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

Influence of individual's age on the characteristics of brain effective connectivity

Given the increasing number of older adults in society, there is a growing need for studies on changes in the aging brain. The aim of this research is to investigate the effective connectivity of different age groups using resting-state functional magnetic resonance imaging (fMRI) and graph theory. By examining connectivity in different age groups, a better understanding of age-related changes can be achieved. Lifespan pilot data from the Human Connectome Project (HCP) were used to examine dynamic effective connectivity (dEC) changes across different age groups. The Granger causality method with time windowing was employed to calculate dEC. After extracting graph measures, statistical analyses were performed to compare the age groups. Support vector machine and decision tree classifiers were used to classify the different age groups based on the extracted graph measures. Based on the obtained results, it can be concluded that there are significant differences in the effective connectivity among the three age groups. Statistical analyses revealed disassortativity. The global efficiency exhibited a decreasing trend, and the transitivity measure showed an increasing trend with the advancing age. The decision tree classifier showed an accuracy of 86.67 % with Kruskal-Wallis selected features. This study demonstrates that changes in effective connectivity across different age brackets can serve as a tool for better understanding brain function during the aging process.

Variation in brain aging: A review and perspective on the utility of individualized approaches to the study of functional networks in aging

Healthy aging is associated with cognitive decline across multiple domains, including executive function, memory, and attention. These cognitive changes can often influence an individual's ability to function and quality of life. However, the degree to which individuals experience cognitive decline, as well as the trajectory of these changes, exhibits wide variability across people. These cognitive abilities are thought to depend on the coordinated activity of large-scale networks. Like behavioral effects, large variation can be seen in brain structure and function with aging, including in large-scale functional networks. However, tracking this variation requires methods that reliably measure individual brain networks and their changes over time. Here, we review the literature on age-related cognitive decline and on age-related differences in brain structure and function. We focus particularly on functional networks and the individual variation that exists in these measures. We propose that novel individual-centered fMRI approaches can shed new light on patterns of inter- and intra-individual variability in aging. These approaches may be instrumental in understanding the neural bases of cognitive decline.

The role of spatial processing in verbal serial order working memory

In a sequence, at least two aspects of information-the identity of items and their serial order-are maintained and supported by distinct working memory (WM) capacities. Verbal serial order WM is modulated by spatial processing, reflected in the Spatial Position Association of Response Codes (SPoARC) effect-the left-beginning, right-end positional association between space and serial position of verbal WM memoranda. We investigated the individual differences in this modulation with both behavioral and neurobiological approaches. We administered a battery of seven behavioral tasks with 160 healthy adults and collected resting-state fMRI data from a subset of 25 participants. With a multilevel mixed-effects modeling approach, we found that the SPoARC effect's magnitude predicts individual differences in verbal serial order WM capacity and is related to spatial item WM capacity. With a graph-theory-based analytic approach, this interaction between verbal serial order WM and spatial WM was corroborated in that the level of interaction between corresponding cortical regions (indexed by modularity) was predictive of the magnitude of the SPoARC effect. Additionally, the modularity of cortical regions associated with verbal serial order WM and spatial attention predicted the SPoARC effect's magnitude, indicating the involvement of spatial attention in this modulation. Together, our findings highlight multiple sources of the interplay between verbal serial order WM and spatial processing.

Salience network segregation mediates the effect of tau pathology on mild behavioral impairment

INTRODUCTION

A recently developed mild behavioral impairment (MBI) diagnostic framework standardizes the early characterization of neuropsychiatric symptoms in older adults. However, the joint contributions of Alzheimer's disease (AD) pathology and brain function to MBI remain unclear.

METHODS

We test a novel model assessing direct relationships between AD biomarker status and MBI symptoms, as well as mediated effects through segregation of the salience and default-mode networks, using data from 128 participants with diagnosis of amnestic mild cognitive impairment or mild dementia-AD type.

RESULTS

We identified a mediated effect of tau positivity on MBI through functional segregation of the salience network from the other high-level, association networks. There were no direct effects of AD biomarkers status on MBI.

DISCUSSION

Our findings suggest that tau pathology contributes to MBI primarily by disrupting salience network function and emphasize the role of the salience network in mediating relationships between neuropathological changes and behavioral manifestations.

HIGHLIGHTS

Network segregation mediates Alzheimer's disease (AD) pathology impact on mild behavioral impairment (MBI). The salience network is pivotal in linking tau pathology and MBI. This study used path analysis with AD biomarkers and network integrity. The study evaluated the roles of salience, default mode, and frontoparietal networks. This is the first study to integrate MBI with AD biomarkers and network functionality.

Age differences in functional connectivity track dedifferentiation of category representations

With advancing age, the distinctiveness of neural representations of information declines. While the finding of this so-called 'age-related neural dedifferentiation' in category-selective neural regions is well-described, the contribution of age-related changes in network organization to dedifferentiation is unknown. Here, we asked whether age differences in a) whole-brain network segregation (i.e., network dedifferentiation) and b) functional connectivity to category-selective neural regions are related to regional dedifferentiation of categorical representations. Younger and older adults viewed blocks of face and house stimuli in the fMRI scanner. We found an age-related decline in neural distinctiveness for faces in the fusiform gyrus (FG) and for houses in the parahippocampal gyrus (PHG). Functional connectivity analyses revealed age-related dedifferentiation of global network structure as well as age differences in connectivity between the FG and early visual cortices. Interindividual correlations demonstrated that regional distinctiveness was related to network segregation. Together, our findings suggest that dedifferentiation of categorical representations may be linked to age-related reorganization of functional networks.

