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

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.

A systematic review and Bayesian meta-analysis provide evidence for an effect of acute physical activity on cognition in young adults

Physical exercise is a potential intervention for enhancing cognitive function across the lifespan. However, while studies employing long-term exercise interventions consistently show positive effects on cognition, studies using single acute bouts have produced mixed results. Here, a systematic review and meta-analysis was conducted to determine the impact of acute exercise on cognitive task performance in healthy young adults. A Bayesian hierarchical model quantified probabilistic evidence for a modulatory relationship by synthesizing 651 effect sizes from 113 studies from PsychInfo and Google Scholar representing 4,390 participants. Publication bias was mitigated using the trim-and-fill method. Acute exercise was found to have a small beneficial effect on cognition (g = 0.13 ± 0.04; BF = 3.67) and decrease reaction time. A meta-analysis restricted to executive function tasks revealed improvements in working memory and inhibition. Meta-analytic estimates were consistent across multiple priors and likelihood functions. Physical activities were categorized based on exercise type (e.g., cycling) because many activities have aerobic and anaerobic components, but this approach may limit comparison to studies that categorize activities based on metabolic demands. The current study provides an updated synthesis of the existing literature and insights into the robustness of acute exercise-induced effects on cognition. Funding provided by the United States Army Research Office.

Brain functional connectivity alterations in patients with anterior cruciate ligament injury

Recent advancements in neuroimaging have illustrated that anterior cruciate ligament (ACL) injuries could impact the central nervous system (CNS), causing neuroplastic changes in the brain beyond the traditionally understood biomechanical consequences. While most of previous functional magnetic resonance imaging (fMRI) studies have focused on localized cortical activity changes post-injury, emerging research has suggested disruptions in functional connectivity across the brain. However, these prior investigations, albeit pioneering, have been constrained by two limitations: a reliance on small-sample participant cohorts, often limited to two to three patients, potentially limiting the generalizability of findings, and an adherence to region of interest based analysis, which may overlook broader network interactions. To address these limitations, our study employed resting-state fMRI to assess whole-brain functional connectivity in 15 ACL-injured patients, comparing them to matched controls using two distinct network analysis methods. Using Network-Based Statistics, we identified widespread reductions in connectivity that spanned across multiple brain regions. Further modular connectivity analysis showed significant decreases in inter-modular connectivity between the sensorimotor and cerebellar modules, and intra-modular connectivity within the default-mode network in ACL-injured patients. Our results thus highlight a shift from localized disruptions to network-wide dysfunctions, suggesting that ACL injuries induce widespread CNS changes. This enhanced understanding has the potential to stimulate the development of strategies aiming to restore functional connectivity and improve recovery outcomes.

Moving Towards a Medicine of Dance: A Scoping Review of Characteristics of Dance Interventions Targeting Older Adults and a Theoretical Framework

Background

Dance combines cultural and aesthetic elements with behaviors important for brain health, including physical activity, social engagement, and cognitive challenge. Therefore, dance could positively impact public health given the rapidly aging population, increasing incidence of Alzheimer's disease and related dementias, and lack of uptake of exercise in many older adults. Despite a high volume of literature, existing literature does not support evidence-based guidelines for dance to support healthy aging.

Objective

To conduct a scoping review of the dance intervention literature in older adults and provide information to facilitate a more consistent approach among scientists in designing dance interventions for older adults that stimulate physical and neurocognitive health adaptations.

Methods

Study characteristics (sample size, population, study design, outcomes, intervention details) were ascertained from 112 separate studies of dance reported in 127 papers that reported outcomes important for brain health (cardiorespiratory fitness, balance and mobility, cognition, mood, and quality of life).

Results

High heterogeneity across studies was evident. Class frequency ranged from < 1 to 5 classes per week, class length from 30-120 minutes, and intervention duration from 2 weeks to 18 months. Studies often did not randomize participants, had small (< 30) sample sizes, and used varied comparator conditions. Over 50 tests of cognition, 40 dance forms, and 30 tests of mobility were identified.

Conclusions

Based on these results, important future directions are establishing common data elements, developing intervention mapping and mechanistic modeling, and testing dosing parameters to strengthen and focus trial design of future studies and generate evidence-based guidelines for dance.

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. First, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes' predictions. Moreover, few of the current graph learning models are interpretable, which may not be capable of providing biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning (HSGPL) model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. To further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using data from human connectome project (HCP) and open access series of imaging studies (OASIS). Our results from extensive experiments demonstrate the superiority of the proposed model compared with several state-of-the-art techniques. In addition, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.

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.

Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks

Top-down processes such as expectations have a strong influence on pain perception. Predicted threat of impending pain can affect perceived pain even more than the actual intensity of a noxious event. This type of threat bias in pain perception is associated with fear of pain and low pain tolerance, and hence the extent of bias varies between individuals. Large-scale patterns of functional brain connectivity are important for integrating expectations with sensory data. Greater integration is necessary for sensory integration; therefore, here we investigate the association between system segregation and top-down threat bias in healthy individuals. We show that top-down threat bias is predicted by less functional connectivity between resting-state networks. This effect was significant at a wide range of network thresholds and specifically in predefined parcellations of resting-state networks. Greater system segregation in brain networks also predicted higher anxiety and pain catastrophizing. These findings highlight the role of integration in brain networks in mediating threat bias in pain perception.

Dynamic functional connectivity MEG features of Alzheimer's disease

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

The Neuroscience of Dance: A Conceptual Framework and Systematic Review

Ancient and culturally universal, dance pervades many areas of life and has multiple benefits. In this article, we provide a conceptual framework and systematic review, as a guide for researching the neuroscience of dance. We identified relevant articles following PRISMA guidelines, and summarised and evaluated all original results. We identified avenues for future research in: the interactive and collective aspects of dance; groove; dance performance; dance observation; and dance therapy. Furthermore, the interactive and collective aspects of dance constitute a vital part of the field but have received almost no attention from a neuroscientific perspective so far. Dance and music engage overlapping brain networks, including common regions involved in perception, action, and emotion. In music and dance, rhythm, melody, and harmony are processed in an active, sustained pleasure cycle giving rise to action, emotion, and learning, led by activity in specific hedonic brain networks. The neuroscience of dance is an exciting field, which may yield information concerning links between psychological processes and behaviour, human flourishing, and the concept of eudaimonia.

