Impaired rich-club connectivity in childhood absence epilepsy

Introduction

Childhood absence epilepsy (CAE) is a well-known pediatric epilepsy syndrome. Recent evidence has shown the presence of a disrupted structural brain network in CAE. However, little is known about the rich-club topology. This study aimed to explore the rich-club alterations in CAE and their association with clinical characteristics.

Methods

Diffusion tensor imaging (DTI) datasets were acquired in a sample of 30 CAE patients and 31 healthy controls. A structural network was derived from DTI data for each participant using probabilistic tractography. Then, the rich-club organization was examined, and the network connections were divided into rich-club connections, feeder connections, and local connections.

Results

Our results confirmed a less dense whole-brain structural network in CAE with lower network strength and global efficiency. In addition, the optimal organization of small-worldness was also damaged. A small number of highly connected and central brain regions were identified to form the rich-club organization in both patients and controls. However, patients exhibited a significantly reduced rich-club connectivity, while the other class of feeder and local connections was relatively spared. Moreover, the lower levels of rich-club connectivity strength were statistically correlated with disease duration.

Discussion

Our reports suggest that CAE is characterized by abnormal connectivity concentrated to rich-club organizations and might contribute to understanding the pathophysiological mechanism of CAE.

Altered structural connectome of children with auditory processing disorder: a diffusion MRI study

Auditory processing disorder (APD) is a listening impairment that some school-aged children may experience despite having normal peripheral hearing. Recent resting-state functional magnetic resonance imaging (MRI) has revealed an alteration in regional functional brain topology in children with APD. However, little is known about the structural organization in APD. We used diffusion MRI data to investigate the structural connectome of 58 children from 8 to 14 years old diagnosed with APD (n = 29) and children without hearing complaints (healthy controls, HC; n = 29). We investigated the rich-club organization and structural connection differences between groups. The APD group showed similar rich-club organization and edge-wise connection compared with the HC group. However, at the regional level, we observed increased average path length (APL) and betweenness centrality in the right inferior parietal lobule and inferior precentral gyrus, respectively, in the APD group. Only HCs demonstrated a positive association between APL and the listening-in-spatialized-noise-sentences task in the left orbital gyrus. In line with previous findings, the current results provide evidence for altered structural networks at the regional level in the APD group, suggesting the involvement of multimodal deficits and a role for structure-function alteration in the listening difficulties of children with APD.

Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment

Objective

Accumulating evidence shows that cognitive impairment (CI) in chronic heart failure (CHF) patients is related to brain network dysfunction. This study investigated brain network structure and rich-club organization in chronic heart failure patients with cognitive impairment based on graph analysis of diffusion tensor imaging data.

Methods

The brain structure networks of 30 CHF patients without CI and 30 CHF patients with CI were constructed. Using graph theory analysis and rich-club analysis, changes in global and local characteristics of the subjects' brain network and rich-club organization were quantitatively calculated, and the correlation with cognitive function was analyzed.

Results

Compared to the CHF patients in the group without CI group, the CHF patients in the group with CI group had lower global efficiency, local efficiency, clustering coefficient, the small-world attribute, and increased shortest path length. The CHF patients with CI group showed lower nodal degree centrality in the fusiform gyrus on the right (FFG.R) and nodal efficiency in the orbital superior frontal gyrus on the left (ORB sup. L), the orbital inferior frontal gyrus on the left (ORB inf. L), and the posterior cingulate gyrus on the right (PCG.R) compared with CHF patients without CI group. The CHF patients with CI group showed a smaller fiber number of edges in specific regions. In CHF patients with CI, global efficiency, local efficiency and the connected edge of the orbital superior frontal gyrus on the right (ORB sup. R) to the orbital middle frontal gyrus on the right (ORB mid. R) were positively correlated with Visuospatial/Executive function. The connected edge of the orbital superior frontal gyrus on the right to the orbital inferior frontal gyrus on the right (ORB inf. R) is positively correlated to attention/calculation. Compared with the CHF patients without CI group, the connection strength of feeder connection and local connection in CHF patients with CI group was significantly reduced, although the strength of rich-club connection in CHF patients complicated with CI group was decreased compared with the control, there was no statistical difference. In addition, the rich-club connection strength was related to the orientation (direction force) of the Montreal cognitive assessment (MoCA) scale, and the feeder and local connection strength was related to Visuospatial/Executive function of MoCA scale in the CHF patients with CI.

