Characteristics Of Functional Brain Networks Research Articles

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma

Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG.

Analysis of Cortico-Muscular Coupling and Functional Brain Network under Different Standing Balance Paradigms.

Maintaining standing balance is essential for people to engage in productive activities in daily life. However, the process of interaction between the cortex and the muscles during balance regulation is understudied. Four balance paradigms of different difficulty were designed by closing eyes and laying sponge pad under feet. Ten healthy subjects were recruited to stand for ten 15 s trials in each paradigm. This study used simultaneously acquired electroencephalography (EEG) and electromyography (EMG) to investigate changes in the human cortico-muscular coupling relationship and functional brain network characteristics during balance control. The coherence and causality of EEG and EMG signals were calculated by magnitude-squared coherence (MSC) and transfer entropy (TE). It was found that changes in balance strategies may lead to a shift in cortico-muscular coherence (CMC) from the beta band to the gamma band when the difficulty of balance increased. As subjects performed the four standing balance paradigms, the causality of the beta band and the gamma band was stronger in the descending neural pathway than that in the ascending neural pathway. A multi-rhythmic functional brain network with 19 EEG channels was constructed and analyzed based on graph theory, showing that its topology also changed with changes in balance difficulty. These results show an active adjustment of the sensorimotor system under different balance paradigms and provide new insights into the endogenous physiological mechanisms underlying the control of standing balance.

Topological data analysis suggests human brain network reconfiguration during the transition from resting state to cognitive load

BACKGROUND: Neural networks of the brain continually adapt to changing environmental demands. The network approach in neuroscience, which focuses on the analysis of structural and functional network characteristics related to cognitive functions, is a highly promising avenue for understanding the psychophysiological mechanisms underlying the adaptive dynamics of cognitive processes. AIM: We aimed to explore how the topological features of functional connectomes in the human brain are linked to different cognitive demands. The focus was on understanding the dynamic changes in brain networks during working memory tasks to identify network characteristics inherent to working memory. METHODS: We examined the topological characteristics of functional brain networks in the resting state and cognitive load provided by the execution of the Sternberg Item Recognition Paradigm based on electroencephalographic data. Electroencephalogram traces from 67 healthy adults were processed to estimate functional connectivity using the coherence method. We propose that the topological properties of functional networks in the human brain are distinct between cognitive load and resting state, with higher integration in the networks during cognitive load. RESULTS: The topological features of functional connectomes depend on the current state of cognitive processing and change with task-induced cognitive load variation. Moreover, functional connectivity during working memory tasks showed a faster emergence of homology group generators, supporting the idea of a relationship between the initial stages of working memory execution and an increase in faster network integration, with connector hubs playing a crucial role. CONCLUSION: Collected evidence suggest that cognitive states, particularly those related to working memory, are associated with distinct topological properties of functional brain networks, highlighting the importance of network dynamics in cognitive processing.

Differences in Brain Functional Network Topology in High and Low Working Memory Performance

Nowadays the network approach in neuroscience provides a promising way of analyzing neurophysiological mechanisms that underlie psychological functions and is widely used to study working memory. To date, data obtained in neuroimaging studies links working memory with topological features of brain networks, such as increased connectivity between frontal, parietal, and temporal regions, as well as increased integration in brain networks as a whole. The present study examines the relationship between the topological characteristics of functional brain networks with the performance in the Sternberg item recognition paradigm based on electroencephalographic data. It is shown that the higher performance in Sternberg paradigm, implying a higher efficiency of the processes of encoding, storage, and retrieval of information from working memory, is associated with an increase in the integration of functional networks, expressed in differences in the clustering coefficient, participation coefficient, Wiener index and eigenvector centrality between the groups of high and low working memory task performance (p < .01). In addition, our data suggest the variability in the topological pattern of connectivity, which can be traced through changes in the magnitude of the standard deviation of the values of topological metrics during the task.

Bimanual coupling is associated with left frontocentral network activity in a task-specific way.

