Integration Of Brain Networks Research Articles

Reported Experiences of Childhood Trauma Does Not Explain Altered Brain Network Integration or Segregation Detected in Schizophrenia

Structural brain alterations in both grey and white-matter regions are more pronounced among individuals with schizophrenia with reported experiences of childhood-trauma (CT).Examining the topological arrangement of connections encompassing both grey and white-matter is key to understanding the role of CT on structural brain network architecture and at a network level may be more informative for examining the mechanisms underlying the additive effects of CT in schizophrenia.

Association of physical activity levels and brain white matter in older Latino adults

ABSTRACT Objective Investigate the associations between self-reported physical activity (PA) engagement and white matter (WM) health (i.e. volume, integrity, and hyperintensities) in older Latinos. Design Cross-sectional study with community-dwelling older adults from predominantly Latino neighborhoods. Participants: Thirty-four cognitively healthy older Latinos from two different cohorts. Measurements: Participants self-reported demographic information, PA engagement [Community Healthy Activities Model Program for Seniors (CHAMPS) Physical Activity Questionnaire for Older Adults] and magnetic resonance imaging (MRI). We used high-resolution three-dimensional T1- and T2-FLAIR weighted images and diffusion tensor imaging acquired via 3 T MRI. We performed a series of hierarchical linear regression models with the addition of relevant covariates to examine the associations between self-reported PA levels and WM volume, integrity, and hyperintensities (separately). We adjusted p-values with the use of the Benjamini-Hochberg's false discovery rate procedure. Results Higher reported levels of leisure-time moderate-to-vigorous PA were significantly associated with higher WM volume of the posterior cingulate (β = 0.220, SE = 0.125, 95% CI 0.009-0.431, p = 0.047) and isthmus cingulate (β = 0.212, SE = 0.110, 95% CI 0.001-0.443, p = 0.044) after controlling for intracranial volume. Higher levels of total PA were significantly associated with higher overall WM volume of these same regions (posterior cingulate: β = 0.220, SE = 0.125, CI 0.024-0.421, p = 0.046; isthmus cingulate: β = 0.220, SE = 0.125, 95% CI 0.003-0.393; p = 0.040). Significant p-values did not withstand Benjamini-Hochberg’s adjustment. PA was not significantly associated with WM integrity or WM hyperintensities. Conclusion Higher levels of PA, particularly higher leisure-time moderate-to-vigorous PA, might be associated with greater WM volume in select white matter regions key to brain network integration for physical and cognitive functioning in older Latinos. More research is needed to further confirm these associations.

Temporal modes of hub synchronization at rest

The brain is a dynamic system that generates a broad repertoire of perceptual, motor, and cognitive states by the integration and segregation of different functional domains represented in large-scale brain networks. However, the fundamental mechanisms underlying brain network integration remain elusive. Here, for the first time to our knowledge, we found that in the resting state the brain visits few synchronization modes defined as clusters of temporally aligned functional hubs. These modes alternate over time and their probability of switching leads to specific temporal loops among them. Notably, although each mode involves a small set of nodes, the brain integration seems highly vulnerable to a simulated attack on this temporal synchronization mechanism. In line with the hypothesis that the resting state represents a prior sculpted by the task activity, the observed synchronization modes might be interpreted as a temporal brain template needed to respond to task/environmental demands .

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane.

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub‐state where integration predominates, and a predominantly segregated sub‐state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting‐state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small‐world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst‐suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub‐state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.

