Structure and function of complex brain networks

Early development of sleep and brain functional connectivity in term-born and preterm infants

The proper development of sleep and sleep-wake rhythms during early neonatal life is crucial to lifelong neurological well-being. Recent data suggests that infants who have poor quality sleep demonstrate a risk for impaired neurocognitive outcomes. Sleep ontogenesis is a complex process, whereby alternations between rudimentary brain states-active vs. wake and active sleep vs. quiet sleep-mature during the last trimester of pregnancy. If the infant is born preterm, much of this process occurs in the neonatal intensive care unit, where environmental conditions might interfere with sleep. Functional brain connectivity (FC), which reflects the brain's ability to process and integrate information, may become impaired, with ensuing risks of compromised neurodevelopment. However, the specific mechanisms linking sleep ontogenesis to the emergence of FC are poorly understood and have received little investigation, mainly due to the challenges of studying causal links between developmental phenomena and assessing FC in newborn infants. Recent advancements in infant neuromonitoring and neuroimaging strategies will allow for the design of interventions to improve infant sleep quality and quantity. This review discusses how sleep and FC develop in early life, the dynamic relationship between sleep, preterm birth, and FC, and the challenges associated with understanding these processes. IMPACT: Sleep in early life is essential for proper functional brain development, which is essential for the brain to integrate and process information. This process may be impaired in infants born preterm. The connection between preterm birth, early development of brain functional connectivity, and sleep is poorly understood. This review discusses how sleep and brain functional connectivity develop in early life, how these processes might become impaired, and the challenges associated with understanding these processes. Potential solutions to these challenges are presented to provide direction for future research.

Topological Abnormalities of Pallido-Thalamo-Cortical Circuit in Functional Brain Network of Patients With Nonchemotherapy With Non-small Cell Lung Cancer

INTRODUCTION

Some previous studies in patients with lung cancer have mainly focused on exploring the cognitive dysfunction and deficits of brain function associated with chemotherapy. However, little is known about functional brain alterations that might occur prior to chemotherapy. Therefore, this study aimed to evaluate brain functional changes in patients with nonchemotherapy before chemotherapy with non-small cell lung cancer (NSCLC).

METHODS

Resting-state functional MRI data of 35 patients with NSCLC and 46 matched healthy controls (HCs) were acquired to construct functional brain networks. Graph theoretical analysis was then applied to investigate the differences of the network and nodal measures between groups. Finally, the receiver operating characteristic (ROC) curve analysis was performed to distinguish between NSCLC and HC.

RESULTS

Decreased nodal strength was found in the left inferior frontal gyrus (opercular part), inferior frontal gyrus (triangular part), inferior occipital gyrus, and right inferior frontal gyrus (triangular part) of patients with NSCLC while increased nodal strength was found in the right pallidum and thalamus. NSCLC also showed decreased nodal betweenness in the right superior occipital gyrus. Different hub regions distribution was found between groups, however, no hub regions showed group differences in the nodal measures. Furthermore, the ROC curve analysis showed good performance in distinguishing NSCLC from HC.

CONCLUSION

These results suggested that topological abnormalities of pallido-thalamo-cortical circuit in functional brain network might be related to NSCLC prior to chemotherapy, which provided new insights concerning the pathophysiological mechanisms of NSCLC and could serve as promising biological markers for the identification of patients with NSCLC.

The Random Plots Graph Generation Model for Studying Systems with Unknown Connection Structures

We consider the problem of modeling complex systems where little or nothing is known about the structure of the connections between the elements. In particular, when such systems are to be modeled by graphs, it is unclear what vertex degree distributions these graphs should have. We propose that, instead of attempting to guess the appropriate degree distribution for a poorly understood system, one should model the system via a set of sample graphs whose degree distributions cover a representative range of possibilities and account for a variety of possible connection structures. To construct such a representative set of graphs, we propose a new random graph generator, Random Plots, in which we (1) generate a diversified set of vertex degree distributions and (2) target a graph generator at each of the constructed distributions, one-by-one, to obtain the ensemble of graphs. To assess the diversity of the resulting ensembles, we (1) substantialize the vague notion of diversity in a graph ensemble as the diversity of the numeral characteristics of the graphs within this ensemble and (2) compare such formalized diversity for the proposed model with that of three other common models (Erdős–Rényi–Gilbert (ERG), scale-free, and small-world). Computational experiments show that, in most cases, our approach produces more diverse sets of graphs compared with the three other models, including the entropy-maximizing ERG. The corresponding Python code is available at GitHub.

Decreased Functional Connectivities of Low-Degree Level Rich Club Organization and Caudate in Post-stroke Cognitive Impairment Based on Resting-State fMRI and Radiomics Features

Background

Stroke is an important cause of cognitive impairment. Rich club organization, a highly interconnected network brain core region, is closely related to cognition. We hypothesized that the disturbance of rich club organization exists in patients with post-stroke cognitive impairment (PSCI).

Methods

We collected data on resting-state functional magnetic resonance imaging (rs-fMRI) with 21 healthy controls (HC), 16 hemorrhagic stroke (hPSCI), and 21 infarct stroke (iPSCI). 3D shape features and first-order statistics of stroke lesions were extracted using 3D slicer software. Additionally, we assessed cognitive function using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE).

Results

Normalized rich club coefficients were higher in hPSCI and iPSCI than HC at low-degree k-levels (k = 1–8 in iPSCI, k = 2–8 in hPSCI). Feeder and local connections were significantly decreased in PSCI patients versus HC, mainly distributed in salience network (SN), default-mode network (DMN), cerebellum network (CN), and orbitofrontal cortex (ORB), especially involving the right and left caudate with changed nodal efficiency. The feeder and local connections of significantly between-group difference were positively related to MMSE and MoCA scores, primarily distributed in the sensorimotor network (SMN) and visual network (VN) in hPSCI, SN, and DMN in iPSCI. Additionally, decreased local connections and low-degree ϕnorm(k) were correlated to 3D shape features and first-order statistics of stroke lesions.

Conclusion

This study reveals the disrupted low-degree level rich club organization and relatively preserved functional core network in PSCI patients. Decreased feeder and local connections in cognition-related networks (DMN, SN, CN, and ORB), particularly involving the caudate nucleus, may offer insight into pathological mechanism of PSCI patients. The shape and signal features of stroke lesions may provide an essential clue for the damage of functional connectivity and the whole brain networks.

