Changes in brain network efficiency and working memory performance in aging

Brain health in diverse settings: How age, demographics and cognition shape brain function

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Sleep functional connectivity, hyperexcitability, and cognition in Alzheimer's disease

INTRODUCTION

Sleep disturbances are common in Alzheimer's disease (AD) and may reflect pathologic changes in brain networks. To date, no studies have examined changes in sleep functional connectivity (FC) in AD or their relationship with network hyperexcitability and cognition.

METHODS

We assessed electroencephalogram (EEG) sleep FC in 33 healthy controls, 36 individuals with AD without epilepsy, and 14 individuals with AD and epilepsy.

RESULTS

AD participants showed increased gamma connectivity in stage 2 sleep (N2), which was associated with longitudinal cognitive decline. Network hyperexcitability in AD was associated with a distinct sleep connectivity signature, characterized by decreased N2 delta connectivity and reversal of several connectivity changes associated with AD. Machine learning algorithms using sleep connectivity features accurately distinguished diagnostic groups and identified "fast cognitive decliners" among study participants who had AD.

DISCUSSION

Our findings reveal changes in sleep functional networks associated with cognitive decline in AD and may have implications for disease monitoring and therapeutic development.

HIGHLIGHTS

Brain functional connectivity (FC) in Alzheimer's disease is altered during sleep. Sleep FC measures correlate with cognitive decline in AD. Network hyperexcitability in AD has a distinct sleep connectivity signature.

The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan

Introduction

The hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive.

Methods

This study utilized resting-state functional MRI data from 350 healthy adults (aged 20-85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan.

Results

The principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal-parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect.

Discussion

Our study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes.

Altered brain network organization in adults with Asperger's syndrome: decreased connectome transitivity and assortativity with increased global efficiency

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that persists into adulthood with both social and cognitive disturbances. Asperger's syndrome (AS) was a distinguished subcategory of autism in the DSM-IV-TR defined by specific symptoms including difficulties in social interactions, inflexible thinking patterns, and repetitive behaviour without any delay in language or cognitive development. Studying the functional brain organization of individuals with these specific symptoms may help to better understand Autism spectrum symptoms.

Methods

The aim of this study is therefore to investigate functional connectivity as well as functional network organization characteristics using graph-theory measures of the whole brain in male adults with AS compared to healthy controls (HC) (AS: n = 15, age range 21-55 (mean ± sd: 39.5 ± 11.6), HC: n = 15, age range 22-57 [mean ± sd: 33.5 ± 8.5]).

Results

No significant differences were found when comparing the region-by-region connectivity at the whole-brain level between the AS group and HC. However, measures of "transitivity," which reflect local information processing and functional segregation, and "assortativity," indicating network resilience, were reduced in the AS group compared to HC. On the other hand, global efficiency, which represents the overall effectiveness and speed of information transfer across the entire brain network, was increased in the AS group.

Discussion

Our findings suggest that individuals with AS may have alterations in the organization and functioning of brain networks, which could contribute to the distinctive cognitive and behavioural features associated with this condition. We suggest further research to explore the association between these altered functional patterns in brain networks and specific behavioral traits observed in individuals with AS, which could provide valuable insights into the underlying mechanisms of its symptomatology.

Effect of Visual Feedback on Behavioral Control and Functional Activity During Bilateral Hand Movement

Previous researches state vision as a vital source of information for movement control and more precisely for accurate hand movement. Further, fine bimanual motor activity may be associated with various oscillatory activities within distinct brain areas and inter-hemispheric interactions. However, neural coordination among the distinct brain areas responsible to enhance motor accuracy is still not adequate. In the current study, we investigated task-dependent modulation by simultaneously measuring high time resolution electroencephalogram (EEG), electromyogram (EMG) and force along with bi-manual and unimanual motor tasks. The errors were controlled using visual feedback. To complete the unimanual tasks, the participant was asked to grip the strain gauge using the index finger and thumb of the right hand thereby exerting force on the connected visual feedback system. Whereas the bi-manual task involved finger abduction of the left index finger in two contractions along with visual feedback system and at the same time the right hand gripped using definite force on two conditions that whether visual feedback existed or not for the right hand. Primarily, the existence of visual feedback for the right hand significantly decreased brain network global and local efficiency in theta and alpha bands when compared with the elimination of visual feedback using twenty participants. Brain network activity in theta and alpha bands coordinates to facilitate fine hand movement. The findings may provide new neurological insight on virtual reality auxiliary equipment and participants with neurological disorders that cause movement errors requiring accurate motor training. The current study investigates task-dependent modulation by simultaneously measuring high time resolution electroencephalogram, electromyogram and force along with bi-manual and unimanual motor tasks. The findings show that visual feedback for right hand decreases the force root mean square error of right hand. Visual feedback for right hand decreases local and global efficiency of brain network in theta and alpha bands.

Functional connectome stability and optimality are markers of cognitive performance

Patterns of whole-brain fMRI functional connectivity, or connectomes, are unique to individuals. Previous work has identified subsets of functional connections within these patterns whose strength predicts aspects of attention and cognition. However, overall features of these connectomes, such as how stable they are over time and how similar they are to a group-average (typical) or high-performance (optimal) connectivity pattern, may also reflect cognitive and attentional abilities. Here, we test whether individuals who express more stable, typical, optimal, and distinctive patterns of functional connectivity perform better on cognitive tasks using data from three independent samples. We find that individuals with more stable task-based functional connectivity patterns perform better on attention and working memory tasks, even when controlling for behavioral performance stability. Additionally, we find initial evidence that individuals with more typical and optimal patterns of functional connectivity also perform better on these tasks. These results demonstrate that functional connectome stability within individuals and similarity across individuals predicts individual differences in cognition.

Brain effective connectivity and functional connectivity as markers of lifespan vascular exposures in middle-aged adults: The Bogalusa Heart Study

Introduction

Effective connectivity (EC), the causal influence that functional activity in a source brain location exerts over functional activity in a target brain location, has the potential to provide different information about brain network dynamics than functional connectivity (FC), which quantifies activity synchrony between locations. However, head-to-head comparisons between EC and FC from either task-based or resting-state functional MRI (fMRI) data are rare, especially in terms of how they associate with salient aspects of brain health.

