Cognition is related to resting-state small-world network topology: an magnetoencephalographic study

Sex Differences in Alterations of Brain Functional Network in Tobacco Use Disorder

INTRODUCTION

Many studies have found sex differences in alterations of brain function in cigarette-smoking adults from the perspective of functional activity or connectivity. However, no studies have systematically found different alteration patterns in brain functional topology of cigarette-smoking men and women from three perspectives: nodal and network efficiency and modular connections.

AIMS AND METHODS

Fifty-six tobacco use disorder (TUD) participants (25 women) and 66 non-TUD participants (28 women) underwent a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed, and a two-way analysis of covariance with false discovery rate correction (q < 0.05) was performed to investigate whether men and women TUD participants had different alterations in the topological features at global, modular, and nodal levels.

RESULTS

Compared to non-TUD participants, men but not women TUD participants showed significantly lower global efficiency (lower intermodular connections between the visual and executive control and between the visual and subcortical modules did not pass the correction) and significantly lower nodal global efficiency in the right superior occipital gyrus, bilateral fusiform gyrus, the right pallidum, right putamen, the bilateral paracentral lobule, the postcentral gyrus, and lower nodal local efficiency in the left paracentral lobule.

CONCLUSIONS

Men and women TUD participants have different topological properties of brain functional network, which may contribute to our understanding of neural mechanisms underlying sex differences in TUD.

IMPLICATIONS

Compared to non-TUD participants, we found men but not women TUD participants with significantly lower network metrics at global, modular, and nodal levels, which could improve our understanding of neural mechanisms underlying sex differences in TUD and lay a solid foundation for future sex-based TUD prevention and treatment.

Sex differences in fetal brain functional network topology

The fetal period is a critical stage in brain development, and understanding the characteristics of the fetal brain is crucial. Although some studies have explored aspects of fetal brain functional networks, few have specifically focused on sex differences in brain network characteristics. We adopted the graph theory method to calculate brain network functional connectivity and topology properties (including global and nodal properties), and further compared the differences in these parameters between male and female fetuses. We found that male fetuses showed an increased clustering coefficient and local efficiency than female fetuses, but no significant group differences concerning other graph parameters and the functional connectivity matrix. Our study suggests the existence of sex-related distinctions in the topological properties of the brain network at the fetal stage of development and demonstrates an increase in brain network separation in male fetuses compared with female fetuses.

Altered grey matter structural covariance in chronic moderate-severe traumatic brain injury

Traumatic brain injury (TBI) alters brain network connectivity. Structural covariance networks (SCNs) reflect morphological covariation between brain regions. SCNs may elucidate how altered brain network topology in TBI influences long-term outcomes. Here, we assessed whether SCN organisation is altered in individuals with chronic moderate-severe TBI (≥ 10 years post-injury) and associations with cognitive performance. This case-control study included fifty individuals with chronic moderate-severe TBI compared to 75 healthy controls recruited from an ongoing longitudinal head injury outcome study. SCNs were constructed using grey matter volume measurements from T1-weighted MRI images. Global and regional SCN organisation in relation to group membership and cognitive ability was examined using regression analyses. Globally, TBI participants had reduced small-worldness, longer characteristic path length, higher clustering, and higher modularity globally (p < 0.05). Regionally, TBI participants had greater betweenness centrality (p < 0.05) in frontal and central areas of the cortex. No significant associations were observed between global network measures and cognitive ability in participants with TBI (p > 0.05). Chronic moderate-severe TBI was associated with a shift towards a more segregated global network topology and altered organisation in frontal and central brain regions. There was no evidence that SCNs are associated with cognition.

Multiscale network neuroscience in neuro-oncology: How tumors, brain networks, and behavior connect across scales

Network neuroscience refers to the investigation of brain networks across different spatial and temporal scales, and has become a leading framework to understand the biology and functioning of the brain. In neuro-oncology, the study of brain networks has revealed many insights into the structure and function of cells, circuits, and the entire brain, and their association with both functional status (e.g., cognition) and survival. This review connects network findings from different scales of investigation, with the combined aim of informing neuro-oncological healthcare professionals on this exciting new field and also delineating the promising avenues for future translational and clinical research that may allow for application of network methods in neuro-oncological care.

Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study

Background

It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking.

Objectives

We aimed to characterize neurooncological patients' network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy.

Methods

Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts.

Results

We included 41 patients (21 men), with a mean age of 60.1 years (range 23-82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p1-30Hz = 0.002, pγ = 0.002, pβ = 0.002, pα = 0.002, pθ = 0.024, and pδ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p1-30Hz = 0.031, pδ = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (pθ = 0.048) and decrease in WB node degree (pα = 0.039) in PSEs versus PNSEs at the uncorrected level.

Conclusion

Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain's functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings.

Functional network segregation is associated with higher functional connectivity in endurance runners

Neuroimaging studies have identified significant differences in brain structure, function, and connectivity between endurance runners and healthy controls. However, the topological organization of large-scale functional brain networks remains unexplored in endurance runners. Using resting-state functional magnetic resonance imaging data, this study examined the differences in the topological organization of functional networks between endurance runners (n = 22) and healthy controls (n = 20). Endurance runners had significantly higher clustering coefficients in the whole-brain functional network than healthy controls, but the two did not differ regarding the shortest path length or small-world index. Using network-based statistics, we identified one subnetwork in endurance runners with higher functional connectivity than healthy controls, and the mean functional connectivity of the subnetwork significantly correlated with the three aforementioned small-world parameters. In this subnetwork, the mean clustering coefficient of nodes associated with short-range connections was higher in endurance runners than in healthy controls, but the mean clustering coefficient of nodes associated with long-range connections did not differ between the two groups. In conclusion, using graph theoretical approaches, we revealed significant differences in the topological organization of the whole-brain functional network and functional connectivity between endurance runners and healthy controls. The relationship between these two features suggests that a more segregated network may arise from the optimization of the identified subnetwork in endurance runners. These findings are possibly the neural basis underlying the good performance of endurance runners in endurance running.

Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score

Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome ProjectN = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.

Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks

The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.

Changes in functional connectivity in newly diagnosed self-limited epilepsy with centrotemporal spikes and cognitive impairment: An MEG study

PURPOSE

Our purpose was to explore the relationship between cognitive impairment and neural network changes in patients newly diagnosed with self-limited epilepsy with centrotemporal spikes (SeLECTS).