Unleashing the potential of dance: a neuroplasticity-based approach bridging from older adults to Parkinson's disease patients

Parkinson's disease (PD) is a neurodegenerative disorder that affects >1% of individuals worldwide and is manifested by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor symptoms such as cognitive impairment and depression. Non-pharmacological interventions such as dance therapy are becoming increasingly popular as complementary therapies for PD, in addition to pharmacological treatments that are currently widely available. Dance as a sensorimotor activity stimulates multiple layers of the neural system, including those involved in motor planning and execution, sensory integration, and cognitive processing. Dance interventions in healthy older people have been associated with increased activation of the prefrontal cortex, as well as enhanced functional connectivity between the basal ganglia, cerebellum, and prefrontal cortex. Overall, the evidence suggests that dance interventions can induce neuroplastic changes in healthy older participants, leading to improvements in both motor and cognitive functions. Dance interventions involving patients with PD show better quality of life and improved mobility, whereas the literature on dance-induced neuroplasticity in PD is sparse. Nevertheless, this review argues that similar neuroplastic mechanisms may be at work in patients with PD, provides insight into the potential mechanisms underlying dance efficacy, and highlights the potential of dance therapy as a non-pharmacological intervention in PD. Further research is warranted to determine the optimal dance style, intensity, and duration for maximum therapeutic benefit and to determine the long-term effects of dance intervention on PD progression.

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.

Influence of goals on modular brain network organization during working memory

Introduction

Top-down control underlies our ability to attend relevant stimuli while ignoring irrelevant, distracting stimuli and is a critical process for prioritizing information in working memory (WM). Prior work has demonstrated that top-down biasing signals modulate sensory-selective cortical areas during WM, and that the large-scale organization of the brain reconfigures due to WM demands alone; however, it is not yet understood how brain networks reconfigure between the processing of relevant versus irrelevant information in the service of WM.

Methods

Here, we investigated the effects of task goals on brain network organization while participants performed a WM task that required participants to detect repetitions (e.g., 0-back or 1-back) and had varying levels of visual interference (e.g., distracting, irrelevant stimuli). We quantified changes in network modularity-a measure of brain sub-network segregation-that occurred depending on overall WM task difficulty as well as trial-level task goals for each stimulus during the task conditions (e.g., relevant or irrelevant).

Results

First, we replicated prior work and found that whole-brain modularity was lower during the more demanding WM task conditions compared to a baseline condition. Further, during the WM conditions with varying task goals, brain modularity was selectively lower during goal-directed processing of task-relevant stimuli to be remembered for WM performance compared to processing of distracting, irrelevant stimuli. Follow-up analyses indicated that this effect of task goals was most pronounced in default mode and visual sub-networks. Finally, we examined the behavioral relevance of these changes in modularity and found that individuals with lower modularity for relevant trials had faster WM task performance.

Discussion

These results suggest that brain networks can dynamically reconfigure to adopt a more integrated organization with greater communication between sub-networks that supports the goal-directed processing of relevant information and guides WM.

Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter

In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20-33, n = 51 and age 60-79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases.

IMOVE: Protocol for a randomized, controlled 2x2 factorial trial of improvisational movement and social engagement interventions in older adults with early Alzheimer's disease

Background

In addition to cognitive impairment, people with Alzheimer's disease (PWAD) experience neuropsychiatric symptoms (e.g., apathy, depression), altered gait, and poor balance that further diminish their quality of life (QoL). Here, we describe a unique, randomized, controlled trial to test the hypothesis that both movement and social engagement aspects of a group dance intervention alter the connectivity of key brain networks involved in motor and social-emotional functioning and lead to improved QoL in PWAD.

Methods

IMOVE (NCT03333837) was a single-center, randomized, controlled 2x2 factorial trial that assigned PWAD/caregiver dyads to one of 4 study conditions (Movement Group, Movement Alone, Social Group, or Usual Care control). The Movement Group participated in twice-weekly group improvisational dance (IMPROVment® Method) classes for 12 weeks. The Movement Alone intervention captured the same dance movement and auditory stimuli as the group class without social interaction, and the Social Group used improvisational party games to recapitulate the fun and playfulness of the Movement Group without the movement. The primary outcome was change in QoL among PWAD. Key secondary outcomes were functional brain network measures assessed using graph-theory analysis of resting-state functional magnetic resonance imaging scans, as well as neuropsychiatric symptoms, gait, and balance.

Results

A total of 111 dyads were randomized; 89 completed the study, despite interruption and modification of the protocol due to COVID-19 restrictions (see companion paper by Fanning et al.). The data are being analyzed and will be submitted for publication in 2023.

The human functional connectome in neurodegenerative diseases: relationship to pathology and clinical progression

INTRODUCTION

Neurodegenerative diseases can be considered as 'disconnection syndromes,' in which a communication breakdown prompts cognitive or motor dysfunction. Mathematical models applied to functional resting-state MRI allow for the organization of the brain into nodes and edges, which interact to form the functional brain connectome.

AREAS COVERED

The authors discuss the recent applications of functional connectomics to neurodegenerative diseases, from preclinical diagnosis, to follow up along with the progressive changes in network organization, to the prediction of the progressive spread of neurodegeneration, to stratification of patients into prognostic groups, and to record responses to treatment. The authors searched PubMed using the terms 'neurodegenerative diseases' AND 'fMRI' AND 'functional connectome' OR 'functional connectivity' AND 'connectomics' OR 'graph metrics' OR 'graph analysis.' The time range covered the past 20 years.

EXPERT OPINION

Considering the great pathological and phenotypical heterogeneity of neurodegenerative diseases, identifying a common framework to diagnose, monitor and elaborate prognostic models is challenging. Graph analysis can describe the complexity of brain architectural rearrangements supporting the network-based hypothesis as unifying pathogenetic mechanism. Although a multidisciplinary team is needed to overcome the limit of methodologic complexity in clinical application, advanced methodologies are valuable tools to better characterize functional disconnection in neurodegeneration.

Evaluating the reliability, validity, and utility of overlapping networks: Implications for network theories of cognition

Brain network definitions typically assume nonoverlap or minimal overlap, ignoring regions' connections to multiple networks. However, new methods are emerging that emphasize network overlap. Here, we investigated the reliability and validity of one assignment method, the mixed membership algorithm, and explored its potential utility for identifying gaps in existing network models of cognition. We first assessed between-sample reliability of overlapping assignments with a split-half design; a bootstrapped Dice similarity analysis demonstrated good agreement between the networks from the two subgroups. Next, we assessed whether overlapping networks captured expected nonoverlapping topographies; overlapping networks captured portions of one to three nonoverlapping topographies, which aligned with canonical network definitions. Following this, a relative entropy analysis showed that a majority of regions participated in more than one network, as is seen biologically, and many regions did not show preferential connection to any one network. Finally, we explored overlapping network membership in regions of the dual-networks model of cognitive control, showing that almost every region was a member of multiple networks. Thus, the mixed membership algorithm produces consistent and biologically plausible networks, which presumably will allow for the development of more complete network models of cognition.