Dynamic reconfiguration of human brain networks across altered states of consciousness

Consciousness is supported by rich neuronal dynamics to orchestrate behaviors and conscious processing can be disrupted by general anesthetics. Previous studies suggested that dynamic reconfiguration of large-scale functional network is critical for learning and higher-order cognitive function. During altered states of consciousness, how brain functional networks are dynamically changed and reconfigured at the whole-brain level is still unclear. To fill this gap, using multilayer network approach and functional magnetic resonance imaging (fMRI) data of 21 healthy subjects, we investigated the dynamic network reconfiguration in three different states of consciousness: wakefulness, dexmedetomidine-induced sedation, and recovery. Applying time-varying community detection algorithm, we constructed multilayer modularity networks to track and quantify dynamic interactions among brain areas that span time and space. We compared four high-level network features (i.e., switching, promiscuity, integration, and recruitment) derived from multilayer modularity across the three conditions. We found that sedation state is primarily characterized by increased switching rates as well as decreased integration, representing a whole-brain pattern with higher modular dynamics and more fragmented communication; such alteration can be mostly reversed after the recovery of consciousness. Thus, our work can provide additional insights to understand the modular network reconfiguration across different states of consciousness and may provide some clinical implications for disorders of consciousness.

Degeneracy measures in biologically plausible random Boolean networks

Background

Degeneracy—the ability of structurally different elements to perform similar functions—is a property of many biological systems. Highly degenerate systems show resilience to perturbations and damage because the system can compensate for compromised function due to reconfiguration of the underlying network dynamics. Degeneracy thus suggests how biological systems can thrive despite changes to internal and external demands. Although degeneracy is a feature of network topologies and seems to be implicated in a wide variety of biological processes, research on degeneracy in biological networks is mostly limited to weighted networks. In this study, we test an information theoretic definition of degeneracy on random Boolean networks, frequently used to model gene regulatory networks. Random Boolean networks are discrete dynamical systems with binary connectivity and thus, these networks are well-suited for tracing information flow and the causal effects. By generating networks with random binary wiring diagrams, we test the effects of systematic lesioning of connections and perturbations of the network nodes on the degeneracy measure.

Results

Our analysis shows that degeneracy, on average, is the highest in networks in which ~ 20% of the connections are lesioned while 50% of the nodes are perturbed. Moreover, our results for the networks with no lesions and the fully-lesioned networks are comparable to the degeneracy measures from weighted networks, thus we show that the degeneracy measure is applicable to different networks.

Conclusions

Such a generalized applicability implies that degeneracy measures may be a useful tool for investigating a wide range of biological networks and, therefore, can be used to make predictions about the variety of systems’ ability to recover function.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04601-5.

Complex functional brain network properties in anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a disorder characterized by an incapacitating fear of weight gain and by a disturbance in the way the body is experienced, facets that motivate dangerous weight loss behaviors. Multimodal neuroimaging studies highlight atypical neural activity in brain networks involved in interoceptive awareness and reward processing.

METHODS

The current study used resting-state neuroimaging to model the architecture of large-scale functional brain networks and characterize network properties of individual brain regions to clinical measures. Resting-state neuroimaging was conducted in 62 adolescents, 22 (21 female) with a history of AN and 40 (39 female) healthy controls (HCs). Sensorimotor and basal ganglia regions, as part of a 165-region whole-brain network, were investigated. Subject-specific functional brain networks were computed to index centrality. A contrast analysis within the general linear model covarying for age was performed. Correlations between network properties and behavioral measures were conducted (significance q < .05).

RESULTS

Compared to HCs, AN had lower connectivity from sensorimotor regions, and greater connectivity from the left caudate nucleus to the right postcentral gyrus. AN demonstrated lower sensorimotor centrality, but higher basal ganglia centrality. Sensorimotor connectivity dyads and centrality exhibited negative correlations with body dissatisfaction and drive for thinness, two essential features of AN.

CONCLUSIONS

These findings suggest that AN is associated with greater communication from the basal ganglia, and lower information propagation in sensorimotor cortices. This is consistent with the clinical presentation of AN, where individuals exhibit patterns of rigid habitual behavior that is not responsive to bodily needs, and seem "disconnected" from their bodies.

Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

Artificial neural networks (ANNs) can be employed as controllers for robotic agents. Their structure is often complex, with many neurons and connections, especially when the robots have many sensors and actuators distributed across their bodies and/or when high expressive power is desirable. Pruning (removing neurons or connections) reduces the complexity of the ANN, thus increasing its energy efficiency, and has been reported to improve the generalization capability, in some cases. In addition, it is well-known that pruning in biological neural networks plays a fundamental role in the development of brains and their ability to learn. In this study, we consider the evolutionary optimization of neural controllers for the case study of Voxel-based soft robots, a kind of modular, bio-inspired soft robots, applying pruning during fitness evaluation. For a locomotion task, and for centralized as well as distributed controllers, we experimentally characterize the effect of different forms of pruning on after-pruning effectiveness, life-long effectiveness, adaptability to new terrains, and behavior. We find that incorporating some forms of pruning in neuroevolution leads to almost equally effective controllers as those evolved without pruning, with the benefit of higher robustness to pruning. We also observe occasional improvements in generalization ability.

Untapped Neuroimaging Tools for Neuro-Oncology: Connectomics and Spatial Transcriptomics

Simple Summary

Brain imaging, specifically magnetic resonance imaging (MRI), plays a key role in the clinical and research aspects of neuro-oncology. Novel neuroimaging techniques enable the transformation of a brain MRI into a so-called average brain. This allows projects using already acquired brain MRIs to perform group analyses and draw conclusions. Once the data are in this average brain, several types of analyses can be performed. For example, determining the most vulnerable locations for certain tumor types or perhaps even the underlying circuitry and gene expression that might cause predisposition to tumor growth. This information may further our understanding of tumor behavior, leading to better patient counseling, surgery timing, and treatment monitoring.

Abstract

Neuro-oncology research is broad and includes several branches, one of which is neuroimaging. Magnetic resonance imaging (MRI) is instrumental for the diagnosis and treatment monitoring of patients with brain tumors. Most commonly, structural and perfusion MRI sequences are acquired to characterize tumors and understand their behaviors. Thanks to technological advances, structural brain MRI can now be transformed into a so-called average brain accounting for individual morphological differences, which enables retrospective group analysis. These normative analyses are uncommonly used in neuro-oncology research. Once the data have been normalized, voxel-wise analyses and spatial mapping can be performed. Additionally, investigations of underlying connectomics can be performed using functional and structural templates. Additionally, a recently available template of spatial transcriptomics has enabled the assessment of associated gene expression. The few published normative analyses have shown relationships between tumor characteristics and spatial localization, as well as insights into the circuitry associated with epileptogenic tumors and depression after cingulate tumor resection. The wide breadth of possibilities with normative analyses remain largely unexplored, specifically in terms of connectomics and imaging transcriptomics. We provide a framework for performing normative analyses in oncology while also highlighting their limitations. Normative analyses are an opportunity to address neuro-oncology questions from a different perspective.