When performing bimanual tasks, hands are typically not controlled individually but rather as a coupled system to achieve high spatiotemporal coordination. On a brain level, intra- and interhemispheric connections networks that control the left and right hand are necessary to exchange information between hemispheres and to couple movements. Behaviorally, coupling is, however, highly task-specific requiring, for example, to maintain a specific relative phase in cyclic tasks (e.g., in- or antiphase) or to perform a role differentiated task where one hand is modulating and the other hands is stabilizing and needs to be kept as still as possible (e.g., holding a notepad and writing on it). In this study, we used electroencephalography to investigate functional brain network characteristics (task-related activation and connectivity) in bimanual force-control tasks with different coordination modes: inphase, antiphase and role-differentiated with the left- or right-hand stabilizing and the other hand manipulating. We aimed to examine i) how network characteristics differ with respect to the coordination mode and ii) how they are related to the performance. Results revealed, task-related differences in the overall activation and connectivity with role-differentiated tasks leading to higher desynchronization as compared to in- and antiphase tasks. In addition, we showed that the strength of bimanual coupling is modulated task-specifically through left-hemispheric networks including C3, FC3 and F3 electrodes. Results highlight the importance of the left frontocentral regions for bimanual coordination.

Functional network characteristics based on EEG of patients in acute ischemic stroke: A pilot study.

Ischemic stroke is a common type of stroke associated with reorganization of functional network of the brain. This pilot study aimed to investigate the characteristics of functional brain networks based on EEG in patients with acute ischemic stroke. Seven patients with ischemic stroke within 72 hours of onset and seven healthy controls were enrolled in the study. Dynamic EEG monitoring and clinical information were repeatedly collected within 72 hours (T1), on the 5th day (T2), and on the 7th day (T3) of stroke onset. A directed transfer function was employed to construct functional brain connection patterns. Graph theoretical analysis was performed to evaluate the characteristics of functional brain networks. First, we found that the brain networks of ischemic stroke patients were quite different from the healthy controls. The clustering coefficient (0.001 < Threshold < 0.2) in Delta, Theta, and Alpha bands for the patients were significantly lower (P < 0.01) and the shortest path length in all bands (0.001 < Threshold < 0.2) for the patients were significantly longer (P < 0.01). Moreover, the peaks of the shortest path length for the patients seemed to be higher in all bands with larger thresholds. Secondly, the brain networks for the patients showed a characterized time-variation pattern. The clustering coefficient (0.001 < Threshold < 0.2) of T1 was higher than that of T2 in alpha band (P < 0.01). The shortest path length (0.001 < Threshold < 0.2) of T3 was shorter than that of T2 (P < 0.01) in all bands, and the peak of T3 was numerically higher than that of T2 in all bands with narrower thresholds. Functional brain networks in patients with acute ischemic stroke showed impaired global functional integration and decreased efficiency of information transmission compared with healthy subjects. The shortening of the shortest path length during the recovery indicates neural plasticity and reorganization.

Transcutaneous Auricular Vagus Nerve Stimulation Differently Modifies Functional Brain Networks of Subjects With Different Epilepsy Types.

Epilepsy types differ by pathophysiology and prognosis. Transcutaneous auricular vagus nerve stimulation (taVNS) is a non-invasive treatment option in epilepsy. Nevertheless, its mode of action and impact on different types of epilepsy are still unknown. We investigated whether short-term taVNS differently affects local and global characteristics of EEG-derived functional brain networks in different types of epilepsy. Thirty subjects (nine with focal epilepsy, 11 with generalized epilepsy, and 10 without epilepsy or seizures) underwent a 3-h continuous EEG-recording (1 h pre-stimulation, 1 h taVNS stimulation, 1 h post-stimulation) from which we derived evolving functional brain networks. We assessed—in a time-resolved manner—important global (topological, robustness, and stability properties) and local (centralities of vertices and edges) network characteristics. Compared to the subjects with focal epilepsies and without epilepsy, those with generalized epilepsies clearly presented with different topological properties of their functional brain network already at rest. Furthermore, subjects with focal and generalized epilepsies reacted differently to the stimulation, expressed as different taVNS-induced immediate and enduring reorganization of global network characteristics. On the local network scale, no discernible spatial pattern could be detected, which points to a rather unspecific and generalized modification of brain activity. Assessing functional brain network characteristics can provide additional information for differentiating between focal and generalized epilepsy. TaVNS-related modifications of global network characteristics clearly differ between epilepsy types. Impact of such a non–pharmaceutical intervention on clinical decision-making in the treatment of different epilepsy types needs to be assessed in future studies.

