Network Connectivity Analysis Research Articles

LCGNet: Local Sequential Feature Coupling Global Representation Learning for Functional Connectivity Network Analysis with fMRI.

Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learning techniques, such as convolutional neural networks (CNNs), have been used to learn high-level feature representations of FCNs for automated brain disease classification. Even though convolution operations in CNNs are good at extracting local properties of FCNs, they generally cannot well capture global temporal representations of FCNs. Recently, the transformer technique has demonstrated remarkable performance in various tasks, which is attributed to its effective self-attention mechanism in capturing the global temporal feature representations. However, it cannot effectively model the local network characteristics of FCNs. To this end, in this paper, we propose a novel network structure for Local sequential feature Coupling Global representation learning (LCGNet) to take advantage of convolutional operations and self-attention mechanisms for enhanced FCN representation learning. Specifically, we first build a dynamic FCN for each subject using an overlapped sliding window approach. We then construct three sequential components (i.e., edge-to-vertex layer, vertex-to-network layer, and network-to-temporality layer) with a dual backbone branch of CNN and transformer to extract and couple from local to global topological information of brain networks. Experimental results on two real datasets (i.e., ADNI and ADHD-200) with rs-fMRI data show the superiority of our LCGNet.

Dynamic phase-locking states and personality in sub-acute mild traumatic brain injury: An exploratory study

Research has shown that maladaptive personality characteristics, such as Neuroticism, are associated with poor outcome after mild traumatic brain injury (mTBI). The current exploratory study investigated the neural underpinnings of this process using dynamic functional network connectivity (dFNC) analyses of resting-state (rs) fMRI, and diffusion MRI (dMRI). Twenty-seven mTBI patients and 21 healthy controls (HC) were included. After measuring the Big Five personality dimensions, principal component analysis (PCA) was used to obtain a superordinate factor representing emotional instability, consisting of high Neuroticism, moderate Openness, and low Extraversion, Agreeableness, and Conscientiousness. Persistent symptoms were measured using the head injury symptom checklist at six months post-injury; symptom severity (i.e., sum of all items) was used for further analyses. For patients, brain MRI was performed in the sub-acute phase (~1 month) post-injury. Following parcellation of rs-fMRI using independent component analysis, leading eigenvector dynamic analysis (LEiDA) was performed to compute dynamic phase-locking brain states. Main patterns of brain diffusion were computed using tract-based spatial statistics followed by PCA. No differences in phase-locking state measures were found between patients and HC. Regarding dMRI, a trend significant decrease in fractional anisotropy was found in patients relative to HC, particularly in the fornix, genu of the corpus callosum, anterior and posterior corona radiata. Visiting one specific phase-locking state was associated with lower symptom severity after mTBI. This state was characterized by two clearly delineated communities (each community consisting of areas with synchronized phases): one representing an executive/saliency system, with a strong contribution of the insulae and basal ganglia; the other representing the canonical default mode network. In patients who scored high on emotional instability, this relationship was even more pronounced. Dynamic phase-locking states were not related to findings on dMRI. Altogether, our results provide preliminary evidence for the coupling between personality and dFNC in the development of long-term symptoms after mTBI.

Reduced Intra- and Inter-Network Functional Connectivity Identified in Patients with Tinnitus with and without Hearing Loss

