Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults

Functional dedifferentiation of associative resting state networks in older adults - A longitudinal study

Healthy aging is associated with weaker functional connectivity within resting state brain networks and stronger functional interaction between these networks. This phenomenon has been characterized as reduced functional segregation and has been investigated mainly in cross-sectional studies. Here, we used a longitudinal dataset which consisted of four occasions of resting state fMRI and psychometric cognitive ability data, collected from a sample of healthy older adults (baseline N = 232, age range: 64-87 y, age M = 70.8 y), to investigate the functional segregation of several well-defined resting state networks encompassing the whole brain. We characterized the ratio of within-network and between-network correlations via the well-established segregation index. Our findings showed a decrease over a 4-year interval in the functional segregation of the default mode, frontoparietal control and salience ventral attention networks. In contrast, we showed an increase in the segregation of the limbic network over the same interval. More importantly, the rate of change in functional segregation of the frontoparietal control network was associated with the rate of change in processing speed. These findings support the hypothesis of functional dedifferentiation in healthy aging as well as its role in cognitive function in elderly.

Age-related differences in functional brain network segregation are consistent with a cascade of cerebrovascular, structural, and cognitive effects

Age-related declines in cognition are associated with widespread structural and functional brain changes, including changes in resting-state functional connectivity and gray and white matter status. Recently we have shown that the elasticity of cerebral arteries also explains some of the variance in cognitive and brain health in aging. Here, we investigated how network segregation, cerebral arterial elasticity (measured with pulse-DOT-the arterial pulse based on diffuse optical tomography) and gray and white matter status jointly account for age-related differences in cognitive performance. We hypothesized that at least some of the variance in brain and cognitive aging is linked to reduced cerebrovascular elasticity, leading to increased cortical atrophy and white matter abnormalities, which, in turn, are linked to reduced network segregation and decreases in cognitive performance. Pairwise comparisons between these variables are consistent with an exploratory hierarchical model linking them, especially when focusing on association network segregation (compared with segregation in sensorimotor networks). These findings suggest that preventing or slowing age-related changes in one or more of these factors may induce a neurophysiological cascade beneficial for preserving cognition in aging.

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011, and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

Brain-wide functional architecture remodeling by alcohol dependence and abstinence

Alcohol abuse and alcohol dependence are key factors in the development of alcohol use disorder, which is a pervasive societal problem with substantial economic, medical, and psychiatric consequences. Although our understanding of the neurocircuitry that underlies alcohol use has improved, novel brain regions that are involved in alcohol use and novel biomarkers of alcohol use need to be identified. The present study used a single-cell whole-brain imaging approach to 1) assess whether abstinence from alcohol in an animal model of alcohol dependence alters the functional architecture of brain activity and modularity, 2) validate our current knowledge of the neurocircuitry of alcohol abstinence, and 3) discover brain regions that may be involved in alcohol use. Alcohol abstinence resulted in the whole-brain reorganization of functional architecture in mice and a pronounced decrease in modularity that was not observed in nondependent moderate drinkers. Structuring of the alcohol abstinence network revealed three major brain modules: 1) extended amygdala module, 2) midbrain striatal module, and 3) cortico-hippocampo-thalamic module, reminiscent of the three-stage theory. Many hub brain regions that control this network were identified, including several that have been previously overlooked in alcohol research. These results identify brain targets for future research and demonstrate that alcohol use and dependence remodel brain-wide functional architecture to decrease modularity. Further studies are needed to determine whether the changes in coactivation and modularity that are associated with alcohol abstinence are causal features of alcohol dependence or a consequence of excessive drinking and alcohol exposure.

Altered brain connectivity in sudden unexpected death in epilepsy (SUDEP) revealed using resting-state fMRI

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The functional architecture among regulatory structures, and the whole brain, is less modular in confirmed cases of SUDEP and those at high-risk.

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Altered functional organisation may mean potential impairment of communication among key regulatory circuits.

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SUDEP is associated with regional connectivity disruptions among cortical and sub-cortical regulatory sites.

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Medial thalamic connectivity was significantly altered in SUDEP compared with all control groups, including those at high-risk.

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Increases in the number, and a shift in organisation, of hubs appears to relate to lower mortality risk.

The circumstances surrounding SUDEP suggest autonomic or respiratory collapse, implying central failure of regulation or recovery. Characterisation of the communication among brain areas mediating such processes may shed light on mechanisms and noninvasively indicate risk.