Altered structural balance of resting-state networks in autism

What makes a network complex, in addition to its size, is the interconnected interactions between elements, disruption of which inevitably results in dysfunction. Likewise, the brain networks’ complexity arises from interactions beyond pair connections, as it is simplistic to assume that in complex networks state of a link is independently determined only according to its two constituting nodes. This is particularly of note in genetically complex brain impairments, such as the autism spectrum disorder (ASD), which has a surprising heterogeneity in manifestations with no clear-cut neuropathology. Accordingly, structural balance theory (SBT) affirms that in real-world signed networks, a link is remarkably influenced by each of its two nodes’ interactions with the third node within a triadic interrelationship. Thus, it is plausible to ask whether ASD is associated with altered structural balance resulting from atypical triadic interactions. In other words, it is the abnormal interplay of positive and negative interactions that matters in ASD, besides and beyond hypo (hyper) pair connectivity. To address this question, we explore triadic interactions based on SBT in the weighted signed resting-state functional magnetic resonance imaging networks of participants with ASD relative to healthy controls (CON). We demonstrate that balanced triads are overrepresented in the ASD and CON networks while unbalanced triads are underrepresented, providing first-time empirical evidence for the strong notion of structural balance on the brain networks. We further analyze the frequency and energy distributions of different triads and suggest an alternative description for the reduced functional integration and segregation in the ASD brain networks. Moreover, results reveal that the scale of change in the whole-brain networks’ energy is more narrow in the ASD networks during development. Last but not least, we observe that energy of the salience network and the default mode network are lower in ASD, which may be a reflection of the difficulty in dynamic switching and flexible behaviors. Altogether, these results provide insight into the atypical structural balance of the ASD brain (sub) networks. It also highlights the potential value of SBT as a new perspective in functional connectivity studies, especially in the case of neurodevelopmental disorders.

Topological Learning and Its Application to Multimodal Brain Network Integration.

A long-standing challenge in multimodal brain network analyses is to integrate topologically different brain networks obtained from diffusion and functional MRI in a coherent statistical framework. Existing multimodal frameworks will inevitably destroy the topological difference of the networks. In this paper, we propose a novel topological learning framework that integrates networks of different topology through persistent homology. Such challenging task is made possible through the introduction of a new topological loss that bypasses intrinsic computational bottlenecks and thus enables us to perform various topological computations and optimizations with ease. We validate the topological loss in extensive statistical simulations with ground truth to assess its effectiveness of discriminating networks. Among many possible applications, we demonstrate the versatility of topological loss in the twin imaging study where we determine the extend to which brain networks are genetically heritable.

NREM sleep stages specifically alter dynamical integration of large-scale brain networks.

SummaryFunctional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.

Functional brain network organization measured with magnetoencephalography predicts cognitive decline in multiple sclerosis

Background: Cognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients. Objective: To investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology. Methods: Resting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage. Results: A more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years (), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band. Conclusions: The level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS.

Aberrant Brain Network Integration and Segregation in Diabetic Peripheral Neuropathy Revealed by Structural Connectomics.

Diabetic peripheral neuropathy (DPN) is one of the most common forms of peripheral neuropathy, and its incidence has been increasing. Mounting evidence has shown that patients with DPN have been associated with widespread alterations in the structure, function and connectivity of the brain, suggesting possible alterations in large-scale brain networks. Using structural covariance networks as well as advanced graph-theory-based computational approaches, we investigated the topological abnormalities of large-scale brain networks for a relatively large sample of patients with DPN (N = 67) compared to matched healthy controls (HCs; N = 88). Compared with HCs, the structural covariance networks of patients with DPN showed an increased characteristic path length, clustering coefficient, sigma, transitivity, and modularity, suggestive of inefficient global integration and increased local segregation. These findings may improve our understanding of the pathophysiological mechanisms underlying alterations in the central nervous system of patients with DPN from the perspective of large-scale structural brain networks.

P300 Analysis Using High-Density EEG to Decipher Neural Response to rTMS in Patients With Schizophrenia and Auditory Verbal Hallucinations.