Towards a model of language neurobiology in early development

Understanding language neurobiology in early childhood is essential for characterizing the developmental structural and functional changes that lead to the mature adult language network. In the last two decades, the field of language neurodevelopment has received increasing attention, particularly given the rapid advances in the implementation of neuroimaging techniques and analytic approaches that allow detailed investigations into the developing brain across a variety of cognitive domains. These methodological and analytical advances hold the promise of developing early markers of language outcomes that allow diagnosis and clinical interventions at the earliest stages of development. Here, we argue that findings in language neurobiology need to be integrated within an approach that captures the dynamic nature and inherent variability that characterizes the developing brain and the interplay between behavior and (structural and functional) neural patterns. Accordingly, we describe a framework for understanding language neurobiology in early development, which minimally requires an explicit characterization of the following core domains: i) computations underlying language learning mechanisms, ii) developmental patterns of change across neural and behavioral measures, iii) environmental variables that reinforce language learning (e.g., the social context), and iv) brain maturational constraints for optimal neural plasticity, which determine the infant's sensitivity to learning from the environment. We discuss each of these domains in the context of recent behavioral and neuroimaging findings and consider the need for quantitatively modeling two main sources of variation: individual differences or trait-like patterns of variation and within-subject differences or state-like patterns of variation. The goal is to enable models that allow prediction of language outcomes from neural measures that take into account these two types of variation. Finally, we examine how future methodological approaches would benefit from the inclusion of more ecologically valid paradigms that complement and allow generalization of traditional controlled laboratory methods.

Plasticity of the language system in children and adults

The language system is perhaps the most unique feature of the human brain's cognitive architecture. It has long been a quest of cognitive neuroscience to understand the neural components that contribute to the hierarchical pattern processing and advanced rule learning required for language. The most important goal of this research is to understand how language becomes impaired when these neural components malfunction or are lost to stroke, and ultimately how we might recover language abilities under these circumstances. Additionally, understanding how the language system develops and how it can reorganize in the face of brain injury or dysfunction could help us to understand brain plasticity in cognitive networks more broadly. In this chapter we will discuss the earliest features of language organization in infants, and how deviations in typical development can-but in some cases, do not-lead to disordered language. We will then survey findings from adult stroke and aphasia research on the potential for recovering language processing in both the remaining left hemisphere tissue and in the non-dominant right hemisphere. Altogether, we hope to present a clear picture of what is known about the capacity for plastic change in the neurobiology of the human language system.

Alterations of dynamic redundancy of functional brain subnetworks in Alzheimer's disease and major depression disorders

The human brain is not only efficiently but also "redundantly" connected. The redundancy design could help the brain maintain resilience to disease attacks. This paper explores subnetwork-level redundancy dynamics and the potential of such metrics in disease studies. As such, we looked into specific functional subnetworks, including those associated with high-level functions. We investigated how the subnetwork redundancy dynamics change along with Alzheimer's disease (AD) progression and with major depressive disorder (MDD), two major disorders that could share similar subnetwork alterations. We found an increased dynamic redundancy of the subcortical-cerebellum subnetwork and its connections to other high-order subnetworks in the mild cognitive impairment (MCI) and AD compared to the normal control (NC). With gained spatial specificity, we found such a redundancy index was sensitive to disease symptoms and could act as a protective mechanism to prevent the collapse of the brain network and functions. The dynamic redundancy of the medial frontal subnetwork and its connections to the frontoparietal subnetwork was also found decreased in MDD compared to NC. The spatial specificity of the redundancy dynamics changes may provide essential knowledge for a better understanding of shared neural substrates in AD and MDD.

Emergent Dynamics and Spatio Temporal Patterns on Multiplex Neuronal Networks

We present a study on the emergence of a variety of spatio temporal patterns among neurons that are connected in a multiplex framework, with neurons on two layers with different functional couplings. With the Hindmarsh-Rose model for the dynamics of single neurons, we analyze the possible patterns of dynamics in each layer separately and report emergent patterns of activity like in-phase synchronized oscillations and amplitude death (AD) for excitatory coupling and anti-phase mixed-mode oscillations (MMO) in multi-clusters with phase regularities when the connections are inhibitory. When they are multiplexed, with neurons of one layer coupled with excitatory synaptic coupling and neurons of the other layer coupled with inhibitory synaptic coupling, we observe the transfer or selection of interesting patterns of collective behavior between the layers. While the revival of oscillations occurs in the layer with excitatory coupling, the transition from anti-phase to in-phase and vice versa is observed in the other layer with inhibitory synaptic coupling. We also discuss how the selection of these spatio temporal patterns can be controlled by tuning the intralayer or interlayer coupling strengths or increasing the range of non-local coupling. With one layer having electrical coupling while the other synaptic coupling of excitatory(inhibitory)type, we find in-phase(anti-phase) synchronized patterns of activity among neurons in both layers.

Evidence for exercise-related plasticity in functional and structural neural network connectivity

The number of studies investigating exercise and cardiorespiratory fitness (CRF)-related changes in the functional and structural organization of brain networks continues to rise. Functional and structural connectivity are critical biomarkers for brain health and many exercise-related benefits on the brain are better represented by network dynamics. Here, we reviewed the neuroimaging literature to better understand how exercise or CRF may facilitate and maintain the efficiency and integrity of functional and structural aspects of brain networks in both younger and older adults. Converging evidence suggests that increased exercise performance and CRF modulate functional connectivity of the brain in a way that corresponds to behavioral changes such as cognitive and motor performance improvements. Similarly, greater physical activity levels and CRF are associated with better cognitive and motor function, which may be brought about by enhanced structural network integrity. This review will provide a comprehensive understanding of trends in exercise-network studies as well as future directions based on the gaps in knowledge that are currently present in the literature.

How prenatal exposures shape the infant brain: Insights from infant neuroimaging studies

Brain development during the prenatal period is rapid and unparalleled by any other time during development. Biological systems undergoing rapid development are at higher risk for disorganizing influences. Therefore, certain prenatal exposures impact brain development, increasing risk for negative neurodevelopmental outcome. While prenatal exposures have been associated with cognitive and behavioral outcomes later in life, the underlying macroscopic brain pathways remain unclear. Here, we review magnetic resonance imaging (MRI) studies investigating the association between prenatal exposures and infant brain development focusing on prenatal exposures via maternal physical health factors, maternal mental health factors, and maternal drug and medication use. Further, we discuss the need for studies to consider multiple prenatal exposures in parallel and suggest future directions for this body of research.