Acute Exercise Improves Large-Scale Brain Network Segregation in Healthy Older Adults

Introduction: Age-related cognitive decline and mental health problems are accompanied by changes in resting-state functional connectivity (rsFC) indices, such as reduced brain network segregation. Meanwhile, exercise can improve cognition, mood, and neural network function in older adults. Studies on effects of exercise on rsFC outcomes in older adults have chiefly focused on changes after exercise training and suggest improved network segregation through enhanced within-network connectivity. However, effects of acute exercise on rsFC measures of neural network integrity in older adults, which presumably underlie changes observed after exercise training, have received less attention. In this study, we hypothesized that acute exercise in older adults would improve functional segregation of major cognition and affect-related brain networks. Methods: To test this, we analyzed rsFC data from 37 healthy and physically active older adults after they completed 30 min of moderate-to-vigorous intensity cycling and after they completed a seated rest control condition. Conditions were performed in a counterbalanced order across separate days in a within-subject crossover design. We considered large-scale brain networks associated with cognition and affect, including the frontoparietal network (FPN), salience network (SAL), default mode network (DMN), and affect-reward network (ARN). Results: We observed that after acute exercise, there was greater segregation between SAL and DMN, as well as greater segregation between SAL and ARN. Conclusion: These findings indicate that acute exercise in active older adults alters rsFC measures in key cognition and affect-related networks in a manner that opposes age-related dedifferentiation of neural networks that may be detrimental to cognition and mental health.

Salience Network Segregation Mediates the Effect of Tau Pathology on Mild Behavioral Impairment

INTRODUCTION

A recently developed mild behavioral impairment (MBI) diagnostic framework standardizes the early characterization of neuropsychiatric symptoms in older adults. However, the links between MBI, brain function, and Alzheimer's disease (AD) biomarkers are unclear.

METHODS

Using data from 128 participants with diagnosis of amnestic mild cognitive impairment and mild dementia - Alzheimer's type, we test a novel model assessing direct relationships between AD biomarker status and MBI symptoms, as well as mediated effects through segregation of the salience and default-mode networks.

RESULTS

We identified a mediated effect of tau positivity on MBI through functional segregation of the salience network from the other high-level, association networks. There were no direct effects of AD biomarkers status on MBI.

DISCUSSION

Our findings suggest an indirect role of tau pathology in MBI through brain network dysfunction and emphasize the role of the salience network in mediating relationships between neuropathological changes and behavioral manifestations.

Community detection in the human connectome: Method types, differences and their impact on inference

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

EngAge - A metacognitive intervention to supplement working memory training: A feasibility study in older adults

Working Memory (WM) training has shown promise in supporting cognitive functioning in older adult populations, but effects that generalize beyond the trained task have been inconsistent. Targeting cognitive processes in isolation might be a limiting factor given that metacognitive and motivational factors have been shown to impact older adults' engagement with challenging cognitive activities, such as WM training. The current feasibility study implemented a novel metacognitive intervention in conjunction with WM training in older adults and examined its potential amplifying short- and long-term effects on cognitive and self-report outcomes as compared to WM or active control training alone. One-hundred and nineteen older adults completed a cognitive training over the course of 20 sessions at home. The cognitive training targeted either WM or general knowledge. In addition, one of the WM training groups completed a metacognitive program via group seminars. We tested for group differences in WM, inhibitory control, and episodic memory, and we assessed participants' perceived self-efficacy and everyday memory failures. At post-test, we replicated earlier work by demonstrating that participants who completed the WM intervention outperformed the active control group in non-trained WM measures, and to some extent, in inhibitory control. However, we found no evidence that the supplemental metacognitive program led to benefits over and above the WM intervention. Nonetheless, we conclude that our metacognitive program is a step in the right direction given the tentative long-term effects and participants' positive feedback, but more longitudinal data with larger sample sizes are needed to confirm these early findings.

Large-Scale Network Connectivity and Cognitive Function Changes After Exercise Training in Older Adults with Intact Cognition and Mild Cognitive Impairment

Background

Despite growing evidence regarding the association between exercise training (ET) and functional brain network connectivity, little is known about the effects of ET on large-scale within- and between-network functional connectivity (FC) of core brain networks.

Objective

We investigated the effects of ET on within- and between-network functional connectivity of the default mode network (DMN), frontoparietal network (FPN), and salience network (SAL) in older adults with intact cognition (CN) and older adults diagnosed with mild cognitive impairment (MCI). The association between ET-induced changes in FC and cognitive performance was examined.

Methods

33 older adults (78.0±7.0 years; 16 MCI and 17 CN) participated in this study. Before and after a 12-week walking ET intervention, participants underwent a graded exercise test, Controlled Oral Word Association Test (COWAT), Rey Auditory Verbal Learning Test (RAVLT), a narrative memory test (logical memory; LM), and a resting-state fMRI scan. We examined the within (_W) and between (_B) network connectivity of the DMN, FPN, and SAL. We used linear regression to examine associations between ET-related changes in network connectivity and cognitive function.

Results

There were significant improvements in cardiorespiratory fitness, COWAT, RAVLT, and LM after ET across participants. Significant increases in DMN_W and SAL_W, and DMN-FPN_B, DMN-SAL_B, and FPN-SAL_B were observed after ET. Greater SAL_W and FPN-SAL_B were associated with enhanced LM immediate recall performance after ET in both groups.

Conclusion

Increased within- and between-network connectivity following ET may subserve improvements in memory performance in older individuals with intact cognition and with MCI due to Alzheimer's disease.

Evaluating brain modularity benefits of an acting intervention: a discriminant-analysis framework

Purpose

Aging is associated with a reduction in brain modularity as well as aspects of executive function, namely, updating, shifting, and inhibition. Previous research has suggested that the aging brain exhibits plasticity. Further, it has been hypothesized that broad-based intervention models may be more effective in eliciting overall gains in executive function than interventions targeted at specific executive skills (e.g., computer-based training). To this end, we designed a 4-week theater-based acting intervention in older adults within an RCT framework. We hypothesized that older adults would show improvements in brain modularity and aspects of executive function, ascribed to the acting intervention.