Shared brain and genetic architectures between mental health and physical activity

Physical activity is correlated with, and effectively treats various forms of psychopathology. However, whether biological correlates of physical activity and psychopathology are shared remains unclear. Here, we examined the extent to which the neural and genetic architecture of physical activity and mental health are shared. Using data from the UK Biobank (N = 6389), we applied canonical correlation analysis to estimate associations between the amplitude and connectivity strength of subnetworks of three major neurocognitive networks (default mode, DMN; salience, SN; central executive networks, CEN) with accelerometer-derived measures of physical activity and self-reported mental health measures (primarily of depression, anxiety disorders, neuroticism, subjective well-being, and risk-taking behaviors). We estimated the genetic correlation between mental health and physical activity measures, as well as putative causal relationships by applying linkage disequilibrium score regression, genomic structural equational modeling, and latent causal variable analysis to genome-wide association summary statistics (GWAS N = 91,105-500,199). Physical activity and mental health were associated with connectivity strength and amplitude of the DMN, SN, and CEN (r's ≥ 0.12, p's < 0.048). These neural correlates exhibited highly similar loading patterns across mental health and physical activity models even when accounting for their shared variance. This suggests a largely shared brain network architecture between mental health and physical activity. Mental health and physical activity (including sleep) were also genetically correlated (|rg| = 0.085-0.121), but we found no evidence for causal relationships between them. Collectively, our findings provide empirical evidence that mental health and physical activity have shared brain and genetic architectures and suggest potential candidate subnetworks for future studies on brain mechanisms underlying beneficial effects of physical activity on mental health.

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.

The functional and structural neural correlates of dynamic balance impairment and recovery in persons with acquired brain injury

Dynamic balance control is associated with the function of multiple brain networks and is impaired following Acquired Brain Injury (ABI). This study aims to characterize the functional and structural correlates of ABI-induced dynamic balance impairments and recovery following a rehabilitation treatment. Thirty-one chronic participants with ABI participated in a novel rehabilitation treatment composed of 22 sessions of a perturbation-based rehabilitation training. Dynamic balance was assessed using the Community Balance and Mobility scale (CB&M) and the 10-Meter Walking Test (10MWT). Brain function was estimated using resting-state fMRI imaging that was analysed using independent component analysis (ICA), and regions-of-interest analyses. Brain morphology was also assessed using structural MRI. ICA revealed a reduction in component-related activation within the sensorimotor and cerebellar networks post-intervention. Improvement in CB&M scale was associated with a reduction in FC within the cerebellar network and with baseline FC within the cerebellar-putamen and cerebellar-thalamic networks. Improvement in 10MWT was associated with baseline FC within the cerebellar-putamen and cerebellar-cortical networks. Brain volume analysis did not reveal structural correlates of dynamic balance, but dynamic balance was correlated with time since injury. Our results show that dynamic balance recovery is associated with FC reduction within and between the cerebellar and sensorimotor networks. The lack of global structural correlates of dynamic balance may point to the involvement of specific networks in balance control.

Power shift and connectivity changes in healthy aging during resting-state EEG

The neural activity of human brain changes in healthy individuals during aging. The most frequent variation in patterns of neural activity are a shift from posterior to anterior areas and a reduced asymmetry between hemispheres. These patterns are typically observed during task execution and by using functional magnetic resonance imaging data. In the present study we investigated whether analogous effects can also be detected during rest and by means of source-space time series reconstructed from electroencephalographic recordings. By analyzing oscillatory power distribution across the brain we indeed found a shift from posterior to anterior areas in older adults. We additionally examined this shift by evaluating connectivity and its changes with age. The findings indicated that inter-area connections among frontal, parietal and temporal areas were strengthened in older individuals. A more complex pattern was shown in intra-area connections, where age-related activity was enhanced in parietal and temporal areas, and reduced in frontal areas. Finally, the resulting network exhibits a loss of modularity with age. Overall, the results extend to resting-state condition the evidence of an age-related shift of brain activity from posterior to anterior areas, thus suggesting that this shift is a general feature of the aging brain rather than being task-specific. In addition, the connectivity results provide new information on the reorganization of resting-state brain activity in aging.

State of Knowledge on Molecular Adaptations to Exercise in Humans: Historical Perspectives and Future Directions

For centuries, regular exercise has been acknowledged as a potent stimulus to promote, maintain, and restore healthy functioning of nearly every physiological system of the human body. With advancing understanding of the complexity of human physiology, continually evolving methodological possibilities, and an increasingly dire public health situation, the study of exercise as a preventative or therapeutic treatment has never been more interdisciplinary, or more impactful. During the early stages of the NIH Common Fund Molecular Transducers of Physical Activity Consortium (MoTrPAC) Initiative, the field is well-positioned to build substantially upon the existing understanding of the mechanisms underlying benefits associated with exercise. Thus, we present a comprehensive body of the knowledge detailing the current literature basis surrounding the molecular adaptations to exercise in humans to provide a view of the state of the field at this critical juncture, as well as a resource for scientists bringing external expertise to the field of exercise physiology. In reviewing current literature related to molecular and cellular processes underlying exercise-induced benefits and adaptations, we also draw attention to existing knowledge gaps warranting continued research effort. © 2021 American Physiological Society. Compr Physiol 12:3193-3279, 2022.

Interrelationships between exercise, functional connectivity, and cognition among healthy adults: A systematic review

The main purpose of this systematic review was to examine past literature focusing on the potential relationship between exercise (or physical activity or cardiorespiratory fitness [CRF]) and functional brain connectivity in healthy adults. Among the studies meeting this purpose, we also evaluated studies investigating whether, and how, functional connectivity may influence the exercise-cognition relationship. A systematic review was employed through several electronic databases (PsychInfo, PubMed, and Google Scholar) in accordance with PRISMA guidelines. The literature search identified 656 records, and a total of 12 studies met the inclusion criteria. Among these 12 studies, there were 4, 7, and 1 study, respectively, examining the relationship between exercise and frontal lobe connectivity, temporal lobe connectivity, and whole-brain connectivity. Also, 7 studies examined the relationship between functional connectivity and cognitive performance across multiple brain regions as a function of exercise. Existing literature suggests that CRF, habitual physical activity, and varying intensities of acute exercise can strengthen functional connections among a wide variety of regions and subcortical structures of the human brain. These exercise-induced functional connectivity changes within and between specific brain structures/networks supporting cognitive processing may improve various domains of cognitive function. Given these complex associations, a thorough understanding of how functional connectivity plays a mediating role in the exercise-cognition interaction is needed in future studies.