We used rs-fMRI to examine network properties among brain structures in people with epilepsy who suffered SUDEP (n = 8) over an 8-year follow-up period, compared with matched high- and low-risk subjects (n = 16/group) who did not suffer SUDEP during that period, and a group of healthy controls (n = 16). Network analysis was employed to explore connectivity within a ‘regulatory-subnetwork’ of brain regions involved in autonomic and respiratory regulation, and over the whole-brain.

Modularity, the extent of network organization into separate modules, was significantly reduced in the regulatory-subnetwork, and the whole-brain, in SUDEP and high-risk. Increased participation, a local measure of inter-modular belonging, was evident in SUDEP and high-risk groups, particularly among thalamic structures. The medial prefrontal thalamus was increased in SUDEP compared with all other control groups, including high-risk. Patterns of hub topology were similar in SUDEP and high-risk, but were more extensive in low-risk patients, who displayed greater hub prevalence and a radical reorganization of hubs in the subnetwork.

SUDEP is associated with reduced functional organization among cortical and sub-cortical brain regions mediating autonomic and respiratory regulation. Living high-risk subjects demonstrated similar patterns, suggesting such network measures may provide prospective risk-indicating value, though a crucial difference between SUDEP and high-risk was altered connectivity of the medial thalamus in SUDEP, which was also elevated compared with all sub-groups. Disturbed thalamic connectivity may reflect a potential non-invasive marker of elevated SUDEP risk.

Detecting modular brain states in rest and task

The human brain is a dynamic networked system that continually reconfigures its functional connectivity patterns over time. Thus, developing approaches able to adequately detect fast brain dynamics is critical. Of particular interest are the methods that analyze the modular structure of brain networks, that is, the presence of clusters of regions that are densely interconnected. In this paper, we propose a novel framework to identify fast modular states that dynamically fluctuate over time during rest and task. We started by demonstrating the feasibility and relevance of this framework using simulated data. Compared with other methods, our algorithm was able to identify the simulated networks with high temporal and spatial accuracies. We further applied the proposed framework on MEG data recorded during a finger movement task, identifying modular states linking somatosensory and primary motor regions. The algorithm was also performed on dense-EEG data recorded during a picture naming task, revealing the subsecond transition between several modular states that relate to visual processing, semantic processing, and language. Next, we tested our method on a dataset of resting-state dense-EEG signals recorded from 124 patients with Parkinson's disease. Results disclosed brain modular states that differentiate cognitively intact patients, patients with moderate cognitive deficits, and patients with severe cognitive deficits. Our new approach complements classical methods, offering a new way to track the brain modular states, in healthy subjects and patients, on an adequate task-specific timescale.

Brain network modularity predicts cognitive training-related gains in young adults

The brain operates via networked activity in separable groups of regions called modules. The quantification of modularity compares the number of connections within and between modules, with high modularity indicating greater segregation, or dense connections within sub-networks and sparse connections between sub-networks. Previous work has demonstrated that baseline brain network modularity predicts executive function outcomes in older adults and patients with traumatic brain injury after cognitive and exercise interventions. In healthy young adults, however, the functional significance of brain modularity in predicting training-related cognitive improvements is not fully understood. Here, we quantified brain network modularity in young adults who underwent cognitive training with casual video games that engaged working memory and reasoning processes. Network modularity assessed at baseline was positively correlated with training-related improvements on untrained tasks. The relationship between baseline modularity and training gain was especially evident in initially lower performing individuals and was not present in a group of control participants that did not show training-related gains. These results suggest that a more modular brain network organization may allow for greater training responsiveness. On a broader scale, these findings suggest that, particularly in low-performing individuals, global network properties can capture aspects of brain function that are important in understanding individual differences in learning.

The effects of lesion and treatment-related recovery on functional network modularity in post-stroke dysgraphia

A better understanding of the neural network properties that support cognitive recovery after a brain lesion is important for our understanding of human neuroplasticity and may have valuable clinical implications. In fifteen individuals with chronic, acquired written language deficits subsequent to left-hemisphere stroke, we used task-based functional connectivity to evaluate the relationship between the graph-theoretic measures (modularity, participation coefficient and within-module degree z-score) and written language production accuracy before and after behavioral treatment. A reference modular structure and local and global hubs identified from healthy controls formed the basis of the analyses. Overall, the investigation revealed that less modular networks with greater global and lower local integration were associated with greater deficit severity and lower response to treatment. Furthermore, we found treatment-induced increases in modularity and local integration measures. In particular, local integration within intact ventral occipital-temporal regions of the spelling network showed the greatest increase in local integration following treatment. This investigation significantly extends previous research by using task-based (rather than resting-state) functional connectivity to examine a larger set of network characteristics in the evaluation of treatment-induced recovery and by including comparisons with control participants. The findings demonstrate the relevance of network modularity for understanding the neuroplasticity supporting functional neural reorganization.

