Correlated Brain Activity Research Articles

Non-reversibility outperforms functional connectivity in characterisation of brain states in MEG data

Characterising brain states during tasks is common practice for many neuroscientific experiments using electrophysiological modalities such as electroencephalography (EEG) and magnetoencephalography (MEG). Brain states are often described in terms of oscillatory power and correlated brain activity, i.e. functional connectivity. It is, however, not unusual to observe weak task induced functional connectivity alterations in the presence of strong task induced power modulations using classical time-frequency representation of the data. Here, we propose that non-reversibility, or the temporal asymmetry in functional interactions, may be more sensitive to characterise task induced brain states than functional connectivity. As a second step, we explore causal mechanisms of non-reversibility in MEG data using whole brain computational models. We include working memory, motor, language tasks and resting-state data from participants of the Human Connectome Project (HCP). Non-reversibility is derived from the lagged amplitude envelope correlation (LAEC), and is based on asymmetry of the forward and reversed cross-correlations of the amplitude envelopes. Using random forests, we find that non-reversibility outperforms functional connectivity in the identification of task induced brain states. Non-reversibility shows especially better sensitivity to capture bottom-up gamma induced brain states across all tasks, but also alpha band associated brain states. Using whole brain computational models we find that asymmetry in the effective connectivity and axonal conduction delays play a major role in shaping non-reversibility across the brain. Our work paves the way for better sensitivity in characterising brain states during both bottom-up as well as top-down modulation in future neuroscientific experiments.

Empirical Bayesian localization of event-related time-frequency neural activity dynamics

Accurate reconstruction of the spatio-temporal dynamics of event-related cortical oscillations across human brain regions is an important problem in functional brain imaging and human cognitive neuroscience with magnetoencephalography (MEG) and electroencephalography (EEG). The problem is challenging not only in terms of localization of complex source configurations from sensor measurements with unknown noise and interference but also for reconstruction of transient event-related time-frequency dynamics of cortical oscillations. We recently proposed a robust empirical Bayesian algorithm for simultaneous reconstruction of complex brain source activity and noise covariance, in the context of evoked and resting-state data. In this paper, we expand upon this empirical Bayesian framework for optimal reconstruction of event-related time-frequency dynamics of regional cortical oscillations, referred to as time-frequency Champagne (TFC). This framework enables imaging of five-dimensional (space, time, and frequency) event-related brain activity from M/EEG data, and can be viewed as a time-frequency optimized adaptive Bayesian beamformer. We evaluate TFC in both simulations and several real datasets, with comparisons to benchmark standards - variants of time-frequency optimized adaptive beamformers (TFBF) as well as the sLORETA algorithm. In simulations, we demonstrate several advantages in estimating time-frequency cortical oscillatory dynamics compared to benchmarks. With real MEG data, we demonstrate across many datasets that the proposed approach is robust to highly correlated brain activity and low SNR data, and is able to accurately reconstruct cortical dynamics with data from just a few epochs.

Brain Functional Connectivity Mapping of Behavioral Flexibility in Rhesus Monkeys.

The predisposition to engage in autonomous habitual behaviors has been associated with behavioral disorders, such as obsessive-compulsive disorder and addiction. Attentional set-shifting tasks (ASSTs), which incorporate changes governing the association of discriminative stimuli with contingent reinforcement, are commonly used to measure underlying processes of cognitive/behavioral flexibility. The purpose of this study was to identify primate brain networks that mediate trait-like deficits in ASST performance using resting-state fMRI. A self-pacing ASST was administered to three cohorts of rhesus monkeys (total n = 35, 18 female). Increased performance over 30 consecutive sessions segregated the monkeys into two populations, termed High Performers (HP, n = 17) and Low Performers (LP, n = 17), with one anomaly. Compared with LPs, HPs had higher rates of improving performance over sessions and completed the 8 sets/sessions with fewer errors. LP monkeys, on the other hand, spent most of each session in the first set and often did not acquire the first reversal. A whole-brain independent components analysis of resting-state fMRI under isoflurane identified four strong networks. Of these, a dual regression analysis revealed that a designated "executive control network," differed between HPs and LPs. Specific areas of connectivity in the rhesus executive control network, including frontal cortices (ventrolateral, ventromedial, and orbital) and the dorsal striatum (caudate, putamen) correlated with perseverative errors and response latency. Overall, the results identify trait-like characteristics of behavioral flexibility that are associated with correlated brain activity involving specific nuclei of frontostriatal networks.SIGNIFICANCE STATEMENT Resting state functional connectivity MRI in rhesus monkeys identified specific nuclei in frontostriatal circuitry that were associated with population differences in perseverative and impulsive aspects of cognitive flexibility.

Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization.

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.

Plasma Orexin Levels Related to Altered Brain Activity During Abstinence in Patients with Alcohol Dependence.

In vivo studies have correlated brain activity with alcohol-seeking behavior, while clinical studies have identified altered brain activity in patients with alcohol dependence (AD) even during abstinence. We aimed to explore the relationship between plasma orexin levels, brain activity, and alcohol-craving scores in patients with AD. In this pilot study, we evaluated 24 male patients with AD in remission and 25 male controls. Alcohol craving was assessed using the Obsessive Compulsive Drinking Scale (OCDS). An adapted MRI technique was used to assess global functional connectivity density (gFCD), and plasma orexin concentrations were measured by radioimmunoassay. Associations were analyzed by the Pearson correlation. Plasma orexin levels in AD patients in remission were significantly higher than those in the controls. OCDS scores correlated to orexin concentrations (r = 0.47, P < .05). Compared to the controls, all AD patients demonstrated reduced gFCD, primarily in the frontal, temporal, and parietal lobes, and increased gFCD in the accumbens nuclei and posterior insular cortex. Mean gFCD values in the accumbens nuclei significantly correlated to craving scores (r = 0.55, P < .05). Although assessed during abstinence, the reward circuits in AD patients exhibited increased activity. Orexin levels correlated to increased activity in the accumbens nuclei and craving scores. The potential clinical utility of plasma orexin levels to assess the risk of relapse in AD patients in treatment and prevention programs deserves further study.

Functional brain connectivity related to surgical skill dexterity in physical and virtual simulation environments.

.Significance: Surgical simulators, both virtual and physical, are increasingly used as training tools for teaching and assessing surgical technical skills. However, the metrics used for assessment in these simulation environments are often subjective and inconsistent.Aim: We propose functional activation metrics, derived from brain imaging measurements, to objectively assess the correspondence between brain activation with surgical motor skills for subjects with varying degrees of surgical skill.Approach: Cortical activation based on changes in the oxygenated hemoglobin (HbO) of 36 subjects was measured using functional near-infrared spectroscopy at the prefrontal cortex (PFC), primary motor cortex, and supplementary motor area (SMA) due to their association with motor skill learning. Inter-regional functional connectivity metrics, namely, wavelet coherence (WCO) and wavelet phase coherence were derived from HbO changes to correlate brain activity to surgical motor skill levels objectively.Results: One-way multivariate analysis of variance found a statistically significant difference in the inter-regional WCO metrics for physical simulator based on Wilk’s Λ for expert versus novice, , . Partial eta squared effect size for the inter-regional WCO metrics was found to be highest between the central prefrontal cortex (CPFC) and SMA, CPFC-SMA (). Two-tailed Mann–Whitney U tests with a 95% confidence interval showed baseline equivalence and a statistically significant () difference in the CPFC-SMA WPCO metrics for the physical simulator training group () versus the untrained control group () following training for 10 consecutive days in addition to the pretest and posttest days.Conclusion: We show that brain functional connectivity WCO metric corresponds to surgical motor skills in the laparoscopic physical simulators. Functional connectivity between the CPFC and the SMA is lower for subjects that exhibit expert surgical motor skills than untrained subjects in laparoscopic physical simulators.

Fatigue in brain tumor patients, towards a neuronal biomarker

BackgroundMany brain tumor patients suffer from fatigue which severely affects their quality of life. There is a lack of objective measurements for fatigue in brain tumor patients. We aimed to find a neurophysiological correlate for fatigue in brain tumor patients. For this purpose, we correlated brain activity associated with phasic alertness with self-reported ratings of fatigue. MethodsPatients with a meningioma, a low-grade glioma or a high-grade glioma (N = 63) participated in this fMRI study. Brain activity in the central executive network (CEN) and default mode network (DMN) associated with phasic alertness was correlated with self-reported fatigue measured with the multidimensional fatigue inventory (MFI-20). Follow-up analyses were performed for MFI-20 domain scores, individual regions within CEN and DMN, and the tumor sub-groups separately. ResultsMFI-20 scores correlated significantly with DMN activity associated with phasic alertness, but not with CEN activity. These results were consistent for each tumor sub-group. Within the DMN, the correlations were strongest in left and right lingual cortex, left and right cuneus, and right precuneus. DiscussionSelf-reported fatigue in brain tumor patients was associated with objective measurements of brain activity, specifically the DMN activity related to phasic alertness. This association represents an important step in the development of a biomarker for fatigue in brain tumor patients, and possibly for other patients that suffer from fatigue.