Evaluating the Effect of Increasing Working Memory Load on EEG-Based Functional Brain Networks

Purpose: Working Memory (WM) plays a crucial role in many cognitive functions of the human brain. Examining how the inter-regional connectivity and characteristics of functional brain networks modulate with increasing WM load could lead to a more in-depth understanding of the WM system.&#x0D; Materials and Methods: To investigate the effect of WM load alterations on the inter-regional synchronization and functional network characteristics, we used Electroencephalogram (EEG) data recorded from 21 healthy participants during an n-back task with three load levels (0-back, 2-back, and 3-back). The networks were constructed based on the weighted Phase Lag Index (wPLI) in the theta, alpha, beta, low-gamma, and high-gamma frequency bands. After constructing the fully connected, weighted, and undirected networks, the node-to-node connections, graph-theory metrics consisting of mean Clustering coefficient (C), characteristic path Length (L), and node strength were analyzed by statistical tests.&#x0D; Results: It was revealed that in the presence of WM load (2- and 3-back tasks) compared with the WM-free condition (0-back task) within the alpha range, the Inter-Regional Functional Connectivity (IRFC), functional integration, functional segregation, and node strength in channels located at the frontal, parietal and occipital regions were significantly reduced. In the high-gamma band, IRFC was significantly higher in the difficult task (3-back) compared to the easy and moderate tasks (0- and 2-back). Besides, locally clustered connections were significantly increased in 3-back relative to the 2-back task.&#x0D; Conclusion: Inter-regional alpha synchronization and alpha-band network metrics can distinguish between the WM and WM-free tasks. In contrast, phase synchronization of high-gamma oscillations can differentiate between the levels of WM load, which demonstrates the potential of the phase-based functional connectivity and brain network metrics for predicting the WM load level.

Investigation of functional brain network reconfiguration during exposure to naturalistic stimuli using graph-theoretical analysis

Objective. One of the most significant features of the human brain is that it can dynamically reconfigure itself to adapt to a changing environment. However, dynamic interaction characteristics of the brain networks in naturalistic scenes remain unclear. Approach. We used open-source functional magnetic resonance imaging (fMRI) data from 15 participants who underwent fMRI scans while watching an audio–visual movie ‘Forrest Gump’. The community detection algorithm based on inter-subject functional correlation was used to study the time-varying functional networks only induced by the movie stimuli. The whole brain reconfiguration patterns were quantified by the temporal co-occurrence matrix that describes the probability of two brain regions engage in the same community (or putative functional module) across time and the time-varying brain modularity. Four graph metrics of integration, recruitment, spatio-temporal diversity and within-community normalised centrality were further calculated to summarise the brain network dynamic roles and hub features in their spatio-temporal topology. Main results. Our results suggest that the networks that were involved in attention and audio–visual information processing, such as the visual network, auditory network, and dorsal attention network, were considered to play a role of ‘stable loners’. By contrast, ‘unstable loner’ networks such as the default mode network (DMN) and fronto-parietal network tended to interact more flexibly with the other networks. In addition, global brain network showed significant fluctuations in modularity. The ‘stable loner’ networks always maintained high functional connectivity (FC) strength while ‘unstable loner’ networks, especially the DMN, exhibited high intra- and inter-network FC only during a low modularity period. Finally, changes in brain modularity were significantly associated with variations in emotions induced by the movie. Significance. Our findings provide new insight for understanding the dynamic interaction characteristics of functional brain networks during naturalistic stimuli.

Abnormal Brain Network Topology During Non-rapid Eye Movement Sleep and Its Correlation With Cognitive Behavioral Abnormalities in Narcolepsy Type 1.

