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

Flexible parametrization of graph-theoretical features from individual-specific networks for prediction

Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance.

Personal Income Performance Correlates with Brain Structural Network Modularity but Not Intelligence Quotient

Introduction: This study aims to use diffusion tensor imaging (DTI) in conjunction with brain graph techniques to define brain structural connectivity and investigate its association with personal income (PI) in individuals of various ages and intelligence quotients (IQ). Methods: MRI examinations were performed on 55 male subjects (mean age: 40.1 ± 9.4 years). Graph data and metrics were generated, and DTI images were analyzed using tract-based spatial statistics (TBSS). All subjects underwent the Wechsler Adult Intelligence Scale for a reliable estimation of the full-scale IQ (FSIQ), which includes verbal comprehension index, perceptual reasoning index, working memory index, and processing speed index. The performance score was defined as the monthly PI normalized by the age of the subject. Results: The analysis of global graph metrics showed that modularity correlated positively with performance score (p = 0.003) and negatively with FSIQ (p = 0.04) and processing speed index (p = 0.005). No significant correlations were found between IQ indices and performance scores. Regional analysis of graph metrics showed modularity differences between right and left networks in sub-cortical (p = 0.001) and frontal (p = 0.044) networks. TBSS analysis showed greater axial and mean diffusivities in the high-performance group in correlation with their modular brain organization. Conclusion: This study showed that PI performance is strongly correlated with a modular organization of brain structural connectivity, which implies short and rapid networks, providing automatic and unconscious brain processing. Additionally, the lack of correlation between performance and IQ suggests a reduced role of academic reasoning skills in performance to the advantage of high uncertainty decision-making networks.

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.

Positive or negative environmental modulations on human brain development: the morpho-functional outcomes of music training or stress

In the last couple of decades, the study of human living brain has benefitted of neuroimaging and non-invasive electrophysiological techniques, which are particularly valuable during development. A number of studies allowed to trace the usual stages leading from pregnancy to adult age, and relate them to functional and behavioral measurements. It was also possible to explore the effects of some interventions, behavioral or not, showing that the commonly followed pathway to adulthood may be steered by external interventions. These events may result in behavioral modifications but also in structural changes, in some cases limiting plasticity or extending/modifying critical periods. In this review, we outline the healthy human brain development in the absence of major issues or diseases. Then, the effects of negative (different stressors) and positive (music training) environmental stimuli on brain and behavioral development is depicted. Hence, it may be concluded that the typical development follows a course strictly dependent from environmental inputs, and that external intervention can be designed to positively counteract negative influences, particularly at young ages. We also focus on the social aspect of development, which starts in utero and continues after birth by building social relationships. This poses a great responsibility in handling children education and healthcare politics, pointing to social accountability for the responsible development of each child.

Exploring neural heterogeneity in inattention and hyperactivity

Inattention and hyperactivity are cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD). These characteristics have also been observed across a range of other neurodevelopmental conditions, such as autism and dyspraxia, suggesting that they might best be studied across diagnostic categories. Here, we evaluated the associations between inattention and hyperactivity behaviours and features of the structural brain network (connectome) in a large transdiagnostic sample of children (Centre for Attention, Learning, and Memory; n = 383). In our sample, we found that a single latent factor explains 77.6% of variance in scores across multiple questionnaires measuring inattention and hyperactivity. Partial Least-Squares (PLS) regression revealed that variability in this latent factor could not be explained by a linear component representing nodewise properties of connectomes. We then investigated the type and extent of neural heterogeneity in a subset of our sample with clinically-elevated levels of inattention and hyperactivity. Multidimensional scaling combined with k-means clustering revealed two neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by nodal communicability-a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had similar behavioural profiles, which included high levels of inattention and hyperactivity. However, one of the clusters scored higher on multiple cognitive assessment measures of executive function. We conclude that inattention and hyperactivity are so common in children with neurodevelopmental difficulties because they emerge through multiple different trajectories of brain development. In our own data, we can identify two of these possible trajectories, which are reflected by measures of structural brain network topology and 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.

Reduced Modular Segregation of White Matter Brain Networks in Attention Deficit Hyperactivity Disorder

OBJECTIVE

Despite studies reporting alterations in the brain networks of patients with ADHD, alterations in the modularity of white matter (WM) networks are still unclear.

METHOD

Based on the results of module division by generalized Louvain algorithm, the modularity of ADHD was evaluated. The correlation between the modular changes of ADHD and its clinical characteristics was analyzed.

RESULTS

The participation coefficient and the connectivity between modules of ADHD increased, and the modularity coefficient decreased. Provincial hubs of ADHD did not change, and the number of connector hubs increased. All results showed that the modular segregation of WM networks of ADHD decreased. Modules with reduced modular segregation are mainly responsible for language and motor functions. Moreover, modularity showed evident correlation with the symptoms of ADHD.

CONCLUSION

The modularity changes in WM network provided a novel insight into the understanding of brain cognitive alterations in ADHD.

The Differential Effects of Adiposity and Fitness on Functional Connectivity in Preadolescent Children

PURPOSE

Childhood obesity is a global health concern, with >340 million youth considered overweight or obese. In addition to contributing greatly to health care costs, excess adiposity associated with obesity is considered a major risk factor for premature mortality from cardiovascular and metabolic diseases and is also negatively associated with cognitive and brain health. A complementary line of research highlights the importance of cardiorespiratory fitness, a by-product of engaging in physical activity, on an abundance of health factors, including cognitive and brain health.