Deep Parametric Mixtures for Modeling the Functional Connectome.

Functional connectivity between brain regions is often estimated by correlating brain activity measured by resting-state fMRI in those regions. The impact of factors (e.g, disorder or substance use) are then modeled by their effects on these correlation matrices in individuals. A crucial step in better understanding their effects on brain function could lie in estimating connectomes, which encode the correlation matrices across subjects. Connectomes are mostly estimated by creating a single average for a specific cohort, which works well for binary factors (such as sex) but is unsuited for continuous ones, such as alcohol consumption. Alternative approaches based on regression methods usually model each pair of regions separately, which generally produces incoherent connectomes as correlations across multiple regions contradict each other. In this work, we address these issues by introducing a deep learning model that predicts connectomes based on factor values. The predictions are defined on a simplex spanned across correlation matrices, whose convex combination guarantees that the deep learning model generates well-formed connectomes. We present an efficient method for creating these simplexes and improve the accuracy of the entire analysis by defining loss functions based on robust norms. We show that our deep learning approach is able to produce accurate models on challenging synthetic data. Furthermore, we apply the approach to the resting-state fMRI scans of 281 subjects to study the effect of sex, alcohol, and HIV on brain function.

A Novel Scanning Algorithm for MEG/EEG imaging using Covariance Partitioning and Noise Learning.

In this paper, we present a novel scanning algorithm, called Covariance Optimization Garnering Noise for Active Cancellation (COGNAC), for magnetoencephalography (MEG) and electroencephalography (EEG) source localization. COGNAC uses a probabilistic graphical generative model for describing sensor data. This novel generative model partitions contributions to sensor data from sources at a particular scan location and from sources outside the scan location, with corresponding multi-resolution variance parameters that are estimated from data. Maximizing a convex upper bound on the marginal likelihood of the data under this generative model results in a cost function that can be optimized efficiently. Importantly, this generative model enables learning of sensor noise without the need for additional baseline or pre-stimulus data. The resulting inference algorithm is quite robust to reconstruction of highly correlated sources and to the effect of high levels of interference and noise sources. Algorithm performance was compared to representative benchmark algorithms on both simulated and real brain activity. In simulations, performance of our novel algorithm is consistently superior to benchmarks. We also demonstrate that the new algorithm is robust to correlated brain activity present in real MEG/EEG data.

Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks.

The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age.

Correspondence Between Resting-State and Episodic Memory-Task Related Networks in Elderly Subjects.

Resting-state fMRI studies demonstrated temporally synchronous fluctuations in brain activity among ensembles of brain regions, suggesting the existence of intrinsic functional networks. A spatial match between some of the resting-state networks and regional brain activation during cognitive tasks has been noted, suggesting that resting-state networks support particular cognitive abilities. However, the spatial match and predictive value of any resting-state network and regional brain activation during episodic memory is only poorly understood. In order to address this research gap, we obtained fMRI acquired both during rest and a face-name association task in 38 healthy elderly subjects. In separate independent component analyses, networks of correlated brain activity during rest or the episodic memory task were identified. For the independent components identified for task-based fMRI, the design matrix of successful encoding or retrieval trials was regressed against the time course of each of the component to identify significantly activated networks. Spatial regression was used to assess the match of resting-state networks against those related to successful memory encoding or retrieval. We found that resting-state networks covering the medial temporal, middle temporal, and frontal areas showed increased activity during successful encoding. Resting-state networks located within posterior brain regions showed increased activity during successful recognition. However, the level of resting-state network connectivity was not predictive of the task-related activity in these networks. These results suggest that a circumscribed number of functional networks detectable during rest become engaged during successful episodic memory. However, higher intrinsic connectivity at rest may not translate into higher network expression during episodic memory.