Schizophrenia is a complex disorder about which much is still unknown. Potential treatments, such as transcranial magnetic stimulation (TMS), have not been exploited, in part because of the variability in behavioral response. This can be overcome with the use of response biomarkers. It has been however shown that repetitive transcranial magnetic stimulation (rTMS) can the relieve positive and negative symptoms of schizophrenia, particularly auditory verbal hallucinations (AVH). This exploratory work aims to establish a quantitative methodological tool, based on high-density electroencephalogram (HD-EEG) data analysis, to assess the effect of rTMS on patients with schizophrenia and AVH. Ten schizophrenia patients with drug-resistant AVH were divided into two groups: the treatment group (TG) received 1 Hz rTMS treatment during 10 daily sessions (900 pulses/session) over the left T3-P3 International 10-20 location. The control group (CG) received rTMS treatment over the Cz (vertex) EEG location. We used the P300 oddball auditory paradigm, known for its reduced amplitude in schizophrenia with AVH, and recorded high-density electroencephalography (HD-EEG, 256 channels), twice for each patient: pre-rTMS and 1 week post-rTMS treatment. The use of HD-EEG enabled the analysis of the data in the time domain, but also in the frequency and source-space connectivity domains. The HD-EEG data were linked with the clinical outcome derived from the auditory hallucinations subscale (AHS) of the Psychotic Symptom Rating Scale (PSYRATS), the Quality of Life Scale (QoLS), and the Depression, Anxiety and Stress Scale (DASS). The general results show a variability between subjects, independent of the group they belong to. The time domain showed a higher N1-P3 amplitude post-rTMS, the frequency domain a higher power spectral density (PSD) in the alpha and beta bands, and the connectivity analysis revealed a higher brain network integration (quantified using the participation coefficient) in the beta band. Despite the small number of subjects and the high variability of the results, this work shows a robust data analysis and an interplay between morphology, spectral, and connectivity data. The identification of a trend post-rTMS for each domain in our results is a first step toward the definition of quantitative neurophysiological parameters to assess rTMS treatment.

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View.

Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer’s disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.

An EEG-based methodology for the estimation of functional brain connectivity networks: Application to the analysis of newborn EEG seizure

This study presents a new methodology for obtaining functional brain networks (FBNs) using multichannel scalp EEG recordings. The developed methodology extracts pair-wise phase synchrony between EEG electrodes to obtain FBNs at δ, θ, and α -bands and investigates their network properties in presence of seizure to detect multiple facets of functional integration and segregation in brain networks. Statistical analysis of the frequency-specific graph measures during seizure and non-seizure intervals reveals their highly discriminative ability between the two EEG states. It is also verified by performing the receiver operating characteristic (ROC) analysis. The results suggest that, for the majority of subjects, the FBNs during seizure intervals exhibit higher modularity and lower global efficiency compared to the FBNs during non-seizure intervals; meaning that during seizure activities the networks become more segregated and less aggregated. Some differences in the results obtained for different subjects can be attributed to the subject-specific nature of seizure networks and the type of epileptic seizure the subject has experienced. The results demonstrate the capacity of the proposed framework for studying different abnormal patterns in multichannel EEG signals.

The Relationship Between Air Pollution and Cognitive Functions in Children and Adolescents: A Systematic Review.

Air pollution has a negative impact on one's health and on the central nervous system. We decided to assess studies that evaluated the relationship between air pollution and cognitive functions in children and adolescents by reviewing studies that had been published between January 2009 and May 2019. We searched three major databases for original works (26 studies) and for studies using brain imaging methods based on MRI (six studies). Adverse effects of air pollutants on selected cognitive or psychomotor functions were found in all of the studies. Exposure to nitrogen dioxide, for example, was linked to impaired working memory, general cognitive functions, and psychomotor functions; particulate matter 2.5 was linked to difficulties in working memory, short-term memory, attention, processing speed, and fine motor function; black carbon was linked to poor verbal intelligence, nonverbal intelligence, and working memory; airborne copper was linked to impaired attentiveness and fine motor skills; isophorone was linked to lower mathematical skills; and polycyclic aromatic hydrocarbons in fetal life were linked to lower intelligence scores. The studies using MRI showed that high concentrations of air pollutants were linked to changes in the brain's white matter or lower functional integration and segregation in children's brain networks. In view of the global increase in air pollution, there is a need for further research to elucidate the relationship between air pollution and cognitive and motor development in children. According to some studies, neuroinflammation, the e4 allele of the apolipoprotein E gene, and gutathione-S-transferase gene polymorphism processes may play a role.