Methods

In this study, 100 cognitively-healthy participants in the Bogalusa Heart Study aged 54.2 ± 4.3years completed Stroop task-based fMRI, resting-state fMRI. EC and FC among 24 regions of interest (ROIs) previously identified as involved in Stroop task execution (EC-task and FC-task) and among 33 default mode network ROIs (EC-rest and FC-rest) were calculated from task-based and resting-state fMRI using deep stacking networks and Pearson correlation. The EC and FC measures were thresholded to generate directed and undirected graphs, from which standard graph metrics were calculated. Linear regression models related graph metrics to demographic, cardiometabolic risk factors, and cognitive function measures.

Results

Women and whites (compared to men and African Americans) had better EC-task metrics, and better EC-task metrics associated with lower blood pressure, white matter hyperintensity volume, and higher vocabulary score (maximum value of p = 0.043). Women had better FC-task metrics, and better FC-task metrics associated with APOE-ε4 3–3 genotype and better hemoglobin-A1c, white matter hyperintensity volume and digit span backwards score (maximum value of p = 0.047). Better EC rest metrics associated with lower age, non-drinker status, and better BMI, white matter hyperintensity volume, logical memory II total score, and word reading score (maximum value of p = 0.044). Women and non-drinkers had better FC-rest metrics (value of p = 0.004).

Discussion

In a diverse, cognitively healthy, middle-aged community sample, EC and FC based graph metrics from task-based fMRI data, and EC based graph metrics from resting-state fMRI data, were associated with recognized indicators of brain health in differing ways. Future studies of brain health should consider taking both task-based and resting-state fMRI scans and measuring both EC and FC analyses to get a more complete picture of functional networks relevant to brain health.

Topological network properties of resting-state functional connectivity patterns are associated with metal mixture exposure in adolescents

Introduction

Adolescent exposure to neurotoxic metals adversely impacts cognitive, motor, and behavioral development. Few studies have addressed the underlying brain mechanisms of these metal-associated developmental outcomes. Furthermore, metal exposure occurs as a mixture, yet previous studies most often consider impacts of each metal individually. In this cross-sectional study, we investigated the relationship between exposure to neurotoxic metals and topological brain metrics in adolescents.

Methods

In 193 participants (53% females, ages: 15-25 years) enrolled in the Public Health Impact of Metals Exposure (PHIME) study, we measured concentrations of four metals (manganese, lead, copper, and chromium) in multiple biological media (blood, urine, hair, and saliva) and acquired resting-state functional magnetic resonance imaging scans. Using graph theory metrics, we computed global and local efficiency (global:GE; local:LE) in 111 brain areas (Harvard Oxford Atlas). We used weighted quantile sum (WQS) regression models to examine association between metal mixtures and each graph metric (GE or LE), adjusted for sex and age.

Results

We observed significant negative associations between the metal mixture and GE and LE [βGE = -0.076, 95% CI (-0.122, -0.031); βLE= -0.051, 95% CI (-0.095, -0.006)]. Lead and chromium measured in blood contributed most to this association for GE, while chromium measured in hair contributed the most for LE.

Discussion

Our results suggest that exposure to this metal mixture during adolescence reduces the efficiency of integrating information in brain networks at both local and global levels, informing potential neural mechanisms underlying the developmental toxicity of metals. Results further suggest these associations are due to combined joint effects to different metals, rather than to a single metal.

The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition

Hyperventilation (HV) is a voluntary activity that causes changes in the neuronal firing characteristics noticeable in the electroencephalogram (EEG) signals. HV-related changes have been scribed to modulation of pO2/pCO2 blood contents. Therefore, an HV test is routinely used for highlighting brain abnormalities including those depending to neurobiological mechanisms at the basis of neurodegenerative disorders. The main aim of the present paper is to study the effectiveness of HV test in modifying the functional connectivity from the EEG signals that can be typical of a prodromal state of Alzheimer's disease (AD), the Mild Cognitive Impairment prodromal to Alzheimer condition. MCI subjects and a group of age-matched healthy elderly (Ctrl) were enrolled and subjected to EEG recording during HV, eyes-closed (EC), and eyes-open (EO) conditions. Since the cognitive decline in MCI seems to be a progressive disconnection syndrome, the approach we used in the present study is the graph theory, which allows to describe brain networks with a series of different parameters. Small world (SW), modularity (M), and global efficiency (GE) indexes were computed among the EC, EO, and HV conditions comparing the MCI group to the Ctrl one. All the three graph parameters, computed in the typical EEG frequency bands, showed significant changes among the three conditions, and more interestingly, a significant difference in the GE values between the MCI group and the Ctrl one was obtained, suggesting that the combination of HV test and graph theory parameters should be a powerful tool for the detection of possible cerebral dysfunction and alteration.

The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability

Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mainly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults (InLang [Interactive networks of Language] database), providing the opportunity to assess language features across a wide range of linguistic processes. Using this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG atlas). The organization of this data-driven neurocognitive atlas of language was examined at multiple levels, uncovering its major components (or crucial subnetworks), and its anatomical and functional correlates. In addition, we estimated its reconfiguration as a function of linguistic demand (flexibility) or several factors such as age or gender (variability). We observed that several discrete networks could be specifically shaped to promote key functional features of language: coding-decoding (Net1), control-executive (Net2), abstract-knowledge (Net3), and sensorimotor (Net4) functions. The architecture of these systems and the functional connectivity of the pivotal brain regions varied according to the nature of the linguistic process, gender, or age. By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enriching and refining existing neurocognitive models of language. The LANG atlas can also be considered a reference for comparative or clinical studies involving various patients and conditions.

Understanding cognitive control in aging: A brain network perspective

Cognitive control decline is a major manifestation of brain aging that severely impairs the goal-directed abilities of older adults. Magnetic resonance imaging evidence suggests that cognitive control during aging is associated with altered activation in a range of brain regions, including the frontal, parietal, and occipital lobes. However, focusing on specific regions, while ignoring the structural and functional connectivity between regions, may impede an integrated understanding of cognitive control decline in older adults. Here, we discuss the role of aging-related changes in functional segregation, integration, and antagonism among large-scale networks. We highlight that disrupted spontaneous network organization, impaired information co-processing, and enhanced endogenous interference promote cognitive control declines during aging. Additionally, in older adults, severe damage to structural network can weaken functional connectivity and subsequently trigger cognitive control decline, whereas a relatively intact structural network ensures the compensation of functional connectivity to mitigate cognitive control impairment. Thus, we propose that age-related changes in functional networks may be influenced by structural networks in cognitive control in aging (CCA). This review provided an integrative framework to understand the cognitive control decline in aging by viewing the brain as a multimodal networked system.

Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures

The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.

The trend of disruption in the functional brain network topology of Alzheimer's disease

Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns

In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified.

Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations

Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.

Frontal-occipital network alterations while viewing 2D & 3D movies: a source-level EEG and graph theory approach

Many researchers have measured the differences in electroencephalography (EEG) while viewing 2D and 3D movies to uncover the neuromechanism underlying distinct viewing experiences. Using whole-brain network analyses of scalp EEG, our previous study reported that beta and gamma bands presented higher global efficiencies while viewing 3D movies. However, scalp EEG is influenced by volume conduction, not allowing inference from a neuroanatomy perspective; thus, source reconstruction techniques are recommended. This paper is the first to measure the differences in the frontal-occipital networks in EEG source space during 2D and 3D movie viewing. EEG recordings from 40 subjects were performed during 2D and 3D movie viewing. We constructed frontal-occipital networks of alpha, beta, and gamma bands in EEG source space and analyzed network efficiencies. We found that the beta band exhibited higher global efficiency in 3D movie viewing than in 2D movie viewing; however, the alpha global efficiency was not statistically significant. In addition, a support vector machine (SVM) classifier, taking functional connectivities as classification features, was built to identify whether the frontal-occipital networks contain patterns that could distinguish 2D and 3D movie viewing. Using the 6 most important functional connectivity features of the beta band, we obtained the best accuracy of 0.933. Our findings shed light on uncovering the neuromechanism underlying distinct experiences while viewing 2D and 3D movies.

Neural alterations in working memory of mild-moderate TBI: An fMRI study in Malaysia

Working memory (WM) encompasses crucial cognitive processes or abilities to retain and manipulate temporary information for immediate execution of complex cognitive tasks in daily functioning such as reasoning and decision-making. The WM of individuals sustaining traumatic brain injury (TBI) was commonly compromised, especially in the domain of WM. The current study investigated the brain responses of WM in a group of participants with mild-moderate TBI compared to their healthy counterparts employing functional magnetic resonance imaging. All consented participants (healthy: n = 26 and TBI: n = 15) performed two variations of the n-back WM task with four load conditions (0-, 1-, 2-, and 3-back). The respective within-group effects showed a right hemisphere-dominance activation and slower reaction in performance for the TBI group. Random-effects analysis revealed activation difference between the two groups in the right occipital lobe in the guided n-back with cues, and in the bilateral occipital lobe, superior parietal region, and cingulate cortices in the n-back without cues. The left middle frontal gyrus was implicated in the load-dependent processing of WM in both groups. Further group analysis identified that the notable activation changes in the frontal gyri and anterior cingulate cortex are according to low and high loads. Though relatively smaller in scale, this study was eminent as it clarified the neural alterations in WM in the mild-moderate TBI group compared to healthy controls. It confirmed the robustness of the phenomenon in TBI with the reproducibility of the results in a heterogeneous non-Western sample.

A network information theoretic framework to characterise muscle synergies in space and time

Objective Current approaches to muscle synergy extraction rely on linear dimensionality reduction algorithms that make specific assumptions on the underlying signals. However, to capture nonlinear time varying, large-scale but also muscle-specific interactions, a more generalised approach is required. Approach Here we developed a novel framework for muscle synergy extraction that relaxes model assumptions by using a combination of information- and network theory and dimensionality reduction. We first quantify informational dynamics between muscles, time-samples or muscle-time pairings using a novel mutual information formulation. We then model these pairwise interactions as multiplex networks and identify modules representing the network architecture. We employ this modularity criterion as the input parameter for dimensionality reduction, which verifiably extracts the identified modules, and also to characterise salient structures within each module. Main results This novel framework captures spatial, temporal and spatiotemporal interactions across two benchmark datasets of reaching movements, producing distinct spatial groupings and both tonic and phasic temporal patterns. Readily interpretable muscle synergies spanning multiple spatial and temporal scales were identified, demonstrating significant task dependence, ability to capture trial-to-trial fluctuations and concordance across participants. Furthermore, our framework identifies submodular structures that represent the distributed networks of co-occurring signal interactions across scales. Significance The capabilities of this framework are illustrated through the concomitant continuity with previous research and novelty of the insights gained. Several previous limitations are circumvented including the extraction of functionally meaningful and multiplexed pairwise muscle couplings under relaxed model assumptions. The extracted synergies provide a holistic view of the movement while important details of task performance are readily interpretable. The identified muscle groupings transcend biomechanical constraints and the temporal patterns reveal characteristics of fundamental motor control mechanisms. We conclude that this framework opens new opportunities for muscle synergy research and can constitute a bridge between existing models and recent network-theoretic endeavours.

Longitudinal changes of connectomes and graph theory measures in aging

Changes in brain structure and connectivity in aging can be probed through diffusion weighted MRI and summarized with structural connectome matrices. Complex network analysis based on graph theory has been applied to provide measures that are correlated with neurobiological variations and can help guide quantitative study of brain function. However, the understanding of how connectomes change longitudinally is limited. In this work, we evaluate modern pipelines to obtain the connectomics data from diffusion weighted MRI scans across different sessions from control subjects (55-99 years old) in the Baltimore Longitudinal Study of Aging and Cambridge Centre for Ageing and Neuroscience. From the connectivity matrices, we compute graph theory measures to understand their brain networks and apply a linear mixed-effects model to study their longitudinal changes with respect to age. With this approach, we computed 14 graph theory measures of 1469 healthy subjects (2476 scans) and found statistically significant correlations between all 14 measures and age. In this analysis: 1) the brain becomes more segregated but less integrated in aging; 2) the overall network cost increases for older subjects; 3) the small-world organizations remain stable; and 4) due to high intra-subject variance, there is not clear trend for longitudinal changes of graph theory measures of individual subjects. Therefore, while useful to investigate brain evolution in aging at the population level, improvements in the connectome reconstruction are needed to decrease single subject variability for individual inference.