Brain Modularity: A Biomarker of Intervention-related Plasticity

Interventions using methods such as cognitive training and aerobic exercise have shown potential to enhance cognitive abilities. However, there is often pronounced individual variability in the magnitude of these gains. Here, we propose that brain network modularity, a measure of brain subnetwork segregation, is a unifying biomarker of intervention-related plasticity. We present work from multiple independent studies demonstrating that individual differences in baseline brain modularity predict gains in cognitive control functions across several populations and interventions, spanning healthy adults to patients with clinical deficits and cognitive training to aerobic exercise. We believe that this predictive framework provides a foundation for developing targeted, personalized interventions to improve cognition.

Sex differences in the relationship between cardiorespiratory fitness and brain function in older adulthood

We investigated sex differences in the association between a measure of physical health, cardiorespiratory fitness (CRF), and brain function using resting-state functional connectivity fMRI. We examined these sex differences in the default, frontoparietal control, and cingulo-opercular networks, assemblies of functionally connected brain regions known to be impacted by both age and fitness level. Healthy older adults ( n = 49; 29 women) were scanned to obtain measures of intrinsic connectivity within and across these 3 networks. We calculated global efficiency (a measure of network integration) and local efficiency (a measure of network specialization) using graph theoretical methods. Across all three networks combined, local efficiency was positively associated with CRF, and this was more robust in male versus female older adults. Furthermore, global efficiency was negatively associated with CRF, but only in males. Our findings suggest that in older adults, associations between brain network integrity and physical health are sex-dependent. These results underscore the importance of considering sex differences when examining associations between fitness and brain function in older adulthood. NEW & NOTEWORTHY We examined the association between cardiorespiratory fitness and resting state functional connectivity in several brain networks known to be impacted by age and fitness level. We found significant associations between fitness and measures of network integration and network specialization, but in a sex-dependent manner, highlighting the interplay between sex differences, fitness, and aging brain health. Our findings underscore the importance of considering sex differences when examining associations between fitness and brain function in older adulthood.

Independent Components of Neural Activation Associated with 100 Days of Cognitive Training

Some cognitive training studies have reported working memory benefits that generalize beyond the trained tasks, whereas others have only found task-specific training effects. What brain networks are associated with general training effects, rather than task-specific effects? We investigated this question in the context of working memory training using the COGITO data set, a longitudinal project including behavioral assessments before and after 100 days of cognitive training in 101 younger (20-31 years) and 103 older (65-80 years) adults. Pre- and postassessments included verbal, numerical, and spatial measures of working memory. It was therefore possible to assess training effects on working memory at a general latent ability level. Previous analyses of these data found training-related improvements on this latent working memory factor in both young and old participants. fMRI data were collected from a subsample of participants (24 young and 15 old) during pre- and post-training sessions. We used independent component analysis to identify networks involved in a perceptual decision-making task performed in the scanner. We identified five task-positive components that were task-related: two frontal networks, a ventral visual network, a motor network, and a cerebellar network. Pre-training activity of the motor network predicted latent working memory performance before training. Additionally, activity in the motor network predicted training-related changes in working memory ability. These findings suggest activity in the motor network plays a role in task-independent working memory improvements and have implications for our understanding of working memory training and for the design and implementation of future training interventions.

Segregation of salience network predicts treatment response of depression to repetitive transcranial magnetic stimulation

Background

The present study tested the hypothesis that network segregation, a graph theoretic measure of functional organization of the brain, is correlated with treatment response in patients with major depressive disorder (MDD) undergoing repetitive transcranial magnetic stimulation (rTMS).

Methods

Network segregation, calculated from resting state functional magnetic resonance imaging scans, was measured in 32 patients with MDD who entered a sham-controlled, double-blinded, randomized trial of rTMS to the left dorsolateral prefrontal cortex, and a cohort of 20 healthy controls (HCs). Half of the MDD patients received sham treatment in the blinded phase, followed by active rTMS in the open-label phase. The analyses focused on segregation of the following networks: default mode (DMN), salience (SN), fronto-parietal (FPN), cingulo-opercular (CON), and memory retrieval (MRN).

Results

There was no differential change in network segregation comparing sham to active treatment. However, in the combined group of patients who completed active rTMS treatment (in the blinded plus open-label phases), higher baseline segregation of SN significantly predicted more symptom improvement after rTMS. Compared to HCs at baseline, MDD patients showed decreased segregation in DMN, and trend-level decreases in SN and MRN.