Hierarchical multiscale Bayesian algorithm for robust MEG/EEG source reconstruction

In this paper, we present a novel hierarchical multiscale Bayesian algorithm for electromagnetic brain imaging using magnetoencephalography (MEG) and electroencephalography (EEG). In particular, we present a solution to the source reconstruction problem for sources that vary in spatial extent. We define sensor data measurements using a generative probabilistic graphical model that is hierarchical across spatial scales of brain regions and voxels. We then derive a novel Bayesian algorithm for probabilistic inference with this graphical model. This algorithm enables robust reconstruction of sources that have different spatial extent, from spatially contiguous clusters of dipoles to isolated dipolar sources. We compare the new algorithm with several representative benchmarks on both simulated and real brain activities. The source locations and the correct estimation of source time courses used for the simulated data are chosen to test the performance on challenging source configurations. In simulations, performance of the novel algorithm shows superiority to several existing benchmark algorithms. We also demonstrate that the new algorithm is more robust to correlated brain activity present in real MEG and EEG data and is able to resolve distinct and functionally relevant brain areas with real MEG and EEG datasets.

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.

Loss of consciousness is related to hyper-correlated gamma-band activity in anesthetized macaques and sleeping humans

Loss of consciousness can result from a wide range of causes, including natural sleep and pharmacologically induced anesthesia. Important insights might thus come from identifying neuronal mechanisms of loss and re-emergence of consciousness independent of a specific manipulation. Therefore, to seek neuronal signatures of loss of consciousness common to sleep and anesthesia we analyzed spontaneous electrophysiological activity recorded in two experiments. First, electrocorticography (ECoG) acquired from 4 macaque monkeys anesthetized with different anesthetic agents (ketamine, medetomidine, propofol) and, second, stereo-electroencephalography (sEEG) from 10 epilepsy patients in different wake-sleep stages (wakefulness, NREM, REM). Specifically, we investigated co-activation patterns among brain areas, defined as correlations between local amplitudes of gamma-band activity. We found that resting wakefulness was associated with intermediate levels of gamma-band coupling, indicating neither complete dependence, nor full independence among brain regions. In contrast, loss of consciousness during NREM sleep and propofol anesthesia was associated with excessively correlated brain activity, as indicated by a robust increase of number and strength of positive correlations. However, such excessively correlated brain signals were not observed during REM sleep, and were present only to a limited extent during ketamine anesthesia. This might be related to the fact that, despite suppression of behavioral responsiveness, REM sleep and ketamine anesthesia often involve presence of dream-like conscious experiences. We conclude that hyper-correlated gamma-band activity might be a signature of loss of consciousness common across various manipulations and independent of behavioral responsiveness.

Exercise Training and Functional Connectivity Changes in Mild Cognitive Impairment and Healthy Elders.

Effective interventions are needed to improve brain function in mild cognitive impairment (MCI), an early stage of Alzheimer's disease (AD). The posterior cingulate cortex (PCC)/precuneus is a hub of the default mode network (DMN) and is preferentially vulnerable to disruption of functional connectivity in MCI and AD. We investigated whether 12 weeks of aerobic exercise could enhance functional connectivity of the PCC/precuneus in MCI and healthy elders. Sixteen MCI and 16 healthy elders (age range = 60-88) engaged in a supervised 12-week walking exercise intervention. Functional MRI was acquired at rest; the PCC/precuneus was used as a seed for correlated brain activity maps. A linear mixed effects model revealed a significant interaction in the right parietal lobe: the MCI group showed increased connectivity while the healthy elders showed decreased connectivity. In addition, both groups showed increased connectivity with the left postcentral gyrus. Comparing pre to post intervention changes within each group, the MCI group showed increased connectivity in 10 regions spanning frontal, parietal, temporal and insular lobes, and the cerebellum. Healthy elders did not demonstrate any significant connectivity changes. The observed results show increased functional connectivity of the PCC/precuneus in individuals with MCI after 12 weeks of moderate intensity walking exercise training. The protective effects of exercise training on cognition may be realized through the enhancement of neural recruitment mechanisms, which may possibly increase cognitive reserve. Whether these effects of exercise training may delay further cognitive decline in patients diagnosed with MCI remains to be demonstrated.

High Dimensional Nonstationary Time Series Modelling with Generalized Dynamic Semiparametric Factor Model

(High dimensional) time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science. In this article, we separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via functional factor analysis. We propose a two-step estimation procedure. At the first step, we detect the deterministic trends of the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under various situations extending current variable selection studies. At the second step, we obtain the detrended low dimensional stochastic process, but it also poses an important question: is it justified, from an inferential point of view, to base further statistical inference on the estimated stochastic time series? We show that the difference of the inference based on the estimated time series and true unobserved time series is asymptotically negligible, which finally allows one to study the dynamics of the whole high-dimensional system with a low dimensional representation together with the deterministic trend. We apply the method to our motivating empirical problems: studies of the dynamic behavior of temperatures (further used for pricing weather derivatives), implied volatilities and risk patterns and correlated brain activities (neuro-economics related) using fMRI data, where a panel version model is also presented.