Analyzing Complex Problem Solving by Dynamic Brain Networks

Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested method models the brain network as an Artificial Neural Network, where the weights correspond to the relationships among the brain anatomic regions. The first step of the model is preprocessing that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using the preprocessed fMRI signal to train the Artificial Neural Network. The properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The representation power of the suggested brain network is shown by decoding the planning and execution subtasks of complex problem solving. Our findings are consistent with the previous results of experimental psychology. Furthermore, it is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution.

Task effects on functional connectivity measures after stroke

Understanding the effect of task compared to rest on detecting stroke-related network abnormalities will inform efforts to optimize detection of such abnormalities. The goal of this work was to determine whether connectivity measures obtained during an overt task are more effective than connectivity obtained during a "resting" state for detecting stroke-related changes in network function of the brain. This study examined working memory, discrete pedaling, continuous pedaling and language tasks. Functional magnetic resonance imaging was used to examine regional and inter-regional brain network function in 14 stroke and 16 control participants. Independent component analysis was used to identify 149 regions of interest (ROI). Using the inter-regional connectivity measurements, the weighted sum was calculated across only regions associated with a given task. Both inter-regional connectivity and regional connectivity were greater during each of the tasks as compared to the resting state. The working memory and discrete pedaling tasks allowed for detection of stroke-related decreases in inter-regional connectivity, while the continuous pedaling and language tasks allowed for detection of stroke-related enhancements in regional connectivity. These observations illustrate that task-based functional connectivity allows for detection of stroke-related changes not seen during resting states. In addition, this work provides evidence that tasks emphasizing different cognitive domains reveal different aspects of stroke-related reorganization. We also illustrate that within the motor domain, different tasks can reveal inter-regional or regional stroke-related changes, in this case suggesting that discrete pedaling required more central drive than continuous pedaling.

Phase synchronized 6 Hz transcranial electric and magnetic stimulation boosts frontal theta activity and enhances working memory

Network-level synchronization of theta oscillations in the cerebral cortex is linked to many vital cognitive functions across daily life, such as executive functions or regulation of arousal and consciousness. However, while neuroimaging has uncovered the ubiquitous functional relevance of theta rhythms in cognition, there remains a limited set of techniques for externally enhancing and stabilizing theta in the human brain non-invasively. Here, we developed and employed a new phase-synchronized low-intensity electric and magnetic stimulation technique to induce and stabilize narrowband 6-Hz theta oscillations in a group of healthy human adult participants, and then demonstrated how this technique also enhances cognitive processing by assaying working memory. Our findings demonstrate a technological advancement of brain stimulation methods, while also validating the causal link between theta activity and concurrent cognitive behavior, which may ultimately help to not only explain mechanisms, but offer perspectives for restoring deficient theta-band network activity observed in neuropsychiatric diseases.

Brain Structural Connectivity Differences in Patients with Normal Cognition and Cognitive Impairment

Advances in magnetic resonance imaging, particularly diffusion imaging, have allowed researchers to analyze brain connectivity. Identification of structural connectivity differences between patients with normal cognition, cognitive impairment, and dementia could lead to new biomarker discoveries that could improve dementia diagnostics. In our study, we analyzed 22 patients (11 control group patients, 11 dementia group patients) that underwent 3T MRI diffusion tensor imaging (DTI) scans and the Montreal Cognitive Assessment (MoCA) test. We reconstructed DTI images and used the Desikan-Killiany-Tourville cortical parcellation atlas. The connectivity matrix was calculated, and graph theoretical analysis was conducted using DSI Studio. We found statistically significant differences between groups in the graph density, network characteristic path length, small-worldness, global efficiency, and rich club organization. We did not find statistically significant differences between groups in the average clustering coefficient and the assortativity coefficient. These statistically significant graph theory measures could potentially be used as quantitative biomarkers in cognitive impairment and dementia diagnostics.

Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain

Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nodes reconfigure with brain state. To date, all graph theory studies of functional connectivity in the brain have used fixed nodes. Here, using fixed-, group-, state-specific, and individualized- parcellations for defining nodes, we demonstrate that functional connectivity changes within the nodes significantly influence the findings at the network level. In some cases, state- or group-dependent changes of the sort typically reported do not persist, while in others, changes are only observed when node reconfigurations are considered. The findings suggest that graph theory investigations into connectivity contrasts between brain states and/or groups should consider the influence of voxel-level changes that lead to node reconfigurations; the fundamental building block of a graph.

Reconfiguration dynamics of a language-and-memory network in healthy participants and patients with temporal lobe epilepsy

Current theoretical frameworks suggest that human behaviors are based on strong and complex interactions between cognitive processes such as those underlying language and memory functions in normal and neurological populations. We were interested in assessing the dynamic cerebral substrate of such interaction between language and declarative memory, as the composite function, in healthy controls (HC, N = 19) and patients with temporal lobe epilepsy (TLE, N = 16). Our assumption was that the language and declarative memory integration is based on a language-and-memory network (LMN) that is dynamic and reconfigures according to task demands and brain status. Therefore, we explored two types of LMN dynamics, a state reconfiguration (intrinsic resting-state compared to extrinsic state assessed with a sentence recall task) and a reorganization of state reconfiguration (TLE compared to HC). The dynamics was evaluated in terms of segregation (community or module detection) and integration (connector hubs). In HC, the level of segregation was the same in both states and the mechanism of LMN state reconfiguration was shown through module change of key language and declarative memory regions with integrative roles. In TLE patients, the reorganization of LMN state reconfiguration was reflected in segregation increase and extrinsic modules that were based on shorter-distance connections. While lateral and mesial temporal regions enabled state reconfiguration in HC, these regions showed reduced flexibility in TLE. We discuss our results in a connectomic perspective and propose a dynamic model of language and declarative memory functioning. We claim that complex and interactive cognitive functions, such as language and declarative memory, should be investigated dynamically, considering the interaction between cognitive networks.