Connectivity modulations induced by reach&grasp movements: a multidimensional approach

Reach&grasp requires highly coordinated activation of different brain areas. We investigated whether reach&grasp kinematics is associated to EEG-based networks changes. We enrolled 10 healthy subjects. We analyzed the reach&grasp kinematics of 15 reach&grasp movements performed with each upper limb. Simultaneously, we obtained a 64-channel EEG, synchronized with the reach&grasp movement time points. We elaborated EEG signals with EEGLAB 12 in order to obtain event related synchronization/desynchronization (ERS/ERD) and lagged linear coherence between Brodmann areas. Finally, we evaluated network topology via sLORETA software, measuring network local and global efficiency (clustering and path length) and the overall balance (small-worldness). We observed a widespread ERD in α and β bands during reach&grasp, especially in the centro-parietal regions of the hemisphere contralateral to the movement. Regarding functional connectivity, we observed an α lagged linear coherence reduction among Brodmann areas contralateral to the arm involved in the reach&grasp movement. Interestingly, left arm movement determined widespread changes of α lagged linear coherence, specifically among right occipital regions, insular cortex and somatosensory cortex, while the right arm movement exerted a restricted contralateral sensory-motor cortex modulation. Finally, no change between rest and movement was found for clustering, path length and small-worldness. Through a synchronized acquisition, we explored the cortical correlates of the reach&grasp movement. Despite EEG perturbations, suggesting that the non-dominant reach&grasp network has a complex architecture probably linked to the necessity of a higher visual control, the pivotal topological measures of network local and global efficiency remained unaffected.

Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer's Disease

Graph theory has been extensively used to investigate brain network topology and its changes in disease cohorts. However, many graph theoretic analysis-based brain network studies focused on the shortest paths or, more generally, cost-efficiency. In this work, we use two new concepts, connectedness and 2-connectedness, to measure different global properties compared to the previously widely adopted ones. We apply them to unravel interesting characteristics in the brain, such as redundancy design and further conduct a time-varying brain functional network analysis for characterizing the progression of Alzheimer's disease (AD). Specifically, we define different connectedness and 2-connectedness states and evaluate their dynamics in AD and its preclinical stage, mild cognitive impairment (MCI), compared to the normal controls (NC). Results indicate that, compared to MCI and NC, brain networks of AD tend to be more frequently connected at a sparse level. For MCI, we found that their brains are more likely to be 2-connected in the minimal connected state as well indicating increasing redundancy in brain connectivity. Such a redundant design could ensure maintained connectedness of the MCI's brain network in the case that pathological damages break down any link or silenced any node, making it possible to preserve cognitive abilities. Our study suggests that the redundancy in the brain functional chronnectome could be altered in the preclinical stage of AD. The findings can be successfully replicated in a retest study and with an independent MCI dataset. Characterizing redundancy design in the brain chronnectome using connectedness and 2-connectedness analysis provides a unique viewpoint for understanding disease affected brain networks.

Topological Data Analysis as a New Tool for EEG Processing

Electroencephalography (EEG) is a widely used cerebral activity measuring device for both clinical and everyday life applications. In addition to denoising and potential classification, a crucial step in EEG processing is to extract relevant features. Topological data analysis (TDA) as an emerging tool enables to analyse and understand data from a different angle than traditionally used methods. As a higher dimensional analogy of graph analysis, TDA can model rich interactions beyond pairwise relations. It also distinguishes different dynamics of EEG time series. TDA remains largely unknown to the EEG processing community while it fits well the heterogeneous nature of EEG signals. This short review aims to give a quick introduction to TDA and how it can be applied to EEG analysis in various applications including brain-computer interfaces (BCIs). After introducing the objective of the article, the main concepts and ideas of TDA are explained. Next, how to implement it for EEG processing is detailed, and lastly the article discusses the benefits and limitations of the method.

Cross-modal connectivity effects in age-related hearing loss

Age-related sensorineural hearing loss (HL) leads to localized brain changes in the primary auditory cortex, long-range functional alterations, and is considered a risk factor for dementia. Nonhuman studies have repeatedly highlighted cross-modal brain plasticity in sensorial brain networks other than those primarily involved in the peripheral damage, thus in this study, the possible cortical alterations associated with HL have been analyzed using a whole-brain multimodal connectomic approach. Fifty-two HL and 30 normal hearing participants were examined in a 3T MRI study along with audiological and neurological assessments. Between-regions functional connectivity and whole-brain probabilistic tractography were calculated in a connectome-based manner and graph theory was used to obtain low-dimensional features for the analysis of brain connectivity at global and local levels. The HL condition was associated with a different functional organization of the visual subnetwork as revealed by a significant increase in global efficiency, density, and clustering coefficient. These functional effects were mirrored by similar (but more subtle) structural effects suggesting that a functional repurposing of visual cortical centers occurs to compensate for age-related loss of hearing abilities.

Association between brain structural network efficiency at term-equivalent age and early development of cerebral palsy in very preterm infants

Very preterm infants (born at less than 32 weeks gestational age) are at high risk for serious motor impairments, including cerebral palsy (CP). The brain network changes that antecede the early development of CP in infants are not well characterized, and a better understanding may suggest new strategies for risk-stratification at term, which could lead to earlier access to therapies. Graph theoretical methods applied to diffusion MRI-derived brain connectomes may help quantify the organization and information transfer capacity of the preterm brain with greater nuance than overt structural or regional microstructural changes. Our aim was to shed light on the pathophysiology of early CP development, before the occurrence of early intervention therapies and other environmental confounders, to help identify the best early biomarkers of CP risk in VPT infants. In a cohort of 395 very preterm infants, we extracted cortical morphometrics and brain volumes from structural MRI and also applied graph theoretical methods to diffusion MRI connectomes, both acquired at term-equivalent age. Metrics from graph network analysis, especially global efficiency, strength values of the major sensorimotor tracts, and local efficiency of the motor nodes and novel non-motor regions were strongly inversely related to early CP diagnosis. These measures remained significantly associated with CP after correction for common risk factors of motor development, suggesting that metrics of brain network efficiency at term may be sensitive biomarkers for early CP detection. We demonstrate for the first time that in VPT infants, early CP diagnosis is anteceded by decreased brain network segregation in numerous nodes, including motor regions commonly-associated with CP and also novel regions that may partially explain the high rate of cognitive impairments concomitant with CP diagnosis. These advanced MRI biomarkers may help identify the highest risk infants by term-equivalent age, facilitating earlier interventions that are informed by early pathophysiological changes.

Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults

Cardiorespiratory fitness was found to influence age-related changes of resting state brain network organization. However, the influence on dedifferentiated involvement of wider and more unspecialized brain regions during task completion is barely understood. We analyzed EEG data recorded during rest and different tasks (sensory, motor, cognitive) with dynamic mode decomposition, which accounts for topological characteristics as well as temporal dynamics of brain networks. As a main feature the dominant spatio-temporal EEG pattern was extracted in multiple frequency bands per participant. To deduce a pattern’s stability, we calculated its proportion of total variance among all activation patterns over time for each task. By comparing fit (N = 15) and less fit older adults (N = 16) characterized by their performance on a 6-min walking test, we found signs of a lower task specificity of the obtained network features for the less fit compared to the fit group. This was indicated by fewer significant differences between tasks in the theta and high beta frequency band in the less fit group. Repeated measures ANOVA revealed that a significantly lower proportion of total variance can be explained by the main pattern in high beta frequency range for the less fit compared to the fit group [F(1,29) = 12.572, p = .001, partial η² = .300]. Our results indicate that the dedifferentiation in task-related brain activation is lower in fit compared to less fit older adults. Thus, our study supports the idea that cardiorespiratory fitness influences task-related brain network organization in different task domains.