Conclusion

The results highlight the importance of network segregation in MDD, particularly in the SN, where more normal baseline segregation of SN may predict better treatment response to rTMS in depression.

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We examined network segregation in a cohort of MDD patients receiving rTMS treatment.

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More normal segregation of SN predicted better response of depression to rTMS.

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Patients with MDD had decreased network segregation in DMN.

Event-related neural oscillation changes following reasoning training in individuals with Mild Cognitive Impairment

Emerging evidence suggests cognitive training programs targeting higher-order reasoning may strengthen not only cognitive, but also neural functions in individuals with Mild Cognitive Impairment (MCI). However, research on direct measures of training-induced neural changes, derivable from electroencephalography (EEG), is limited. The current pilot study examined effects of Gist Reasoning training (n = 16) compared to New Learning training (n = 16) in older adults with amnestic MCI on measures of event-related neural oscillations (theta and alpha band power) corresponding to Go/NoGo tasks during basic and superordinate semantic categorization. EEG data were recorded while participants performed the Go/NoGo task pre- and post-training, and power in theta and alpha frequency bands was examined. Both groups were comparable at pre-training on all measures and both groups showed greater event-related theta synchronization post-training. Furthermore, the Gist Reasoning group had enhanced event-related desynchronization in low-frequency alpha band (8-10 Hz) on response inhibition (NoGo) trials and high-frequency alpha band (11-13 Hz) on response execution (Go) trials during superordinate categorization, relative to the New Learning group. These findings suggest that Gist Reasoning training in MCI impacted neural processing linked to strategic processing of Go and NoGo trials during the more complex superordinate categorization task. Targeting higher-order top-down cognitive processing seems to better harness residual neuroplastic potential in MCI. ClinicalTrials.gov ID: NCT02588209.

Aging of the frontal lobe

Healthy aging is associated with numerous deficits in cognitive function, which have been attributed to changes within the prefrontal cortex (PFC). This chapter summarizes some of the most prominent cognitive changes associated with age-related alterations in the anatomy and physiology of the PFC. Specifically, aging of the PFC results in deficient aspects of cognitive control, including sustained attention, selective attention, inhibitory control, working memory, and multitasking abilities. Yet, not all cognitive functions associated with the PFC exhibit age-related declines, such as arithmetic, comprehension, emotion perception, and emotional control. Moreover, not all older adults exhibit declines in cognition. Multiple life-course and lifestyle factors, as well as genetics, play a role in the trajectory of cognitive performance across the life span. Thus many adults retain cognitive function well into advanced age. Moreover, the brain remains plastic throughout life and there is increasing evidence that most age-related declines in cognition can be remediated by various methods such as physical exercise, cognitive training, or noninvasive brain stimulation. Overall, because cognitive aging is associated with numerous life-course and lifestyle factors, successful aging likely begins in early life, while maintaining cognition or remediating declines is a life-long process.

A mechanistic model of connector hubs, modularity and cognition

The human brain network is modular-comprised of communities of tightly interconnected nodes1. This network contains local hubs, which have many connections within their own communities, and connector hubs, which have connections diversely distributed across communities2,3. A mechanistic understanding of these hubs and how they support cognition has not been demonstrated. Here, we leveraged individual differences in hub connectivity and cognition. We show that a model of hub connectivity accurately predicts the cognitive performance of 476 individuals in four distinct tasks. Moreover, there is a general optimal network structure for cognitive performance-individuals with diversely connected hubs and consequent modular brain networks exhibit increased cognitive performance, regardless of the task. Critically, we find evidence consistent with a mechanistic model in which connector hubs tune the connectivity of their neighbors to be more modular while allowing for task appropriate information integration across communities, which increases global modularity and cognitive performance.

Working Memory and Reasoning Benefit from Different Modes of Large-scale Brain Dynamics in Healthy Older Adults

Researchers have proposed that solving complex reasoning problems, a key indicator of fluid intelligence, involves the same cognitive processes as solving working memory tasks. This proposal is supported by an overlap of the functional brain activations associated with the two types of tasks and by high correlations between interindividual differences in performance. We replicated these findings in 53 older participants but also showed that solving reasoning and working memory problems benefits from different configurations of the functional connectome and that this dissimilarity increases with a higher difficulty load. Specifically, superior performance in a typical working memory paradigm ( n-back) was associated with upregulation of modularity (increased between-network segregation), whereas performance in the reasoning task was associated with effective downregulation of modularity. We also showed that working memory training promotes task-invariant increases in modularity. Because superior reasoning performance is associated with downregulation of modular dynamics, training may thus have fostered an inefficient way of solving the reasoning tasks. This could help explain why working memory training does little to promote complex reasoning performance. The study concludes that complex reasoning abilities cannot be reduced to working memory and suggests the need to reconsider the feasibility of using working memory training interventions to attempt to achieve effects that transfer to broader cognition.