Widespread Positive Direct and Indirect Effects of Regular Physical Activity on the Developing Functional Connectome in Early Adolescence

Adolescence is a period of profound but incompletely understood changes in the brain's neural circuitry (the connectome), which is vulnerable to risk factors such as unhealthy weight, but may be protected by positive factors such as regular physical activity. In 5955 children (median age = 120 months; 50.86% females) from the Adolescent Brain Cognitive Development (ABCD) cohort, we investigated direct and indirect (through impact on body mass index [BMI]) effects of physical activity on resting-state networks, the backbone of the functional connectome that ubiquitously affects cognitive function. We estimated significant positive effects of regular physical activity on network connectivity, efficiency, robustness and stability (P ≤ 0.01), and on local topologies of attention, somatomotor, frontoparietal, limbic, and default-mode networks (P < 0.05), which support extensive processes, from memory and executive control to emotional processing. In contrast, we estimated widespread negative BMI effects in the same network properties and brain regions (P < 0.05). Additional mediation analyses suggested that physical activity could also modulate network topologies leading to better control of food intake, appetite and satiety, and ultimately lower BMI. Thus, regular physical activity may have extensive positive effects on the development of the functional connectome, and may be critical for improving the detrimental effects of unhealthy weight on cognitive health.

Temporal modes of hub synchronization at rest

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

Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification.

Modulation of glutamatergic functional connectivity by a prototypical antipsychotic: Translational inference from a postsynaptic density immediate-early gene-based network analysis

BACKGROUND

Although extensively studied, the effect of antipsychotics is not completely understood at a network level. We tested the hypothesis that acute administration of haloperidol would modulate functional connectivity of brain regions relevant to schizophrenia pathophysiology. To assess putative changes in brain network properties and regional interactivity, we studied the expression of Homer1a, an Immediate Early Gene (IEG) demonstrated to be induced by antipsychotic administration and coding for a protein involved in glutamatergic synapses remodeling.

METHODS

Sprague-Dawley rats (n = 26) assigned to vehicle (VEH; NaCl 0.9%) or haloperidol (HAL; 0.8 mg/kg) were included in the network analysis. Homer1a mRNA induction was evaluated by in situ hybridization. Signal intensity analysis was performed in 33 Regions of Interest (ROIs) in the cortex, the caudate putamen, and the nucleus accumbens. A signal correlation analysis was performed, computing all possible pairwise Pearson correlations among ROIs in the two groups. Two networks were generated for HAL and VEH groups, and their properties and topography were explored.

RESULTS

VEH and HAL networks showed qualitative differences in global efficiency and clustering coefficient. The HAL network showed enhanced interactivity between cortical and striatal regions, and within caudate putamen subdivisions. On the other hand, it exhibited reduced inter-correlations between cingulate cortex and anterior insula and caudate putamen and nucleus accumbens. Moreover, haloperidol was able to modulate centrality of crucial functional hubs. These preclinical results corroborate and expand the clinical evidence that antipsychotics may modulate specific brain network properties and disease-related circuits' interactivity.

Integration and segregation across large-scale intrinsic brain networks as a marker of sustained attention and task-unrelated thought

Sustained attention is a fundamental cognitive process that can be decoupled from distinct external events, and instead emerges from ongoing intrinsic large-scale network interdependencies fluctuating over seconds to minutes. Lapses of sustained attention are commonly associated with the subjective experience of mind wandering and task-unrelated thoughts. Little is known about how fluctuations in information processing underpin sustained attention, nor how mind wandering undermines this information processing. To overcome this, we used fMRI to investigate brain activity during subjects' performance (n=29) of a cognitive task that was optimized to detect and isolate continuous fluctuations in both sustained attention (via motor responses) and task-unrelated thought (via subjective reports). We then investigated sustained attention with respect to global attributes of communication throughout the functional architecture, i.e., by the segregation and integration of information processing across large scale-networks. Further, we determined how task-unrelated thoughts related to these global information processing markers of sustained attention. The results show that optimal states of sustained attention favor both enhanced segregation and reduced integration of information processing in several task-related large-scale cortical systems with concurrent reduced segregation and enhanced integration in the auditory and sensorimotor systems. Higher degree of mind wandering was associated with losses of the favored segregation and integration of specific subsystems in our sustained attention model. Taken together, we demonstrate that intrinsic ongoing neural fluctuations are characterized by two converging communication modes throughout the global functional architecture, which give rise to optimal and suboptimal attention states. We discuss how these results might potentially serve as neural markers for clinically abnormal attention. SIGNIFICANCE STATEMENT: Most of our brain activity unfolds in an intrinsic manner, i.e., is unrelated to immediate external stimuli or tasks. Here we use a gradual continuous performance task to map this intrinsic brain activity to both fluctuations of sustained attention and mind wandering. We show that optimal sustained attention is associated with concurrent segregation and integration of information processing within many large-scale brain networks, while task-unrelated thought is related to sub-optimal information processing in specific subsystems of this sustained attention network model. These findings provide a novel information processing framework for investigating the neural basis of sustained attention, by mapping attentional fluctuations to genuinely global features of intra-brain communication.

Age-Related Compensatory Reconfiguration of PFC Connections during Episodic Memory Retrieval

During demanding cognitive tasks, older adults (OAs) frequently show greater prefrontal cortex (PFC) activity than younger adults (YAs). This age-related increase in PFC activity is often associated with enhanced cognitive performance, suggesting functional compensation. However, the brain is a complex network of interconnected regions, and it is unclear how network connectivity of PFC regions differs for OAs versus YAs. To investigate this, we examined the age-related difference on the functional brain networks mediating episodic memory retrieval. YAs and OAs participants encoded and recalled visual scenes, and age-related differences in network topology during memory retrieval were investigated as a function of memory performance. We measured both changes in functional integration and reconfiguration in connectivity patterns. The study yielded three main findings. First, PFC regions were more functionally integrated with the rest of the brain network in OAs. Critically, this age-related increase in PFC integration was associated with better retrieval performance. Second, PFC regions showed stronger performance-related reconfiguration of connectivity patterns in OAs. Finally, the PFC reconfiguration increases in OAs tracked reconfiguration reductions in the medial temporal lobe (MTL)-a core episodic memory region, suggesting that PFC connectivity in OAs may be compensating for MTL deficits.

An imbalance between functional segregation and integration in patients with pontine stroke: A dynamic functional network connectivity study

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Pontine stroke patients show abnormal time-varying brain activity.