Aberrant brain network topology in the frontoparietal-limbic circuit in bipolar disorder: a graph-theory study

Characterizing the properties of brain networks across mood states seen in bipolar disorder (BP) can provide a deeper insight into the mechanisms involved in this type of affective disorder. In this study, graph theoretical methods were used to examine global, modular and nodal brain network topology in the resting state using functional magnetic resonance imaging data acquired from 95 participants, including those with bipolar depression (BPD; n = 30) and bipolar mania (BPM; n = 39) and healthy control (HC) subjects (n = 26). The threshold value of the individual subjects' connectivity matrix varied from 0.15 to 0.30 with steps of 0.01. We found that: (1) at the global level, BP patients showed a significantly increased global efficiency and synchronization and a decreased path length; (2) at the nodal level, BP patients showed impaired nodal parameters, predominantly within the frontoparietal and limbic sub-network; (3) at the module level, BP patients were characterized by denser FCs (edges) between Module III (the front-parietal system) and Module V (limbic/paralimbic systems); (4) at the nodal level, the BPD and BPM groups showed state-specific differences in the orbital part of the left superior-frontal gyrus, right putamen, right parahippocampal gyrus and left fusiform gyrus. These results revealed abnormalities in topological organization in the whole brain, especially in the frontoparietal-limbic circuit in both BPD and BPM. These deficits may reflect the pathophysiological processes occurring in BP. In addition, state-specific regional nodal alterations in BP could potentially provide biomarkers of conversion across different mood states.

Exploring intensity-dependent modulations in EEG resting-state network efficiency induced by exercise

PURPOSE

Exhaustive cardiovascular load can affect neural processing and is associated with decreases in sensorimotor performance. The purpose of this study was to explore intensity-dependent modulations in brain network efficiency in response to treadmill running assessed from resting-state electroencephalography (EEG) measures.

METHODS

Sixteen trained participants were tested for individual peak oxygen uptake (VO2 peak) and performed an incremental treadmill exercise at 50% (10 min), 70% (10 min) and 90% speed VO2 peak (all-out) followed by cool-down running and active recovery. Before the experiment and after each stage, borg scale (BS), blood lactate concentration (BLa), resting heartrate (HRrest) and 64-channel EEG resting state were assessed. To analyze network efficiency, graph theory was applied to derive small world index (SWI) from EEG data in theta, alpha-1 and alpha-2 frequency bands.

RESULTS

Analysis of variance for repeated measures revealed significant main effects for intensity on BS, BLa, HRrest and SWI. While BS, BLa and HRrest indicated maxima after all-out, SWI showed a reduction in the theta network after all-out.

CONCLUSION

Our explorative approach suggests intensity-dependent modulations of resting-state brain networks, since exhaustive exercise temporarily reduces brain network efficiency. Resting-state network assessment may prospectively play a role in training monitoring by displaying the readiness and efficiency of the central nervous system in different training situations.

Prestimulus Low-Alpha Frontal Networks Are Associated with Pareidolias in Parkinson's Disease

Background: Pareidolias are visual phenomena wherein ambiguous, abstract forms or shapes appear meaningful due to incorrect perception. In Parkinson's disease (PD), patients susceptible to visual hallucinations experience visuo-perceptual deficits in the form of pareidolias. Although pareidolias necessitate top-down modulation of visual processing, the cortical dynamics of internally generated perceptual priors on these visual misperceptions is unknown. Objectives: To study prestimulus-related electroencephalography (EEG) spectral and network abnormalities in PD patients experiencing pareidolias. Methods: Twenty-one PD in-patients and 10 age-matched controls were evaluated. Neuropsychological assessments included tests for cognition, attention, and executive functions. Pareidolias were quantified by using the "noise pareidolia test" with simultaneous EEG recording. The PD patients were subdivided into two groups-those with high pareidolia counts (n = 10) and those without (n = 11). The EEG was analyzed 1000 msec before stimulus presentation in the spectral domain (theta, low-alpha, and high-alpha frequencies) with corresponding graph networks to evaluate network properties. Statistical analysis included analysis of variance and multiple regression to evaluate the differences. Results: The PD patients with high pareidolia counts were older with lower scores on neuropsychological tests. Their prestimulus EEG low-alpha band showed a tendency toward higher frontal activity (p = 0.07). Graph networks showed increased normalized clustering coefficient (p = 0.05) and lower frontal degree centrality (p = 0.005). These network indices correlated positively to patients' pareidolia scores. Discussion: We suggest that pareidolias in PD are a consequence of an abnormal top-down modulation of visual processing; they are defined by their frontal low-alpha spectral and network alterations in the prestimulus phase due to a dissonance between patients' internally generated mental processing with external stimuli. Impact statement Pareidolias in Parkinson's disease (PD) are considered to be promising early markers of visual hallucinations and an indicator of PD prognosis. In certain susceptible PD patients, pareidolias can be evoked and studied. Here, via electroencephalography, we aimed at understanding this visual phenomenon by studying how neural information is processed before stimulus presentation in such patients. Using spectral and graph network measures, we revealed how top-down modulated internally generated processes affect visual perception in patients with pareidolias. Our findings highlight how prestimulus network alterations in the frontal cortex shape poststimulus pareidolic manifestations in PD.

Cohesive communities in dynamic brain functional networks

In large-scale brain network dynamics, brain nodes switching between modules has been found to correlate with cognition. However, how the brain nodes engage in this kind of reorganization of modules is unclear. Based on a functional magnetic resonance imaging dataset, we construct dynamic brain functional networks and investigate nodal module temporal dynamic behavior by applying the multilayer network analysis approach. We reveal three cohesive communities that are groups of brain nodes linked in the same community during brain module dynamic reorganization. We show that the cohesive communities have higher clustering coefficients and lower characteristic path lengths than the controlled community, indicating cohesive communities are the parts of brain networks with high information processing efficiency. The smaller sample entropy of functional connectivity in cohesive communities also proves their property of being more "static" compared with the controlled community in brain dynamics. Specifically, compared with the controlled community, the functional connectivity of cohesive communities is restricted strictly by structure connectivity and shows more similarity to structure connectivity. More importantly, we find that the cohesive communities are stable not only in the resting state but also when processing cognitive tasks. Our results not only show that cohesive communities may be the fundamental community organization to support brain network dynamics but also provide insights into the intrinsic structural relationship between the resting state and task states of the brain.