METHODS

The Wechsler Intelligence Scale for Children, fourth edition was used to divide all SeLECTS patients into two groups: patients with full-scale intelligence quotient (FSIQ) below 80 that corresponded to cognitive impairment, and patients with FSIQ above 80 that corresponded to a normal cognitive function. The data on the resting state were recorded using magnetoencephalography. The properties of the networks were analyzed using graph theory (GT) analysis.

RESULTS

The functional connectivity (FC) of the frontal cortex in patients with FSIQ < 80 was reduced in the 12-30 Hz frequency band, and the FC of the posterior cingulate cortex was reduced in the 80-250 and 250-500 Hz frequency bands. The GT analysis showed that patients in the FSIQ < 80 group had higher strength in the 8-12 and 12-30 Hz frequency bands than those in the healthy control and FSIQ > 80 group. However, the path length was reduced in the 80-250 Hz band, and the clustering coefficient was reduced in the 12-30, 80-250, and 250-500 Hz frequency bands. Moreover, the receiver operator characteristic analysis showed that the clustering coefficient in the 12-30 and 80-250 Hz frequency bands, as well as the path length in the 80-250 Hz frequency band possessed a good discriminative ability in distinguishing the FSIQ > 80 group.

CONCLUSIONS

SeLECTS patients with cognitive impairment in the early stage of the disease developed disordered networks in cognitive-related brain regions. The clustering coefficient in the 12-30 and 80-250 Hz frequency bands as well as the path length in the 80-250 Hz frequency band might be good indicators to distinguish the cognitive impairment of SeLECTS patients at the early stage.

Assessment of EEG-based functional connectivity in response to haptic delay

Haptic technologies enable users to physically interact with remote or virtual environments by applying force, vibration, or motion via haptic interfaces. However, the delivery of timely haptic feedback remains a challenge due to the stringent computation and communication requirements associated with haptic data transfer. Haptic delay disrupts the realism of the user experience and interferes with the quality of interaction. Research efforts have been devoted to studying the neural correlates of delayed sensory stimulation to better understand and thus mitigate the impact of delay. However, little is known about the functional neural networks that process haptic delay. This paper investigates the underlying neural networks associated with processing haptic delay in passive and active haptic interactions. Nineteen participants completed a visuo-haptic task using a computer screen and a haptic device while electroencephalography (EEG) data were being recorded. A combined approach based on phase locking value (PLV) functional connectivity and graph theory was used. To assay the effects of haptic delay on functional connectivity, we evaluate a global connectivity property through the small-worldness index and a local connectivity property through the nodal strength index. Results suggest that the brain exhibits significantly different network characteristics when a haptic delay is introduced. Haptic delay caused an increased manifestation of the small-worldness index in the delta and theta bands as well as an increased nodal strength index in the middle central region. Inter-regional connectivity analysis showed that the middle central region was significantly connected to the parietal and occipital regions as a result of haptic delay. These results are expected to indicate the detection of conflicting visuo-haptic information at the middle central region and their respective resolution and integration at the parietal and occipital regions.

Regional healthy brain activity, glioma occurrence and symptomatology

It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients’ tumour locations relates to tumour and patient characteristics.

Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients’ individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics.

As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients’ individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients.

In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients’ individual tumour locations may capture both tumour biology and patients’ performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of ‘cancer neuroscience’.

MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.

Connectomic Profiles and Cognitive Trajectories After Epilepsy Surgery in Children

OBJECTIVE

Neurocognitive outcomes following surgery for temporal lobe epilepsy in childhood are variable. Postoperative changes are not directly predicted by seizure-freedom and associations between epilepsy, neuropsychological function, and developing neural networks are poorly understood. Here, we leveraged whole-brain connectomic profiling in magnetoencephalography (MEG) to retrospectively study associations between brain connectivity and neuropsychological function in children with temporal lobe epilepsy undergoing resective surgery.

METHODS

Clinical and MEG data were retrospectively analyzed for children who underwent temporal lobe epilepsy surgery at the Hospital for Sick Children from 2000 to 2021. Resting-state connectomes were constructed from neuromagnetic oscillations via the weighted phase lag index. Using a partial least-squares (PLS) approach, multidimensional associations between patient connectomes, neuropsychological scores, and clinical covariates were assessed. Bootstrap resampling statistics were performed to assess statistical significance.

RESULTS

A total of 133 medical records were reviewed, and 5 PLS analyses were performed. Each PLS analysis probed a particular neuropsychological domain and the associations between its baseline and post-operative scores and the connectomic data. In each PLS analysis, a significant latent variable was identified, representing a specific percentage of the variance in the data, and relating neural networks to clinical covariates, which included changes in rote verbal memory (N=41, p = 0.01, σ² = 0.38), narrative/verbal memory (N=57, p = 0.00, σ² = 0.52), visual memory (N=51, p = 0.00, σ² = 0.43), working memory (N=44, p = 0.00, σ² = 0.52), and overall intellectual function (N=59, p = 0.00, σ² = 0.55). Children with more diffuse, bilateral intrinsic connectivity across several frequency bands showed lower scores on all neuropsychological assessments but demonstrated a greater propensity for gains following resective surgery.

CONCLUSION

Here, we report that connectomes characterized by diffuse connectivity, reminiscent of developmentally immature networks, are associated with lower pre-operative cognition and post-operative cognitive improvement. These findings provide a potential means to understand neurocognitive function in children with temporal lobe epilepsy and expected changes post-operatively.

Global and localized network characteristics of the resting brain predict and adapt to foreign language learning in older adults

Resting brain (rs) activity has been shown to be a reliable predictor of the level of foreign language (L2) proficiency younger adults can achieve in a given time-period. Since rs properties change over the lifespan, we investigated whether L2 attainment in older adults (aged 64-74 years) is also predicted by individual differences in rs activity, and to what extent rs activity itself changes as a function of L2 proficiency. To assess how neuronal assemblies communicate at specific frequencies to facilitate L2 development, we examined localized and global measures (Minimum Spanning Trees) of connectivity. Results showed that central organization within the beta band (~ 13-29.5 Hz) predicted measures of L2 complexity, fluency and accuracy, with the latter additionally predicted by a left-lateralized centro-parietal beta network. In contrast, reduced connectivity in a right-lateralized alpha (~ 7.5-12.5 Hz) network predicted development of L2 complexity. As accuracy improved, so did central organization in beta, whereas fluency improvements were reflected in localized changes within an interhemispheric beta network. Our findings highlight the importance of global and localized network efficiency and the role of beta oscillations for L2 learning and suggest plasticity even in the ageing brain. We interpret the findings against the background of networks identified in socio-cognitive processes.