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Pontine stroke patients exist aberrant functional segregation and integration.

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The alterations of dFNC may lead to a poor functional recovery after stroke.

Background

Previous studies on brain functional connectivity have revealed the neural physiopathology in patients with pontine stroke (PS). However, those studies focused only on the static features of intrinsic fluctuations, rather than on the time-varying effects throughout the entire scan. In the present study, we sought to explore the underlying mechanism of PS using the dynamic functional network connectivity (dFNC) method.

Methods

Resting-state functional magnetic resonance imaging (fMRI) data were collected from 58 patients with PS and 52 healthy controls (HC). Independent component analysis (ICA), the sliding window method, and k-means clustering analysis were performed to extract different functional networks, to calculate dFNC matrices, and to estimate distinct dynamic connectivity states. Additionally, temporal features were compared between the two groups in each state to explore the brain’s preference for different dynamic connectivity states in PS, and global and local efficiency were compared among states to explore the differences of topologic organization across different dFNC states. The correlations between clinical scales and the temporal features that differed between the two groups also were calculated.

Results

The dFNC analyses suggested four recurring states; in two of these states, the PS group showed a different duration from that of the HC group. Patients with PS spent significantly more time in a sparsely connected state (State 1), which was characterized by relatively low levels of connectivity within and between all brain networks. In contrast, patients with PS spent significantly less time in a highly segregated state (State 2), which was characterized by high levels of positive connectivities within primary perceptional domains and within higher cognitive control domains, and by high levels of negative inter-functional connectivities (inter-FCs) among primary perceptional and higher cognitive control domains. Additionally, the dwell time in State 2 was positively correlated with HC group’s long-term memory scores in the Rey Auditory Verbal Learning Test (RAVLT-L), whereas there was no correlation between the State-2 dwell time and RAVLT-L scores in the PS group. Furthermore, the sparsely connected state and the highly segregated state mentioned above had the highest global efficiency and the highest local efficiency among the four states, respectively.

Conclusions

In summary, we observed a preference in the aberrant brain for dynamic connectivity states with different network topologic organization in patients with PS, indicating abnormal functional segregation and integration of the whole brain and confirming the imperfection of functional network connectivity in patients with PS. These findings provide new evidence for the dynamic neural mechanisms underlying clinical symptoms in patients with PS.

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.

Altered Functional Brain Networks in Patients with Traumatic Anosmia: Resting-State Functional MRI Based on Graph Theoretical Analysis

OBJECTIVE

Traumatic anosmia is a common disorder following head injury; however, little is known regarding its neural basis and influence on the functional networks. Therefore, we aimed to investigate the functional connectivity changes in patients with traumatic anosmia compared to healthy controls using resting-state functional magnetic resonance imaging (rs-fMRI).

MATERIALS AND METHODS

Sixteen patients with traumatic anosmia and 12 healthy controls underwent rs-fMRI. Differences in the connectivity of the olfactory and whole brain networks were compared between the two groups. Graph theoretical parameters, such as modularity and global efficiency of the whole brain or olfactory networks, were calculated and compared. Correlation analyses were performed between the parameters and disease severity.

RESULTS

Patients with traumatic anosmia showed decreased intra-network connectivity in the olfactory network (false discovery rate [FDR]-corrected p < 0.05) compared with that in healthy controls. Furthermore, the inter-network connectivity was increased in both the olfactory (FDR-corrected p < 0.05) and whole brain networks (degree-based statistic-corrected p < 0.05) in the anosmia group. The whole brain networks showed decreased modularity (p < 0.001) and increased global efficiency (p = 0.019) in patients with traumatic anosmia. The modularity and global efficiency were correlated with disease severity in patients with anosmia (p < 0.001 and p = 0.002, respectively).

CONCLUSION

Traumatic anosmia increased the inter-network connectivity observed with rs-fMRI in the olfactory and global brain functional networks. rs-fMRI parameters may serve as potential biomarkers for traumatic anosmia by revealing a more widespread functional damage than previously expected.

Connectome analysis of brain functional network alterations in breast cancer survivors with and without chemotherapy

Purpose

Treatment modalities for breast cancer, the leading cause of cancer-related deaths in women worldwide, include surgery, radiotherapy, adjuvant chemotherapy, targeted therapy, and hormonal therapy. The advancement in medical technology has facilitated substantial reduction in breast cancer mortality. However, patients may experience cognitive impairment after chemotherapy. This phenomenon called chemotherapy-induced cognitive impairment (i.e., “chemobrain”) is common among breast cancer survivors. However, cognitive function deficits may exist before chemotherapy initiation. This study examined the functional network alterations in breast survivors by using resting-state functional magnetic resonance imaging (fMRI).

Methods

We recruited 172 female participants and separated them into three groups: C+ (57 breast cancer survivors who had finished 3–12-month-long chemotherapy), C- (45 breast cancer survivors who had not undergone chemotherapy), and HC (70 participants with no breast cancer history). We analyzed mean fractional amplitudes of low-frequency fluctuation and graph theoretical topologies from resting-state fMRI and applied network-based analysis to portray functional changes among the three groups.

Results

Among the three groups, the C- group demonstrated hyperactivity in the prefrontal cortex, bilateral middle temporal gyrus, right inferior temporal gyrus and right angular gyrus. Only the left caudate demonstrated significantly more hypoactivity in the C- group than in the C+ group. Graph theoretical analysis demonstrated that the brains of the C+ group became inclined toward regular networks and the brains of the C- group became inclined toward random networks.

Conclusion

Subtle alterations were noted in the brain activity and networks of our cancer survivors. Moreover, functional network disruptions occurred regardless of chemotherapeutic agent administration.

EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by differences in network size, density, and standardization. To address this issue and improve classification accuracy, we propose a novel method, in which the hybrid features of the brain function based on the bilevel network are extracted. Minimum spanning tree (MST) based on electroencephalogram (EEG) signal nodes in different MIs is constructed as the first network layer to solve the global network connectivity problem. In addition, the regional network in different movement patterns is constructed as the second network layer to determine the network characteristics, which is consistent with the correspondence between limb movement patterns and cerebral cortex in neurophysiology. We attempt to apply MST to the classification of the MI EEG signals, and the bilevel network has better interpretability. Thereafter, a vector is formed by combining the MST fundamental features with the directional features of the regional network. Our method is validated using the BCI Competition IV Dataset I. Experimental results verify the feasibility of the bilevel network framework. Furthermore, the average classification performance of the proposed method reaches 89.50%, which is higher than that of other competing methods, thereby indicating that the bilevel network is effective for MI classification.