Materials and methods

The participants were 179 adults from the community, aged 60-89 years and on average, college educated. They completed a battery of executive function tasks and resting state functional MRI scans to measure brain network modularity pre- and post-intervention. Participants in the active intervention group (n = 93) enacted scenes with a partner that involved executive function, whereas the active control group (n = 86) learned about the history and styles of acting. Both groups met two times/week for 75-min for 4 weeks. A mixed model was used to evaluate intervention effects related to brain modularity. Discriminant-analysis was used to determine the role of seven executive functioning tasks in discriminating the two groups. These tasks indexed subdomains of updating, switching, and inhibition. Discriminant tasks were subject to a logistic regression analysis to determine how post-intervention executive function performance interacted with changes in modularity to predict group membership.

Results

We noted an increase in brain modularity in the acting group, relative to pre-intervention and controls. Performance on updating tasks were representative of the intervention group. However, post-intervention performance on updating did not interact with the observed increase in brain modularity to distinguish groups.

Conclusion

An acting intervention can facilitate improvements in modularity and updating, both of which are sensitive to aging and may confer benefits to daily functioning and the ability to learn.

Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach

Age-associated changes in brain function play an important role in the development of neurodegenerative diseases. Although previous work has examined age-related changes in static functional connectivity, accumulating evidence suggests that advancing age is especially associated with alterations in the dynamic interactions and transitions between different brain states, which hitherto have received less attention. Conclusions of previous studies in this domain are moreover limited by suboptimal replicability of resting-state functional magnetic resonance imaging (fMRI) and culturally homogenous cohorts. Here, we investigate the robustness of age-associated changes in dynamic functional connectivity (dFC) by capitalizing on the availability of fMRI cohorts from two cultures (Western European and Chinese). In both the LEMON (Western European) and SALD (Chinese) cohorts, we consistently identify two distinct states: a more frequent segregated within-network connectivity state (state I) and a less frequent integrated between-network connectivity state (state II). Moreover, in both these cohorts, older (55-80 years) compared to younger participants (20-35 years) exhibited lower occurrence of and spent less time in state I. Older participants also tended to exhibit more transitions between networks and greater variance in global efficiency. Overall, our cross-cultural replication of age-associated changes in dFC metrics implies that advancing age is robustly associated with a reorganization of dynamic brain activation that favors the use of less functionally specific networks.

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.

Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach

Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.

Association between Long-Term Air Pollution, Chronic Traffic Noise, and Resting-State Functional Connectivity in the 1000BRAINS Study

BACKGROUND

Older adults show a high variability in cognitive performance that cannot be explained by aging alone. Although research has linked air pollution and noise to cognitive impairment and structural brain alterations, the potential impact of air pollution and noise on functional brain organization is unknown.

OBJECTIVE

This study examined the associations between long-term air pollution and traffic noise with measures of functional brain organization in older adults. We hypothesize that exposures to high air pollution and noise levels are associated with age-like changes in functional brain organization, shown by less segregated brain networks.

METHODS

Data from 574 participants (44.1% female, 56-85 years of age) in the German 1000BRAINS study (2011-2015) were analyzed. Exposure to particulate matter (PM10, PM2.5, and PM2.5 absorbance), accumulation mode particle number (PNAM), and nitrogen dioxide (NO2) was estimated applying land-use regression and chemistry transport models. Noise exposures were assessed as weighted 24-h (Lden) and nighttime (Lnight) means. Functional brain organization of seven established brain networks (visual, sensorimotor, dorsal and ventral attention, limbic, frontoparietal and default network) was assessed using resting-state functional brain imaging data. To assess functional brain organization, we determined the degree of segregation between networks by comparing the strength of functional connections within and between networks. We estimated associations between air pollution and noise exposure with network segregation, applying multiple linear regression models adjusted for age, sex, socioeconomic status, and lifestyle variables.

RESULTS

Overall, small associations of high exposures with lesser segregated networks were visible. For the sensorimotor networks, we observed small associations between high air pollution and noise and lower network segregation, which had a similar effect size as a 1-y increase in age [e.g., in sensorimotor network, -0.006 (95% CI: -0.021, 0.009) per 0.3 ×10-5/m increase in PM2.5 absorbance and -0.004 (95% CI: -0.006, -0.002) per 1-y age increase].

CONCLUSION

High exposure to air pollution and noise was associated with less segregated functional brain networks. https://doi.org/10.1289/EHP9737.

Leveraging technology to personalize cognitive enhancement methods in aging

As population aging advances at an increasing rate, efforts to help people maintain or improve cognitive function late in life are critical. Although some studies have shown promise, the question of whether cognitive training is an effective tool for improving general cognitive ability remains incompletely explored, and study results to date have been inconsistent. Most approaches to cognitive enhancement in older adults have taken a 'one size fits all' tack, as opposed to tailoring interventions to the specific needs of individuals. In this Perspective, we argue that modern technology has the potential to enable large-scale trials of public health interventions to enhance cognition in older adults in a personalized manner. Technology-based cognitive interventions that rely on closed-loop systems can be tailored to individuals in real time and have the potential for global testing, extending their reach to large and diverse populations of older adults. We propose that the future of cognitive enhancement in older adults will rely on harnessing new technologies in scientifically informed ways.

Beneficial Effect of High-Frequency Repetitive Transcranial Magnetic Stimulation for the Verbal Memory and Default Mode Network in Healthy Older Adults

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) is a non-invasive effective treatment for cognitive disorder, but its underlying mechanism of action remains unknown. The aim of this study was to explore the effect of a 2-week high-frequency (HF) active or sham 10 Hz rTMS on verbal memory in 40 healthy older adults. Resting-state functional magnetic resonance imaging (rs-fMRI) was used to measure functional connectivity (FC) within the default mode network (DMN). Verbal memory performance was evaluated using an auditory verbal learning test (AVLT). Additionally, we evaluated the relationship between memory improvement and FC changes within the DMN. The results revealed that HF-rTMS can enhance immediate recall and delayed recall of verbal memory and increased the FC of the bilateral precuneus (PCUN) within the DMN. The positive correlations between the immediate recall memory and the FC of the left PCUN after a 2-week intervention of HF-rTMS were detected. In conclusion, HF-rTMS may have the potential to improve verbal memory performance in older adults, which relation to FC changes in the DMN. The current findings are useful for increasing the understanding of the mechanisms of HF-rTMS, as well as guiding HF-rTMS treatment of cognitive disorders.