Objective: Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) were applied to investigate the abnormalities in the topological characteristics of functional brain networks during non-rapid eye movement(NREM)sleep. And we investigated its relationship with cognitive abnormalities in patients with narcolepsy type 1 (NT1) disorder in the current study.Methods: The Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) and EEG-fMRI were applied in 25 patients with NT1 and 25 age-matched healthy controls. All subjects participated in a nocturnal video polysomnography(PSG)study, and total sleep time (TST), percentage of TST (%TST) for each sleep stage and arousal index were calculated. The Epworth Sleepiness Score (ESS) was used to measure the degree of daytime sleepiness. The EEG-fMRI study was performed simultaneously using a 3T MRI system and a 32-channel MRI-compatible EEG system during sleep. Visual scoring of EEG data was used for sleep staging. Cognitive function was assessed for all subjects using the MoCA-BJ. The fMRI data were applied to establish a whole-brain functional connectivity network for all subjects, and the topological characteristics of the whole-brain functional network were analyzed using a graph-theoretic approach. The topological parameters were compared between groups. Lastly, the correlation between topological parameters and the assessment scale using Montreal Cognition was analyzed.Results: The MoCA-BJ scores were lower in patients with NT1 than in normal controls. Whole-brain global efficiency during stage N2 sleep in patients with NT1 displayed significantly lower small-world properties than in normal controls. Whole-brain functional network global efficiency in patients with NT1 was significantly correlated with MoCA-BJ scores.Conclusion: The global efficiency of the functional brain network during stage N2 sleep in patients with NT1 and the correspondingly reduced small-world attributes were associated with cognitive impairment.

Functional brain network characteristics are associated with epilepsy severity in childhood absence epilepsy.

While cognitive impairments are not generally considered to be part of the childhood absence epilepsy (CAE) syndrome, some recent studies report cognitive, mainly attentional, deficits. Here we set out to investigate the whole brain functional network of children with CAE and controls. Furthermore, the possible relation of the functional network abnormalities with epilepsy and neurocognitive characteristics is studied. Seventeen children with childhood CAE (aged 9.2 ± 2.1 years) and 15 controls (aged 9.8 ± 1.8 years) were included. Resting state functional MRI was acquired to study the functional network. Using graph theoretical analysis, three global metrics of the functional network were investigated: the characteristic path length, the clustering coefficient, and the small-worldness. A multivariable linear regression model including age, sex, and subject motion as covariates was used to investigate group differences in the graph metrics. Subsequently, relations of the graph metrics with epilepsy and neurocognitive characteristics were assessed. Longer path lengths, weaker clustering and a lower small-world network topology were observed in children with CAE compared to controls. Moreover, longer path lengths were related to a longer duration of CAE and a higher number of absence seizure per hour. Clustering and small-worldness were not significantly related to epilepsy or neurocognitive characteristics. The organization of the functional network of children with CAE is less efficient compared to controls, and is related to disease duration. These preliminary findings suggest that CAE is associated with alterations in the functional network.

Optimized Configuration of Functional Brain Network for Processing Semantic Audiovisual Stimuli Underlying the Modulation of Attention: A Graph-Based Study.

Semantic audiovisual stimuli have a facilitatory effect on behavioral performance and influence the integration of multisensory inputs across sensory modalities. Many neuroimaging and electrophysiological studies investigated the neural mechanisms of multisensory semantic processing and reported that attention modulates the response to multisensory semantic inputs. In the present study, we designed an functional magnetic resonance imaging (fMRI) experiment of semantic discrimination using the unimodal auditory, unimodal visual and bimodal audiovisual stimuli with semantic information. By manipulating the stimuli present on attended and unattended position, we recorded the task-related fMRI data corresponding to the unimodal auditory, unimodal visual and bimodal audiovisual stimuli in attended and unattended conditions. We also recorded the fMRI data in resting state. Then the fMRI method was used together with a graph theoretical analysis to construct the functional brain networks in task-related and resting states and quantitatively characterize the topological network properties. The aim of our present study is to explore the characteristics of functional brain networks that process semantic audiovisual stimuli in attended and unattended conditions, revealing the neural mechanism of multisensory processing and the modulation of attention. The behavioral results showed that the audiovisual stimulus presented simultaneously promoted the performance of semantic discrimination task. And the analyses of network properties showed that compared with the resting-state condition, the functional networks of processing semantic audiovisual stimuli (both in attended and unattended conditions) had greater small-worldness, global efficiency, and lower clustering coefficient, characteristic path length, global efficiency and hierarchy. In addition, the hubs were concentrated in the bilateral temporal lobes, especially in the anterior temporal lobes (ATLs), which were positively correlated to reaction time (RT). Moreover, attention significantly altered the degree of small-worldness and the distribution of hubs in the functional network for processing semantic audiovisual stimuli. Our findings suggest that the topological structure of the functional brain network for processing semantic audiovisual stimulus is modulated by attention, and has the characteristics of high efficiency and low wiring cost, which maintains an optimized balance between functional segregation and integration for multisensory processing efficiently.