Adaptive and maladaptive brain functional network reorganization after stroke in hemianopia patients: an EEG-tracking study

OBJECTIVE

Hemianopia following occipital stroke is believed to be mainly due to local damage at or near the lesion site. Yet, MRI studies suggest functional connectivity network (FCN) reorganization also in distant brain regions. Because it is unclear if reorganization is adaptive or maladaptive, compensating for, or aggravating vision loss, we characterized FCNs electrophysiologically to explore local and global brain plasticity and correlated FCN reorganization with visual performance.

METHODS

Resting-state EEG was recorded in chronic, unilateral stroke patients and healthy age-matched controls (n=24 each). The correlation of oscillating EEG activity was calculated with the imaginary part of coherence between pairs of interested regions, and FCN graph theory metrics (degree, strength, clustering coefficient) were correlated with stimulus detection and reaction time.

RESULTS

Stroke brains showed altered FCNs in the alpha- and beta-band in numerous occipital, temporal and frontal brain structures. On a global level, FCN had a less efficient network organization while on the local level node networks reorganized especially in the intact hemisphere. Here, the occipital network was 58% more rigid (with a more "regular" network structure) while the temporal network was 32% more efficient (showing greater "small-worldness"), both of which correlated with worse or better visual processing, respectively.

CONCLUSIONS

Occipital stroke is associated with both local and global FCN reorganization, but this can be both, adaptive and maladaptive. We propose that the more "regular" FCN structure in the intact visual cortex indicates maladaptive plasticity where less processing efficacy with reduced signal/noise ratio may cause perceptual deficits in the intact visual field. In contrast, reorganization in intact temporal brain regions is presumably adaptive, possibly supporting enhanced peripheral movement perception.

Sex-specific disruption in corticospinal excitability and hemispheric (a)symmetry in multiple sclerosis

Multiple Sclerosis (MS) is a neurodegenerative disease in which pathophysiology and symptom progression presents differently between the sexes. In a cohort of people with MS (n = 110), we used transcranial magnetic stimulation (TMS) to investigate sex differences in corticospinal excitability (CSE) and sex-specific relationships between CSE and cognitive function. Although demographics and disease characteristics did not differ between sexes, males were more likely to have cognitive impairment as measured by the Montreal Cognitive Assessment (MoCA); 53.3% compared to females at 26.3%. Greater CSE asymmetry was noted in females compared to males. Females demonstrated higher active motor thresholds and longer silent periods in the hemisphere corresponding to the weaker hand which was more typical of hand dominance patterns in healthy individuals. Males, but not females, exhibited asymmetry of nerve conduction latency (delayed MEP latency in the hemisphere corresponding to the weaker hand). In males, there was also a relationship between delayed onset of ipsilateral silent period (measured in the hemisphere corresponding to the weaker hand) and MoCA, suggestive of cross-callosal disruption. Our findings support that a sex-specific disruption in CSE exists in MS, pointing to interhemispheric disruption as a potential biomarker of cognitive impairment and target for neuromodulating therapies.

A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high-resolution atlas: Performance, precision, and parcellation

Noninvasive functional neuroimaging of the human brain can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms are available, and have been widely evaluated in the context of localizing task-evoked activities. However, no consensus yet exists on the optimum algorithm for resting-state data. Here, we evaluated the performance of six commonly-used source reconstruction algorithms based on minimum-norm and beamforming estimates. Using human resting-state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor-level data. Next, we proposed a data-driven approach to reduce the atlas from the Human Connectome Project's multi-modal parcellation of the human cortex based on metrics such as MEG signal-to-noise-ratio and resting-state functional connectivity gradients. This procedure produced a reduced cortical atlas with 230 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners. Our results show that there is no "one size fits all" algorithm, and make recommendations on the appropriate algorithms depending on the data and aimed analyses. Our comprehensive comparisons and recommendations can serve as a guide for choosing appropriate methodologies in future studies of resting-state MEG.

Altered functional connectivity in newly diagnosed benign epilepsy with unilateral or bilateral centrotemporal spikes: A multi-frequency MEG study

OBJECTIVE

Rolandic epilepsy (RE) is one of the most common forms of epilepsy syndromes in children. The condition is usually accompanied with either unilateral or bilateral centrotemporal epileptic discharge. Despite the term "benign", many studies have reported that children with benign epilepsy with centrotemporal spikes (BECTS) display a range of pervasive cognitive difficulties. In addition, existing research suggests that unilateral and bilateral centrotemporal spikes may affect cognition through different mechanisms. Consequently, the present study aimed to investigate cognitive impairment and the resting-state network topology of children with benign epilepsy with unilateral centrotemporal spikes (U-BECTS) and with bilateral centrotemporal spikes (B-BECTS).

METHODS

This study recruited 14 children with U-BECTS and 14 with B-BECTS. Thereafter, cognition was assessed in 28 children with BECTS and 14 healthy controls, using the fourth edition of the Wechsler Intelligence Scale (WISC-IV). Additionally, the functional network of the brain was constructed through magnetoencephalography (MEG) to record the resting-state brain magnetic signals of the brain and by computing virtual sensor waveforms at the source level. Moreover, graph theory (GT) analysis was used to assess the properties of the brain network.

RESULTS

Children in the B-BECTS group had an earlier onset of epilepsy compared to those in the U-BECTS category. In addition, both the B-BECTS and U-BECTS groups had lower Full Scale Intelligence Quotient (FSIQ), Verbal Comprehension Index (VCI), and Working Memory Index (WMI) scores, compared to the healthy controls although only children in the B-BECTS category had lower Perceptual Reasoning Index (PRI) scores. The results also showed that both BECTS groups had increased frontal cortex connectivity in specific frequency bands. Notably, children with B-BECTS showed a more disorderly and randomized network in the 1-4-Hz and 80-250-Hz frequency bands. Moreover, GT analysis showed that children with B-BECTS had lower clustering coefficient and characteristic path length in the 80-250-Hz frequency bands and higher connection strength in the 4-8-Hz frequency bands. On the other hand, the U-BECTS group had a higher clustering coefficient in the 8-12-Hz frequency bands, compared to the healthy controls. Correlation analysis revealed that there were negative correlations between network parameters, clinical characteristics, and neuropsychological data in the U-BECTS category.