Introduction: The aim of the study was to investigate differences in the intra- and inter-network functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) in patients with tinnitus, with (T + H) or without hearing loss (T). Methods: We performed rs-fMRI on 82 participants (21 T, 32 T + H, and 29 healthy controls). An independent component analysis (ICA) was performed to obtain the resting-state networks (RSNs) and calculate the differences in FC. Moreover, we investigated the relationships between networks using functional network connectivity analysis. Results: We identified nine major RSNs, including the auditory network; default mode network; executive control network (ECN), including the right frontoparietal network and left frontoparietal network (LFPN); somatomotor network (SMN); dorsal attention network; ventral attention network; salience network (SN); and visual network (VN). These RSNs were extracted in all groups using ICA. Compared with that in the control group, we observed reduced FC between the LFPN and VN in the T group and between the LFPN and SN in the T + H group. The inter-network connectivity analysis revealed decreased network interactions in the SMN (IC 22)-ECN (IC 2), SMN (IC 22)-VN (IC 8), and VN (IC 14)-SN (IC 3) connections in the T + H group, compared with the healthy control group. Furthermore, we observed significantly decreased network interactions in the SMN (IC 22)-VN (IC 8) in the T group. Conclusions: Our results indicated abnormalities within the brain networks of the T and T + H groups, including the SMN, ECN, and VN, compared with the control group. Furthermore, both T and T + H groups demonstrated reduced FC between the LFPN, VN, and SMN. There were no significant differences between the T and the T + H groups. Furthermore, we observed reduced FC between the right olfactory cortex and the orbital part of the right middle frontal gyrus, right precentral gyrus, left dorsolateral superior frontal gyrus, and right triangular part of the inferior frontal gyrus within the T and T + H groups. Thus, disruptions in brain regions responsible for attention, stimulus monitoring, and auditory orientation contribute to tinnitus generation.

An 8‐week compassion and mindfulness–based exposure therapy program improves posttraumatic stress symptoms

AbstractThe persistence of posttraumatic stress symptoms (PTSS) can be debilitating. However, many people experiencing such symptoms may not qualify for or may not seek treatment. Potentially contributing to ongoing residual symptoms of PTSS is emotion dysregulation. Meanwhile, the research area of mindfulness and compassion has grown to imply emotion regulation as one of its underlying mechanisms; yet, its influence on emotion regulation in PTSS cohort is unknown. Here, we explored the potential effectiveness of an 8‐week Compassion‐oriented and Mindfulness‐based Exposure Therapy (CoMET) for individuals with PTSS using a waitlist control design. A total of 28 individuals (27 females, age range = 18–39 years) participated in the study (17 CoMET; 11 waitlist control). Following CoMET, participants reported significant decreases in PTSS severity (from clinical to non‐clinical levels), emotion dysregulation and experiential avoidance, as well as significant increases in mindfulness, self‐compassion and quality of life. Electroencephalogram‐based brain network connectivity analysis revealed an increase in alpha‐band connectivity following CoMET in a network that includes the amygdala, suggesting that CoMET successfully induced changes in functional connectivity between brain regions that play a crucial role in emotion regulation. In sum, the current study demonstrated promising intervention outcomes of CoMET in effectively alleviating the symptoms of PTSS via enhanced emotion regulation.

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease

BackgroundNeuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer’s disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response.MethodsSixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline.ResultsWe found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54).ConclusionOur findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response.Relevance statementDynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease.Key points• rTMS at the left angular gyrus could induce cognitive improvement.• rTMS could suppress primary network connections to compensate for higher-order networks.• Dynamic reorganization of brain networks could predict individual treatment response to rTMS.Graphical

Mapping the human parafoveal vascular network to understand flow variability in capillaries.

Capillary flow is known to be non-homogenous between vessels and variable over time, for reasons that are poorly understood. The local properties of individual vessels have been shown to have limited explanatory power in this regard. This exploratory study investigates the association of network-level properties such as vessel depth, branch order, and distance from the feeding arteriole with capillary flow. Detailed network connectivity analysis was undertaken in 3 healthy young subjects using flood-illuminated adaptive optics retinal imaging, with axial depth of vessels determined via optical coherence tomography angiography. Forty-one out of 70 vessels studied were of terminal capillary type, i.e. fed from an arterial junction and drained by a venous junction. Approximately half of vessel junctions were amenable to fitting with a model of relative branch diameters, with only a few adhering to Murray's Law. A key parameter of the model (the junction exponent) was found to be inversely related to the average velocity (r = -0.59, p = 0.015) and trough velocity (r = -0.67, p = 0.004) in downstream vessels. Aspects of cellular flow, such as the minimum velocity, were also moderately correlated (r = 0.46, p = 0.009) with distance to the upstream feeding arteriole. Overall, this study shows that capillary network topology contributes significantly to the flow variability in retinal capillaries in human eyes. Understanding the heterogeneity in capillary flow is an important first step before pathological flow states can be properly understood. These results show that flow within capillary vessels is not affected by vessel depths but significantly influenced by the upstream feeder distance as well as the downstream vessel junction exponents, but there remains much to be uncovered regarding healthy capillary flow.