Conclusion

Chronic heart failure patients with CI exhibited lower global and local brain network properties, reduced white matter fiber connectivity, as well as a decreased strength in local and feeder connections in key brain regions. The disrupted brain network characteristics and connectivity was associated with cognitive impairment in CHF patients. Our findings suggest that impaired brain network properties and decreased connectivity, a feature of progressive disruption of brain networks, predict the development of cognitive impairment in patients with chronic heart failure.

Highly connected and highly variable: A Core brain network during resting state supports Propofol-induced unconsciousness

Despite that leading theories of consciousness make diverging predictions for where and how neural activity gives rise to subjective experience, they all seem to partially agree that the neural correlates of consciousness (NCC) require globally integrated brain activity across a network of functionally specialized modules. However, it is not clear yet whether such functional configurations would be able to identify the NCC. We scanned resting-state fMRI data from 21 subjects during wakefulness, propofol-induced sedation, and anesthesia. Graph-theoretical analyses were conducted on awake fMRI data to search for the NCC candidates as brain regions that exhibit both high rich-clubness and high modular variability, which were found to locate in prefrontal and temporoparietal cortices. Another independent data set of 10 highly-sampled subjects was used to validate the NCC distribution at the individual level. Brain module-based dynamic analysis revealed two discrete reoccurring brain states, one of which was dominated by the NCC candidates (state 1), while the other state was predominately composed of primary sensory/motor regions (state 2). Moreover, state 1 appeared to be temporally more stable than state 2, suggesting that the identified NCC members could sustain conscious content as metastable network representations. Finally, we showed that the identified NCC was modulated in terms of functional connectedness and modular variability in response to the loss of consciousness induced by propofol anesthesia. This work offers a framework to search for neural correlates of consciousness by charting the brain network topology and provides new insights into understanding the roles of different regions in underpinning human consciousness.

The Local Topological Reconfiguration in the Brain Network After Targeted Hub Dysfunction Attacks in Patients With Juvenile Myoclonic Epilepsy

The central brain regions of brain networks have been extensively studied in terms of their roles in various diseases. This study provides a direct measure of the brain's responses to targeted attacks on central regions, revealing the critical role these regions play in patients with juvenile myoclonic epilepsy (JME). The resting-state data of 37 patients with JME and 37 healthy subjects were collected, and brain functional networks were constructed for the two groups of data according to their Pearson correlation coefficients. The left middle cingulate gyrus was defined as the central brain region by the eigenvector centrality algorithm and was attacked by the CLM sequential failure model. The rich-club connection differences between the patients with JME and healthy controls before and after the attacks were compared according to graph theory indices and the number of rich-club connections. We found that the numbers of rich connections in the brain networks of the healthy control group and the group of patients with JME were significantly reduced [p < 0.05, false discovery rate (FDR) correction] before the CLM sequential failure attacks, and no significant differences were observed between the feeder connections and local connections. In the healthy control group, significant rich connection differences were obtained (p < 0.01, FDR correction), and no statistically significant differences were observed regarding the feeder connections and local connections in the brain network before and after CLM failure attacks on the central brain region. No significant differences were obtained between the rich connections, feeder connections, and local connections in patients with JME before and after CLM successive failure attacks on the central brain area. The rich connections, feeder connections, and local connections were not significantly different in the brain networks of the healthy control group and the group of patients with JME after CLM successive failure attacks on the central brain region. We concluded that the damage to the left middle cingulate gyrus is closely linked to various brain disorders, suggesting that this region is of great importance for understanding the pathophysiological basis of myoclonic seizures in patients with JME.

Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club

Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50-64 years versus older group (n = 310); age range, 65-91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

Structural Brain Network Disruption at Preclinical Stage of Cognitive Impairment Due to Cerebral Small Vessel Disease

Cerebral small vessel disease (CSVD) is a common disease among elderly individuals and recognized as a major cause of vascular cognitive impairment. Recent studies demonstrated that CSVD is a disconnection syndrome. However, due to the covert neurological symptoms and subtle changes in clinical performance, the connection alterations during the stage of preclinical cognitive impairment (PCI) and mild cognitive impairment (MCI) are usually neglected and still largely unknown. Using diffusion tensor imaging (DTI), we investigated the early structural network changes in PCI and MCI patients. The PCI group demonstrated well preserved rich-club organization, less nodal strength loss, and disruption of connections centered in the feeder and local connections. Nevertheless, the MCI group manifested a disruption in the rich-club organization, a worse nodal strength loss especially in hub nodes, and an overall disturbance in rich-club, feeder and local connections. Moreover, in all CSVD patients, the strength of the rich-club, feeder and local connections was significantly correlated with multiple cognitive scores, especially in attention, executive, and memory domains; while in MCI patients, only the strength of the rich-club connections was significantly correlated with cognition. Furthermore, based on the network-based statistic analysis, we also identified distinct network component disruption pattern between the PCI group and the MCI group, validating the results described above. These results suggest a disruption pattern from peripheral to central connections with the change of cognitive status, shedding light on the early identification and the underlying disruption of CSVD.

Unifying the Notions of Modularity and Core-Periphery Structure in Functional Brain Networks during Youth

At rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core-periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core-periphery structure. Here, we leverage a recently-developed model-based approach-the weighted stochastic block model-that simultaneously uncovers modular and core-periphery structure, and we apply it to functional magnetic resonance imaging data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich mesoscale organization beyond that sought by modularity maximization techniques. Moreover, we show that this mesoscale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core-periphery structure in functional brain networks, offering novel insights into their development and implications for behavior.

Bipolar Disorder and Gender Are Associated with Frontolimbic and Basal Ganglia Dysconnectivity: A Study of Topological Variance Using Network Analysis

Well-established structural abnormalities, mostly involving the limbic system, have been associated with disorders of emotion regulation. Understanding the arrangement and connections of these regions with other functionally specialized cortico-subcortical subnetworks is key to understanding how the human brain's architecture underpins abnormalities of mood and emotion. We investigated topological patterns in bipolar disorder (BD) with the anatomically improved precision conferred by combining subject-specific parcellation/segmentation with nontensor-based tractograms derived using a high-angular resolution diffusion-weighted approach. Connectivity matrices were constructed using 34 cortical and 9 subcortical bilateral nodes (Desikan-Killiany), and edges that were weighted by fractional anisotropy and streamline count derived from deterministic tractography using constrained spherical deconvolution. Whole-brain and rich-club connectivity alongside a permutation-based statistical approach was used to investigate topological variance in predominantly euthymic BD relative to healthy volunteers. BP patients (n = 40) demonstrated impairments across whole-brain topological arrangements (density, degree, and efficiency), and a dysconnected subnetwork involving limbic and basal ganglia relative to controls (n = 45). Increased rich-club connectivity was most evident in females with BD, with frontolimbic and parieto-occipital nodes not members of BD rich-club. Increased centrality in females relative to males was driven by basal ganglia and fronto-temporo-limbic nodes. Our subject-specific cortico-subcortical nontensor-based connectome map presents a neuroanatomical model of BD dysconnectivity that differentially involves communication within and between emotion-regulatory and reward-related subsystems. Moreover, the female brain positions more dependence on nodes belonging to these two differently specialized subsystems for communication relative to males, which may confer increased susceptibility to processes dependent on integration of emotion and reward-related information.