Eight-week multi-domain cognitive training does not impact large-scale resting-state brain networks in Parkinson's disease

There is meta-analytic evidence for the efficacy of cognitive training (CT) in Parkinson's disease (PD). We performed a randomized controlled trial where we found small positive effects of CT on executive function and processing speed in individuals with PD (ntotal = 140). In this study, we assessed the effects of CT on brain network connectivity and topology in a subsample of the full study population (nmri = 86). Participants were randomized into an online multi-domain CT and an active control condition and performed 24 sessions of either intervention in eight weeks. Resting-state functional MRI scans were acquired in addition to extensive clinical and neuropsychological assessments pre- and post-intervention. In line with our preregistered analysis plan (osf.io/3st82), we computed connectivity between 'cognitive' resting-state networks and computed topological outcomes at the whole-brain and sub-network level. We assessed group differences after the intervention with mixed-model analyses adjusting for baseline performance and analyzed the association between network and cognitive performance changes with repeated measures correlation analyses. The final analysis sample consisted of 71 participants (n CT = 37). After intervention there were no group differences on between-network connectivity and network topological outcomes. No associations between neural network and neuropsychological performance change were found. CT increased segregated network topology in a small sub-sample of cognitively intact participants. Post-hoc nodal analyses showed post-intervention enhanced connectivity of both the dorsal anterior cingulate cortex and dorsolateral prefrontal cortex in the CT group. The results suggest no large-scale brain network effects of eight-week computerized CT, but rather localized connectivity changes of key regions in cognitive function, that potentially reflect the specific effects of the intervention.

Resting state functional connectivity provides mechanistic predictions of future changes in sedentary behavior

Sedentary behaviors are increasing at the cost of millions of dollars spent in health care and productivity losses due to physical inactivity-related deaths worldwide. Understanding the mechanistic predictors of sedentary behaviors will improve future intervention development and precision medicine approaches. It has been posited that humans have an innate attraction towards effort minimization and that inhibitory control is required to overcome this prepotent disposition. Consequently, we hypothesized that individual differences in the functional connectivity of brain regions implicated in inhibitory control and physical effort decision making at the beginning of an exercise intervention in older adults would predict the change in time spent sedentary over the course of that intervention. In 143 healthy, low-active older adults participating in a 6-month aerobic exercise intervention (with three conditions: walking, dance, stretching), we aimed to use baseline neuroimaging (resting state functional connectivity of two a priori defined seed regions), and baseline accelerometer measures of time spent sedentary to predict future pre-post changes in objectively measured time spent sedentary in daily life over the 6-month intervention. Our results demonstrated that functional connectivity between (1) the anterior cingulate cortex and the supplementary motor area and (2) the right anterior insula and the left temporoparietal/temporooccipital junction, predicted changes in time spent sedentary in the walking group. Functional connectivity of these brain regions did not predict changes in time spent sedentary in the dance nor stretch and tone conditions, but baseline time spent sedentary was predictive in these conditions. Our results add important knowledge toward understanding mechanistic associations underlying complex out-of-session sedentary behaviors within a walking intervention setting in older adults.

Inter- and Intra-Hemispheric Age-Related Remodeling in Visuo-Spatial Working Memory

Electroencephalography (EEG) studies investigating visuo-spatial working memory (vWM) in aging typically adopt an event-related potential (ERP) analysis approach that has shed light on the age-related changes during item retention and retrieval. However, this approach does not fully enable a detailed description of the time course of the neural dynamics related to aging. The most frequent age-related changes in brain activity have been described by two influential models of neurocognitive aging, the Hemispheric Asymmetry Reduction in Older Adults (HAROLD) and the Posterior-Anterior Shift in Aging (PASA). These models posit that older adults tend to recruit additional brain areas (bilateral as predicted by HAROLD and anterior as predicted by PASA) when performing several cognitive tasks. We tested younger (N = 36) and older adults (N = 35) in a typical vWM task (delayed match-to-sample) where participants have to retain items and then compare them to a sample. Through a data-driven whole scalp EEG analysis we aimed at characterizing the temporal dynamics of the age-related activations predicted by the two models, both across and within different stages of stimulus processing. Behaviorally, younger outperformed older adults. The EEG analysis showed that older adults engaged supplementary bilateral posterior and frontal sites when processing different levels of memory load, in line with both HAROLD and PASA-like activations. Interestingly, these age-related supplementary activations dynamically developed over time. Indeed, they varied across different stages of stimulus processing, with HAROLD-like modulations being mainly present during item retention, and PASA-like activity during both retention and retrieval. Overall, the present results suggest that age-related neural changes are not a phenomenon indiscriminately present throughout all levels of cognitive processing.

Improvisational Movement to Improve Quality of Life in Older Adults With Early-Stage Dementia: A Pilot Study

Alzheimer's disease has profound effects on quality of life, affecting not only cognition, but mobility and opportunities for social engagement. Dance is a form of movement that may be uniquely suited to help maintain quality of life for older adults, including those with dementia, because it inherently incorporates movement, social engagement, and cognitive stimulation. Here, we describe the methods and results of the pilot study for the IMOVE trial (NCT03333837, www.clinicaltrials.gov), a clinical trial designed to use improvisational dance classes to test the effects of movement and social engagement in people with mild cognitive impairment (MCI) or early-stage dementia. The pilot study was an 8-week investigation into the feasibility and potential effects of an improvisational dance intervention on people with MCI or early-stage dementia (PWD/MCI) and their caregivers (CG). The pilot aimed to assess changes in quality of life, balance, mood, and functional brain networks in PWD/MCI and their CG. Participants were recruited as dyads (pairs) that included one PWD/MCI and one CG. Ten total dyads were enrolled in the pilot study with five dyads assigned to the usual care control group and five dyads participating in the dance intervention. The intervention arm met twice weekly for 60 min for 8 weeks. Attendance and quality of life assessed with the Quality of Life in Alzheimer's disease (QoL-AD) questionnaire were the primary outcomes. Secondary outcomes included balance, mood and brain network connectivity assessed through graph theory analysis of functional magnetic resonance imaging (fMRI). Class attendance was 96% and qualitative feedback reflected participants felt socially connected to the group. Increases in quality of life and balance were observed, but not mood. Brain imaging analysis showed increases in multiple brain network characteristics, including global efficiency and modularity. Further investigation into the positive effects of this dance intervention on both imaging and non-imaging metrics will be carried out on the full clinical trial data. Results from the trial are expected in the summer of 2022.