High-definition transcranial direct current stimulation enhances network segregation during spatial navigation in mild cognitive impairment

Spatial navigation is essential for everyday life and relies on complex network-level interactions. Recent evidence suggests that transcranial direct current stimulation (tDCS) can influence the activity of large-scale functional brain networks. We characterized brain-wide changes in functional network segregation (i.e. the balance of within vs. between-network connectivity strength) induced by high-definition (HD) tDCS in older adults with mild cognitive impairment (MCI) during virtual spatial navigation. Twenty patients with MCI and 22 cognitively intact older adults (healthy controls-HC) underwent functional magnetic resonance imaging following two counterbalanced HD-tDCS sessions (one active, one sham) that targeted the right parietal cortex (center anode at P2) and delivered 2 mA for 20 min. Compared to HC, MCI patients showed lower brain-wide network segregation following sham HD-tDCS. However, following active HD-tDCS, MCI patients' network segregation increased to levels similar to those in HC, suggesting functional normalization. Follow-up analyses indicated that the increase in network segregation for MCI patients was driven by HD-tDCS effects on the "high-level"/association brain networks, in particular the dorsal-attention and default-mode networks. HD-tDCS over the right parietal cortex may normalize the segregation/integration balance of association networks during spatial navigation in MCI patients, highlighting its potential to restore brain activity in Alzheimer's disease.

Impact of cognitive reserve on dance intervention-induced changes in brain plasticity

Dance is a complex sensorimotor activity with positive effects on physical fitness, cognition, and brain plasticity in the aging population. We explored whether individual levels of cognitive reserve (CR) proxied by education moderate dance intervention (DI)-induced plasticity assessed by resting-state functional connectivity (rs-FC) changes of the sensorimotor network (SMN), and between the dorsal attention network (DAN) and anterior default mode network (aDMN). Our cohort consisted of 99 subjects, randomly assigned to either a DI group who underwent a 6-month intervention (n = 49, M_age = 69.02 ± 5.40) or a control group (n = 50, M_age = 69.37 ± 6.10). Moderation analyses revealed that CR moderated DI-induced increase of the SMN rs-FC with significant changes observed in participants with ≥ 15 years of education (b = 0.05, t(62) = 3.17, p = 0.002). Only DI alone was a significant predictor of the DAN-aDMN crosstalk change (b = 0.06, t(64) = 2.16, p = 0.035). The rs-FC increase in the SMN was correlated with an improved physical fitness measure, and changes in the DAN-aDMN connectivity were linked to better performance on figural fluency. Consistent with the passive CR hypothesis, we observed that CR correlated only with baseline behavioral scores, not their change.

Alterations in Brain Network Organization in Adults With Obesity as Compared With Healthy-Weight Individuals and Seniors

OBJECTIVE

Life expectancy and obesity rates have drastically increased in recent years. An unhealthy weight is related to long-lasting medical disorders that might compromise the normal course of aging. The aim of the current study of brain connectivity patterns was to examine whether adults with obesity would show signs of premature aging, such as lower segregation, in large-scale networks.

METHODS

Participants with obesity (n = 30, mean age = 32.8 ± 5.68 years) were compared with healthy-weight controls (n = 33, mean age = 30.9 ± 6.24 years) and senior participants who were stroke-free and without dementia (n = 30, mean age = 67.1 ± 6.65 years) using resting-state magnetic resonance imaging and graph theory metrics (i.e., small-world index, clustering coefficient, characteristic path length, and degree).

RESULTS

Contrary to our hypothesis, participants with obesity exhibited a higher clustering coefficient compared with senior participants (t = 5.06, p < .001, d = 1.23, 95% CIbca = 0.64 to 1.88). Participants with obesity also showed lower global degree relative to seniors (t = -2.98, p = .014, d = -0.77, 95% CIbca = -1.26 to -0.26) and healthy-weight controls (t = -2.92, p = .019, d = -0.72, 95% CIbca = -1.19 to -0.25). Regional degree alterations in this group were present in several functional networks.

CONCLUSIONS

Participants with obesity displayed greater network clustering than did seniors and also had lower degree compared with seniors and individuals with normal weight, which is not consistent with the notion that obesity is associated with premature aging of the brain. Although the cross-sectional nature of the study precludes causal inference, the overly clustered network patterns in obese participants could be relevant to age-related changes in brain function because regular networks might be less resilient and metabolically inefficient.

Alzheimer's Pathology Is Associated with Dedifferentiation of Intrinsic Functional Memory Networks in Aging

In presymptomatic Alzheimer's disease (AD), beta-amyloid plaques (Aβ) and tau tangles accumulate in distinct spatiotemporal patterns within the brain, tracking closely with episodic memory decline. Here, we tested whether age-related changes in the segregation of the brain's intrinsic functional episodic memory networks-anterior-temporal (AT) and posterior-medial (PM) networks-are associated with the accumulation of Aβ, tau, and memory decline using fMRI and PET. We found that AT and PM networks were less segregated in older than that in younger adults and this reduced specialization was associated with more tau and Aβ in the same regions. The effect of network dedifferentiation on memory depended on the amount of Aβ and tau, with low segregation and pathology associated with better performance at baseline and low segregation and high pathology related to worse performance over time. This pattern suggests a compensation phase followed by a degenerative phase in the early, preclinical phase of AD.

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.

Age-related differences in network structure and dynamic synchrony of cognitive control

Cognitive trajectories vary greatly across older individuals, and the neural mechanisms underlying these differences remain poorly understood. Here, we investigate the cognitive variability in older adults by linking the influence of white matter microstructure on the task-related organization of fast and effective communications between brain regions. Using diffusion tensor imaging and electroencephalography, we show that individual differences in white matter network organization are associated with network clustering and efficiency in the alpha and high-gamma bands, and that functional network dynamics partly explain individual differences in cognitive control performance in older adults. We show that older individuals with high versus low structural network clustering differ in task-related network dynamics and cognitive performance. These findings were corroborated by investigating magnetoencephalography networks in an independent dataset. This multimodal (fMRI and biological markers) brain connectivity framework of individual differences provides a holistic account of how differences in white matter microstructure underlie age-related variability in dynamic network organization and cognitive performance.