Dispersion of functional gradients across the adult lifespan

Ageing is commonly associated with changes to segregation and integration of functional brain networks, but, in isolation, current network-based approaches struggle to elucidate changes across the many axes of functional organisation. However, the advent of gradient mapping techniques in neuroimaging provides a new means of studying functional organisation in a multi-dimensional connectivity space. Here, we studied ageing and behaviourally-relevant differences in a three-dimensional connectivity space using the Cambridge Centre for Ageing Neuroscience cohort (n = 643). Building on gradient mapping techniques, we developed a set of measures to quantify the dispersion within and between functional communities. We detected a strong shift of the visual network across the adult lifespan from an extreme to a more central position in the 3D gradient space. In contrast, the dispersion distance between transmodal communities (dorsal attention, ventral attention, frontoparietal and default mode) did not change. However, these communities themselves were increasingly dispersed with increasing age, reflecting more dissimilar functional connectivity profiles within each community. Increasing dispersion of frontoparietal, attention and default mode networks, in particular, were associated negatively with cognition, measured by fluid intelligence. By using a technique that explicitly captures the ordering of functional systems in a multi-dimensional hierarchical framework, we identified behaviorally-relevant age-related differences of within and between network organisation. We propose that the study of functional gradients across the adult lifespan could provide insights that may facilitate the development of new strategies to maintain cognitive ability across the lifespan in health and disease.

Functional Integration and Separation of Brain Network Based on Phase Locking Value During Emotion Processing

The functional connection patterns of the brain during the processing of human emotions are complex and changeable. Therefore, the use of brain network information at the global scale is not sufficient to reflect the coupling relationship between brain regions, and it is impossible to accurately analyze the differences in information interaction patterns of the brain under different emotions. The purpose of this article is to study the functional connectivity of the brain by using the phase synchronization of EEG (Electroencephalogram) channel information in different emotional states. Considering that the Phase Locking Value (PLV) can effectively reflect the phase synchronization relationship between the EEG channels, it is adopted to describe the functional connection relationship of the brain. After the brain network based on PLV is constructed, considering that the topological structure of the brain network is complex and volatile, we merge the two types of attributes: functional integration and functional separation to analyze the differences in brain connectivity for different emotions. Further, the modular structure of the brain network is constructed through community detection to extract its more comprehensive local characteristics for connectivity analysis. The results show that, compared with positive emotions, brain regions under negative emotions have higher phase synchronization, more complex brain connectivity patterns, and more obvious modular structures. In addition, key cortical brain regions associated with emotional stimulation have been identified as brain network hubs. The verification on the DEAP dataset demonstrates that the analysis framework of this study effectively improved the accuracy of emotion recognition.

Older adults benefit from more widespread brain network integration during working memory

Neuroimaging evidence suggests that the aging brain relies on a more distributed set of cortical regions than younger adults in order to maintain successful levels of performance during demanding cognitive tasks. However, it remains unclear how task demands give rise to this age-related expansion in cortical networks. To investigate this issue, functional magnetic resonance imaging was used to measure univariate activity, network connectivity, and cognitive performance in younger and older adults during a working memory (WM) task. Here, individuals performed a WM task in which they held letters online while reordering them alphabetically. WM load was titrated to obtain four individualized difficulty levels with different set sizes. Network integration—defined as the ratio of within-versus between-network connectivity—was linked to individual differences in WM capacity. The study yielded three main findings. First, as task difficulty increased, network integration decreased in younger adults, whereas it increased in older adults. Second, age-related increases in network integration were driven by increases in right hemisphere connectivity to both left and right cortical regions, a finding that helps to reconcile existing theories of compensatory recruitment in aging. Lastly, older adults with higher WM capacity demonstrated higher levels of network integration in the most difficult task condition. These results shed light on the mechanisms of age-related network reorganization by demonstrating that changes in network connectivity may act as an adaptive form of compensation, with older adults recruiting a more distributed cortical network as task demands increase.

Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease.

