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

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a severe derangement of cognitive functions, primarily memory, in elderly subjects. As far as the functional impairment is concerned, growing evidence supports the “disconnection syndrome” hypothesis. Recent investigations using fMRI have revealed a generalized alteration of resting state networks (RSNs) in patients affected by AD and mild cognitive impairment (MCI). However, it was unclear whether the changes in functional connectivity were accompanied by corresponding structural network changes. In this work, we have developed a novel structural/functional connectomic approach: resting state fMRI was used to identify the functional cortical network nodes and diffusion MRI to reconstruct the fiber tracts to give a weight to internodal subcortical connections. Then, local and global efficiency were determined for different networks, exploring specific alterations of integration and segregation patterns in AD and MCI patients compared to healthy controls (HC). In the default mode network (DMN), that was the most affected, axonal loss, and reduced axonal integrity appeared to compromise both local and global efficiency along posterior-anterior connections. In the basal ganglia network (BGN), disruption of white matter integrity implied that main alterations occurred in local microstructure. In the anterior insular network (AIN), neuronal loss probably subtended a compromised communication with the insular cortex. Cognitive performance, evaluated by neuropsychological examinations, revealed a dependency on integration and segregation of brain networks. These findings are indicative of the fact that cognitive deficits in AD could be associated not only with cortical alterations (revealed by fMRI) but also with subcortical alterations (revealed by diffusion MRI) that extend beyond the areas primarily damaged by neurodegeneration, toward the support of an emerging concept of AD as a “disconnection syndrome.” Since only AD but not MCI patients were characterized by a significant decrease in structural connectivity, integrated structural/functional connectomics could provide a useful tool for assessing disease progression from MCI to AD.

The analysis of the topology and organization of brain networks is known to greatly benefit from network measures in graph theory. However, to evaluate dynamic changes of brain functional connectivity, more sophisticated quantitative metrics characterizing temporal evolution of brain topological features are required. To simplify conversion of time-varying brain connectivity to a static graph representation is straightforward but the procedure loses temporal information that could be critical in understanding the brain functions. To extend the understandings of functional segregation and integration to a dynamic fashion, we recommend dynamic graph metrics to characterise temporal changes of topological features of brain networks. This study investigated functional segregation and integration of brain networks over time by dynamic graph metrics derived from EEG signals during an experimental protocol: performance of complex flight simulation tasks with multiple levels of difficulty. We modelled time-varying brain functional connectivity as multi-layer networks, in which each layer models brain connectivity at time window t + Δt. Dynamic graph metrics were calculated to quantify temporal and topological properties of the network. Results show that brain networks under the performance of complex tasks reveal a dynamic small-world architecture with a number of frequently connected nodes or hubs, which supports the balance of information segregation and integration in brain over time. The results also show that greater cognitive workloads caused by more difficult tasks induced a more globally efficient but less clustered dynamic small-world functional network. Our study illustrates that task-related changes of functional brain network segregation and integration can be characterized by dynamic graph metrics.

The hierarchical modular organization of functional networks in the brain is crucial for supporting diverse cognitive functions. Functional disorders in the brain are associated with an abnormal hierarchical modular organization. The default mode network (DMN) is a complex dynamic network that is linked to specialized cognitive functions and clinically relevant information. In this study, we hypothesize that hierarchical functional segregation and integration of the DMN within attention-deficit/hyperactivity disorder (ADHD) is abnormal. We investigated topological metrics of both segregation and integration in different hierarchical subnetworks of the DMN between patients with ADHD and healthy controls. We found that the hierarchical functional integration and segregation of the DMN decreased and increased, respectively, in ADHD. Our results also indicated that the abnormalities in the DMN are intrinsically caused by changes in functional segregation and integration in its higher-level subnetworks. To better understand the temporally dynamic changes in the hierarchical functional integration and segregation of the DMN within ADHD, we further analyzed the dynamic transitions between functional segregation and integration. We found that the adaptive reorganizational ability of brain network states decreased in ADHD patients, which indicated less adaptive regulation between the DMN subnetworks in ADHD for supporting correspondingly normal cognitive function. From the perspective of hierarchical functional segregation and integration, our results further provide evidence to support dysfunctional brain cognitive functions within ADHD linked to brain network segregation and integration.

