has far-reaching implications. Defining and applying causality in biomedical research has posed significant challenges to the scientific community. In this perspective, we attempt to connect the partly disparate fields of systems biology, causal reasoning, and machine learning to inform future approaches in the field of systems biology and molecular medicine.

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.

Identifying the genetic association between the cerebral cortex and fibromyalgia.

Fibromyalgia (FM) is a central sensitization syndrome that is strongly associated with the cerebral cortex. This study used bidirectional two-sample Mendelian randomization (MR) analysis to investigate the bidirectional causality between FM and the cortical surface area and cortical thickness of 34 brain regions. Inverse variance weighted (IVW) was used as the primary method for this study, and sensitivity analyses further supported the results. The forward MR analysis revealed that genetically determined thinner cortical thickness in the parstriangularis (OR = 0.0567 mm, PIVW = 0.0463), caudal middle frontal (OR = 0.0346 mm, PIVW = 0.0433), and rostral middle frontal (OR = 0.0285 mm, PIVW = 0.0463) was associated with FM. Additionally, a reduced genetically determined cortical surface area in the pericalcarine (OR = 0.9988 mm2, PIVW = 0.0085) was associated with an increased risk of FM. Conversely, reverse MR indicated that FM was associated with cortical thickness in the caudal middle frontal region (ß = -0.0035 mm, PIVW = 0.0265), fusiform region (ß = 0.0024 mm, SE = 0.0012, PIVW = 0.0440), the cortical surface area in the supramarginal (ß = -9.3938 mm2, PIVW = 0.0132), and postcentral regions (ß = -6.3137 mm2, PIVW = 0.0360). Reduced cortical thickness in the caudal middle frontal gyrus is shown to have a significant relationship with FM prevalence in a bidirectional causal analysis.

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.

Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors.

Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.

The dynamic causality brain network reflects whether the working memory is solidified.

Working memory, which is foundational to higher cognitive function, is the "sketchpad of volitional control." Successful working memory is the inevitable outcome of the individual's active control and manipulation of thoughts and turning them into internal goals during which the causal brain processes information in real time. However, little is known about the dynamic causality among distributed brain regions behind thought control that underpins successful working memory. In our present study, given that correct responses and incorrect ones did not differ in either contralateral delay activity or alpha suppression, further rooting on the high-temporal-resolution EEG time-varying directed network analysis, we revealed that successful working memory depended on both much stronger top-down connections from the frontal to the temporal lobe and bottom-up linkages from the occipital to the temporal lobe, during the early maintenance period, as well as top-down flows from the frontal lobe to the central areas as the delay behavior approached. Additionally, the correlation between behavioral performance and casual interactions increased over time, especially as memory-guided delayed behavior approached. Notably, when using the network metrics as features, time-resolved multiple linear regression of overall behavioral accuracy was exactly achieved as delayed behavior approached. These results indicate that accurate memory depends on dynamic switching of causal network connections and shifting to more task-related patterns during which the appropriate intervention may help enhance memory.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data.

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

Decomposing predictability to identify dominant causal drivers in complex ecosystems.

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.

Associations between ticagrelor use and the risk of infections: A Mendelian randomization study.

AIMS: We conducted a Mendelian randomization (MR) study to elucidate the anti-infective effects of ticagrelor. METHODS AND RESULTS: Single-nucleotide polymorphisms (SNPs) associated with serum levels of ticagrelor or its major metabolite AR-C124910XX (ARC) in the PLATelet inhibition and patient Outcomes trial were selected as genetic proxies for ticagrelor exposure. Positive control analyses indicated that genetically surrogated serum ticagrelor levels (six SNPs) but not ARC levels (two SNPs) were significantly associated with lower risks of coronary heart disease. Therefore, the six SNPs were used as genetic instruments for ticagrelor exposure, and the genome-wide association study data for five infection outcomes were derived from the UK Biobank and FinnGen consortium. The two-sample MR analyses based on inverse variance-weighted methods indicated that genetic liability to ticagrelor exposure could reduce the risk of bacterial pneumonia (odds ratio [OR]: 0.82, 95% confidence interval [CI]: 0.71-0.95, P = 8.75E-03) and sepsis (OR: 0.83, 95% CI: 0.73-0.94, P = 3.69E-03); however, no causal relationship between ticagrelor exposure and upper respiratory infection, pneumonia, and urinary tract infection was detected. Extensive sensitivity analyses corroborated these findings. CONCLUSION: Our MR study provides further evidence for the preventive effects of ticagrelor on bacterial pneumonia and sepsis.

Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins.

An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD-dependent isocitrate dehydrogenase are determined by a perturbation-scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality.

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.

Causal associations of immune cell phenotypes with migraine: A mendelian randomization study.

The interaction between the immune system and the brain, crucial for blood-brain barrier integrity, is a potential factor in migraine development. Although there's evidence of a connection between immune dysregulation and migraine, a clear causal link has been lacking. To bridge this knowledge gap, we performed a two-sample Mendelian randomization (MR) analysis of 731 immune cell phenotypes to determine their causality with migraine, of which parameters included fluorescence, cell abundance, count, and morphology. Sensitivity and pleiotropy checks validated our findings. After applying a false discovery rate correction, our MR study identified 35 of 731 immune phenotypes with a significant causal link to migraine (p < 0.05). Of these, 24 showed a protective effect (inverse variance weighting : p < 0.05, odds ratio <1), and 11 were risk factors (inverse variance weighting : p < 0.05, odds ratio >1). Although limited by population sample size and potential population-specific genetic variations, our study uncovers a significant genetic link between certain immune cell markers and migraine, providing new insights into the disorder's pathophysiology. These discoveries are crucial for developing targeted biomarkers and personalized treatments. The research enhances our understanding of immune cells' role in migraine and may substantially improve patient outcomes and lessen its socio-economic impact.

Beta- and gamma-band cortico-cortical interactions support naturalistic reading of continuous text.

Large-scale integration of information across cortical structures, building on neural connectivity, has been proposed to be a key element in supporting human cognitive processing. In electrophysiological neuroimaging studies of reading, quantification of neural interactions has been limited to the level of isolated words or sentences due to artefacts induced by eye movements. Here, we combined magnetoencephalography recording with advanced artefact rejection tools to investigate both cortico-cortical coherence and directed neural interactions during naturalistic reading of full-page texts. Our results show that reading versus visual scanning of text was associated with wide-spread increases of cortico-cortical coherence in the beta and gamma bands. We further show that the reading task was linked to increased directed neural interactions compared to the scanning task across a sparse set of connections within a wide range of frequencies. Together, the results demonstrate that neural connectivity flexibly builds on different frequency bands to support continuous natural reading.