Expertise-related functional brain network efficiency in healthy older adults

BackgroundIn view of age-related brain changes, identifying factors that are associated with healthy aging are of great interest. In the present study, we compared the functional brain network characteristics of three groups of healthy older participants aged 61–75 years who had a different cognitive and motor training history (multi-domain group: participants who had participated in a multi-domain training; visuomotor group: participants who had participated in a visuomotor training; control group: participants with no specific training history). The study’s basic idea was to examine whether these different training histories are associated with differences in behavioral performance as well as with task-related functional brain network characteristics. Based on a high-density electroencephalographic measurement one year after training, we calculated graph-theoretical measures representing the efficiency of functional brain networks.ResultsBehaviorally, the multi-domain group performed significantly better than the visuomotor and the control groups on a multi-domain task including an inhibition domain, a visuomotor domain, and a spatial navigation domain. In terms of the functional brain network features, the multi-domain group showed significantly higher functional connectivity in a network encompassing visual, motor, executive, and memory-associated brain areas in the theta frequency band compared to the visuomotor group. These brain areas corresponded to the multi-domain task demands. Furthermore, mean connectivity of this network correlated positively with performance across both the multi-domain and the visuomotor group. In addition, the multi-domain group showed significantly enhanced processing efficiency reflected by a higher mean weighted node degree (strength) of the network as compared to the visuomotor group.ConclusionsTaken together, our study shows expertise-dependent differences in task-related functional brain networks. These network differences were evident even a year after the acquisition of the different expertise levels. Hence, the current findings can foster understanding of how expertise is positively associated with brain functioning during aging.

Attentional performance is correlated with the local regional efficiency of intrinsic brain networks.

Attention is a crucial brain function for human beings. Using neuropsychological paradigms and task-based functional brain imaging, previous studies have indicated that widely distributed brain regions are engaged in three distinct attention subsystems: alerting, orienting and executive control (EC). Here, we explored the potential contribution of spontaneous brain activity to attention by examining whether resting-state activity could account for individual differences of the attentional performance in normal individuals. The resting-state functional images and behavioral data from attention network test (ANT) task were collected in 59 healthy subjects. Graph analysis was conducted to obtain the characteristics of functional brain networks and linear regression analyses were used to explore their relationships with behavioral performances of the three attentional components. We found that there was no significant relationship between the attentional performance and the global measures, while the attentional performance was associated with specific local regional efficiency. These regions related to the scores of alerting, orienting and EC largely overlapped with the regions activated in previous task-related functional imaging studies, and were consistent with the intrinsic dorsal and ventral attention networks (DAN/VAN). In addition, the strong associations between the attentional performance and specific regional efficiency suggested that there was a possible relationship between the DAN/VAN and task performances in the ANT. We concluded that the intrinsic activity of the human brain could reflect the processing efficiency of the attention system. Our findings revealed a robust evidence for the functional significance of the efficiently organized intrinsic brain network for highly productive cognitions and the hypothesized role of the DAN/VAN at rest.

Homological scaffolds of brain functional networks.

Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10, 186–198. (doi:10.1038/nrn2618)). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects—homological cycles—associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds, a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle, we apply these tools to compare resting-state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin—the main psychoactive component of magic mushrooms. The results show that the homological structure of the brain's functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo.

Association of Structural Global Brain Network Properties with Intelligence in Normal Aging

Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60–85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R) and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient) were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience.

Functional brain network efficiency predicts intelligence

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.

Effect of tumor resection on the characteristics of functional brain networks

Brain functioning such as cognitive performance depends on the functional interactions between brain areas, namely, the functional brain networks. The functional brain networks of a group of patients with brain tumors are measured before and after tumor resection. In this work, we perform a weighted network analysis to understand the effect of neurosurgery on the characteristics of functional brain networks. Statistically significant changes in network features have been discovered in the beta (13-30 Hz) band after neurosurgery: the link weight correlation around nodes and within triangles increases which implies improvement in local efficiency of information transfer and robustness; the clustering of high link weights in a subgraph becomes stronger, which enhances the global transport capability; and the decrease in the synchronization or virus spreading threshold, revealed by the increase in the largest eigenvalue of the adjacency matrix, which suggests again the improvement of information dissemination.