Age differences in functional network reconfiguration with working memory training

Demanding cognitive functions like working memory (WM) depend on functional brain networks being able to communicate efficiently while also maintaining some degree of modularity. Evidence suggests that aging can disrupt this balance between integration and modularity. In this study, we examined how cognitive training affects the integration and modularity of functional networks in older and younger adults. Twenty three younger and 23 older adults participated in 10 days of verbal WM training, leading to performance gains in both age groups. Older adults exhibited lower modularity overall and a greater decrement when switching from rest to task, compared to younger adults. Interestingly, younger but not older adults showed increased task-related modularity with training. Furthermore, whereas training increased efficiency within, and decreased participation of, the default-mode network for younger adults, it enhanced efficiency within a task-specific salience/sensorimotor network for older adults. Finally, training increased segregation of the default-mode from frontoparietal/salience and visual networks in younger adults, while it diffusely increased between-network connectivity in older adults. Thus, while younger adults increase network segregation with training, suggesting more automated processing, older adults persist in, and potentially amplify, a more integrated and costly global workspace, suggesting different age-related trajectories in functional network reorganization with WM training.

Activity Engagement and Cognitive Performance Amongst Older Adults

Research supporting cognitive reserve theory suggests that engaging in a variety of cognitive, social, and physical activities may serve as protective factors against age-related changes in mental functioning, especially if the activities are cognitively engaging. Individuals who participate in a variety of cognitive activities have been found to be more likely to maintain a higher level of cognitive functioning and be less likely to develop dementia. In this study, we explore the relationship between engaging in a variety of activities and cognitive performance amongst 206 healthy older adults between the ages of 65–85. Age and years of education were found to be the most significant predictors of a global composite representing cognitive performance, consistent with previous work linking these variables to age-related changes in cognition and the cognitive reserve. We interpret these results to suggest that age and education are better predictors of global cognitive performance in older adults than self-reported activity engagement.

Accrual of functional redundancy along the lifespan and its effects on cognition

Despite the necessity to understand how the brain endures the initial stages of age-associated cognitive decline, no brain mechanism has been quantitatively specified to date. The brain may withstand the effects of cognitive aging through redundancy, a design feature in engineered and biological systems, which entails the presence of substitute elements to protect it against failure. Here, we investigated the relationship between functional network redundancy and age over the human lifespan and their interaction with cognition, analyzing resting-state functional MRI images and cognitive measures from 579 subjects. Network-wide redundancy was significantly associated with age, showing a stronger link with age than other major topological measures, presenting a pattern of accumulation followed by old-age decline. Critically, redundancy significantly mediated the association between age and executive function, with lower anti-correlation between age and cognition in subjects with high redundancy. The results suggest that functional redundancy accrues throughout the lifespan, mitigating the effects of age on cognition.

Task-based functional connectivity in aging: How task and connectivity methodology affect discovery of age effects

INTRODUCTION

Past studies have found that healthy aging has a significant effect on the organization and function of networks in the human brain. Many of these studies have examined how functional connectivity during one task or at rest is affected by aging; however, few studies have systematically examined how the effect of age on functional connectivity may vary as a function of choice of in-scanner task.

METHODS

The present study included healthy adults between the ages of 20 and 80 and examined a variety of metrics of functional connectivity during performance of 11 in-scanner tasks, falling into 4 cognitive domains: vocabulary, processing speed, fluid reasoning, and episodic memory. Functional connectivity was assessed at three levels: average correlations within and between 10 networks, system segregation (sensorimotor vs. association networks), and whole-brain graph theory metrics (global efficiency and modularity).

RESULTS

Results showed that the effect of age on these metrics differed as a function of task-for example, age had a more consistent effect on functional connectivity metrics computed during fluid reasoning tasks; however, there was less of an effect of age on functional connectivity metrics computed during tasks of episodic memory. Further, some of these measures showed relationships with behavioral performance on the in-scanner task, with different networks playing a role in the different cognitive domains.

CONCLUSION

These findings suggest that while aging may be generally associated with reductions in within- and between-network connectivity, system segregation, global efficiency, and modularity, the magnitude and presence of these effects varies by in-scanner task.

Longitudinal functional brain network reconfiguration in healthy aging

Healthy aging is associated with changes in cognitive performance and functional brain organization. In fact, cross-sectional studies imply lower modularity and significant heterogeneity in modular architecture across older subjects. Here, we used a longitudinal dataset consisting of four occasions of resting-state-fMRI and cognitive testing (spanning 4 years) in 150 healthy older adults. We applied a graph-theoretic analysis to investigate the time-evolving modular structure of the whole-brain network, by maximizing the multilayer modularity across four time points. Global flexibility, which reflects the tendency of brain nodes to switch between modules across time, was significantly higher in healthy elderly than in a temporal null model. Further, global flexibility, as well as network-specific flexibility of the default mode, frontoparietal control, and somatomotor networks, were significantly associated with age at baseline. These results indicate that older age is related to higher variability in modular organization. The temporal metrics were not associated with simultaneous changes in processing speed or learning performance in the context of memory encoding. Finally, this approach provides global indices for longitudinal change across a given time span and it may contribute to uncovering patterns of modular variability in healthy and clinical aging populations.