Socioeconomic status moderates age-related differences in the brain's functional network organization and anatomy across the adult lifespan

An individual’s socioeconomic status (SES) is a central feature of their environmental surroundings and has been shown to relate to the development and maturation of their brain in childhood. Here, we demonstrate that an individual’s present (adult) SES relates to their brain function and anatomy across a broad range of middle-age adulthood. In middle-aged adults (35–64 years), lower SES individuals exhibit less organized functional brain networks and reduced cortical thickness compared with higher SES individuals. These relationships cannot be fully explained by differences in health, demographics, or cognition. Additionally, childhood SES does not explain the relation between SES and brain network organization. These observations provide support for a powerful relationship between the environment and the brain that is evident in adult middle age.

An individual’s environmental surroundings interact with the development and maturation of their brain. An important aspect of an individual’s environment is his or her socioeconomic status (SES), which estimates access to material resources and social prestige. Previous characterizations of the relation between SES and the brain have primarily focused on earlier or later epochs of the lifespan (i.e., childhood, older age). We broaden this work to examine the relationship between SES and the brain across a wide range of human adulthood (20–89 years), including individuals from the less studied middle-age range. SES, defined by education attainment and occupational socioeconomic characteristics, moderates previously reported age-related differences in the brain’s functional network organization and whole-brain cortical structure. Across middle age (35–64 years), lower SES is associated with reduced resting-state system segregation (a measure of effective functional network organization). A similar but less robust relationship exists between SES and age with respect to brain anatomy: Lower SES is associated with reduced cortical gray matter thickness in middle age. Conversely, younger and older adulthood do not exhibit consistent SES-related difference in the brain measures. The SES–brain relationships persist after controlling for measures of physical and mental health, cognitive ability, and participant demographics. Critically, an individual’s childhood SES cannot account for the relationship between their current SES and functional network organization. These findings provide evidence that SES relates to the brain’s functional network organization and anatomy across adult middle age, and that higher SES may be a protective factor against age-related brain decline.

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

Brain Network Modularity Predicts Exercise-Related Executive Function Gains in Older Adults

Recent work suggests that the brain can be conceptualized as a network comprised of groups of sub-networks or modules. The extent of segregation between modules can be quantified with a modularity metric, where networks with high modularity have dense connections within modules and sparser connections between modules. Previous work has shown that higher modularity predicts greater improvements after cognitive training in patients with traumatic brain injury and in healthy older and young adults. It is not known, however, whether modularity can also predict cognitive gains after a physical exercise intervention. Here, we quantified modularity in older adults (N = 128, mean age = 64.74) who underwent one of the following interventions for 6 months (NCT01472744 on ClinicalTrials.gov): (1) aerobic exercise in the form of brisk walking (Walk), (2) aerobic exercise in the form of brisk walking plus nutritional supplement (Walk+), (3) stretching, strengthening and stability (SSS), or (4) dance instruction. After the intervention, the Walk, Walk+ and SSS groups showed gains in cardiorespiratory fitness (CRF), with larger effects in both walking groups compared to the SSS and Dance groups. The Walk, Walk+ and SSS groups also improved in executive function (EF) as measured by reasoning, working memory, and task-switching tests. In the Walk, Walk+, and SSS groups that improved in EF, higher baseline modularity was positively related to EF gains, even after controlling for age, in-scanner motion and baseline EF. No relationship between modularity and EF gains was observed in the Dance group, which did not show training-related gains in CRF or EF control. These results are consistent with previous studies demonstrating that individuals with a more modular brain network organization are more responsive to cognitive training. These findings suggest that the predictive power of modularity may be generalizable across interventions aimed to enhance aspects of cognition and that, especially in low-performing individuals, global network properties can capture individual differences in neuroplasticity.

Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on "resting-state" networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.

The diverse club

A complex system can be represented and analyzed as a network, where nodes represent the units of the network and edges represent connections between those units. For example, a brain network represents neurons as nodes and axons between neurons as edges. In many networks, some nodes have a disproportionately high number of edges as well as many edges between each other and are referred to as the "rich club". In many different networks, the nodes of this club are assumed to support global network integration. Here we show that another set of nodes, which have edges diversely distributed across the network, form a "diverse club". The diverse club exhibits, to a greater extent than the rich club, properties consistent with an integrative network function-these nodes are more highly interconnected and their edges are more critical for efficient global integration. Finally, these two clubs potentially evolved via distinct selection pressures.