Topological impact of negative links on the stability of resting-state brain network

Vertex-based parameters of hierarchal lattice tube with an application of metric dimension

Architectural metamaterials that span different length scales and are either self-similar or dissimilar to one another make up hierarchical lattices. Comparing hierarchical lattices to traditional ones reveals that they offer superior and customizable properties, which allows for a wide variety of material property manipulation and optimization. Each computer network can be represented as a graph, where nodes alternate as vertices and links are edges. The recent advanced topic of resolvability parameters of a graph involves shaping the entire structure to obtain each nodes' specific position. In this article, we computed the metric, fault metric, and partition dimension of the hierarchal lattic tube. The application of the metric dimension is also covered in this paper.

Altered brain network stability in OCD following rTMS intervention: Insights from structural balance theory

Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising intervention for Obsessive-Compulsive Disorder (OCD). However, understanding brain network changes following rTMS remains limited, despite its potential to enhance treatment efficacy. In this retrospective study, we investigated brain network reorganization in OCD patients after rTMS, using structural balance theory as a framework. We hypothesized that rTMS-induced functional plasticity would alter brain network topology, particularly affecting triadic associations, and leading to increased balance energy levels, indicative of a less stable network state. Brain functional networks were constructed from resting-state EEGs of OCD patients, with phase lag indexes calculated both before and after rTMS treatment. These networks were analyzed by comparing global parameters, including positive and negative links, triadic interactions (balanced/unbalanced), hub formation tendencies, and balance energy levels. We observed a significant decrease in weak-balanced triads and an increase in strong-unbalanced triads within the Beta І frequency band (12-15 Hz). Additionally, there was a notable reduction in the tendency of negative links to form hubs across certain frequency bands. These changes led to an increase in the network's balanced energy level, pushing it toward a less stable state. We hope these findings will refine rTMS strategies by facilitating brain network reorganization.

The brain selectively allocates energy to functional brain networks under cognitive control

Network energy has been conceptualized based on structural balance theory in the physics of complex networks. We utilized this framework to assess the energy of functional brain networks under cognitive control and to understand how energy is allocated across canonical functional networks during various cognitive control tasks. We extracted network energy from functional connectivity patterns of subjects who underwent fMRI scans during cognitive tasks involving working memory, inhibitory control, and cognitive flexibility, in addition to task-free scans. We found that the energy of the whole-brain network increases when exposed to cognitive control tasks compared to the task-free resting state, which serves as a reference point. The brain selectively allocates this elevated energy to canonical functional networks; sensory networks receive more energy to support flexibility for processing sensory stimuli, while cognitive networks relevant to the task, functioning efficiently, require less energy. Furthermore, employing network energy, as a global network measure, improves the performance of predictive modeling, particularly in classifying cognitive control tasks and predicting chronological age. Our results highlight the robustness of this framework and the utility of network energy in understanding brain and cognitive mechanisms, including its promising potential as a biomarker for mental conditions and neurological disorders.

Common and unique brain aging patterns between females and males quantified by large-scale deep learning

There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently between males and females. Based on resting-state brain functional connectivity (FC) of 25,582 healthy participants (13,373 females) aged 49-76 years from the UK Biobank project, we employ deep learning with explainable AI to discover primary FCs related to progressive aging and reveal similarity and difference between females and males in brain aging. Using a nested cross-validation scheme, we conduct 4200 deep learning models to classify all paired age groups on the main data for females and males separately and then extract gender-common and gender-specific aging-related FCs. Next, we validate those FCs using additional 21,000 classifiers on the independent data. Our results support that aging results in reduced brain functional interactions for both females and males, primarily relating to the positive connectivity within the same functional domain and the negative connectivity between different functional domains. Regions linked to cognitive control show the most significant age-related changes in both genders. Unique aging effects in males and females mainly involve the interaction between cognitive control and the default mode, vision, auditory, and frontoparietal domains. Results also indicate females exhibit faster brain functional changes than males. Overall, our study provides new evidence about common and unique patterns of brain aging in females and males.