Age differences in predicting working memory performance from network-based functional connectivity

Deterioration in working memory capacity (WMC) has been associated with normal aging, but it remains unknown how age affects the relationship between WMC and connectivity within functional brain networks. We therefore examined the predictability of WMC from fMRI-based resting-state functional connectivity (RSFC) within eight meta-analytically defined functional brain networks and the connectome in young and old adults using relevance vector machine in a robust cross-validation scheme. Particular brain networks have been associated with mental functions linked to WMC to a varying degree and are associated with age-related differences in performance. Comparing prediction performance between the young and old sample revealed age-specific effects: In young adults, we found a general unpredictability of WMC from RSFC in networks subserving WM, cognitive action control, vigilant attention, theory-of-mind cognition, and semantic memory, whereas in older adults each network significantly predicted WMC. Moreover, both WM-related and WM-unrelated networks were differently predictive in older adults with low versus high WMC. These results indicate that the within-network functional coupling during task-free states is specifically related to individual task performance in advanced age, suggesting neural-level reorganization. In particular, our findings support the notion of a decreased segregation of functional brain networks, deterioration of network integrity within different networks and/or compensation by reorganization as factors driving associations between individual WMC and within-network RSFC in older adults. Thus, using multivariate pattern regression provided novel insights into age-related brain reorganization by linking cognitive capacity to brain network integrity.

Brain Network Modularity Predicts Improvements in Cognitive and Scholastic Performance in Children Involved in a Physical Activity Intervention

Introduction: Brain network modularity is a principle that quantifies the degree to which functional brain networks are divided into subnetworks. Higher modularity reflects a greater number of within-module connections and fewer connections between modules, and a highly modular brain is often interpreted as a brain that contains highly specialized brain networks with less integration between networks. Recent work in younger and older adults has demonstrated that individual differences in brain network modularity at baseline can predict improvements in performance after cognitive and physical interventions. The use of brain network modularity as a predictor of training outcomes has not yet been examined in children.

Method: In the present study, we examined the relationship between baseline brain network modularity and changes (post-intervention performance minus pre-intervention performance) in cognitive and academic performance in 8- to 9-year-old children who participated in an after-school physical activity intervention for 9 months (N = 78) as well as in children in a wait-list control group (N = 72).

Results: In children involved in the after-school physical activity intervention, higher modularity of brain networks at baseline predicted greater improvements in cognitive performance for tasks of executive function, cognitive efficiency, and mathematics achievement. There were no associations between baseline brain network modularity and performance changes in the wait-list control group.

Discussion: Our study has implications for biomarkers of cognitive plasticity in children. Understanding predictors of cognitive performance and academic progress during child development may facilitate the effectiveness of interventions aimed to improve cognitive and brain health.

Neural correlates of working memory training: Evidence for plasticity in older adults

Brain activity typically increases with increasing working memory (WM) load, regardless of age, before reaching an apparent ceiling. However, older adults exhibit greater brain activity and reach ceiling at lower loads than younger adults, possibly reflecting compensation at lower loads and dysfunction at higher loads. We hypothesized that WM training would bolster neural efficiency, such that the activation peak would shift towards higher memory loads after training. Pre-training, older adults showed greater recruitment of the WM network than younger adults across all loads, with decline at the highest load. Ten days of adaptive training on a verbal WM task improved performance and led to greater brain responsiveness at higher loads for both groups. For older adults the activation peak shifted rightward towards higher loads. Finally, training increased task-related functional connectivity in older adults, both within the WM network and between this task-positive network and the task-negative/default-mode network. These results provide new evidence for functional plasticity with training in older adults and identify a potential signature of improvement at the neural level.

Resting-state and task-based centrality of dorsolateral prefrontal cortex predict resilience to 1 Hz repetitive transcranial magnetic stimulation

Repetitive transcranial magnetic stimulation (rTMS) is used to investigate normal brain function in healthy participants and as a treatment for brain disorders. Various subject factors can influence individual response to rTMS, including brain network properties. A previous study by our group showed that “virtually lesioning” the left dorsolateral prefrontal cortex (dlPFC; important for cognitive flexibility) using 1 Hz rTMS reduced performance on a set‐shifting task. We aimed to determine whether this behavioural response was related to topological features of pre‐TMS resting‐state and task‐based functional networks. 1 Hz (inhibitory) rTMS was applied to the left dlPFC in 16 healthy participants, and to the vertex in 17 participants as a control condition. Participants performed a set‐shifting task during fMRI at baseline and directly after a single rTMS session 1–2 weeks later. Functional network topology measures were calculated from resting‐state and task‐based fMRI scans using graph theoretical analysis. The dlPFC‐stimulated group, but not the vertex group, showed reduced setshifting performance after rTMS, associated with lower task‐based betweenness centrality (BC) of the dlPFC at baseline (p = .030) and a smaller reduction in task‐based BC after rTMS (p = .024). Reduced repeat trial accuracy after rTMS was associated with higher baseline resting state node strength of the dlPFC (p = .017). Our results suggest that behavioural response to 1 Hz rTMS to the dlPFC is dependent on baseline functional network features. Individuals with more globally integrated stimulated regions show greater resilience to rTMS effects, while individuals with more locally well‐connected regions show greater vulnerability.

Investigating the Effects of Spacing on Working Memory Training Outcome: A Randomized, Controlled, Multisite Trial in Older Adults

OBJECTIVE

The majority of the population will experience some cognitive decline with age. Therefore, the development of effective interventions to mitigate age-related decline is critical for older adults' cognitive functioning and their quality of life.

METHODS

In our randomized controlled multisite trial, we target participants' working memory (WM) skills, and in addition, we focus on the intervention's optimal scheduling in order to test whether and how the distribution of training sessions might affect task learning, and ultimately, transfer. Healthy older adults completed an intervention targeting either WM or general knowledge twice per day, once per day, or once every-other-day. Before and after the intervention and 3 months after training completion, participants were tested in a variety of cognitive domains, including those representing functioning in everyday life.

RESULTS

In contrast to our hypotheses, spacing seems to affect learning only minimally. We did observe some transfer effects, especially within the targeted cognitive domain (WM and inhibition/interference), which remained stable at the 3-month follow-up.

DISCUSSION

Our findings have practical implications by showing that the variation in training schedule, at least within the range used here, does not seem to be a crucial element for training benefits.

Altered brain network topology related to working memory in internet addiction

BACKGROUND AND AIMS

The working memory (WM) ability of internet addicts and the topology underlying the WM processing in internet addiction (IA) are poorly understood. In this study, we employed a graph theoretical framework to characterize the topological properties of the IA brain network in the source cortical space during WM task.