Alterations in Structural and Functional Connectivities in Patients with End-Stage Renal Disease

Background and Purpose

The aim of this study was to evaluate the structural and functional connectivities of brain network using graph theoretical analysis in neurologically asymptomatic patients with end-stage renal disease (ESRD). We further investigated the prevalence of cognitive impairment (CI) in ESRD patients and analyzed the association between network measures of brain connectivity and cognitive function.

Methods

We prospectively enrolled 40 neurologically asymptomatic ESRD patients, 40 healthy controls, and 20 disease controls. All of the subjects underwent diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI). We calculated measures of structural and functional connectivities based on DTI and rs-fMRI, respectively, and investigated differences therein between the ESRD patients and the healthy controls. We assessed cognitive function in the ESRD patients using the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological battery.

Results

The ESRD patients exhibited decreased global structural and functional brain connectivities, as well as alterations of network hubs compared to the healthy controls and disease controls. About 70% of the ESRD patients had CI. Moreover, ESRD patients without CI exhibited decreased global connectivity and alterations of network hubs. Furthermore, there was a significant positive association between measures of brain connectivity and cognitive function.

Conclusions

We found that ESRD patients exhibited decreased structural and functional brain connectivities, and that there was a significant association between brain connectivity and cognitive function. These alterations in the brain network may contribute to the pathophysiological mechanism of CI in ESRD patients.

Resting-state network topology and planning ability in healthy adults

Functional magnetic resonance imaging (fMRI) studies have been used extensively to investigate the brain areas that are recruited during the Tower of London (ToL) task. Nevertheless, little research has been devoted to study the neural correlates of the ToL task using a network approach. Here we investigated the association between functional connectivity and network topology during resting-state fMRI and ToL task performance, that was performed outside the scanner. Sixty-two (62) healthy subjects (21-74 years) underwent eyes-closed rsfMRI and performed the task on a laptop. We studied global (whole-brain) and within subnetwork resting-state topology as well as functional connectivity between subnetworks, with a focus on the default-mode, fronto-parietal and dorsal and ventral attention networks. Efficiency and clustering coefficient were calculated to measure network integration and segregation, respectively, at both the global and subnetwork level. Our main finding was that higher global efficiency was associated with slower performance (β = 0.22, Pbca = 0.04) and this association seemed mainly driven by inter-individual differences in default-mode network connectivity. The reported results were independent of age, sex, education-level and motion. Although this finding is contrary to earlier findings on general cognition, we tentatively hypothesize that the reported association may indicate that individuals with a more integrated brain during the resting-state are less able to further increase network efficiency when transitioning from a rest to task state, leading to slower responses. This study also adds to a growing body of literature supporting a central role for the default-mode network in individual differences in cognitive performance.

Intrinsic Functional Connectivity of the Brain in Adults with a Single Cerebral Hemisphere

A reliable set of functional brain networks is found in healthy people and thought to underlie our cognition, emotion, and behavior. Here, we investigated these networks by quantifying intrinsic functional connectivity in six individuals who had undergone surgical removal of one hemisphere. Hemispherectomy subjects and healthy controls were scanned with identical parameters on the same scanner and compared to a large normative sample (n = 1,482). Surprisingly, hemispherectomy subjects and controls all showed strong and equivalent intrahemispheric connectivity between brain regions typically assigned to the same functional network. Connectivity between parts of different networks, however, was markedly increased for almost all hemispherectomy participants and across all networks. These results support the hypothesis of a shared set of functional networks that underlie cognition and suggest that between-network interactions may characterize functional reorganization in hemispherectomy.

Cognitive functioning in glioma patients is related to functional connectivity measures of the non-tumoural hemisphere

Previous studies have shown that cognitive functioning in patients with brain tumour is associated with the functional network characteristics of specific resting‐state networks or with whole‐brain network characteristics. These studies, however, did not acknowledge the functional contribution of areas in the contralesional, non‐tumoural hemisphere, even though these healthy remote areas likely play a critical role in compensating for the loss of function in damaged tissue. In the current study, we examined whether there is an association between cognitive performance and functional network features of the contralesional hemisphere of patients with glioma. We found that local efficiency of the contralesional hemisphere was associated with performance on the reaction time domain, whereas contralesional assortativity was associated with complex attention and cognitive flexibility scores. Our results suggest that a less segregated organization of the contralesional hemisphere is associated with better reaction time scores, whereas a better spread of information over the contralesional hemisphere through mutually interconnected contralesional hubs is associated with better cognitive flexibility and better complex attention scores. These findings urge researchers to recognize the functional contribution of remote, undamaged regions and to focus more on the graph metrics of the contralesional hemisphere in the search for predictors of cognitive functioning in patients with brain tumour.

Cognitive profiles and associated structural brain networks in a multimorbid sample of marginalized adults

Introduction

Cognition is impaired in homeless and vulnerably housed persons. Within this heterogeneous and multimorbid group, distinct profiles of cognitive dysfunction are evident. However, little is known about the underlying neurobiological substrates. Imaging structural covariance networks provides a novel investigative strategy to characterizing relationships between brain structure and function within these different cognitive subgroups.

Method

Participants were 208 homeless and vulnerably housed persons. Cluster analysis was used to group individuals on the basis of similarities in cognitive functioning in the areas of attention, memory, and executive functioning. The principles of graph theory were applied to construct two brain networks for each cognitive group, using measures of cortical thickness and gyrification. Global and regional network properties were compared across networks for each of the three cognitive clusters.

Results

Three cognitive groups were defined by: higher cognitive functioning across domains (Cluster 1); lower cognitive functioning with a decision-making strength (Cluster 3); and an intermediate group with a relative executive functioning weakness (Cluster 2). Between-group differences were observed for cortical thickness, but not gyrification networks. The lower functioning cognitive group exhibited higher segregation and reduced integration, higher centrality in select nodes, and less spatially compact modules compared with the two other groups.