Medial to lateral frontal functional connectivity mapping reveals the organization of cingulate cortex

The functional organization of the frontal lobe is a source of debate, focusing on broad functional subdivisions, large-scale networks, or local refined specificities. Multiple neurocognitive models have tried to explain how functional interactions between cingulate and lateral frontal regions contribute to decision making and cognitive control, but their neuroanatomical bases remain unclear. We provide a detailed description of the functional connectivity between cingulate and lateral frontal regions using resting-state functional MRI in rhesus macaques. The analysis focuses on the functional connectivity of the rostral part of the cingulate sulcus with the lateral frontal cortex. Data-driven and seed-based analysis revealed three clusters within the cingulate sulcus organized along the rostro-caudal axis: the anterior, mid, and posterior clusters display increased functional connectivity with, respectively, the anterior lateral prefrontal regions, face-eye lateral frontal motor cortical areas, and hand lateral frontal motor cortex. The location of these clusters can be predicted in individual subjects based on morphological landmarks. These results suggest that the anterior cluster corresponds to the anterior cingulate cortex, whereas the posterior clusters correspond to the face-eye and hand cingulate motor areas within the anterior midcingulate cortex. These data provide a comprehensive framework to identify cingulate subregions based on functional connectivity and local organization.

Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features

Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.

Statistical Brain Network Analysis

The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, methods for statistically analyzing networks at the group and individual levels have lagged behind. We have attempted to address this need by developing three complementary statistical frameworks-a mixed modeling framework, a distance regression framework, and a hidden semi-Markov modeling framework. These tools serve as synergistic fusions of statistical approaches with network science methods, providing needed analytic foundations for whole-brain network data. Here we delineate these approaches, briefly survey related tools, and discuss potential future avenues of research. We hope this review catalyzes further statistical interest and methodological development in the field.

Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis

INTRODUCTION

High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders.

METHODS

Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R.

RESULTS

Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen's d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition.

CONCLUSION

These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain's ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.

Impact of negative links on the structural balance of brain functional network during emotion processing

Activation of specific brain areas and synchrony between them has a major role in process of emotions. Nevertheless, impact of anti-synchrony (negative links) in this process still requires to be understood. In this study, we hypothesized that quantity and topology of negative links could influence a network stability by changing of quality of its triadic associations. Therefore, a group of healthy participants were exposed to pleasant and unpleasant images while their brain responses were recorded. Subsequently, functional connectivity networks were estimated and quantity of negative links, balanced and imbalanced triads, tendency to make negative hubs, and balance energy levels of two conditions were compared. The findings indicated that perception of pleasant stimuli was associated with higher amount of negative links with a lower tendency to make a hub in theta band; while the opposite scenario was observed in beta band. It was accompanied with smaller number of imbalanced triads and more stable network in theta band, and smaller number of balanced triads and less stable network in beta band. The findings highlighted that inter regional communications require less changes to receive new information from unpleasant stimuli, although by decrement in beta band stability prepares the network for the upcoming events.

Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings

BACKGROUND

Glioblastoma multiforme (GBM) is one of the most malignant types of central nervous system tumors. GBM patients usually have a poor prognosis. Identification of genes associated with the progression of the disease is essential to explain the mechanisms or improve the prognosis of GBM by catering to targeted therapy. It is crucial to develop a methodology for constructing a biological network and analyze it to identify potential biomarkers associated with disease progression.

METHODS

Gene expression datasets are obtained from TCGA data repository to carry out this study. A survival analysis is performed to identify survival associated genes of GBM patient. A gene co-expression network is constructed based on Pearson correlation between the gene's expressions. Various topological measures along with set operations from graph theory are applied to identify most influential genes linked with the progression of the GBM.

RESULTS

Ten key genes are identified as a potential biomarkers associated with GBM based on centrality measures applied to the disease network. These genes are SEMA3B, APS, SLC44A2, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, CTSZ, and KRTAP4.2. Higher expression values of two genes, SLC44A2 and KRTAP4.2 are found to be associated with progression and lower expression values of seven gens SEMA3B, APS, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, and CTSZ are linked with the progression of the GBM.

CONCLUSIONS

The proposed methodology employing a network topological approach to identify genetic biomarkers associated with cancer.

Balance-energy of resting state network in obsessive-compulsive disorder

Stability of the brain functional network is directly linked to organization of synchronous and anti-synchronous activities. Nevertheless, impact of arrangement of positive and negative links called links topology requires to be well understood. In this study, we investigated how topology of the functional links reduce balance-energy of the brain network in obsessive-compulsive disorder (OCD) and push the network to a more stable state as compared to healthy controls. Therefore, functional associations between the regions were measured using the phase synchrony between the EEG activities. Subsequently, balance-energy of the brain functional network was estimated based on the quality of triadic interactions. Occurrence rates of four different types of triadic interactions including weak and strong balanced, and unbalanced interactions were compared. In addition, impact of the links topology was also investigated by looking at the tendency of positive and negative links to making hubs. Our results showed although the number of positive and negative links were not statistically different between OCD and healthy controls, but positive links in OCDs' brain networks have more tendency to make hub. Moreover, lower number of unbalanced triads and higher number of strongly balanced triad reduced the balance-energy in OCDs' brain networks that conceptually has less requirement to change. We hope these findings could shed a light on better understanding of brain functional network in OCD.