METHODS

A sample of 24 subjects with IA and 23 matched healthy controls (HCs) performed visual 2-back task. Exact Low Resolution Electromagnetic Tomography was adopted to project the pre-processed EEG signals into source space. Subsequently, Lagged phase synchronization was calculated between all pairs of Brodmann areas, the graph theoretical approaches were then employed to estimate the brain topological properties of all participants during the WM task.

RESULTS

We found better WM behavioral performance in IA subjects compared with the HCs. Moreover, compared to the HC group, more integrated and hierarchical brain network was revealed in the IA subjects in alpha band. And altered regional centrality was mainly resided in frontal and limbic lobes. In addition, significant relationships between the IA severity and the significant altered graph indices were found.

CONCLUSIONS

In conclusion, these findings provide evidence to support the notion that altered topological configuration may underline changed WM function observed in IA.

Functional dedifferentiation of the brain during healthy aging

Nonpathological aging is associated with significant cognitive deficits. Thus, the underlying neurobiology of aging-associated cognitive decline warrants investigation. In a recent study, Chong et al. (Chong JSX, Ng KK, Tandi J, Wang C, Poh J-H, Lo JC, Chee MWL, Zhou JH. J Neurosci 39: 5534-5550, 2019) provided insights into the association between cognitive decline and the loss of functional specialization in the brains of older adults. Here, we introduce the novel graph theoretical approach utilized and discuss the significance of their findings and broader implications on aging. We also provide alternate perspectives of their findings and suggest directions for future work.

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 neural dedifferentiation and cognition

This review focuses on possible contributions of neural dedifferentiation to age-related cognitive decline. Neural dedifferentiation is held to reflect a breakdown in the functional specificity of brain regions and networks that compromises the fidelity of neural representations supporting episodic memory and related cognitive functions. The evidence for age-related dedifferentiation is robust when it is operationalized as neural selectivity for different categories of perceptual stimuli or as decreased segregation or modularity of resting-state functional brain networks. Neural dedifferentiation for perceptual categories appears to demonstrate a negative, age-invariant relationship with performance on tests of memory and fluid processing. Whether this pattern extends to network-level measures of dedifferentiation cannot currently be determined due to insufficient evidence. The existing data highlight the importance of further examination of neural dedifferentiation as a factor contributing to episodic memory and to cognitive performance more generally.

Multiscale Imaging Reveals Aberrant Functional Connectome Organization and Elevated Dorsal Striatal Arc Expression in Advanced Age

The functional connectome reflects a network architecture enabling adaptive behavior that becomes vulnerable in advanced age. The cellular mechanisms that contribute to altered functional connectivity in old age, however, are not known. Here we used a multiscale imaging approach to link age-related changes in the functional connectome to altered expression of the activity-dependent immediate-early gene Arc as a function of training to multitask on a working memory (WM)/biconditional association task (BAT). Resting-state fMRI data were collected from young and aged rats longitudinally at three different timepoints during cognitive training. After imaging, rats performed the WM/BAT and were immediately sacrificed to examine expression levels of Arc during task performance. Aged behaviorally impaired, but not young, rats had a subnetwork of increased connectivity between the anterior cingulate cortex (ACC) and dorsal striatum (DS) that was correlated with the use of a suboptimal response-based strategy during cognitive testing. Moreover, while young rats had stable rich-club organization across three scanning sessions, the rich-club organization of old rats increased with cognitive training. In a control group of young and aged rats that were longitudinally scanned at similar time intervals, but without cognitive training, ACC-DS connectivity and rich-club organization did not change between scans in either age group. These findings suggest that aberrant large-scale functional connectivity in aged animals is associated with altered cellular activity patterns within individual brain regions.

The Effect of Aging on Resting State Connectivity of Predefined Networks in the Brain

Recent studies have found a deleterious effect of age on a wide variety of measures of functional connectivity, and some hints at a relationship between connectivity at rest and cognitive functioning. However, few studies have combined multiple functional connectivity methods, or examined them over a wide range of adult ages, to try to uncover which metrics and networks seem to be particularly sensitive to age-related decline across the adult lifespan. The present study utilized multiple resting state functional connectivity methods in a sample of adults from 20–80 years old to gain a more complete understanding of the effect of aging on network function and integrity. Whole-brain results showed that aging results in weakening average within-network connectivity, lower system segregation and local efficiency, and higher participation coefficient. Network-level results suggested that nearly every primary sensory and cognitive network faces some degree of age-related decline, including reduced within-network connectivity, higher network-based participation coefficient, and reduced network-level local efficiency. Further, some of these connectivity metrics showed relationships with cognitive performance. Thus, these results suggest that a multi-method analysis of functional connectivity data may be critical to capture the full effect of aging on the health of brain networks.

Self-initiated learning reveals memory performance and electrophysiological differences between younger, older and older adults with relative memory impairment

Older adults display difficulties in encoding and retrieval of information, resulting in poorer memory. This may be due to an inability of older adults to engage elaborative encoding strategies during learning. This study examined behavioural and electrophysiological effects of explicit cues to self-initiate learning during encoding and subsequent recognition of words in younger adults (YA), older control adults (OA) and older adults with relative memory impairment (OD). The task was a variation of the old/new paradigm, some study items were preceded by a cue to learn the word (L) while others by a do not learn cue (X). Behaviourally, YA outperformed OA and OD on the recognition task, with no significant difference between OA and OD. Event-related potentials at encoding revealed enhanced early visual processing (70-140 ms) for L- versus X-words in young and old. Only YA exhibited a greater late posterior positivity (LPP; 200-500 ms) for all words during encoding perhaps reflecting superior encoding strategy. During recognition, only YA differentiated L- versus X-words with enhanced frontal P200 (150-250 ms) suggesting impaired early word selection for retrieval in older groups; however, OD had enhanced P200 activity compared to OA during L-word retrieval. The LPP (250-500 ms) was reduced in amplitude for L-words compared to both X- and new words. However, YA showed greater LPP amplitude for all words compared to OA. For older groups, we observed reduced left parietal hemispheric asymmetry apparent in YA during encoding and recognition, especially for OD. Findings are interpreted in the light of models of compensation and dedifferentiation associated with age-related changes in memory function.