Opioid use disorder (OUD) is associated with altered brain network connectivity, particularly in the fronto-parietal (FPN), default mode, and salience (SN) networks. At rest, brain networks that are distinct from each other but are partially connected can optimize neural efficiency and support cognitive performance. Previous research found lower network segregation in people with cognitive impairment, alcohol use disorder, and as people age. Here, we examined brain network segregation (a graph theory-based metric of the network integration/segregation balance) in individuals with OUD and hypothesized that recent drug use severity would be linked to reduced network segregation. Forty adults with OUD completed resting-state functional magnetic resonance imaging, the drug use severity subscale of the Addiction Severity Index, and measures of cognition (IQ and working memory), mood, and affect. We grouped 264 brain regions into 10 networks, categorized as association (higher-order cognition) or sensorimotor (sensory and motor) networks. Regression analyses showed that drug use severity predicted lower brain network segregation in the association networks, with the FPN and SN driving this effect. Age predicted lower brain network segregation in the sensorimotor networks, while an interaction with age showed that drug use severity only predicted lower sensorimotor network segregation in younger adults. Cognition did not relate to brain network segregation, but positive affect related to greater SN segregation. Brain network segregation remained stable across OUD treatment. These findings elucidate alterations in brain network segregation related to drug use severity in people with OUD, which may contribute to cognitive impairment and accelerated brain aging.

Measures of functional brain network segregation and integration vary with an individual’s age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant’s scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

This chapter presents the existing modelling frameworks for multiplex and multilayer networks. Multiplex network models are divided into growing multiplex network models and null models of multiplex networks. Growing multiplex networks are here shown to explain the main dynamical rules responsible to the emergent properties of multiplex networks, including the scale-free degree distribution, interlayer degree correlations and multilayer communities. Null models of multiplex networks are described in the context of maximum-entropy multiplex network ensembles. Randomization algorithms to test the relevant of network properties against null models are here described. Moreover, Multi-slice temporal networks Models capturing main properties of real temporal network data are presented. Finally, null models of general multilayer networks and networks of networks are characterized.

The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: anais.baudot@univ-amu.fr.

Older adults typically exhibit decline in language production. However, how the brain supports or fails to support these processes is unclear. Moreover, there are competing hypotheses about the nature of age-related neural changes and whether age-related increases in neural activity reflect compensation or a decline in neural efficiency. In the current study, we investigated the neural bases of language production focusing on resting state functional connectivity. We hypothesized that language production performance, functional connectivity, and their relationship would differ as a function of age. Consistent with prior work, older age was associated with worse language production performance. Functional connectivity analyses showed that network segregation within the left hemisphere language network was maintained across adulthood. However, increased age was associated with lower whole brain network segregation. Moreover, network segregation was related to language production ability. In both network analyses, there were significant interactions with age-higher network segregation was associated with better language production abilities for younger and middle-aged adults, but not for older adults. Interestingly, there was a stronger relationship between language production and the whole brain network segregation than between production and the language network. These results highlight the utility of network segregation measures as an index of brain function, with higher network segregation associated with better language production ability. Moreover, these results are consistent with stability in the left hemisphere language network across adulthood and suggest that dedifferentiation among brain networks, outside of the language network, is a hallmark of aging and may contribute to age-related language production difficulties.

Alzheimer's disease (AD) is the most common type of dementia, comprising an estimated 60-80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging (structural magnetic resonance imaging (MRI), diffusion MRI, and functional MRI) and neurophysiological techniques (electroencephalography and magnetoencephalography) with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment (MCI), the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks (i.e., connectomics) and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.