Omnipresence of the sensorimotor-association axis topography in the human connectome

Low-dimensional representations are increasingly used to study meaningful organizational principles within the human brain. Most notably, the sensorimotor-association axis consistently explains the most variance in the human connectome as its so-called principal gradient, suggesting that it represents a fundamental organizational principle. While recent work indicates these low dimensional representations are relatively robust, they are limited by modeling only certain aspects of the functional connectivity structure. To date, the majority of studies have restricted these approaches to the strongest connections in the brain, treating weaker or negative connections as noise despite evidence of meaningful structure among them. The present work examines connectivity gradients of the human connectome across a full range of connectivity strengths and explores the implications for outcomes of individual differences, identifying potential dependencies on thresholds and opportunities to improve prediction tasks. Interestingly, the sensorimotor-association axis emerged as the principal gradient of the human connectome across the entire range of connectivity levels. Moreover, the principal gradient of connections at intermediate strengths encoded individual differences, better followed individual-specific anatomical features, and was also more predictive of intelligence. Taken together, our results add to evidence of the sensorimotor-association axis as a fundamental principle of the brain's functional organization, since it is evident even in the connectivity structure of more lenient connectivity thresholds. These more loosely coupled connections further appear to contain valuable and potentially important information that could be used to improve our understanding of individual differences, diagnosis, and the prediction of treatment outcomes.

Stable decoding of working memory load through frequency bands

Numerous studies have shown that working memory modulates every frequency band's power in the human brain. Yet, the question of how the highly distributed working memory adapts to external demands remains unresolved. Here, we explored frequency band modulations underlying working memory load, taking executive control under account. We hypothesized that synchronizations underlying various cognitive functions may be sequenced in time to avoid interference and that transient modulation of decoding accuracy of task difficulty would vary with increasing difficulty. We recorded whole scalp EEG data from 12 healthy participants, while they performed a visuo-spatial n-back task with three conditions of increasing difficulty, after an initial learning phase. We analyzed evoked spectral perturbations and time-resolved decoding of individual synchronization. Surprisingly, our results provide evidence for persistent decoding above the level-of-chance (83.17% AUC) for combined frequency bands. In fact, the decoding accuracy was higher for the combined than for isolated frequency bands (AUC from 65.93% to 74.30%). However, in line with our hypothesis, frequency band clusters transiently emerged in parieto-occipital regions within two separate time windows for alpha-/beta-band (relative synchronization from approximately 200 to 600 ms) and for the delta-/theta-band (relative desynchronization from approximately 600 to 1000 ms). Overall, these findings highlight concurrent sustained and transient measurable features of working memory load. This could reflect the emergence of stability within and between functional networks of the complex working memory system. In turn, this process allows energy savings to cope with external demands.

Sex differences in multilayer functional network topology over the course of aging in 37543 UK Biobank participants

Aging is a major risk factor for cardiovascular and neurodegenerative disorders, with considerable societal and economic implications. Healthy aging is accompanied by changes in functional connectivity between and within resting-state functional networks, which have been associated with cognitive decline. However, there is no consensus on the impact of sex on these age-related functional trajectories. Here, we show that multilayer measures provide crucial information on the interaction between sex and age on network topology, allowing for better assessment of cognitive, structural, and cardiovascular risk factors that have been shown to differ between men and women, as well as providing additional insights into the genetic influences on changes in functional connectivity that occur during aging. In a large cross-sectional sample of 37,543 individuals from the UK Biobank cohort, we demonstrate that such multilayer measures that capture the relationship between positive and negative connections are more sensitive to sex-related changes in the whole-brain connectivity patterns and their topological architecture throughout aging, when compared to standard connectivity and topological measures. Our findings indicate that multilayer measures contain previously unknown information on the relationship between sex and age, which opens up new avenues for research into functional brain connectivity in aging.