Altered morphological characteristics and structural covariance connectivity associated with verbal working memory performance in ADHD children.

Deficits in verbal working memory (VWM) observed in attention deficit hyperactivity disorder (ADHD)children can persist into adulthood. Although previous studies have identified brain regions that are activated during VWM tasks, the neural mechanisms underlying the relationship between VWM deficits remain unclear. The objective of this study was to investigate the structural covariance network connectivity and brain morphology changes that are associated with VWM performance in ADHD children. For this study, we selected 26 ADHD children and 26 healthy control (HC) participants. Participants were instructed to perform a n-back VWM task and their accuracy and response times were subsequently recorded. This research utilized voxel-based morphometry to measure the grey matter (GM) volume, and conducted structural covariance connectivity network analysis to explore the changes of brain in ADHD. Voxel-based morphometry analysis showed that lower GM volume in the right cerebellum lobule VI and the left parahippocampal gryus in ADHD children. Moreover, a positive correlation was found between the GM volume in the right cerebellum lobule VI and the accuracy of 2-back VWM task with verbal, small reward, and delayed feedback (VSD). Structural covariance network analysis found decreased structural connectivity between right cerebellum lobule VI and right precentral gyrus, right postcentral gyrus, left paracentral lobule, right superior parietal gyrus, left hippocampus in ADHD children. The low GM volume and altered structural covariance connectivity in the right cerebellum lobule VI might potentially affect VWM performance in ADHD children. The innovation of this study lies in its more focused discussion on the morphological characteristics and structural covariance connectivity of verbal working memory deficits in ADHD children, and the innovative finding of a positive correlation between gray matter volume in the right cerebellum lobule VI and accuracy in completing the 2-back VWM task with verbal instructions, small reward, and delayed feedback(VSD). This expands upon previous research by elucidating the specific brain structures involved in verbal working memory deficits in ADHD children and highlights the potential importance of the cerebellum in this cognitive process. Overall, these innovative findings advance our understanding of the neural basis of ADHD and may have important implications for the development of targeted interventions for verbal working memory deficits.

Abstract PO-020: Gene regulatory network connectivity analysis identifies novel candidate effectors of HNSC tumorigenesis

Abstract PO-020: Gene regulatory network connectivity analysis identifies novel candidate effectors of HNSC tumorigenesis

A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis.

Resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) analysis has illuminated brain network interactions across many neuropsychiatric disorders. A common analysis approach involves using hard clustering methods to identify transitory states of brain activity, and in response to this, other methods have been developed to quantify the importance of specific dFNC interactions to identified states. Some of these methods involve perturbing individual features and examining the number of samples that switch states. However, only a minority of samples switch states. As such, these methods actually identify the importance of dFNC features to the clustering of a subset of samples rather than the overall clustering. In this study, we present a novel approach that more capably identifies the importance of each feature to the overall clustering. Our approach uses fuzzy clustering to output probabilities of each sample belonging to states and then measures their Kullback-Leibler divergence after perturbation. We show the viability of our approach in the context of schizophrenia (SZ) default mode network analysis, identifying significant differences in state dynamics between individuals with SZ and healthy controls. We further compare our approach with an existing approach, showing that it captures the effects of perturbation upon most samples. We also find that interactions between the posterior cingulate cortex (PCC) and the anterior cingulate cortex and the PCC and precuneus are important across methods. We expect that our novel explainable clustering approach will enable further progress in rs-fMRI analysis and to other clustering applications.