Relationships between enriching early life experiences and cognitive function later in life are mediated by educational attainment

The study of how engagement in enriching cognitive, physical and social activities in childhood impacts cognitive function decades later will advance our understanding of how modifiable lifestyle activities promote cognition across the lifespan. 88 healthy older adults (aged 60-80 years) returned a retrospective questionnaire regarding their participation in seven lifestyle activities (musical instrument playing, language learning, sport participation, art/dance lessons, scouting, volunteering, family vacations) before age 13 years. After controlling for current age, educational attainment, socioeconomic status of the mother and current engagement in lifestyle activities, a greater number of activities were significantly associated with better vocabulary abilities, episodic memory and fluid intelligence. The relationships with vocabulary and fluid intelligence were mediated by educational attainment. We postulate that engagement in a higher number of enriching early life activities is a reflection of both one's sociocontextual environment and engagement with that environment. This engagement leads to attributes relevant for educational aspirations/attainment, ultimately contributing to factors that have a lifespan impact on cognitive function.

Low-Frequency Repetitive Transcranial Magnetic Stimulation Restores Dynamic Functional Connectivity in Subcortical Stroke

Background and Purpose: Strokes consistently result in brain network dysfunction. Previous studies have focused on the resting-state characteristics over the study period, while dynamic recombination remains largely unknown. Thus, we explored differences in dynamics between brain networks in patients who experienced subcortical stroke and the effects of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) on dynamic functional connectivity (dFC). Methods: A total of 41 patients with subcortical stroke were randomly divided into the LF-rTMS (n = 23) and the sham stimulation groups (n = 18). Resting-state functional MRI data were collected before (1 month after stroke) and after (3 months after stroke) treatment; a total of 20 age- and sex-matched healthy controls were also included. An independent component analysis, sliding window approach, and k-means clustering were used to identify different functional networks, estimate dFC matrices, and analyze dFC states before treatment. We further assessed the effect of LF-rTMS on dFCs in patients with subcortical stroke. Results: Compared to healthy controls, patients with stroke spent significantly more time in state I [p = 0.043, effect size (ES) = 0.64] and exhibited shortened stay in state II (p = 0.015, ES = 0.78); the dwell time gradually returned to normal after LF-rTMS treatment (p = 0.015, ES = 0.55). Changes in dwell time before and after LF-rTMS treatment were positively correlated with changes in the Fugl-Meyer Assessment for Upper Extremity (pr = 0.48, p = 0.028). Moreover, patients with stroke had decreased dFCs between the sensorimotor and cognitive control domains, yet connectivity within the cognitive control network increased. These abnormalities were partially improved after LF-rTMS treatment. Conclusion: Abnormal changes were noted in temporal and spatial characteristics of sensorimotor domains and cognitive control domains of patients who experience subcortical stroke; LF-rTMS can promote the partial recovery of dFC. These findings offer new insight into the dynamic neural mechanisms underlying effect of functional recombination and rTMS in subcortical stroke. Registration: http://www.chictr.org.cn/index.aspx, Unique.identifier: ChiCTR1800019452.

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

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

Brain network modularity predicts changes in cortical thickness in children involved in a physical activity intervention

Individual differences in brain network modularity at baseline can predict improvements in cognitive performance after cognitive and physical interventions. This study is the first to explore whether brain network modularity predicts changes in cortical brain structure in 8- to 9-year-old children involved in an after-school physical activity intervention (N = 62), relative to children randomized to a wait-list control group (N = 53). For children involved in the physical activity intervention, brain network modularity at baseline predicted greater decreases in cortical thickness in the anterior frontal cortex and parahippocampus. Further, for children involved in the physical activity intervention, greater decrease in cortical thickness was associated with improvements in cognitive efficiency. The relationships among baseline modularity, changes in cortical thickness, and changes in cognitive performance were not present in the wait-list control group. Our exploratory study has promising implications for the understanding of brain network modularity as a biomarker of intervention-related improvements with physical activity.

Problem-solving therapy-induced amygdala engagement mediates lifestyle behavior change in obesity with comorbid depression: a randomized proof-of-mechanism trial

BACKGROUND

Depression hinders obesity treatment; elucidating mechanisms may enable treatment enhancements.

OBJECTIVES

The aim was to investigate whether changes in neural targets in the negative affect circuit following psychotherapy mediate subsequent changes in weight and behaviors.

METHODS

Adults (n = 108) with obesity and depression were randomly assigned to usual care or an intervention that delivered problem-solving therapy (PST) for depression over 2 mo. fMRI for brain imaging was performed at baseline and 2 mo. BMI, physical activity, and diet were measured at baseline and 12 mo. Mediation analysis assessed between-group differences in neural target changes using t test and correlations between neural target changes and outcome changes (simple and interaction effect) using ordinary least-squares regression.

RESULTS

Compared with usual care, PST led to reductions in left amygdala activation (-0.75; 95% CI: -1.49, -0.01) and global scores of the negative affect circuit (-0.43; -0.81, -0.06), engaged by threat stimuli. Increases in amygdala activation and global circuit scores at 2 mo correlated with decreases in physical activity outcomes at 12 mo in the usual-care group; these relations were altered by PST. In relation to change in leisure-time physical activity, standardized β-coefficients were -0.67 in usual care and -0.01 in the intervention (between-group difference: 0.66; 0.02, 1.30) for change in left amygdala activation and -2.02 in usual care and -0.11 in the intervention (difference: 1.92; 0.64, 3.20) for change in global circuit scores. In relation to change in total energy expenditure, standardized β-coefficients were -0.65 in usual care and 0.08 in the intervention (difference: 0.73; 0.29, 1.16) for change in left amygdala activation and -1.65 in usual care and 0.08 in the intervention (difference: 1.74; 0.85, 2.63) for change in global circuit scores. Results were null for BMI and diet.

CONCLUSIONS

Short-term changes in the negative affect circuit engaged by threat stimuli following PST for depression mediated longer-term changes in physical activity. This trial was registered at www.clinicaltrials.gov as NCT02246413 (https://clinicaltrials.gov/ct2/show/NCT02246413).