Pattern of frustration formation in the functional brain network

The brain is a frustrated system that contains conflictual link arrangements named frustration. The frustration as a source of disorder prevents the system from settling into low-energy states and provides flexibility for brain network organization. In this research, we tried to identify the pattern of frustration formation in the brain at the levels of region, connection, canonical network, and hemisphere. We found that frustration formation has no uniform pattern. Some subcortical elements have an active role in frustration formation, despite low contributions from many cortical elements. Frustrating connections are mostly between-network connections, and triadic frustrations are mainly formed between three regions from three distinct canonical networks. We did not find any significant differences between brain hemispheres or any robust differences between the frustration formation patterns of various life-span stages. Our results may be interesting for those who study the organization of brain links and promising for those who want to manipulate brain networks.

Hybrid balance theory: Heider balance under higher-order interactions

Heider's balance theory in signed networks, which consists of friendship or enmity relationships, is a model that relates the type of relationship between two people to the third person. In this model, there is an assumption of the independence of triadic relations, which means that the balance or imbalance of one triangle does not affect another and the energy only depends on the number of each type of triangle. There is evidence that in real network data, in addition to third-order interactions (Heider balance), higher-order interactions also play a role. One step beyond the Heider balance, the effect of quartic balance has been studied by removing the assumption of triangular independence. The application of quartic balance results in the influence of the balanced or imbalanced state of neighboring triangles on each specific one. Here, a question arises as to how the Heider balance is affected by the existence of quartic balance (fourth order). To answer this question, we presented a model which has both third- and fourth-order interactions and we called it a hybrid balance theory. The phase diagram obtained from the mean-field approximation shows there is a threshold for higher-order interaction strength, below which a third-order interaction dominates and there are no imbalance triangles in the network, and above this threshold, squares effectively determine the balance state in which the imbalance triangles can survive. The solution of the mean-field indicates that we have a first-order phase transition in terms of the random behavior of agents (temperature) which is in accordance with the Monte Carlo simulation results.

Exploration of the Specific Pathology of HXMM Tablet Against Retinal Injury Based on Drug Attack Model to Network Robustness

Retinal degenerative diseases are related to retinal injury because of the activation of the complement cascade, oxidative stress-induced cell death mechanisms, dysfunctional mitochondria, chronic neuroinflammation, and production of the vascular endothelial growth factor. Anti-VEGF therapy demonstrates remarkable clinical effects and benefits in retinal degenerative disease patients. Hence, new drug development is necessary to treat patients with severe visual loss. He xue ming mu (HXMM) tablet is a CFDA-approved traditional Chinese medicine (TCM) for retinal degenerative diseases, which can alleviate the symptoms of age-related macular degeneration (AMD) and diabetic retinopathy (DR) alone or in combination with anti-VEGF agents. To elucidate the mechanisms of HXMM, a quantitative evaluation algorithm for the prediction of the effect of multi-target drugs on the disturbance of the disease network has been used for exploring the specific pathology of HXMM and TCM precision positioning. Compared with anti-VEGF agents, the drug disturbance of HXMM on the functional subnetwork shows that HXMM reduces the network robustness on the oxidative stress subnetwork and inflammatory subnetwork to exhibit the anti-oxidation and anti-inflammation activity. HXMM provides better protection to ARPE-19 cells against retinal injury after H₂O₂ treatment. HXMM can elevate GSH and reduce LDH levels to exhibit antioxidant activity and suppress the expression of IL-6 and TNF-α for anti-inflammatory activity, which is different from the anti-VEGF agent with strong anti-VEGF activity. The experimental result confirmed the accuracy of the computational prediction. The combination of bioinformatics prediction based on the drug attack on network robustness and experimental validation provides a new strategy for precision application of TCM.

A regression framework for brain network distance metrics

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: standard F test, F test with individual level effects (ILE), feasible generalized least squares (FGLS), and permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing multivariate distance matrix regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

Requirement to change of functional brain network across the lifespan

Many studies have focused on neural changes and neuroplasticity, while the signaling demand for neural modification needs to be explored. In this study, we traced this issue in the organization of brain functional links where the conflictual arrangement of signed links makes a request to change. We introduced the number of frustrations (unsatisfied closed triadic interactions) as a measure for assessing "requirement to change" of functional brain network. We revealed that the requirement to change of the resting-state network has a u-shape functionality over the lifespan with a minimum in early adulthood, and it's correlated with the presence of negative links. Also, we discovered that brain negative subnetwork has a special topology with a log-normal degree distribution in all stages, however, its global measures are altered by adulthood. Our results highlight the study of collective behavior of functional negative links as the source of the brain's between-regions conflicts and we propose exploring the attribute of the requirement to change besides other neural change factors.