CONCLUSION

The findings revealed that children with BECTS display a diffuse early cognitive deficit. In addition, resting-state suboptimal network topology may be the mechanism of cognitive impairment in children with BECTS. The study also showed that and children with B-BECTS may be at a higher risk of cognitive impairment.

The trajectory of thought: Heavy-tailed distributions in memory foraging promote efficiency

Free-recall tasks suggest human memory foraging may follow a heavy-tailed distribution, such as a Lévy flight, patch foraging, or area-restricted search - walk procedures that are common in other activities of cognitive agents, such as food foraging in both animals and humans. To date, research merely equates memory foraging with hunting in the physical world based on similarities in statistical structure. The current work supports that memory foraging follows a heavy-tailed distribution by using categories with quantitative distances between each item: countries, which have physical distances, and animals, from which cognitive distances can be derived using a multidimensional scaling (MDS) procedure. Likewise, inter-item lag times follow a heavy-tailed distribution. The current work also demonstrates that inter-item distances and times are positively correlated, suggesting the organization of items in memory may be akin to the organization of a physical landscape. Finally, both studies show that participants' original, heavy-tailed lists of countries and animal names produce shorter overall distances traveled than random selection. Human memory foraging follows the same pattern as foraging in the natural world - perhaps because exposure to ecological settings informs our inner cognitive experience - leading to a processing and retrieval time benefit.

Connecting brain and behavior in clinical neuroscience: A network approach

In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.

The organization of functional neurocognitive networks in focal epilepsy correlates with domain-specific cognitive performance

Understanding and diagnosing cognitive impairment in epilepsy remains a prominent challenge. New etiological models suggest that cognitive difficulties might not be directly linked to seizure activity, but are rather a manifestation of a broader brain pathology. Consequently, treating seizures is not sufficient to alleviate cognitive symptoms, highlighting the need for novel diagnostic tools. Here, we investigated whether the organization of three intrinsic, resting-state functional connectivity networks was correlated with domain-specific cognitive test performance. Using individualized EEG source reconstruction and graph theory, we examined the association between network small worldness and cognitive test performance in 23 patients with focal epilepsy and 17 healthy controls, who underwent a series of standardized pencil-and-paper and digital cognitive tests. We observed that the specific networks robustly correlated with test performance in distinct cognitive domains. Specifically, correlations were evident between the default mode network and memory in patients, the central-executive network and executive functioning in controls, and the salience network and social cognition in both groups. Interestingly, the correlations were evident in both groups, but in different domains, suggesting an alteration in these functional neurocognitive networks in focal epilepsy. The present findings highlight the potential clinical relevance of functional brain network dysfunction in cognitive impairment.

Electroencephalogram in dementia with Lewy bodies: a systematic review

Dementia with Lewy bodies (DLB) belongs to the spectrum of Lewy body dementia (LBD) that also encompasses Parkinson's disease dementia (PDD). It is a common neurodegenerative disorder characterized by memory decline, cognitive fluctuations, visual hallucinations, autonomic nervous system disturbance, REM sleep behavior disorder, and parkinsonism. Definite diagnosis can be established only through neuropathological confirmation of Lewy bodies' presence in brain tissue. Probable or possible diagnosis relies upon clinical features, imaging, polysomnography, and electroencephalogram (EEG) findings. Potential neurophysiological biomarkers for the diagnosis, management, and evaluation of treatment-response in DLB should be affordable and widely available outside academic centers. Increasing evidence supports the use of quantitative EEG (qEEG) as a potential DLB biomarker, with promising results in discriminating DLB from other dementias and in identifying subjects who are on the trajectory to develop DLB. Several studies evaluated the diagnostic value of EEG in DLB. Visual analysis and qEEG techniques have been implemented, showing a superiority of the last in terms of sensitivity and objectivity. In this systematic review, we attempt to provide a general synthesis of the current knowledge on EEG application in DLB. We review the findings from original studies and address the issues remaining to be further clarified.

Modifications in the Topological Structure of EEG Functional Connectivity Networks during Listening Tonal and Atonal Concert Music in Musicians and Non-Musicians

The present work aims to demonstrate the hypothesis that atonal music modifies the topological structure of electroencephalographic (EEG) connectivity networks in relation to tonal music. To this, EEG monopolar records were taken in musicians and non-musicians while listening to tonal, atonal, and pink noise sound excerpts. EEG functional connectivities (FC) among channels assessed by a phase synchronization index previously thresholded using surrogate data test were computed. Sound effects, on the topological structure of graph-based networks assembled with the EEG-FCs at different frequency-bands, were analyzed throughout graph metric and network-based statistic (NBS). Local and global efficiency normalized (vs. random-network) measurements (NLE|NGE) assessing network information exchanges were able to discriminate both music styles irrespective of groups and frequency-bands. During tonal audition, NLE and NGE values in the beta-band network get close to that of a small-world network, while during atonal and even more during noise its structure moved away from small-world. These effects were attributed to the different timbre characteristics (sounds spectral centroid and entropy) and different musical structure. Results from networks topographic maps for strength and NLE of the nodes, and for FC subnets obtained from the NBS, allowed discriminating the musical styles and verifying the different strength, NLE, and FC of musicians compared to non-musicians.

Synergistic effects of HIV and marijuana use on functional brain network organization

HIV is associated with disruptions in cognition and brain function. Marijuana use is highly prevalent in HIV but its effects on resting brain function in HIV are unknown. Brain function can be characterized by brain activity that is correlated between regions over time, called functional connectivity. Neuropsychiatric disorders are increasingly being characterized by disruptions in such connectivity. We examined the synergistic effects of HIV and marijuana use on functional whole-brain network organization during resting state. Our sample included 78 adults who differed on HIV and marijuana status (19 with co-occurring HIV and marijuana use, 20 HIV-only, 17 marijuana-only, and 22 controls). We examined differences in local and long-range brain network organization using eight graph theoretical metrics: transitivity, local efficiency, within-module degree, modularity, global efficiency, strength, betweenness, and participation coefficient. Local and long-range connectivity were similar between the co-occurring HIV and marijuana use and control groups. In contrast, the HIV-only and marijuana-only groups were both associated with disruptions in brain network organization. These results suggest that marijuana use in HIV may normalize disruptions in brain network organization observed in persons with HIV. However, future work is needed to determine whether this normalization is suggestive of a beneficial or detrimental effect of marijuana on cognitive functioning in HIV.