Dance, aging, and neuroplasticity: an integrative review

This study aimed to summarize investigations that examined the benefits of dance on the neuroplasticity of older healthy adults, report structural and functional changes in the brain, and identify the strategies used in training protocols. The integrative review was perfomed in PubMed, Web of Science and Scopus databases, including randomized clinical trials, quasi-experimental, cross-sectional and cohort studies published in English, between 2010-2020. Twelve articles ware selected. Dance practice was associated with an improvement in functional connectivity, cognitive performance, and incresead brain volumes. Our results main supports studies on the plasticity induced by dance training in healthy older people.

White matter plasticity in healthy older adults: The effects of aerobic exercise

White matter deterioration is associated with cognitive impairment in healthy aging and Alzheimer's disease. It is critical to identify interventions that can slow down white matter deterioration. So far, clinical trials have failed to demonstrate the benefits of aerobic exercise on the adult white matter using diffusion Magnetic Resonance Imaging. Here, we report the effects of a 6-month aerobic walking and dance interventions (clinical trial NCT01472744) on white matter integrity in healthy older adults (n = 180, 60-79 years) measured by changes in the ratio of calibrated T1- to T2-weighted images (T1w/T2w). Specifically, the aerobic walking and social dance interventions resulted in positive changes in the T1w/T2w signal in late-myelinating regions, as compared to widespread decreases in the T1w/T2w signal in the active control. Notably, in the aerobic walking group, positive change in the T1w/T2w signal correlated with improved episodic memory performance. Lastly, intervention-induced increases in cardiorespiratory fitness did not correlate with change in the T1w/T2w signal. Together, our findings suggest that white matter regions that are vulnerable to aging retain some degree of plasticity that can be induced by aerobic exercise training. In addition, we provided evidence that the T1w/T2w signal may be a useful and broadly accessible measure for studying short-term within-person plasticity and deterioration in the adult human white matter.

The Daily Activity Study of Health (DASH): A pilot randomized controlled trial to enhance physical activity in sedentary older adults

Sedentary behavior increases the risk for multiple chronic diseases, early mortality, and accelerated cognitive decline in older adults. Interventions to reduce sedentary behavior among older adults are needed to improve health outcomes and reduce the burden on healthcare systems. We designed a randomized controlled trial that uses a self-affirmation manipulation and gain-framed health messaging to effectively reduce sedentary behavior in older adults. This message-based intervention lasts 6 weeks, recruiting 80 healthy but sedentary older adults from the community, between the ages of 60 and 95 years. Participants are randomly assigned to one of two groups: 1) an intervention group, which receives self-affirmation followed by gain-framed health messages daily or 2) a control group, which receives daily loss-framed health messages only. Objective physical activity engagement is measured by accelerometers. Accelerometers are deployed a week before, during, and the last week of intervention to examine potential changes in sedentary time and physical activity engagement. Participants undertake structural and functional (resting and task-based) MRI scans, neuropsychological tests, computerized behavioral measures, and neurobehavioral inventories at baseline and after the intervention. A 3-month follow-up assesses the long-term maintenance of any engendered behaviors from the intervention period. This study will assess the effectiveness of a novel behavioral intervention at reducing sedentarism in older adults and examine the neurobehavioral mechanisms underlying any such changes.

Maladaptive brain organization at 1 month into abstinence as an indicator for future relapse in patients with alcohol use disorder

Abstinence is a lifelong endeavor, and the risk of a relapse is always present for patients with Alcohol Use Disorder (AUD). The aim of the study was to better understand specific characteristics of the intrinsic whole-brain-network architecture of 34 AUD patients that may support abstinence or relapse. We used Graph Theory Analysis (GTA) of resting-state fMRI data from treatment seekers at 1 month of abstinence and their follow-up data as abstainers or relapsers 3 months later, together with data from 30 light/non-drinking controls scanned at the same interval. We determined the group-specific intrinsic community configurations at both timepoints as well as the corresponding modularity Q, a GTA measure that quantifies how well individual network communities are separated from each other. Both AUD groups at both timepoints had community configurations significantly different from those of controls, but the three groups did not significantly differ in their Q values. However, relapsers showed a maladaptive community configuration at baseline, which became more similar to the controls' community organization after the relapsers had started consuming alcohol again during the study interval. Additionally, successful recovery from AUD was not associated with re-gaining the intrinsic brain organization found in light/non-drinkers, but with a re-configuration resulting in a new brain organization distinctly different from that of healthy controls. Resting-state fMRI provides useful measures reflecting neuroplastic adaptations related to AUD treatment outcome.

Increased dynamic flexibility in the medial temporal lobe network following an exercise intervention mediates generalization of prior learning

Recent work has conceptualized the brain as a network comprised of groups of sub-networks or modules. "Flexibility" of brain network(s) indexes the dynamic reconfiguration of comprising modules. Using novel techniques from dynamic network neuroscience applied to high-resolution resting-state functional magnetic resonance imaging (fMRI), the present study investigated the effects of an aerobic exercise intervention on the dynamic rearrangement of modular community structure-a measure of neural flexibility-within the medial temporal lobe (MTL) network. The MTL is one of the earliest brain regions impacted by Alzheimer's disease. It is also a major site of neuroplasticity that is sensitive to the effects of exercise. In a two-group non-randomized, repeated measures and matched control design with 34 healthy older adults, we observed an exercise-related increase in flexibility within the MTL network. Furthermore, MTL network flexibility mediated the beneficial effect aerobic exercise had on mnemonic flexibility, as measured by the ability to generalize past learning to novel task demands. Our results suggest that exercise exerts a rehabilitative and protective effect on MTL function, resulting in dynamically evolving networks of regions that interact in complex communication patterns. These reconfigurations may underlie exercise-induced improvements on cognitive measures of generalization, which are sensitive to subtle changes in the MTL.

Does dance counteract age-related cognitive and brain declines in middle-aged and older adults? A systematic review

BACKGROUND

Dance is a multidomain activity that combines aerobic, coordination and cognitive exercise. This music-associated physical and cognitive exercise is a leisure activity that motivates people, elicits emotions, and avoids boredom, promoting adherence to practice. Continuing physical activity is of paramount importance, since cognitive benefits tend to disappear or even reverse when training ceases.

OBJECTIVE

The question we addressed in this systematic review is what influence dance has on the brain and cognition of healthy middle-aged and older adults.

LITERATURE SURVEY

We systematically reviewed the effects of dance on brain and cognition in older adults using MEDLINE, Psyc-Info, PubMed and Scopus databases.

METHODOLOGY

After screening 1051 studies, thirty-five met the eligibility inclusion criteria. These studies showed that dance improves brain structure and function as well as physical and cognitive functions.