A comparative analysis of pedestrian network connectivity and accessibility using network approximation

A comparative analysis of pedestrian network connectivity and accessibility using network approximation

Effective connectivity analysis of brain networks of mathematically gifted adolescents using transfer entropy

Using functional neuroimaging, electrophysiological techniques and neural data processing techniques, neuroscientists have found that mathematically gifted adolescents exhibit unusual neurocognitive features in the activation of task-related brain regions. Hemispheric information interaction, functional reorganization of networks, and utilization of task-related brain regions are beneficial to rapid and efficient task processing. Based on Granger causality channel selection, the transfer entropy (TE) value between effective channels was computed, and the information flow patterns in the directed functional brain networks derived from electroencephalography (EEG) data during deductive reasoning tasks were explored. We evaluated the workspace configuration patterns of the brain network and the global integration characteristics of separated brain regions using node strength, motif, directed clustering coefficient and characteristic path length in the brain networks of mathematically gifted adolescents with effective connectivity. The empirical results demonstrated that a more integrated functional network at the global level and a more efficient clique at the local level support a pattern of workspace configuration in the mathematically gifted brain that is more conducive to task-related information processing.

Supplementary materials for Effects of Repetitive Transcranial Magnetic Stimulation and Their Underlying Neural Mechanisms Evaluated with Magnetic Resonance Imaging-Based Brain Connectivity Network Analyses

supplementary materials for Effects of Repetitive Transcranial Magnetic Stimulation and Their Underlying Neural Mechanisms Evaluated with Magnetic Resonance Imaging-Based Brain Connectivity Network Analyses

Structural connectivity and brain network analyses in Parkinson's disease: A cross-sectional and longitudinal study.

Parkinson's disease (PD) is an idiopathic disease of the central nervous system characterized by both motor and non-motor symptoms. It is the second most common neurodegenerative disease. Magnetic resonance imaging (MRI) can reveal underlying brain changes associated with PD. In this study, structural connectivity and white matter networks were analyzed by diffusion MRI and graph theory in a cohort of patients with PD and a cohort of healthy controls (HC) obtained from the Parkinson's Progression Markers Initiative (PPMI) database in a cross-sectional analysis. Furthermore, we investigated longitudinal changes in the PD cohort over 36 months. Compared with the control group, participants with PD showed lower structural connectivity in several brain areas, including the corpus callosum, fornix, and uncinate fasciculus, which were also confirmed by a large effect-size. Additionally, altered connectivity between baseline and after 36 months was found in different network paths inside the white matter with a medium effect-size. Network analysis showed trends toward lower network density in PD compared with HC at baseline and after 36 months, though not significant after correction. Significant differences were observed in nodal degree and strength in several nodes. In conclusion, altered structural and network metrics in several brain regions, such as corpus callosum, fornix, and cingulum were found in PD, compared to HC. We also report altered connectivity in the PD group after 36 months, reflecting the impact of both PD pathology and aging processes. These results indicate that structural and network metrics might yield insight into network reorganization that occurs in PD.

Tracking functional network connectivity dynamics in the elderly.

Functional magnetic resonance imaging (fMRI) has shown that aging disturbs healthy brain organization and functional connectivity. However, how this age-induced alteration impacts dynamic brain function interaction has not yet been fully investigated. Dynamic function network connectivity (DFNC) analysis can produce a brain representation based on the time-varying network connectivity changes, which can be further used to study the brain aging mechanism for people at different age stages. This presented investigation examined the dynamic functional connectivity representation and its relationship with brain age for people at an elderly stage as well as in early adulthood. Specifically, the resting-state fMRI data from the University of North Carolina cohort of 34 young adults and 28 elderly participants were fed into a DFNC analysis pipeline. This DFNC pipeline forms an integrated dynamic functional connectivity (FC) analysis framework, which consists of brain functional network parcellation, dynamic FC feature extraction, and FC dynamics examination. The statistical analysis demonstrates that extensive dynamic connection changes in the elderly concerning the transient brain state and the method of functional interaction in the brain. In addition, various machine learning algorithms have been developed to verify the ability of dynamic FC features to distinguish the age stage. The fraction time of DFNC states has the highest performance, which can achieve a classification accuracy of over 88% by a decision tree. The results proved there are dynamic FC alterations in the elderly, and the alteration was found to be correlated with mnemonic discrimination ability and could have an impact on the balance of functional integration and segregation.