The relationship between epilepsy and cognitive function in benign childhood epilepsy with centrotemporal spikes

INTRODUCTION

This study was aimed to explore the relationship between neural network changes in newly diagnosed children with Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS) and cognitive impairment.

METHODS

Children's cognition was evaluated using the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV). Magnetoencephalographic (MEG) data of 18 healthy children and 22 BECTS patients were recorded in order to construct a functional connectivity (FC) network, which was quantified by graph theory (GT).

RESULTS

The mean age of the control group was 7.94 ± 1.89 years, and the mean age of BECTS patients was 8.14 ± 1.73 years. Our results show that the WISC-IV index scores in the BECTS group were significantly lower than those in the control group. Besides, the FC network pattern of BECTS patients changed significantly in the 12-30, 30-80, and 250-500 Hz frequency band. The local functional connections between posterior cingulate cortex (PCC) and frontal lobe varied significantly in 12-30, 80-250, and 250-500 Hz. Our GT analysis shows that the connection strength of BECTS patients increases significantly in the 12-30 Hz frequency band, the path length decreases significantly in the 12-30 Hz and 30-80 Hz frequency bands, with the clustering coefficient decreasing significantly in the 12-30 Hz, 30-80 Hz, and 250-500 Hz frequency bands. Correlation analysis showed that the full-scale IQ (FSIQ) was positively correlated with the 12-30 Hz clustering coefficient, verbal comprehension index (VCI) was positively correlated with the 250-500 Hz clustering coefficient, perceptual reasoning index (PRI) was positively correlated with the 12-30 Hz clustering coefficient, and perceptual reasoning index (PSI) was negatively correlated with the 12-30 Hz path length.

CONCLUSION

There is a trend of cognitive impairment in patients with early BECTS. This trend of cognitive impairment in early BECTS children may be related to the changes in the FC network pattern.

Cognitive function: holarchy or holacracy?

Cognition is the most complex function of the brain. When exploring the inner workings of cognitive processes, it is crucial to understand the complexity of the brain's dynamics. This paper aims to describe the integrated framework of the cognitive function, seen as the result of organization and interactions between several systems and subsystems. We briefly describe several organizational concepts, spanning from the reductionist hierarchical approach, up to the more dynamic theory of open complex systems. The homeostatic regulation of the mechanisms responsible for cognitive processes is showcased as a dynamic interplay between several anticorrelated mechanisms, which can be found at every level of the brain's organization, from molecular and cellular level to large-scale networks (e.g., excitation-inhibition, long-term plasticity-long-term depression, synchronization-desynchronization, segregation-integration, order-chaos). We support the hypothesis that cognitive function is the consequence of multiple network interactions, integrating intricate relationships between several systems, in addition to neural circuits.

To the self and beyond: Arousal and functional connectivity of the temporo-parietal junction contributes to spontaneous sensations perception

The temporoparietal junction (TPJ), along with the anterior insula (AI) and the extrastriate body area (EBA), play a major part in embodiment and self-awareness. However, these connections also appear to be frequently engaged in arousal and attentional processing of external events. Considering that these networks may focus attention both toward and away from the self, we set to investigate how they contribute to the perception of spontaneous sensations (SPS), a common phenomenon related to self-awareness and mediated by both interoceptive and attentional processes. In Experiment 1, resting-state EEG was recorded, as well as arousal reported via a questionnaire, followed by a SPS task. Functional TPJ-AI and TPJ-EBA connectivity were computed using eLORETA. Spatial correlational analyses showed that less frequent SPS coincided with greater TPJ-AI and TPJ-EBA functional connectivity, especially in the theta and alpha frequency bands. High self-reported arousal predicted low intensity and low confidence in the location of SPS. Resting-state skin conductance level (SCL) was recorded in Experiment 2, followed by the SPS task. Less frequent SPS coincided with greater SCL. Findings are interpreted in terms of attention and self-related processes, and a discussion of the TPJ participation in self-awareness through SPS is presented.

The Effects of Cognitive Training on Brain Network Activity and Connectivity in Aging and Neurodegenerative Diseases: a Systematic Review

Cognitive training (CT) is an increasingly popular, non-pharmacological intervention for improving cognitive functioning in neurodegenerative diseases and healthy aging. Although meta-analyses support the efficacy of CT in improving cognitive functioning, the neural mechanisms underlying the effects of CT are still unclear. We performed a systematic review of literature in the PubMed, Embase and PsycINFO databases on controlled CT trials (N > 20) in aging and neurodegenerative diseases with pre- and post-training functional MRI outcomes up to November 23rd 2018 (PROSPERO registration number CRD42019103662). Twenty articles were eligible for our systematic review. We distinguished between multi-domain and single-domain CT. CT induced both increases and decreases in task-related functional activation, possibly indicative of an inverted U-shaped curve association between regional brain activity and task performance. Functional connectivity within ‘cognitive’ brain networks was consistently reported to increase after CT while a minority of studies additionally reported increased segregation of frontoparietal and default mode brain networks. Although we acknowledge the large heterogeneity in type of CT, imaging methodology, in-scanner task paradigm and analysis methods between studies, we propose a working model of the effects of CT on brain activity and connectivity in the context of current knowledge on compensatory mechanisms that are associated with aging and neurodegenerative diseases.

Alterations in the whole brain network organization after prenatal ethanol exposure

BACKGROUND

People with fetal alcohol spectrum disorder (FASD) often have structural or functional alterations of the central nervous system, including changes in brain network organization. These have been associated with neuropsychological deficits, but outcomes are not consistent across studies. We used a rat model of FASD to assess brain network alterations in males and females following ethanol exposure during a prenatal period similar to the first half of gestation in humans.

METHODS

Pregnant Long Evans rats were given an ethanol-containing or isocaloric non-ethanol diet from gestation day 6 to 20. Resting-state functional magnetic resonance imaging was performed on offspring in young adulthood. Graph theoretical analysis was used to assess properties associated with the whole brain network organization, with a focus on segregation, integration, and small-world organization-a feature which allows specialized local information processing (segregation) and simultaneously efficient global information sharing (integration).

RESULTS

Ethanol-exposed females showed a significant decrease in small-worldness compared with control females or with ethanol-exposed males. Compared to control females, the proportion of animals with atypically high path length (1 standard deviation higher than the grand average) was significantly higher in ethanol-exposed females, indicating that the alteration in small-world organization is driven by decreased network integration. No significant effects were seen in males.