Traumatic brain injury (TBI) often results in balance impairment, increasing the risk of falls, and the chances of further injuries. However, the underlying neural mechanisms of postural control after TBI are not well understood. To this end, we conducted a pilot study to explore the neural mechanisms of unpredictable balance perturbations in 17 chronic TBI participants and 15 matched healthy controls (HC) using the EEG, MRI, and diffusion tensor imaging (DTI) data. As quantitative measures of the functional integration and segregation of the brain networks during the postural task, we computed the global graph‐theoretic network measures (global efficiency and modularity) of brain functional connectivity derived from source‐space EEG in different frequency bands. We observed that the TBI group showed a lower balance performance as measured by the center of pressure displacement during the task, and the Berg Balance Scale (BBS). They also showed reduced brain activation and connectivity during the balance task. Furthermore, the decrease in brain network segregation in alpha‐band from baseline to task was smaller in TBI than HC. The DTI findings revealed widespread structural damage. In terms of the neural correlates, we observed a distinct role played by different frequency bands: theta‐band modularity during the task was negatively correlated with the BBS in the TBI group; lower beta‐band network connectivity was associated with the reduction in white matter structural integrity. Our future studies will focus on how postural training will modulate the functional brain networks in TBI.

BackgroundThe topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is disrupted in Alzheimer’s disease(AD) and its prodromal stage, mild cognitive impairment(MCI). We investigated on the fitting of nodal degree distribution of the whole‐brain functional networks and 7 subnetworks in healthy controls(HC) and individuals with MCI and AD.MethodWe included 40 HCs, 34 MCI and 34 early‐stage AD subjects who were recruited from a memory clinic at the Peking University Sixth Hospital. Resting‐state fMRI scans underwent standard preprocessing and were spatially normalized to the standard space. For each subject, we built whole‐brain FC matrices by computing Pearson’s correlations among brain voxels, followed by a threshold process(r from 0.4 to 0.6). Seven pre‐defined subnetworks were obtained from previous studies, including visual, sensorimotor, dorsal attention, ventral attention, limbic, frontoparietal and default mode (DMN). For a given subnetwork, degree distributions were calculated within and between subnetworks, respectively. Maximum likelihood estimation was used to determine model parameters. The goodness of fit of each fitting was evaluated by bootstrapping and the Kolmogorov‐Smirnov test. Group differences in the estimated parameters were tested by ANCOVAs, controlled for age, gender and education.ResultIn the whole‐brain networks and all subnetworks, the connectivity degree distributions were fitted better by a Weibull distribution(f(x)∼x^(β‐1)e^(‐λx^β)) than power law or alternative models. Compared with the HC group, the MCI group showed lower Weibull β parameter (shape factor) in both the whole‐brain and subnetworks, especially in visual, somatomotor and DMN across all threshold(ps<0.05), some of which were distributed in within‐network(somatomotor), between‐networks(dorsal attention and limbic), or both(visual, ventral attention, frontoparietal and DMN). In the AD group, the β parameters were lower in the frontalparietal(p<0.05) and DMN(p<0.05, except for r=0.6) than those in the HC.ConclusionWe show that the Weibull model fits human brain functional networks better than a power law model, and this network structure is disrupted in AD and MCI. Such a short‐tailed model may capture intrinsic network structure of the human brain in health and disease.

Aging is typically associated with substantial declines in motor functioning as well as robust changes in the functional organization of brain networks. Previous research has investigated the link between these 2 age-varying factors but examinations were predominantly limited to the functional organization within motor-related brain networks. Little is known about the relationship between age-related behavioral impairments and changes in functional organization at the whole brain (i.e., multiple network) level. This knowledge gap is surprising given that the decreased segregation of brain networks (i.e., increased internetwork connectivity) can be considered a hallmark of the aging process. Accordingly, we investigated the association between declines in motor performance across the adult lifespan (20-75 years) and age-related modulations of functional connectivity within and between resting state networks. Results indicated that stronger internetwork resting state connectivity observed as a function of age was significantly related to worse motor performance. Moreover, performance had a significantly stronger association with the strength of internetwork as compared with intranetwork connectivity, including connectivity within motor networks. These findings suggest that age-related declines in motor performance may be attributed to a breakdown in the functional organization of large-scale brain networks rather than simply age-related connectivity changes within motor-related networks.