Immune-related toxicity and soluble profile in patients affected by solid tumors: a network approach

BackgroundImmune checkpoint inhibitors (ICIs) have particular, immune-related adverse events (irAEs), as a consequence of interfering with self-tolerance mechanisms. The incidence of irAEs varies depending on ICI class, administered dose and treatment schedule. The aim of this study was to define a baseline (T0) immune profile (IP) predictive of irAE development.MethodsA prospective, multicenter study evaluating the immune profile (IP) of 79 patients with advanced cancer and treated with anti-programmed cell death protein 1 (anti-PD-1) drugs as a first- or second-line setting was performed. The results were then correlated with irAEs onset. The IP was studied by means of multiplex assay, evaluating circulating concentration of 12 cytokines, 5 chemokines, 13 soluble immune checkpoints and 3 adhesion molecules. Indoleamine 2, 3-dioxygenase (IDO) activity was measured through a modified liquid chromatography–tandem mass spectrometry using the high-performance liquid chromatography-mass spectrometry (HPLC–MS/MS) method. A connectivity heatmap was obtained by calculating Spearman correlation coefficients. Two different networks of connectivity were constructed, based on the toxicity profile.ResultsToxicity was predominantly of low/moderate grade. High-grade irAEs were relatively rare, while cumulative toxicity was high (35%). Positive and statistically significant correlations between the cumulative toxicity and IP10 and IL8, sLAG3, sPD-L2, sHVEM, sCD137, sCD27 and sICAM-1 serum concentration were found. Moreover, patients who experienced irAEs had a markedly different connectivity pattern, characterized by disruption of most of the paired connections between cytokines, chemokines and connections of sCD137, sCD27 and sCD28, while sPDL-2 pair-wise connectivity values seemed to be intensified. Network connectivity analysis identified a total of 187 statistically significant interactions in patients without toxicity and a total of 126 statistically significant interactions in patients with toxicity. Ninety-eight interactions were common to both networks, while 29 were specifically observed in patients who experienced toxicity.ConclusionsA particular, common pattern of immune dysregulation was defined in patients developing irAEs. This immune serological profile, if confirmed in a larger patient population, could lead to the design of a personalized therapeutic strategy in order to prevent, monitor and treat irAEs at an early stage.

Basal ganglia functional connectivity network analysis does not support the 'noisy signal' hypothesis of Parkinson's disease.

The 'noisy signal' hypothesis of basal ganglia dysfunction in Parkinson's disease (PD) suggests that major motor symptoms of the disorder are caused by the development of abnormal basal ganglia activity patterns resulting in the propagation of 'noisy' signals to target systems. While such abnormal activity patterns might be useful biomarkers for the development of therapeutic interventions, correlation between specific changes in activity and PD symptoms has been inconsistently demonstrated, and raises questions concerning the accuracy of the hypothesis. Here, we tested this hypothesis by considering three nodes of the basal ganglia network, the subthalamus, globus pallidus interna, and cortex during self-paced and cued movements in patients with PD. Interactions between these regions were analyzed using measures that assess both linear and non-linear relationships. Marked changes in the network are observed with dopamine state. Specifically, we detected functional disconnection of the basal ganglia from the cortex and higher network variability in untreated PD, but various patterns of directed functional connectivity with lower network variability in treated PD. When we examine the system output, significant correlation is observed between variability in the cortico-basal ganglia network and muscle activity variability but only in the treated state. Rather than supporting a role of the basal ganglia in the transmission of noisy signals in patients with PD, these findings suggest that cortico-basal ganglia network interactions by fault or design, in the treated Parkinsonian state, are actually associated with improved cortical network output variability.