Inferring objects from a multitude of oscillations

Oscillations often provide us with information of the origin. For instance, electrical oscillations measured by electroencephalograms and electrocardiograms afford clues to cognitive disorders and cardiac dysfunction, respectively. In particular, vibrations in air-pressure or sounds present rich information about circumstances. Here, we consider the problem of inferring types of coins from the sounds of their collision, and search for mechanisms that make such an inference possible. By devising a Bayesian learning algorithm and using a deep neural network, we reveal that optimizing the inference naturally leads the machines to select frequencies at which individual coins exhibit specific peaks in their sound spectra, indicating that inferences can be efficiently made by detecting the resonance sounds inherent in different coins. Both learning machines achieve high performances in correctly inferring coins. The developed methods are general and may be applicable to not only other sound identification tasks, but also various oscillatory phenomena, such as correlating brain activity to behavior as in the brain–computer interfaces.

Risk Patterns and Correlated Brain Activities. Multidimensional Statistical Analysis of fMRI Data with Application to Risk Patterns

Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we reanalyze functional magnetic resonance imaging (fMRI) data on 17 subjects which were exposed to an investment decision task from Mohr et al. (2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. Our goal is to capture the dynamic behavior of specic brain regions of all subjects in this high-dimensional time series data, by a exible factor approach resulting in a low dimensional representation. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park et al. (2009) and identify task-related activations in space and dynamics in time. Further, we classify the risk attitudes of all subjects based on the estimated lowdimensional time series. Our classication analysis successfully conrms the

Comparison of Brain Activity Correlating with Self-Report versus Narrative Attachment Measures during Conscious Appraisal of an Attachment Figure.

Objectives: The Adult Attachment Interview (AAI) has been the gold standard of attachment assessment, but requires special training. The Relationship Scales Questionnaire (RSQ) is a widely used self-report measure. We investigate how each correlates with brain activity during appraisal of subjects’ mothers.Methods: Twenty-eight women were scored on the AAI, RSQ, and mood measures. During functional magnetic resonance imaging, subjects viewed their mothers in neutral-, valence-, and salience-rating conditions. We identified regions where contrasts in brain activity between appraisal and neutral viewing conditions correlated with each measure of attachment after covarying for mood. AAI and RSQ measures were then compared in terms of the extent to which regions of correlating brain activity overlapped with “default mode network” (DMN) vs. executive frontal network (EFN) masks and cortical vs. subcortical masks. Additionally, interactions with mood were examined.Results: Salience and valence processing associated with increased thalamo-striatal, posterior cingulate, and visual cortex activity. Salience processing decreased PFC activity, whereas valence processing increased left insula activity. Activity correlating with AAI vs. RSQ measures demonstrated significantly more DMN and subcortical involvement. Interactions with mood were observed in the middle temporal gyrus and precuneus for both measures.Conclusion: The AAI appears to disproportionately correlate with conscious appraisal associated activity in DMN and subcortical structures, while the RSQ appears to tap EFN structures more extensively. Thus, the AAI may assess more interoceptive, ‘core-self’-related processes, while the RSQ captures higher-order cognitions involved in attachment. Shared interaction effects between mood and AAI and RSQ-measures may suggest that processes tapped by each belong to a common system.

Relation between functional connectivity and rhythm discrimination in children who do and do not stutter.

Our ability to perceive and produce rhythmic patterns in the environment supports fundamental human capacities ranging from music and language processing to the coordination of action. This article considers whether spontaneous correlated brain activity within a basal ganglia-thalamocortical (rhythm) network is associated with individual differences in auditory rhythm discrimination. Moreover, do children who stutter with demonstrated deficits in rhythm perception have weaker links between rhythm network functional connectivity and rhythm discrimination? All children in the study underwent a resting-state fMRI session, from which functional connectivity measures within the rhythm network were extracted from spontaneous brain activity. In a separate session, the same children completed an auditory rhythm-discrimination task, where behavioral performance was assessed using signal detection analysis. We hypothesized that in typically developing children, rhythm network functional connectivity would be associated with behavioral performance on the rhythm discrimination task, but that this relationship would be attenuated in children who stutter. Results supported our hypotheses, lending strong support for the view that (1) children who stutter have weaker rhythm network connectivity and (2) the lack of a relation between rhythm network connectivity and rhythm discrimination in children who stutter may be an important contributing factor to the etiology of stuttering.