Conclusions

The cortical thickness network differences of Cluster 3 suggest that major disruptions in structural connectivity underlie cognitive dysfunction in a subgroup of people who have a high multimorbid illness burden and who are vulnerably housed or homeless. The origins, and possible plasticity of these structure-function relationships identified with network analysis warrant further study.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Aging, lifestyle and dementia

Aging is the greatest risk factor for most diseases including cancer, cardiovascular disorders, and neurodegenerative disease. There is emerging evidence that interventions that improve metabolic health with aging may also be effective for brain health. The most robust interventions are non-pharmacological and include limiting calorie or protein intake, increasing aerobic exercise, or environmental enrichment. In humans, dietary patterns including the Mediterranean, Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) and Okinawan diets are associated with improved age-related health and may reduce neurodegenerative disease including dementia. Rapamycin, metformin and resveratrol act on nutrient sensing pathways that improve cardiometabolic health and decrease the risk for age-associated disease. There is some evidence that they may reduce the risk for dementia in rodents. There is a growing recognition that improving metabolic function may be an effective way to optimize brain health during aging.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Disrupted Brain Network Efficiency and Decreased Functional Connectivity in Multi-sensory Modality Regions in Male Patients With Alcohol Use Disorder

Background: Recent studies have reported altered efficiency in selective brain regions and functional networks in patients with alcohol use disorder (AUD). Inefficient processing can reflect or arise from the disorganization of information being conveyed from place to place. However, it remains unknown whether the efficiency and functional connectivity are altered in large-scale topological organization of patients with AUD.

Methods: Resting-state functional magnetic resonance imaging (rsfMRI) data were experimentally collected from 21 right-handed males with AUD and 21 right-handed, age-, gender- and education-matched healthy controls (HCs). Graph theory was used to investigate inter-group differences in the topological parameters (global and nodal) of networks and inter-regional functional connectivity. Correlations between group differences in network properties and clinical variables were also investigated in the AUD group.

Results: The brain networks of the AUD group showed decreased global efficiency when compared with the HC group. Besides, increased nodal efficiency was found in the left orbitofrontal cortex (OFC), while reduced nodal efficiency was observed in the right OFC, right fusiform gyrus (FFG), right superior temporal gyrus, right inferior occipital gyrus (IOG), and left insula. Moreover, hypo-connectivity was detected between the right dorsolateral prefrontal cortex (DLPFC) and right superior occipital gyrus (SOG) in the AUD group when compared with the HC group. The nodal efficiency of the left OFC was associated with cognitive performance in the AUD group.

Conclusions: AUD patients exhibited alterations in brain network efficiency and functional connectivity, particularly in regions linked to multi-sensory modalities. These disrupted topological properties may help to obtain a more comprehensive understanding of large-scale brain network activity. Furthermore, these data provide a potential neural mechanism of impaired cognition in individuals with AUD.

Neural Correlates of Working Memory Maintenance in Advanced Aging: Evidence From fMRI

Working memory (WM)-related brain activity is known to be modulated by aging; particularly, older adults demonstrate greater activity than young adults. However, it is still unclear whether the activity increase in older adults is also observed in advanced aging. The present functional magnetic resonance imaging (fMRI) study was designed to clarify the neural correlates of WM in advanced aging. Further, we set out to investigate in the case that adults of advanced age do show age-related increase in WM-related activity, what the functional significance of this over-recruitment might be. Two groups of older adults – “young–old” (61–70 years, n = 17) and “old–old” (77–82 years, n = 16) – were scanned while performing a visual WM task (the n-back task: 0-back and 1-back). WM effects (1-back > 0-back) common to both age groups were identified in several regions, including the bilateral dorsolateral prefrontal cortex (DLPFC), the inferior parietal cortex, and the insula. Greater WM effects in the old–old than in the young–old group were identified in the right caudal DLPFC. These results were replicated when we performed a separate analysis between two age groups with the same level of WM performance (the young–old vs. a “high-performing” subset of the old–old group). There were no regions where WM effects were greater in the young–old group than in the old–old group. Importantly, the magnitude of the over-recruitment WM effects positively correlated with WM performance in the old–old group, but not in the young–old group. The present findings suggest that cortical over-recruitment occurs in advanced old age, and that increased activity may serve a compensatory function in mediating WM performance.

Process-Specific Alliances (PSAs) in Cognitive Neuroscience

Most cognitive neuroscience theories have focused on the functions of individual brain regions, but cognitive abilities depend also on functional interactions among multiple regions. Many recent studies on these interactions have examined large-scale, resting-state networks, but these networks are difficult to link to theories about specific cognitive processes. Cognitive theories are easier to link to the mini-networks we call process specific alliances (PSAs). A PSA is a small team of brain regions that rapidly assemble to mediate a cognitive process in response to task demands but quickly disassemble when the process is no longer needed. We compare PSAs to resting-state networks and to other connectivity-based, task-related networks, and we characterize the advantages and disadvantages of each type of network.

Key Brain Network Nodes Show Differential Cognitive Relevance and Developmental Trajectories during Childhood and Adolescence

Human adolescence is a period of rapid changes in cognition and goal-directed behavior, and it constitutes a major transitional phase towards adulthood. One of the mechanisms suggested to underlie the protracted maturation of functional brain networks, is the increased network integration and segregation enhancing neural efficiency. Importantly, the increasing coordinated network interplay throughout development is mediated through functional hubs, which are highly connected brain areas suggested to be pivotal nodes for the regulation of neural activity. To elucidate brain hub development during childhood and adolescence, we estimated voxel-wise eigenvector centrality (EC) using functional magnetic resonance imaging (fMRI) data from two different psychological contexts (resting state and a working memory task), in a large cross-sectional sample (n = 754) spanning the age from 8 to 22 years, and decomposed the maps using independent component analysis (ICA). Our results reveal significant age-related centrality differences in cingulo-opercular, visual, and sensorimotor network nodes during both rest and task performance, suggesting that common neurodevelopmental processes manifest across different mental states. Supporting the functional significance of these developmental patterns, the centrality of the cingulo-opercular node was positively associated with task performance. These findings provide evidence for protracted maturation of hub properties in specific nodes of the brain connectome during the course of childhood and adolescence and suggest that cingulo-opercular centrality is a key factor supporting neurocognitive development.