CONCLUSION

The results revealed that prenatal ethanol exposure disrupts the balance between network segregation and integration in young adult female rats. The whole brain network is less integrated after ethanol exposure in the females, suggesting wide-spread reduction of long-range regional communication.

Alterations of neural network organization during REM sleep in women: implication for sex differences in vulnerability to mood disorders

BACKGROUND

Sleep plays an important role in vulnerability to mood disorders. However, despite the existence of sex differences in vulnerability to mood disorders, no study has yet investigated the sex effect on sleep network organization and its potential involvement in vulnerability to mood disorders. The aim of our study was to empirically investigate the sex effect on network organization during REM and slow-wave sleep using the effective connectivity measured by Granger causality.

METHODS

Polysomnographic data from 44 healthy individuals (28 men and 16 women) recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realized by importing these connectivity matrices into EEGNET Toolbox.

RESULTS

In men and women, all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep. However, compared to men, women present greater small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage, which indicates the presence of a small-world network organization less marked during REM sleep as well as for all EEG bands during this sleep stage in women. In addition, in women, these small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage are positively correlated with the presence of subclinical symptoms of depression.

CONCLUSIONS

Thus, the highlighting of these sex differences in network organization during REM sleep indicates the presence of differences in the global and local processing of information during sleep between women and men. In addition, this small-world network organization less marked during REM sleep appears to be a marker of vulnerability to mood disorders specific to women, which opens up new perspectives in understanding sex differences in the occurrence of mood disorders.

Modified models and simulations for estimating dynamic functional connectivity in resting state functional magnetic resonance imaging

As understanding the nature of brain networks through dynamic functional connectivity (dFC) estimation is of paramount significant, the introduction and revision of blood-oxygen-level dependent (BOLD) signal simulation methods in brain regions and dFC estimation methods have gained significant ground in recent years. Based on the observation of BOLD signals with multivariate nonnormal distribution in functional magnetic resonance imaging (fMRI) images, we first propose a copula-based method for the production of these signals, in which nonnormal data are generated with a selected time-varying covariance matrix. Therefore, we can compare the performance of models in the cases where brain signals have a multivariate nonnormal distribution. Then, two kendallized exponentially weighted moving average (KEWMA) and kendallized dynamic conditional correlation (KDCC) multivariate volatility models are introduced which are based on two well-known and commonly used exponentially weighted moving average (EMWA) and dynamic conditional correlation (DCC) models. The results show that KDCC model can estimate conditional correlation significantly far better than the former ones (ie, DCC, standardized dynamic conditional correlation, EWMA, and standardized exponentially weighted moving average) on both types of data (ie, multivariate normal and nonnormal). In the next step, the bivariate normal distribution in Iranian resting state fMRI data is confirmed by using statistical tests, and it is shown that the dynamic nature of FC is not optimally detected using prevalent methods. Two alternative Portmanteau and rank-based tests are proposed for the examination of conditional heteroscedasticity in data. Finally, dFC in these data is estimated by employing the KDCC model.

Distinct Functional Connectivity Patterns Are Associated With Social and Cognitive Lifestyle Factors: Pathways to Cognitive Reserve

The importance of diverse lifestyle factors in sustaining cognition during aging and delaying the onset of decline in Alzheimer's disease and related dementias cannot be overstated. We explored the influence of cognitive, social, and physical lifestyle factors on resting-state lagged linear connectivity (LLC) in high-density electroencephalography (EEG) in adults, ages 35-75 years. Diverse lifestyle factors build cognitive reserve (CR), protecting cognition in the presence of physical brain decline. Differences in LLC were examined between high- and low-CR groups formed using cognitive, social, and exercise lifestyle factors. LLC is a measure of lagged coherence that excludes zero phase contributions and limits the effects of volume conduction on connectivity estimates. Significant differences in LLC were identified for cognitive and social factors, but not exercise. Participants high in social CR possessed greater local and long-range connectivity in theta and low alpha for eyes-open and eyes-closed recording conditions. In contrast, participants high in cognitive CR exhibited greater eyes-closed long-range connectivity between the occipital lobe and other cortical regions in low alpha. Greater eyes-closed local LLC in delta was also present in men high in cognitive CR. Cognitive factor scores correlated with sustained attention, whereas social factors scores correlated with spatial working memory. Gender was a significant covariate in our analyses, with women displaying higher local and long-range LLC in low beta. Our findings support distinct relationships between CR and LLC, as well as CR and cognitive function for cognitive and social subcomponents. These patterns reflect the importance of diverse lifestyle factors in building CR.

Alterations of neural network organisation during rapid eye movement sleep and slow-wave sleep in major depression: Implications for diagnosis, classification, and treatment

The aim of this study was to empirically investigate the network organisation during rapid eye movement sleep (REMS) and slow-wave sleep (SWS) using the effective connectivity measured using the Granger causality to identify new potential biomarkers for the diagnosis, classification, and potential favourable response to treatment in major depression. Polysomnographic data were analysed from 24 healthy individuals and 16 major depressed individuals recruited prospectively. To obtain the 19×19 connectivity matrix of all possible pairwise combinations of electrodes by the Granger causality method from our electroencephalographic data, we used the Toolbox MVGC multivariate Granger causality. The computation of network measures was realised by importing these connectivity matrices into the EEGNET Toolbox. Major depressed individuals (versus healthy individuals) and those with endogenous depression (versus those with neurotic depression) present alterations of small-world network organisation during REMS, whereas major depressed individuals with potential favourable response to electroconvulsive therapy (versus those with potential unfavourable response) have a less efficient small-world network organisation during SWS. Thus, alterations in network organisation during REMS could be biomarkers for the diagnosis and classification of major depressive episodes, whereas alterations of network organisation during SWS could be a biomarker to predict potential favourable response to treatment by electroconvulsive therapy.

Frequency-Dependent Changes in Resting State Electroencephalogram Functional Networks after Traumatic Brain Injury in Piglets

Traumatic brain injury (TBI) is a major health concern in children, as it can cause chronic cognitive and behavioral deficits. The lack of objective involuntary metrics for the diagnosis of TBI makes prognosis more challenging, especially in the pediatric context, in which children are often unable to articulate their symptoms. Resting state electroencephalograms (EEG), which are inexpensive and non-invasive, and do not require subjects to perform cognitive tasks, have not yet been used to create functional brain networks in relation to TBI in children or non-human animals; here we report the first such study. We recorded resting state EEG in awake piglets before and after TBI, from which we generated EEG functional networks from the alpha (8-12 Hz), beta (16.5-25 Hz), broad (1-35 Hz), delta (1-3.5 Hz), gamma (30-35 Hz), sigma (13-16 Hz), and theta (4-7.5 Hz) frequency bands. We hypothesize that mild TBI will induce persistent frequency-dependent changes in the 4-week-old piglet at acute and chronic time points. Hyperconnectivity was found in several frequency band networks after TBI. This study serves as proof of concept that the study of EEG functional networks in awake piglets may be useful for the development of diagnostic metrics for TBI in children.