Mobile network connectivity analysis for device to device communication in 5G network

<p><span>Since long term evolved release 14 (LTE R14), the device to device (D2D) communications have become a promising technology for in-band or out-band mobile communication networks. In addition, D2D communications constitute an essential component of the fifth-generation mobile network (5G). For example, to improve capability communication, reduce the power dissipation, reduce latency within the networks and implement new applications and services. However, reducing the congestion in D2D communications and improving the mobile network connectivity are the essential problems to propose these new applications or services. This paper presents new solutions to reduce the congestion of devices around a base station and improve the performance of the D2D network; in terms of the number of connected devices or user equipment (UE). The simulation results show that our proposed solution can improve the network capacity by doubling the number of connected devices (or UE) and reducing the congestion. For this reason, our proposition makes it possible to reduce the financial cost by reducing the cost of deploying equipment. For example, instead of using two base stations, we can use only one station to connect the same number of devices.</span></p>

Practice-based post-disaster road network connectivity analysis using a data-driven percolation theory-based method

This paper proposes a data-driven percolation theory-based approach that analyzes post-disaster traffic network connectivity in the case of earthquakes. The proposed approach’s main contribution is integrating real disaster-related data and percolation theory to analyze scenario-based post-disaster traffic network connectivity. The proposed approach adopts a data-driven estimation function to associate the link-based data, including the permanent ground deformation provided by a government-sponsored disaster-related information platform. This study validates the feasibility of the proposed approach through an empirical study. We find that network connectivity decreases with a similar slope in the scenarios of earthquakes with magnitudes between 5.0 and 6.5 but begins to flatten at larger magnitudes. In addition, most of the isolated areas are located in mountainous and coastal areas.

The continuum of attention dysfunction: Evidence from dynamic functional network connectivity analysis in neurotypical adolescents.

The question of whether attention-related disorders such as attention-deficit/hyperactivity disorder (ADHD) are best understood as clinical categories or as extreme ends of a spectrum is an ongoing debate. Assessing individuals with varying degrees of attention problems and utilizing novel methodologies to assess relationships between attention and brain activity may provide key information to support the spectrum hypothesis. We scanned 91 neurotypical adolescents during rest using functional magnetic resonance imaging. We conducted static and dynamic functional network connectivity (FNC) analysis and correlated findings to behavioral metrics of ADHD, attention problems, and impulsivity. We found that dynamic FNC analysis detects significant differences in large-scale neural connectivity as a function of individual differences in attention and impulsivity that are obscured in static analysis. We show ADHD manifestations and attention problems are associated with diminished Salience Network-centered FNC and that ADHD manifestations and impulsivity are associated with prolonged periods of dynamically hyperconnected states. Importantly, our meta-state analysis results reveal a relationship between ADHD manifestations and exhibiting variable and volatile dynamic behavior such as changing meta-states more often and traveling over a greater dynamic range. These findings in non-clinical adolescents provide support for the continuum model of attention disorders.

Electroencephalographic Effective Connectivity Analysis of the Neural Networks during Gesture and Speech Production Planning in Young Adults.

Gestures and speech, as linked communicative expressions, form an integrated system. Previous functional magnetic resonance imaging studies have suggested that neural networks for gesture and spoken word production share similar brain regions consisting of fronto-temporo-parietal brain regions. However, information flow within the neural network may dynamically change during the planning of two communicative expressions and also differ between them. To investigate dynamic information flow in the neural network during the planning of gesture and spoken word generation in this study, participants were presented with spatial images and were required to plan the generation of gestures or spoken words to represent the same spatial situations. The evoked potentials in response to spatial images were recorded to analyze the effective connectivity within the neural network. An independent component analysis of the evoked potentials indicated 12 clusters of independent components, the dipoles of which were located in the bilateral fronto-temporo-parietal brain regions and on the medial wall of the frontal and parietal lobes. Comparison of effective connectivity indicated that information flow from the right middle cingulate gyrus (MCG) to the left supplementary motor area (SMA) and from the left SMA to the left precentral area increased during gesture planning compared with that of word planning. Furthermore, information flow from the right MCG to the left superior frontal gyrus also increased during gesture planning compared with that of word planning. These results suggest that information flow to the brain regions for hand praxis is more strongly activated during gesture planning than during word planning.