Rich-Club Organization: An Important Determinant of Functional Outcome After Acute Ischemic Stroke

Objective: To determine whether the rich-club organization, essential for information transport in the human connectome, is an important biomarker of functional outcome after acute ischemic stroke (AIS). Methods: Consecutive AIS patients (N = 344) with acute brain magnetic resonance imaging (MRI) (<48 h) were eligible for this study. Each patient underwent a clinical MRI protocol, which included diffusion weighted imaging (DWI). All DWIs were registered to a template on which rich-club regions have been defined. Using manual outlines of stroke lesions, we automatically counted the number of affected rich-club regions and assessed its effect on the National Institute of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS; obtained at 90 days post-stroke) scores through ordinal regression. Results: Of 344 patients (median age 65, inter-quartile range 54-76 years) with a median DWI lesion volume (DWIv) of 3cc, 64% were male. We established that an increase in number of rich-club regions affected by a stroke increases the odds of poor stroke outcome, measured by NIHSS (OR: 1.77, 95%CI 1.41-2.21) and mRS (OR: 1.38, 95%CI 1.11-1.73). Additionally, we demonstrated that the OR exceeds traditional markers, such as DWIv (OR_NIHSS 1.08, 95%CI 1.06-1.11; OR_mRS 1.05, 95%CI 1.03-1.07) and age (OR_NIHSS 1.03, 95%CI 1.01-1.05; OR_mRS 1.05, 95%CI 1.03-1.07). Conclusion: In this proof-of-concept study, the number of rich-club nodes affected by a stroke lesion presents a translational biomarker of stroke outcome, which can be readily assessed using standard clinical AIS imaging protocols and considered in functional outcome prediction models beyond traditional factors.

Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks

Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different species and scales ranging from C. elegans to mouse, rat, cat, macaque, and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.

Multiplex core-periphery organization of the human connectome

What is the core of the human brain is a fundamental question that has been mainly addressed by studying the anatomical connections between differently specialized areas, thus neglecting the possible contributions from their functional interactions. While many methods are available to identify the core of a network when connections between nodes are all of the same type, a principled approach to define the core when multiple types of connectivity are allowed is still lacking. Here, we introduce a general framework to define and extract the core-periphery structure of multi-layer networks by explicitly taking into account the connectivity patterns at each layer. We first validate our algorithm on synthetic networks of different size and density, and with tunable overlap between the cores at different layers. We then use our method to merge information from structural and functional brain networks, obtaining in this way an integrated description of the core of the human connectome. Results confirm the role of the main known cortical and subcortical hubs, but also suggest the presence of new areas in the sensori-motor cortex that are crucial for intrinsic brain functioning. Taken together these findings provide fresh evidence on a fundamental question in modern neuroscience and offer new opportunities to explore the mesoscale properties of multimodal brain networks.

Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits

Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients.

Structural Network Disorganization in Subjects at Clinical High Risk for Psychosis

Previous network studies in chronic schizophrenia patients revealed impaired structural organization of the brain's rich-club members, a set of highly interconnected hub regions that play an important integrative role for global brain communication. Moreover, impaired rich-club connectivity has also been found in unaffected siblings of schizophrenia patients, suggesting that abnormal rich-club connectivity is related to familiar, possibly reflecting genetic, vulnerability for schizophrenia. However, no study has yet investigated whether structural rich-club organization is also impaired in individuals with a clinical risk syndrome for psychosis. Diffusion tensor imaging and probabilistic tractography was used to construct structural whole-brain networks in 24 healthy controls and 24 subjects with an at-risk mental state (ARMS). Graph theory was applied to quantify the structural rich-club organization and global network properties. ARMS subjects revealed a significantly altered structural rich-club organization compared with the control group. The disruption of rich-club organization was associated with the severity of negative psychotic symptoms and led to an elevated level of modularity in ARMS subjects. This study shows that abnormal structural rich-club organization is already evident in clinical high-risk subjects for psychosis and further demonstrates the impact of rich-club disorganization on global network communication. Together with previous evidence in chronic schizophrenia patients and unaffected siblings, our findings suggest that abnormal structural rich-club organization may reflect an endophenotypic marker of psychosis.

Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain

White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.