Cognitive functioning in glioma patients is related to functional connectivity measures of the non-tumoural hemisphere

Previous studies have shown that cognitive functioning in patients with brain tumour is associated with the functional network characteristics of specific resting‐state networks or with whole‐brain network characteristics. These studies, however, did not acknowledge the functional contribution of areas in the contralesional, non‐tumoural hemisphere, even though these healthy remote areas likely play a critical role in compensating for the loss of function in damaged tissue. In the current study, we examined whether there is an association between cognitive performance and functional network features of the contralesional hemisphere of patients with glioma. We found that local efficiency of the contralesional hemisphere was associated with performance on the reaction time domain, whereas contralesional assortativity was associated with complex attention and cognitive flexibility scores. Our results suggest that a less segregated organization of the contralesional hemisphere is associated with better reaction time scores, whereas a better spread of information over the contralesional hemisphere through mutually interconnected contralesional hubs is associated with better cognitive flexibility and better complex attention scores. These findings urge researchers to recognize the functional contribution of remote, undamaged regions and to focus more on the graph metrics of the contralesional hemisphere in the search for predictors of cognitive functioning in patients with brain tumour.

Graph theory analysis of resting-state functional magnetic resonance imaging in essential tremor

Essential tremor (ET) is a neurological disease with both motor and nonmotor manifestations; however, little is known about its underlying brain basis. Furthermore, the overall organization of the brain network in ET remains largely unexplored. We investigated the topological properties of brain functional network, derived from resting-state functional magnetic resonance imaging (MRI) data, in 23 ET patients versus 23 healthy controls. Graph theory analysis was used to assess the functional network organization. At the global level, the functional network of ET patients was characterized by lower small-worldness values than healthy controls-less clustered functionality of the brain. At the regional level, compared with the healthy controls, ET patients showed significantly higher values of global efficiency, cost and degree, and a shorter average path length in the left inferior frontal gyrus (pars opercularis), right inferior temporal gyrus (posterior division and temporo-occipital part), right inferior lateral occipital cortex, left paracingulate, bilateral precuneus bilaterally, left lingual gyrus, right hippocampus, left amygdala, nucleus accumbens bilaterally, and left middle temporal gyrus (posterior part). In addition, ET patients showed significant higher local efficiency and clustering coefficient values in frontal medial cortex bilaterally, subcallosal cortex, posterior cingulate cortex, parahippocampal gyri bilaterally (posterior division), right lingual gyrus, right cerebellar flocculus, right postcentral gyrus, right inferior semilunar lobule of cerebellum and culmen of vermis. Finally, the right intracalcarine cortex and the left orbitofrontal cortex showed a shorter average path length in ET patients, while the left frontal operculum and the right planum polare showed a higher betweenness centrality in ET patients. In conclusion, the efficiency of the overall brain functional network in ET is disrupted. Further, our results support the concept that ET is a disorder that disrupts widespread brain regions, including those outside of the brain regions responsible for tremor.

Increased segregation of functional networks in developing brains

A growing literature conceptualises typical brain development from a network perspective. However, largely due to technical and methodological challenges inherent in paediatric functional neuroimaging, there remains an important gap in our knowledge regarding the typical development of functional brain networks in "preschool" childhood (i.e., children younger than 6 years of age). In this study, we recorded brain oscillatory activity using age-appropriate magnetoencephalography in 24 children, including 14 preschool children aged from 4 to 6 years and 10 school children aged from 7 to 12 years. We compared the topology of the resting-state brain networks in these children, estimated using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series, with that of 24 adults. Our results show that during childhood the MST topology shifts from a star-like (centralised) toward a more line-like (de-centralised) configuration, indicating the functional brain networks become increasingly segregated. In addition, the increasing global network segregation is frequency-independent and accompanied by decreases in centrality (or connectedness) of cortical regions with age, especially in areas of the default mode network. We propose a heuristic MST model of "network space", which posits a clear developmental trajectory for the emergence of complex brain networks. Our results not only revealed topological reorganisation of functional networks across multiple temporal and spatial scales in childhood, but also fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years of childhood.

Magnetoencephalography applied to the study of Alzheimer's disease

Magnetoencephalography (MEG) is a relatively modern neuroimaging technique able to study normal and pathological brain functioning with temporal resolution in the order of milliseconds and adequate spatial resolution. Although its clinical applications are still relatively limited, great advances have been made in recent years in the field of dementia and Alzheimer's disease (AD) in particular. In this chapter, we briefly describe the physiological phenomena underlying MEG brain signals and the different metrics that can be computed from these data in order to study the alterations disrupting brain activity not only in demented patients, but also in the preclinical and prodromal stages of the disease. Changes in non-linear brain dynamics, power spectral properties, functional connectivity and network topological changes observed in AD are narratively summarized in the context of the pathophysiology of the disease. Furthermore, the potential of MEG as a potential biomarker to identify AD pathology before dementia onset is discussed in the light of current knowledge and the relationship between potential MEG biomarkers and current established hallmarks of the disease is also reviewed. To this aim, findings from different approaches such as resting state or during the performance of different cognitive paradigms are discussed.Lastly, there is an increasing interest in current scientific literature in promoting interventions aimed at modifying certain lifestyles, such as nutrition or physical activity among others, thought to reduce or delay AD risk. We discuss the utility of MEG as a potential marker of the success of such interventions from the available literature.