Structural and functional brain connectivity in moderate-late preterm infants with low-grade intraventricular hemorrhage

PURPOSE

Brain functional connectivity (FC) changes and microstructural abnormalities are reported in infants born moderate and late preterm (MLPT). We evaluated the effect of low-grade (grades I, II) intraventricular hemorrhage (IVH) in MLPT babies on brain structural connectivity (SC) and FC.

METHODS

Babies born MLPT between January 2014 and May 2017 underwent brain ultrasound (US) at 72 h and 7 days after birth, and MRI at around term equivalent. The MRI protocol comprised T1- and T2-weighted sequences, diffusion tensor imaging (DTI), and resting-state functional MRI (fMRI). SC and FC were assessed using graph analysis.

RESULTS

Of 350 MLPT neonates, 15 showed low-grade IVH on US at 72 h, for which brain MRI was available in 10. These 10 infants, with mean gestational age (GA) 34.0 ± 0.8 weeks, comprised the study group, and 10 MLPT infants of mean GA 33.9 ± 1.1 weeks, with no abnormalities on brain US and MRI, were control subjects. All study subjects presented modularity, small world topology, and rich club organization for both SC and FC. The patients with low-grade IVH had lower FC rich club coefficient and lower SC betweenness centrality in the left frontoparietal operculum, and lower SC rich club coefficient in the right superior orbitofrontal cortex than the control subjects.

CONCLUSIONS

Topological and functional properties of mature brain connectivity are present in MLPT infants. IVH in these infants was associated with structural and functional abnormalities in the left frontoparietal operculum and right orbitofrontal cortex, regions related to language and cognition.

Surface-Based Connectivity Integration: An atlas-free approach to jointly study functional and structural connectivity

There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose from and this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas-free approach, named Surface-Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra-cortical gray matter. SBCI represents both SC and FC in a continuous manner on the white surface, avoiding the need for prespecified atlases. The continuous SC is represented as a probability density function and is smoothed for better facilitation of its integration with FC. To infer the relationship between SC and FC, three novel sets of SC-FC coupling (SFC) measures are derived. Using data from the Human Connectome Project, we introduce the high-quality SFC measures produced by SBCI and demonstrate the use of these measures to study sex differences in a cohort of young adults. Compared with atlas-based methods, this atlas-free framework produces more reproducible SFC features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies.

Simplifying functional network representation and interpretation through causality clustering

Functional networks, i.e. networks representing the interactions between the elements of a complex system and reconstructed from the observed elements’ dynamics, are becoming a fundamental tool to unravel the structures created by the movement of information in systems like the human brain. They also present drawbacks, one of the most important being the inherent difficulty in representing and interpreting the resulting structures for large number of nodes and links. I here propose a causality clustering approach, based on grouping nodes into clusters according to their similarity in the overall information dynamics, the latter one being measured by a causality metric. The whole system can then arbitrarily be simplified, with nodes being grouped in e.g. sources, brokers and sinks of information. The advantages and limitations of the proposed approach are discussed using a set of synthetic and real-world data sets, the latter ones representing two neuroscience and technological problems.

Genetic Influence on Nociceptive Processing in the Human Brain-A Twin Study

Nociceptive processing in the human brain is complex and involves several brain structures and varies across individuals. Determining the structures that contribute to interindividual differences in nociceptive processing is likely to improve our understanding of why some individuals feel more pain than others. Here, we found specific parts of the cerebral response to nociception that are under genetic influence by employing a classic twin-design. We found genetic influences on nociceptive processing in the midcingulate cortex and bilateral posterior insula. In addition to brain activations, we found genetic contributions to large-scale functional connectivity (FC) during nociceptive processing. We conclude that additive genetics influence specific brain regions involved in nociceptive processing. The genetic influence on FC during nociceptive processing is not limited to core nociceptive brain regions, such as the dorsal posterior insula and somatosensory areas, but also involves cognitive and affective brain circuitry. These findings improve our understanding of human pain perception and increases chances to find new treatments for clinical pain.

Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks

Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective-direct and causal-connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.

Study of Human Tacit Knowledge Based on Electroencephalogram Signal Characteristics

Tacit knowledge is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. In the mineral grinding process, the proficiency of the operators depends on the tacit knowledge gained from their experience and training rather than on knowledge learned from a handbook. This article proposed a method combining the electroencephalogram (EEG) signals and the industrial process to detect the proficiency of the operators in the mineral grinding process to reveal the effect of tacit knowledge on the functional cortical connection. The functional brain networks of operators were established based on partial direct coherence and directed transfer function of EEG, and the multi-classifiers were used with the graph-theoretic indexes of the FBNs as input to distinguish the trained operators (Hps) from the non-trained operators (Lps). The results showed that the brain networks of Hps had a better connectivity than those of Lps (p < 0.01), and the accuracy of classification was up to 94.2%. Our studies confirm that based on the performance of EEG features and the combination of industrial operational operation and cognitive processes, the proficiency of the operators can be detected.

Drinking coffee enhances neurocognitive function by reorganizing brain functional connectivity

The purpose of this study was to identify the mechanisms underlying effects of coffee on cognition in the context of brain networks. Here we investigated functional connectivity before and after drinking coffee using graph-theoretic analysis of electroencephalography (EEG). Twenty-one healthy adults voluntarily participated in this study. The resting-state EEG data and results of neuropsychological tests were consecutively acquired before and 30 min after coffee consumption. Graph analyses were performed and compared before and after coffee consumption. Correlation analyses were conducted to assess the relationship between changes in graph measures and those in cognitive function tests. Functional connectivity (FC) was reorganized toward more efficient network properties after coffee consumption. Performance in Digit Span tests and Trail Making Test Part B improved after coffee consumption, and the improved performance in executive function was correlated with changes in graph measures, reflecting a shift toward efficient network properties. The beneficial effects of coffee on cognitive function might be attributed to the reorganization of FC toward more efficient network properties. Based on our findings, the patterns of network reorganization could be used as quantitative markers to elucidate the mechanisms underlying the beneficial effects of coffee on cognition, especially executive function.

Abnormal Rich Club Organization in Hemispheric White Matter Networks of ADHD

Objective: Brain network studies have revealed abnormal topology asymmetry of white matter (WM) in ADHD. Recently, rich club organization was proposed to be a key feature of brain network topology. However, abnormalities in the rich club organization of hemispheric WM networks in ADHD remain unclear. Method: Forty ADHD patients and 51 normal controls participated in this study. Structural networks were reconstructed based on diffusion tensor imaging (DTI) and analyzed with graph theory. Results: The two groups exhibited different patterns of asymmetry in connectivity measures of rich club connections. ADHD patients showed more feeder connections than normal controls. Reduced rightward asymmetry was observed in connectivity measures of local connections involving several peripheral regions of the ADHD patients. In addition, abnormal regional asymmetry scores were associated with ADHD symptoms. Conclusion: The topological changes in rich club organization provide a novel insight into the alteration of WM connections in ADHD.

Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective

The coordinated dynamic interactions of large-scale brain circuits and networks have been associated with cognitive functions and behavior. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts fundamental constraints on the dynamical landscape of brain activity, i.e., the different states, or patterns of regional activation, and transition between states the brain can display. Specifically, it has been shown that densely connected, central regions control the transition between states that are "easily" reachable (in terms of expended energy), whereas weakly connected areas control transitions to states that are hard-to-reach. Changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia, especially regarding cognitive symptoms. Therefore, an analysis of graph and control theoretic measures of large-scale brain connectivity in patients offers to give insight into the emergence of cognitive disturbances in the disorder. To investigate these potential differences between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we used structural MRI data to assess the microstructural organization of white matter. We first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability, which identifies brain areas that can guide brain activity into different, easily reachable states with little input energy and modal controllability, which characterizes regions that can push the brain into difficult-to-reach states, i.e., states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. We found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

Diffuse white matter abnormality in very preterm infants at term reflects reduced brain network efficiency

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Most preterm infants exhibit regions of high signal intensity on T2 MRI at term.

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Debate remains as to whether this signal (DWMA) is pathological.

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We quantified DWMA and used graph theory to measure brain network efficiency.

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Whole-brain and regional network efficiency at term decreased with greater DWMA.

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DWMA in very preterm infants is associated with reduced brain efficiency at term.

Between 50 and 80% of very preterm infants (<32 weeks gestational age) exhibit increased white matter signal intensity on T2-weighted MRI at term-equivalent age, known as diffuse white matter abnormality (DWMA). A few studies have linked DWMA with microstructural abnormalities, but the exact relationship remains poorly understood. We related DWMA extent to graph theory measures of network efficiency at term in a representative cohort of 343 very preterm infants. We performed anatomic and diffusion MRI at term and quantified DWMA volume with our novel, semi-automated algorithm. From diffusion-weighted structural connectomes, we calculated the graph theory metrics local efficiency and clustering coefficient, which measure the ability of groups of nodes to perform specialized processing, and global efficiency, which assesses the ability of brain regions to efficiently combine information. We computed partial correlations between these measures and DWMA volume, adjusted for confounders. Increasing DWMA volume was associated with decreased global efficiency of the entire very preterm brain and decreased local efficiency and clustering coefficient in a variety of regions supporting cognitive, linguistic, and motor function. We show that DWMA is associated with widespread decreased brain network efficiency, suggesting that it is pathologic and likely has adverse developmental consequences.

DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

Inter-individual body mass variations relate to fractionated functional brain hierarchies

Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Studying 325 healthy young adults, we examined associations between functional connectivity and inter-individual BMI variations. We utilized non-linear connectome manifold learning techniques to represent macroscale functional organization along continuous hierarchical axes that dissociate low level and higher order brain systems. We observed an increased differentiation between unimodal and heteromodal association networks in individuals with higher BMI, indicative of a disrupted modular architecture and hierarchy of the brain. Transcriptomic decoding and gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings illustrate functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations.

Stochastic and vibrational resonance in complex networks of neurons

The concept of resonance in nonlinear systems is crucial and traditionally refers to a specific realization of maximum response provoked by a particular external perturbation. Depending on the system and the nature of perturbation, many different resonance types have been identified in various fields of science. A prominent example is in neuroscience where it has been widely accepted that a neural system may exhibit resonances at microscopic, mesoscopic and macroscopic scales and benefit from such resonances in various tasks. In this context, the two well-known forms are stochastic and vibrational resonance phenomena which manifest that detection and propagation of a feeble information signal in neural structures can be enhanced by additional perturbations via these two resonance mechanisms. Given the importance of network architecture in proper functioning of the nervous system, we here present a review of recent studies on stochastic and vibrational resonance phenomena in neuronal media, focusing mainly on their emergence in complex networks of neurons as well as in simple network structures that represent local behaviours of neuron communities. From this perspective, we aim to provide a secure guide by including theoretical and experimental approaches that analyse in detail possible reasons and necessary conditions for the appearance of stochastic resonance and vibrational resonance in neural systems. This article is part of the theme issue 'Vibrational and stochastic resonance in driven nonlinear systems (part 2)'.

Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning

Uncovering the complex network of the brain is of great interest to the field of neuroimaging. Mining from these rich datasets, scientists try to unveil the fundamental biological mechanisms in the human brain. However, neuroimaging data collected for constructing brain networks is generally costly, and thus extracting useful information from a limited sample size of brain networks is demanding. Currently, there are two common trends in neuroimaging data collection that could be exploited to gain more information: 1) multimodal data, and 2) longitudinal data. It has been shown that these two types of data provide complementary information. Nonetheless, it is challenging to learn brain network representations that can simultaneously capture network properties from multimodal as well as longitudinal datasets. Here we propose a general fusion framework for multi-source learning of brain networks - multimodal brain network fusion with longitudinal coupling (MMLC). In our framework, three layers of information are considered, including cross-sectional similarity, multimodal coupling, and longitudinal consistency. Specifically, we jointly factorize multimodal networks and construct a rotation-based constraint to couple network variance across time. We also adopt the consensus factorization as the group consistent pattern. Using two publicly available brain imaging datasets, we demonstrate that MMLC may better predict psychometric scores than some other state-of-the-art brain network representation learning algorithms. Additionally, the discovered significant brain regions are synergistic with previous literature. Our new approach may boost statistical power and sheds new light on neuroimaging network biomarkers for future psychometric prediction research by integrating longitudinal and multimodal neuroimaging data.

No support for white matter connectivity differences in the combined and inattentive ADHD presentations

Evidence from functional neuroimaging studies support neural differences between the Attention Deficit Hyperactivity Disorder (ADHD) presentation types. It remains unclear if these neural deficits also manifest at the structural level. We have previously shown that the ADHD combined, and ADHD inattentive types demonstrate differences in graph properties of structural covariance suggesting an underlying difference in neuroanatomical organization. The goal of this study was to examine and validate white matter brain organization between the two subtypes using both scalar and connectivity measures of brain white matter. We used both tract-based spatial statistical (TBSS) and tractography analyses with network-based Statistics (NBS) and graph-theoretical analyses in a cohort of 35 ADHD participants (aged 8-17 years) defined using DSM-IV criteria as combined (ADHD-C) type (n = 19) or as predominantly inattentive (ADHD-I) type (n = 16), and 28 matched neurotypical controls. We performed TBSS analyses on scalar measures of fractional anisotropy (FA), mean (MD), radial (RD), and axial (AD) diffusivity to assess differences in WM between ADHD types and controls. NBS and graph theoretical analysis of whole brain inter-regional tractography examined connectomic differences and brain network organization, respectively. None of the scalar measures significantly differed between ADHD types or relative to controls. Similarly, there were no tractography connectivity differences between the two subtypes and relative to controls using NBS. Global and regional graph measures were also similar between the groups. A single significant finding was observed for nodal degree between the ADHD-C and controls, in the right insula (corrected p = .029). Our result of no white matter differences between the subtypes is consistent with most previous findings. These findings together might suggest that the white matter structural architecture is largely similar between the DSM-based ADHD presentations is similar to the extent of being undetectable with the current cohort size.

Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI

Dynamic network analysis using resting-state functional magnetic resonance imaging (rs-fMRI) provides a great insight into fundamentally dynamic characteristics of human brains, thus providing an efficient solution to automated brain disease identification. Previous studies usually pay less attention to evolution of global network structures over time in each brain's rs-fMRI time series, and also treat network-based feature extraction and classifier training as two separate tasks. To address these issues, we propose a temporal dynamics learning (TDL) method for network-based brain disease identification using rs-fMRI time-series data, through which network feature extraction and classifier training are integrated into the unified framework. Specifically, we first partition rs-fMRI time series into a sequence of segments using overlapping sliding windows, and then construct longitudinally ordered functional connectivity networks. To model the global temporal evolution patterns of these successive networks, we introduce a group-fused Lasso regularizer in our TDL framework, while the specific network architecture is induced by an ℓ₁-norm regularizer. Besides, we develop an efficient optimization algorithm to solve the proposed objective function via the Alternating Direction Method of Multipliers (ADMM). Compared with previous studies, the proposed TDL model can not only explicitly model the evolving connectivity patterns of global networks over time, but also capture unique characteristics of each network defined at each segment. We evaluate our TDL on three real autism spectrum disorder (ASD) datasets with rs-fMRI data, achieving superior results in ASD identification compared with several state-of-the-art methods.

Elucidating the Neuropathologic Mechanisms of SARS-CoV-2 Infection

The current pandemic caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a public health emergency. To date, March 1, 2021, coronavirus disease 2019 (COVID-19) has caused about 114 million accumulated cases and 2.53 million deaths worldwide. Previous pieces of evidence suggest that SARS-CoV-2 may affect the central nervous system (CNS) and cause neurological symptoms in COVID-19 patients. It is also known that angiotensin-converting enzyme-2 (ACE2), the primary receptor for SARS-CoV-2 infection, is expressed in different brain areas and cell types. Thus, it is hypothesized that infection by this virus could generate or exacerbate neuropathological alterations. However, the molecular mechanisms that link COVID-19 disease and nerve damage are unclear. In this review, we describe the routes of SARS-CoV-2 invasion into the central nervous system. We also analyze the neuropathologic mechanisms underlying this viral infection, and their potential relationship with the neurological manifestations described in patients with COVID-19, and the appearance or exacerbation of some neurodegenerative diseases.

A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity

Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analysis into a single spatial scale. In addition, most methods focus on the pairwise relationships between the ROIs and ignore high-order associations between subjects. In this letter, we propose a mutual multi-scale triplet graph convolutional network (MMTGCN) to analyze functional and structural connectivity for brain disorder diagnosis. We first employ several templates with different scales of ROI parcellation to construct coarse-to-fine brain connectivity networks for each subject. Then, a triplet GCN (TGCN) module is developed to learn functional/structural representations of brain connectivity networks at each scale, with the triplet relationship among subjects explicitly incorporated into the learning process. Finally, we propose a template mutual learning strategy to train different scale TGCNs collaboratively for disease classification. Experimental results on 1,160 subjects from three datasets with fMRI or dMRI data demonstrate that our MMTGCN outperforms several state-of-the-art methods in identifying three types of brain disorders.

Altered electroencephalographic networks in developmental dyslexia after remedial training: a prospective case-control study

Electroencephalographic studies using graph theoretic analysis have found aberrations in functional connectivity in children with developmental dyslexia. However, how the training with visual tasks can change the functional connectivity of the semantic network in developmental dyslexia is still unclear. We looked for differences in local and global topological properties of functional networks between 21 healthy controls and 22 dyslexic children (8-9 years old) before and after training with visual tasks in this prospective case-control study. The minimum spanning tree method was used to construct the subjects' brain networks in multiple electroencephalographic frequency ranges during a visual word/pseudoword discrimination task. We found group differences in the theta, alpha, beta and gamma bands for four graph measures suggesting a more integrated network topology in dyslexics before the training compared to controls. After training, the network topology of dyslexic children had become more segregated and similar to that of the controls. In the θ, α and β1-frequency bands, compared to the controls, the pre-training dyslexics exhibited a reduced degree and betweenness centrality of the left anterior temporal and parietal regions. The simultaneous appearance in the left hemisphere of hubs in temporal and parietal (α, β1), temporal and superior frontal cortex (θ, α), parietal and occipitotemporal cortices (β1), identified in the networks of normally developing children was not present in the brain networks of dyslexics. After training, the hub distribution for dyslexics in the theta and beta1 bands had become similar to that of the controls. In summary, our findings point to a less efficient network configuration in dyslexics compared to a more optimal global organization in the controls. This is the first study to investigate the topological organization of functional brain networks of Bulgarian dyslexic children. Approval for the study was obtained from the Ethics Committee of the Institute of Neurobiology and the Institute for Population and Human Studies, Bulgarian Academy of Sciences (approval No. 02-41/12.07.2019) on March 28, 2017, and the State Logopedic Center and the Ministry of Education and Science (approval No. 09-69/14.03.2017) on July 12, 2019.

Constructing Connectome Atlas by Graph Laplacian Learning

The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a "common" brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods.

Acitretin reverses early functional network degradation in a mouse model of familial Alzheimer's disease

Aberrant activity of local functional networks underlies memory and cognition deficits in Alzheimer's disease (AD). Hyperactivity was observed in microcircuits of mice AD-models showing plaques, and also recently in early stage AD mutants prior to amyloid deposition. However, early functional effects of AD on cortical microcircuits remain unresolved. Using two-photon calcium imaging, we found altered temporal distributions (burstiness) in the spontaneous activity of layer II/III visual cortex neurons, in a mouse model of familial Alzheimer's disease (5xFAD), before plaque formation. Graph theory (GT) measures revealed a distinct network topology of 5xFAD microcircuits, as compared to healthy controls, suggesting degradation of parameters related to network robustness. After treatment with acitretin, we observed a re-balancing of those network measures in 5xFAD mice; particularly in the mean degree distribution, related to network development and resilience, and post-treatment values resembled those of age-matched controls. Further, behavioral deficits, and the increase of excitatory synapse numbers in layer II/III were reversed after treatment. GT is widely applied for whole-brain network analysis in human neuroimaging, we here demonstrate the translational value of GT as a multi-level tool, to probe networks at different levels in order to assess treatments, explore mechanisms, and contribute to early diagnosis.