Age-related differences in the structural and effective connectivity of cognitive control: a combined fMRI and DTI study of mental arithmetic

Cognitive changes with aging are highly variable across individuals. This study investigated whether cognitive control performance might depend on preservation of structural and effective connectivity in older individuals. Specifically, we tested inhibition following working memory (WM) updating and maintenance. We analyzed diffusion tensor imaging and functional magnetic resonance imaging data in thirty-four young adults and thirty-four older adults, who performed an arithmetic verification task during functional magnetic resonance imaging. Results revealed larger arithmetic interference in older adults relative to young adults after WM updating, whereas both groups showed similar interference after WM maintenance. In both groups, arithmetic interference was associated with larger activations and stronger effective connectivity among bilateral anterior cingulate, bilateral inferior frontal gyrus, and left angular gyrus, with larger activations of frontal regions in older adults than in younger adults. In older adults, preservation of frontoparietal structural microstructure, especially involving the inferior frontaloccipital fasciculus, was associated with reduced interference, and stronger task-related effective connectivity. These results highlight how both structural and functional changes in the cognitive control network contribute to individual variability in performance during aging.

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.

Strong-Weak Pruning for Brain Network Identification in Connectome-Wide Neuroimaging: Application to Amyotrophic Lateral Sclerosis Disease Stage Characterization

Magnetic resonance imaging allows acquiring functional and structural connectivity data from which high-density whole-brain networks can be derived to carry out connectome-wide analyses in normal and clinical populations. Graph theory has been widely applied to investigate the modular structure of brain connections by using centrality measures to identify the "hub" of human connectomes, and community detection methods to delineate subnetworks associated with diverse cognitive and sensorimotor functions. These analyses typically rely on a preprocessing step (pruning) to reduce computational complexity and remove the weakest edges that are most likely affected by experimental noise. However, weak links may contain relevant information about brain connectivity, therefore, the identification of the optimal trade-off between retained and discarded edges is a subject of active research. We introduce a pruning algorithm to identify edges that carry the highest information content. The algorithm selects both strong edges (i.e. edges belonging to shortest paths) and weak edges that are topologically relevant in weakly connected subnetworks. The newly developed "strong-weak" pruning (SWP) algorithm was validated on simulated networks that mimic the structure of human brain networks. It was then applied for the analysis of a real dataset of subjects affected by amyotrophic lateral sclerosis (ALS), both at the early (ALS2) and late (ALS3) stage of the disease, and of healthy control subjects. SWP preprocessing allowed identifying statistically significant differences in the path length of networks between patients and healthy subjects. ALS patients showed a decrease of connectivity between frontal cortex to temporal cortex and parietal cortex and between temporal and occipital cortex. Moreover, degree of centrality measures revealed significantly different hub and centrality scores between patient subgroups. These findings suggest a widespread alteration of network topology in ALS associated with disease progression.

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.

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.

Brain connectivity tracks effects of chemotherapy separately from behavioral measures

Several studies in cancer research have suggested that cognitive dysfunction following chemotherapy, referred to in lay terms as "chemobrain", is a serious problem. At present, the changes in integrative brain function that underlie such dysfunction remain poorly understood. Recent developments in neuroimaging suggest that patterns of functional connectivity can provide a broadly applicable neuromarker of cognitive performance and other psychometric measures. The current study used multivariate analysis methods to identify patterns of disruption in resting state functional connectivity of the brain due to chemotherapy and the degree to which the disruptions can be linked to behavioral measures of distress and cognitive performance. Sixty two women (22 healthy control, 18 patients treated with adjuvant chemotherapy, and 22 treated without chemotherapy) were evaluated with neurocognitive measures followed by self-report questionnaires and open eyes resting-state fMRI scanning at three time points: diagnosis (M0, pre-adjuvant treatment), 1 month (M1), and 7 months (M7) after treatment. The results indicated deficits in cognitive health of breast cancer patients immediately after chemotherapy that improved over time. This psychological trajectory was paralleled by a disruption and later recovery of resting-state functional connectivity, mostly in the parietal and frontal brain regions. Mediation analysis showed that the functional connectivity alteration pattern is a separable treatment symptom from the decreased cognitive health. Current study indicates that more targeted support for patients should be developed to ameliorate these multi-faceted side effects of chemotherapy treatment on neural functioning and cognitive health.

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.

Connectome-based models predict attentional control in aging adults

There are well-characterized age-related differences in behavioral and neural responses to tasks of attentional control. However, there is also increasing recognition of individual variability in the process of neurocognitive aging. Using connectome-based predictive modeling, a method for predicting individual-level behaviors from whole-brain functional connectivity, a sustained attention connectome-based prediction model (saCPM) has been derived in young adults. The saCPM consists of two large-scale functional networks: a high-attention network whose strength predicts better attention and a low-attention network whose strength predicts worse attention. Here we examined the generalizability of the saCPM for predicting inhibitory control in an aging sample. Forty-two healthy young adults (n = 21, ages 18-30) and older adults (n = 21, ages 60-80) performed a modified Stroop task, on which older adults exhibited poorer performance, indexed by higher reaction time cost between incongruent and congruent trials. The saCPM generalized to predict reaction time cost across age groups, but did not account for age-related differences in performance. Exploratory analyses were conducted to characterize the effects of age on functional connectivity and behavior. We identified subnetworks of the saCPM that exhibited age-related differences in strength. The strength of two low-attention subnetworks, consisting of frontoparietal, medial frontal, default mode, and motor nodes that were more strongly connected in older adults, mediated the effect of age group on performance. These results support the saCPM's ability to capture attention-related patterns reflected in each individual's functional connectivity signature across both task context and age. However, older and younger adults exhibit functional connectivity differences within components of the saCPM networks, and it is these connections that better account for age-related deficits in attentional control.