CONCLUSIONS

The protective effect of dance training on cognition in older adults, together with the possibility of adapting intensity and style to suit possible physical limitations makes this activity very suitable for older adults.

Sensor-measured sedentariness and physical activity are differentially related to fluid and crystallized abilities in aging

Aerobic exercise and physical activity (PA) are known to benefit cognition in adulthood. However, a typical older adult spends most of the day sedentary or in light PA, behaviors that are typically poorly captured by questionnaires. To better understand the associations between time spent in different intensities of lifestyle PA and cognition, we measured average time spent daily in sedentariness, light, and moderate to vigorous PA using hip-worn sensors (ActiGraph accelerometers). We studied baseline data from 228 cognitively normal adults (Age 60-80) who took part in a clinical trial (clinical study identifier: NCT01472744). Fluid (processing speed, memory, and reasoning) and crystallized abilities (vocabulary knowledge) were assessed with the Virginia Cognitive Aging Battery. Adjusting for age, sex, and several modifiable socioeconomic, physical and functional health factors, time spent daily in moderate to vigorous PA was positively related with fluid abilities (perceptual speed and reasoning). Furthermore, we found that those spending more time sedentary performed better on vocabulary knowledge and reasoning tasks. In contrast, time spent in light PA was not related to either fluid or crystallized abilities. Our results add to the previous literature by providing the first sensor-based evidence that crystallized and fluid abilities in older age may be associated with engagement in different intensities of daily activity. Moreover, our findings suggest that the behavior of moderate to vigorous PA is at least as important in relation to cognition as the desirable long-term physiological effects of higher intensity PA and exercise. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The Role of Exercise in Management of Mental Health Disorders: An Integrative Review

A large and growing body of evidence suggests that physical activity (PA) may hold therapeutic promise in the management of mental health disorders. Most evidence linking PA to mental health outcomes has focused on the effects of aerobic exercise training on depression, although a growing body of work supports the efficacy of both aerobic and resistance exercise paradigms in the treatment of anxiety and post-traumatic stress disorder. Despite abundant evidence linking PA and mental health, use of exercise training as a mental health treatment remains limited due to three important sources of uncertainty: (a) large individual differences in response to exercise treatment within multiple mental health domains; (b) the critical importance of sustained PA engagement, not always achieved, for therapeutic benefit; and (c) disagreement regarding the relative importance of putative therapeutic mechanisms. Our review of treatment data on exercise interventions and mental health outcomes focuses primarily on depression and anxiety within a health neuroscience framework. Within this conceptual framework, neurobiological and behavioral mechanisms may have additiveor synergistic influences on key cognitive and behavioral processes that influence mental health outcomes. We therefore highlight sources of treatment heterogeneity by integrating the critical influences of (a) neurobiological mechanisms enhancing neuroplasticity and (b) behavioral learning of self-regulatory skills. Understanding the interrelationships between dynamic neurobiological and behavioral mechanisms may help inform personalized mental health treatments and clarify why, and for whom, exercise improves mental health outcomes. The review concludes with recommendations for future studies leveraging individual differences to refine treatment approaches to optimize mental health benefits.

Role of modularity in self-organization dynamics in biological networks

Interconnected ensembles of biological entities are perhaps some of the most complex systems that modern science has encountered so far. In particular, scientists have concentrated on understanding how the complexity of the interacting structure between different neurons, proteins, or species influences the functioning of their respective systems. It is well established that many biological networks are constructed in a highly hierarchical way with two main properties: short average paths that join two apparently distant nodes (neuronal, species, or protein patches) and a high proportion of nodes in modular aggregations. Although several hypotheses have been proposed so far, still little is known about the relation of the modules with the dynamical activity in such biological systems. Here we show that network modularity is a key ingredient for the formation of self-organizing patterns of functional activity, independently of the topological peculiarities of the structure of the modules. In particular, we propose a self-organizing mechanism which explains the formation of macroscopic spatial patterns, which are homogeneous within modules. This may explain how spontaneous order in biological networks follows their modular structural organization. We test our results on real-world networks to confirm the important role of modularity in creating macroscale patterns.

Effects of physical exercise on executive function in cognitively healthy older adults: A systematic review and meta-analysis of randomized controlled trials: Physical exercise for executive function

OBJECTIVE

To assess the effect of physical exercise interventions on executive function in cognitively healthy adults aged 60 years and older.

METHODS

Four electronic databases, the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Web of Science and Embase, were comprehensively searched from their inception to November 25, 2019. Randomized controlled trials (RCTs) examining the effect of physical exercise on executive function in cognitively healthy older adults were included.

RESULTS

Twenty-five eligible trials with fair methodological quality were identified. Compared to a no-exercise intervention, physical exercise had positive effect on working memory (Hedge's g=0.127, p<0.01, I²= 0%), cognitive flexibility (Hedge's g=0.511; p=0.007, I²=89.08%), and inhibitory control (Hedge's g=0.136; p=0.001, I²=0%) in cognitively healthy older adults. The moderator analysis indicated that more than 13 weeks of aerobic exercise significantly improved working memory and cognitive flexibility, and intervention lasting more than 26 weeks significantly improved inhibition; mind-body exercise significantly improved working memory. No significant effect on planning or semantic verbal fluency (SVF) was found.

CONCLUSION

Regular physical exercise training, especially aerobic exercise and mind-body exercise, had positive benefit for improving working memory, cognitive flexibility and inhibitory control of executive function in cognively healthy older adults. Further well-designed RCTs should focus on the impact of specific exercise forms with a standardized exercise scheme on executive function in cognitively healthy older adults.

MRI Predictors of Cognitive Training Outcomes

The prospect of improving or maintaining cognitive functioning has provoked a steadily increasing number of cognitive training interventions over the last years, especially for clinical and elderly populations. However, there are discrepancies between the findings of the studies. One of the reasons behind these heterogeneous findings is that there are vast inter-individual differences in how people benefit from the training and in the extent that training-related gains are transferred to other untrained tasks and domains. In this paper, we address the value of incorporating neural measures to cognitive training studies in order to fully understand the mechanisms leading to inter-individual differences in training gains and their generalizability to other tasks. Our perspective is that it is necessary to collect multimodal neural measures in the pre- and post-training phase, which can enable us to understand the factors contributing to successful training outcomes. More importantly, this understanding can enable us to predict who will benefit from different types of interventions, thereby allowing the development of individually tailored intervention programs.

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.