Growing evidence links impairment of brain functions in Alzheimer's disease (AD) with disruptions of brain functional connectivity. However, whether the AD brain shows similar changes from a dynamic or cross-frequency view remains poorly explored. This paper provides an effective framework to investigate the properties of multiplex brain networks in AD considering inter-frequency and temporal dynamics. Using resting-state EEG signals, two types of multiplex networks were reconstructed separately considering the network interactions between different frequency bands or time points. We further applied multiplex network features to characterize functional integration and segregation of the cross-frequency or time-varying networks. Finally, machine learning methods were employed to evaluate the performance of multiplex-network-based indexes for detection of AD. Results revealed that the brain networks of AD patients are disrupted with reduced segregation particularly in the left occipital area for both cross-frequency and time-varying networks. However, the alteration of integration differs among brain regions and may show an increasing trend in the frontal area of AD brain. By combining the features of integration and segregation in time-varying networks, the best classification performance was achieved with an accuracy of 92.5%. These findings suggest that our multiplex framework can be applied to explore functional integration and segregation of brain networks and characterize the abnormalities of brain function. This may shed new light on the brain network analysis and extend our understanding of brain function in patients with neurological diseases.

P.325 Childhood trauma does not explain altered brain network integration or segregation detected in schizophrenia

P.325 Childhood trauma does not explain altered brain network integration or segregation detected in schizophrenia

Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: Part 2: Default mode network -preliminary evidence

Auditory hallucinations (AHs) are one of the most distressing symptoms of schizophrenia (SZ) and are often resistant to medication. Imaging studies of individuals with SZ show hyperactivation of the default mode network (DMN) and the superior temporal gyrus (STG). Studies in SZ show DMN hyperconnectivity and reduced anticorrelation between DMN and the central executive network (CEN). DMN hyperconnectivity has been associated with positive symptoms such as AHs while reduced DMN anticorrelations with cognitive impairment. Using real-time fMRI neurofeedback (rt-fMRI-NFB) we trained SZ patients to modulate DMN and CEN networks. Meditation is effective in reducing AHs in SZ and to modulate brain network integration and increase DMN anticorrelations. Consequently, patients were provided with meditation strategies to enhance their abilities to modulate DMN/CEN. Results show a reduction of DMN hyperconnectivity and increase in DMNCEN anticorrelation. Furthermore, the change in individual DMN connectivity significantly correlated with reductions in AHs. This is the first time that meditation enhanced through rt-fMRI-NFB is used to reduce AHs in SZ. Moreover, it provides the first empirical evidence for a direct causal relation between meditation enhanced rt-fMRI-NFB modulation of DMNCEN activity and post-intervention modulation of resting state networks ensuing in reductions in frequency and severity of AHs.

Healthy Subjects With Extreme Patterns of Performance Differ in Functional Network Topology and Benefits From Nicotine.

Do subjects with atypical patterns in attentional and executive behaviour show different brain network topology and react differently towards nicotine administration? The efficacy of pro-cognitive drugs like nicotine considerably varies between subjects and previous theoretical and empirical evidence suggest stronger behavioural nicotine effects in subjects with low performance. One problem is, however, how to best define low performance, especially if several cognitive functions are assessed for subject characterisation. We here present a method that used a multivariate, robust outlier detection algorithm to identify subjects with suspicious patterns of performance in attentional and executive functioning. In contrast to univariate approaches, this method is sensitive towards extreme positions within the multidimensional space that do not have to be extreme values in the individual behavioural distributions. The method was applied to a dataset of healthy, non-smoking subjects (n = 34) who were behaviorally characterised by an attention and executive function test on which N = 12 volunteers were classified as outliers. All subjects then underwent a resting-state functional magnetic resonance imaging (fMRI) scan to characterise brain network topology and an experimental behavioural paradigm under placebo and nicotine (7 mg patch) that gauged aspects of attention and executive function. Our results indicate that subjects with an atypical multivariate pattern in attention and executive functioning showed significant differences in nodal brain network integration in visual association and pre-motor brain regions during resting state. These differences in brain network topology significantly predicted larger individual nicotine effects on attentional processing. In summary, the current approach successfully identified a subgroup of healthy volunteers with low behavioural performance who differ in brain network topology and attentional benefit from nicotine.