Single-Trial Recognition of Imagined Forces and Speeds of Hand Clenching Based on Brain Topography and Brain Network

To provide optional force and speed control parameters for brain-computer interfaces (BCIs), an effective feature extraction method of imagined force and speed of hand clenching based on electroencephalography (EEG) was explored. Twenty subjects were recruited to participate in the experiment. They were instructed to perform three different actual/imagined hand clenching force tasks (4 kg, 10 kg, and 16 kg) and three different hand clenching speed tasks (0.5 Hz, 1 Hz, and 2 Hz). Topographical maps parameters and brain network parameters of EEG were calculated as new features of imagined force and speed of hand clenching, which were classified by three classifiers: linear discrimination analysis, extreme learning machines and support vector machines. Topographical maps parameters were better for recognition of the hand clenching force task, while brain network parameters were better for recognition of the hand clenching speed task. After a combination of five types of features (energy, power spectrum of the autoregressive model, wavelet packet coefficients, topographical maps parameters and brain network parameters), the recognition rate of the hand clenching force task was 97%, and that of the hand clenching speed task was as high as 100%. The brain topographical and the brain network parameters are expected to improve the accuracy of decoding the EEG signal of imagined force and speed of hand clenching. A more efficient brain network may facilitate the recognition of force/speed of hand clenching. Combined features could significantly improve the single-trial recognition rate of imagined forces and speeds of hand clenching. The current study provides a new idea for the imagined force and speed of hand clenching that offers more control intention instructions for BCIs.

Structural network disruption markers explain disability in multiple sclerosis

OBJECTIVE

To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions.

METHODS

This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing-remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects' groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT.

RESULTS

Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing-remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed.

CONCLUSIONS

Structural topological changes exist between subjects' groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology.

Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG

Mentally imagining rather physically executing the motor behaviors is defined as motor imagery (MI). During MI, the mu rhythmical oscillation of cortical neurons is the event-related desynchronization (ERD) subserving the physiological basis of MI-based brain-computer interface. In our work, we investigated the specific brain network reconfiguration from rest idle to MI task states, and also probed the underlying relationship between the brain network reconfiguration and MI related ERD. Findings revealed that comparing to rest state, the MI showed the enhanced motor area related linkages and the deactivated activity of default mode network. In addition, the reconfigured network index was closely related to the ERDs, i.e., the higher the reconfigured network index was, the more obvious the ERDs were. These findings consistently implied that the reconfiguration from rest to task states underlaid the reallocation of related brain resources, and the efficient brain reconfiguration corresponded to a better MI performance, which provided the new insights into understanding the mechanism of MI as well as the potential biomarker to evaluate the rehabilitation quality for those patients with deficits of motor function.

Dynamic Functional Connectivity and Symptoms of Parkinson's Disease: A Resting-State fMRI Study

Research has shown that dynamic functional connectivity (dFC) in Parkinson’s disease (PD) is associated with better attention performance and with motor symptom severity. In the current study, we aimed to investigate dFC of both the default mode network (DMN) and the frontoparietal network (FPN) as neural correlates of cognitive functioning in patients with PD. Additionally, we investigated pain and motor problems as symptoms of PD in relation to dFC. Twenty-four PD patients and 27 healthy controls participated in this study. Memory and executive functioning were assessed with neuropsychological tests. Pain was assessed with the Numeric Rating Scale (NRS); motor symptom severity was assessed with the Unified Parkinson’s Disease Rating Scale (UPDRS). All subjects underwent resting-state functional magnetic resonance imaging (fMRI), from which dFC was defined by calculating the variability of functional connectivity over a number of sliding windows within each scan. dFC of both the DMN and FPN with the rest of the brain was calculated. Patients performed worse on tests of visuospatial memory, verbal memory and working memory. No difference existed between groups regarding dFC of the DMN nor the FPN with the rest of the brain. A positive correlation existed between dFC of the DMN and visuospatial memory. Our results suggest that dynamics during the resting state are a neural correlate of visuospatial memory in PD patients. Furthermore, we suggest that brain dynamics of the DMN, as measured with dFC, could be a phenomenon specifically linked to cognitive functioning in PD, but not to other symptoms.

Rifle Shooting Performance Correlates with Electroencephalogram Beta Rhythm Network Activity during Aiming

To study the relationship between brain network and shooting performance during shooting aiming, we collected electroencephalogram (EEG) signals from 40 skilled shooters during rifle shooting and calculated the EEG functional coupling, functional brain network topology, and correlation coefficients between these EEG characteristics and shooting performance. Our result shows a significant negative correlation between shooting performance and functional coupling between the prefrontal, frontal, and temporal regions of the right brain in the Beta1 and Beta2 frequency bands. Global and local brain network topology characteristics were also significantly correlated with shooting performance. These findings indicate that under these experimental conditions, shooters with higher shooting performances exhibit lower functional coupling, higher global, and lower local information integration efficiency during shooting. These conclusions may provide a theoretical basis of the EEG brain network for studying the mental status of shooters while shooting.

Effect of 5-HTTLPR on current source density, connectivity, and topological properties of resting state EEG networks

The S allele of serotonin transporter gene (5-HTTLPR) has been found to increase the risk of depression and other mental health problems, but some evidence suggests that S-allele carriers outperform subjects carrying the long allele in an array of cognitive tasks. Evidence linking this polymorphism with individual variation in electrophysiological properties of resting state brain networks is very limited. This study investigated the effect of 5-HTTLPR polymorphism on EEG current source density, connectivity, and topological properties of resting state networks. We collected genetic and resting state EEG data in 113 Caucasians. As compared to L-homozygotes, S-allele carriers showed lower current source density and connectivity in most frequency bands in areas overlapping with the default mode and emotion regulation regions. The analysis of graph-theoretical measures showed that S-allele carriers, as compared to L-homozygotes, have less optimal topological properties of brain networks in theta, but more optimal in alpha band. This dissociation may reflect the predisposition to emotional disorders, which is inherent to S-allele carriers, and, on the other hand, their superior functioning in some cognitive domains.

EEG Resting State Functional Connectivity in Adult Dyslexics Using Phase Lag Index and Graph Analysis

Developmental dyslexia may involve deficits in functional connectivity across widespread brain networks that enable fluent reading. We investigated the large-scale organization of electroencephalography (EEG) functional networks at rest in 28 dyslexics and 36 typically reading adults. For each frequency band (delta, theta alpha and beta), we assessed functional connectivity strength with the phase lag index (PLI). Network topology was examined using minimum spanning tree (MST) graphs derived from the functional connectivity matrices. We found significant group differences in the alpha band (8-13 Hz). The graph analysis indicated more interconnected nodes, in dyslexics compared to typical readers. The graph metrics were significantly correlated with age in dyslexics but not in typical readers, which may indicate more heterogeneity in maturation of brain networks in dyslexics. The present findings support the involvement of alpha oscillations in higher cognition and the sensitivity of graph metrics to characterize functional networks in adult dyslexia. Finally, the current results extend our previous findings on children.