Hypnosis has been shown to be of clinical utility; however, its underlying neural mechanisms remain unclear. This study aims to investigate altered brain dynamics during the non-ordinary state of consciousness induced by hypnosis. We studied high-density EEG in 9 healthy participants during eyes-closed wakefulness and during hypnosis, induced by a muscle relaxation and eyes fixation procedure. Using hypotheses based on internal and external awareness brain networks, we assessed region-wise brain connectivity between six ROIs (right and left frontal, right and left parietal, upper and lower midline regions) at the scalp level and compared across conditions. Data-driven, graph-theory analyses were also carried out to characterize brain network topology in terms of brain network segregation and integration. During hypnosis, we observed (1) increased delta connectivity between left and right frontal, as well as between right frontal and parietal regions; (2) decreased connectivity for alpha (between right frontal and parietal and between upper and lower midline regions) and beta-2 bands (between upper midline and right frontal, frontal and parietal, also between upper and lower midline regions); and (3) increased network segregation (short-range connections) in delta and alpha bands, and increased integration (long-range connections) in beta-2 band. This higher network integration and segregation was measured bilaterally in frontal and right parietal electrodes, which were identified as central hub regions during hypnosis. This modified connectivity and increased network integration-segregation properties suggest a modification of the internal and external awareness brain networks that may reflect efficient cognitive-processing and lower incidences of mind-wandering during hypnosis.

BackgroundThe sialic‐acid binding immunoglobulin‐like lectin 3 receptor (Siglec‐3 / CD33) expressed on microglia, regulates immune functions relevant to Alzheimer's disease (AD). Clusterin (CLU) and apolipoprotein E (ApoE) are soluble, sialylated proteins implicated in AD pathogenesis through genetic associations and their interactions with amyloid‐beta (Aβ). However, the role of these proteins as potential CD33 ligands remains unclear. This study explores whether CLU and/or ApoE bind CD33 and examines the functional impact of these interactions on Aβ uptake and amyloid plaque clearance.MethodsThe binding of CD33 to CLU and ApoE was assessed through co‐immunoprecipitation using U937 cells (endogenously expressing CD33) and HEK293 cells (expressing exogenous CD33). Quantitative bio‐layer interferometry (BLI) and microscale thermophoresis determined binding affinities, focusing on the role of CD33's Arg119 sialic acid binding site. In situ proximity ligation assays (PLA) and co‐immunoprecipitation from AD and control human brain lysates validated in vivo interactions. Functional assays examined Aβ uptake and amyloid plaque clearance in monocytes and U937 cells, with or without CLU treatment.ResultsThe quantitative binding assay revealed that CLU, but not ApoE, was a sialylation‐dependent ligand for CD33, binding with high affinity (Kd = 28.9 ± 10.3 nM). Binding required an intact Arg119 residue and dimeric CD33 structure. PLA and co‐immunoprecipitation studies demonstrated colocalization of CD33 and CLU on microglia in AD brains, especially near amyloid plaques. Functionally, sialylated CLU inhibited Aβ uptake in monocytes from CD33 “CC” risk allele carriers and reduced amyloid plaque clearance in U937 cells. Desialylated CLU showed no significant effects. Notably, CLU + Aβ oligomers induced stronger CD33 ITIM signaling than CLU alone, enhancing phosphorylation and SHP‐1 recruitment.ConclusionThis study identifies CLU as a specific CD33 ligand and highlights its role in modulating microglial functions via CD33 ITIM signaling. Sialylated CLU inhibits Aβ uptake and amyloid plaque clearance, suggesting a potential mechanism underlying microglial dysfunction in AD. These findings underscore the therapeutic potential of targeting the CD33‐CLU axis to restore microglial homeostasis and enhance amyloid clearance in AD.

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.