Exercise benefits on Alzheimer's disease: State-of-the-science

Although there is no unanimity, growing evidence supports the value of regular physical exercise to prevent Alzheimer's disease as well as cognitive decline in affected patients. Together with an introductory summary on epidemiological evidence, the aim of this review is to summarize the current knowledge on the potential biological mechanisms underlying exercise benefits in this condition. Regular physical exercise has proven to be beneficial for traditional cardiovascular risk factors (e.g., reduced vascular flow, diabetes) involved in the pathogenesis of Alzheimer's disease. Exercise also promotes neurogenesis via increases in exercise-induced metabolic factors (e.g., ketone bodies, lactate) and muscle-derived myokines (cathepsin-B, irisin), which in turn stimulate the production of neurotrophins such as brain-derived neurotrophic factor. Finally, regular exercise exerts anti-inflammatory effects and improves the brain redox status, thereby ameliorating the pathophysiological hallmarks of Alzheimer's disease (e.g., amyloid-β deposition). In summary, physical exercise might provide numerous benefits through different pathways that might, in turn, help prevent risk and progression of Alzheimer's disease. More evidence is needed, however, based on human studies.

Effects of Exercise Training Interventions on Executive Function in Older Adults: A Systematic Review and Meta-Analysis

BACKGROUND

Chronic exercise training has been shown be to positively associated with executive function (EF) in older adults. However, whether the exercise training effect on EF is affected by moderators including the specific sub-domain of EF, exercise prescription variables, and sample characteristics remains unknown.

OBJECTIVES

This systematic and meta-analytic review of randomized controlled trials (RCTs) investigated the effects of exercise training on EF in older adults and explored potential moderators underlying the effects of exercise training on EF.

METHODS

In accordance with the PRISMA guidelines, the electronic databases MEDLINE (PubMed) and EMBASE (Scopus) were searched from January 2003 to November 2019. All studies identified for inclusion were peer-reviewed and published in English. To be included, studies had to report findings from older (> 55 years old), cognitively normal adults or adults with mild cognitive impairment (MCI) randomized to an exercise training or a control group. The risk of bias in each study was appraised using the Cochrane risk-of-bias tool. Fixed-effects models were used to compare the effects of exercise training and control conditions on EF assessed at baseline and post-intervention. In addition, subgroup analyses were performed for three moderators (i.e., the specific sub-domain of EF, exercise prescription variables, and sample characteristics).

RESULTS

Thirty-three RCTs were included. Overall, exercise training was associated with a significant small improvement in EF [Q(106) = 260.09, Hedges' g = 0.21; p < 0.01]. The EF sub-domain moderator was not significant [Q(2) = 4.33, p > 0.05], showing that the EF improvement in response to exercise is evident for measures of inhibition, updating, and shifting. Regarding exercise prescription variables, results were significantly moderated by frequency of exercise training [Q(1) = 10.86, p < 0.05], revealing that effect sizes (ESs) were larger for moderate frequency (g = 0.31) as compared to low frequency exercise (g = 0.15). The results also showed type of exercise training moderated the ESs [Q(4) = 26.18, p < 0.05], revealing that ESs were largest for other forms of exercise (g = 0.44), followed by Tai Chi and yoga (g = 0.38), resistance exercise (g = 0.22), aerobic exercise (g = 0.14), and combined exercise (g = 0.10). In addition, The results showed moderated length of training the ESs [Q(2) = 16.64, p < 0.05], revealing that ESs were largest for short length (g = 0.32), followed by mid length (g = 0.26) and long length (g = 0.09). No significant difference in effects was observed as a function of exercise intensity [Q(1) = 2.87 p > 0.05] and session time [Q(2) = 0.21, p > 0.05]. Regarding sample characteristics, the results were significantly moderated by age [Q(2) = 20.64, p < 0.05], with significant benefits for young-old (55-65 years old) (g = 0.30) and mid-old (66-75 years old) (g = 0.25), but no effect on EF for old-old (more than 75 years old). The results were also significantly moderated by physical fitness levels [Q(1) = 10.80, p < 0.05], revealing that ESs were larger for sedentary participants (g = 0.33) as compared to physically fit participants (g = 0.16). In addition, results were also significantly moderated by cognitive status [Q(1) = 11.44, p < 0.05], revealing that ESs were larger for participants with cognitively normal (g = 0.26) as compared to those with mild cognitive impairment (g = 0.08). No significant differences in effects were observed as a function of sex [Q(2) = 5.38, p > 0.05].

CONCLUSIONS

Exercise training showed a small beneficial effect on EF in older adults and the magnitude of the effect was different across some moderators.

Occupational Physical Stress Is Negatively Associated With Hippocampal Volume and Memory in Older Adults

Our jobs can provide intellectually and socially enriched environments but also be the source of major psychological and physical stressors. As the average full-time worker spends >8 h at work per weekday and remains in the workforce for about 40 years, occupational experiences must be important factors in cognitive and brain aging. Therefore, we studied whether occupational complexity and stress are associated with hippocampal volume and cognitive ability in 99 cognitively normal older adults. We estimated occupational complexity, physical stress, and psychological stress using the Work Design Questionnaire (Morgeson and Humphrey, 2006), Quantitative Workload Inventory and Interpersonal Conflict at Work Scale (Spector and Jex, 1998). We found that physical stress, comprising physical demands and work conditions, was associated with smaller hippocampal volume and poorer memory performance. These associations were independent of age, gender, brain size, socioeconomic factors (education, income, and job title), duration of the job, employment status, leisure physical activity and general stress. This suggests that physical demands at work and leisure physical activity may have largely independent and opposite effects on brain and cognitive health. Our findings highlight the importance of considering midlife occupational experiences, such as work physical stress, in understanding individual trajectories of cognitive and brain aging.

Brain network topology predicts participant adherence to mental training programs

Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain networks have been suggested to predict learning performance, but it is unclear whether these patterns contribute to motivational aspects of learning such as adherence. This study tests whether configurations of brain connections in resting-state fMRI scans can be used to predict adherence to two programs: meditation and creative writing. Results indicate that greater system segregation and clustering predict the number of practice sessions and class participation in both programs at a wide range of network thresholds (corrected p value < 0.05). At a local level, regions in subcortical circuitry such as striatum and accumbens predicted adherence in all subjects. Furthermore, there were also some important distinctions between groups: Adherence to meditation was predicted by connectivity within local network of the anterior insula and default mode network; and in the writing program, adherence was predicted by network neighborhood of frontal and temporal regions. Four machine learning methods were applied to test the robustness of the brain metric for classifying individual capacity for adherence and yielded reasonable accuracy. Overall, these findings underscore the fact that adherence and the ability to perform prescribed exercises is associated with organizational patterns of brain connectivity.