METHODS

This study investigated the relationship among excess adiposity (visceral adipose tissue [VAT], subcutaneous abdominal adipose tissue), total abdominal adipose tissue, whole-body percent fat [WB%FAT], body mass index (BMI), and fat-free cardiorespiratory fitness (FF-V̇O 2max ) on resting-state functional connectivity (RSFC) in 121 ( f = 68) children (7-11 yr) using a data-driven whole-brain multivoxel pattern analysis.

RESULTS

Multivoxel pattern analysis revealed brain regions that were significantly associated with VAT, BMI, WB%FAT, and FF-V̇O 2 measures. Yeo's (2011) RSFC-based seven-network cerebral cortical parcellation was used for labeling the results . Post hoc seed-to-voxel analyses found robust negative correlations of VAT and BMI with areas involved in the visual, somatosensory, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks. Further, positive correlations of FF-V̇O 2 were observed with areas involved in the ventral attention and frontoparietal networks. These novel findings indicate that negative health factors in childhood may be selectively and negatively associated with the 7 Yeo-defined functional networks, yet positive health factors (FF-V̇O 2 ) may be positively associated with these networks.

CONCLUSIONS

These novel results extend the current literature to suggest that BMI and adiposity are negatively associated with, and cardiorespiratory fitness (corrected for fat-free mass) is positively associated with, RSFC networks in children.

Shedding Light on the Effects of Orienteering Exercise on Spatial Memory Performance in College Students of Different Genders: An fNIRS Study

Objective: To investigate the intervention effect of orienteering exercises on the spatial memory ability of college students of different genders and its underlying mechanism. Methods: Forty-eight college students were randomly screened into experimental and control groups, 12 each of male and female, by SBSOD scale. The effects of 12 weeks of orienteering exercises on the behavioral performance and brain activation patterns during the spatial memory tasks of college students of different genders were explored by behavioral tests and the fNIRS technique. Results: After the orienteering exercise intervention in the experimental group, the male students had significantly greater correct rates and significantly lower reaction times than the female students; left and right dorsolateral prefrontal activation was significantly reduced in the experimental group, and the male students had a significantly greater reduction in the left dorsolateral prefrontal than the female students. The degree of activation in the left and right dorsolateral prefrontals of the male students and the right dorsolateral prefrontals of the female students correlated significantly with behavioral performance, and the functional coupling between the brain regions showed an enhanced performance. Discussion: Orienteering exercises improve the spatial memory ability of college students, more significantly in male students. The degree of activation of different brain regions correlated with behavioral performance and showed some gender differences.

Resting-state network organisation in children with traumatic brain injury

Children with traumatic brain injury are at risk of neurocognitive and behavioural impairment. Although there is evidence for abnormal brain activity in resting-state networks after TBI, the role of resting-state network organisation in paediatric TBI outcome remains poorly understood. This study is the first to investigate the impact of paediatric TBI on resting-state network organisation using graph theory, and its relevance for functional outcome. Participants were 8-14 years and included children with (i) mild TBI and risk factors for complicated TBI (mild^RF+, n = 20), (ii) moderate/severe TBI (n = 15), and (iii) trauma control injuries (n = 27). Children underwent resting-state functional magnetic resonance imaging (fMRI), neurocognitive testing, and behavioural assessment at 2.8 years post-injury. Graph theory was applied to fMRI timeseries to evaluate the impact of TBI on global and local organisation of the resting-state network, and relevance for neurocognitive and behavioural functioning. Children with TBI showed atypical global network organisation as compared to the trauma control group, reflected by lower modularity (mild^RF + TBI and moderate/severe TBI), higher smallworldness (mild^RF + TBI) and lower assortativity (moderate/severe TBI ps < .04, Cohen's ds: > .6). Regarding local network organisation, the relative importance of hub regions in the network did not differ between groups. Regression analyses showed relationships between global as well as local network parameters with neurocognitive functioning (i.e., working memory, memory encoding; R² = 23.3 - 38.5%) and behavioural functioning (i.e., externalising problems, R² = 36.1%). Findings indicate the impact of TBI on global functional network organisation, and the relevance of both global and local network organisation for long-term neurocognitive and behavioural outcome after paediatric TBI. The results suggest potential prognostic value of resting-state network organisation for outcome after paediatric TBI.

A mixed-modeling framework for whole-brain dynamic network analysis

The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks.

In recent years, a growing body of studies have aimed at analyzing the brain as a complex dynamic system by using various neuroimaging data. This has opened new avenues to answer compelling questions about the brain function in health and disease. However, methods that allow for providing statistical inference about how the complex interactions of the brain are associated with desired phenotypes are to be developed for a more profound insight. This study introduces a promising regression-based model to relate dynamic brain networks to desired phenotypes and provide statistical inference. Moreover, it can be used for simulating dynamic brain networks with respect to desired phenotypes at the group and individual levels.

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.

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.

Effects of Physical Exercise Interventions on Spatial Orientation in Children and Adolescents: A Systematic Scoping Review

Background: Regular physical exercise plays an integral part in the psychomotor and psychosocial development of children and adolescents, with complex motor and cognitive processes closely linked. Spatial abilities, one aspect of cognitive functioning start to evolve from earliest childhood and reach adult-like levels by early adolescence. As they have been associated with good spatial orientation, wayfinding, map-reading skills, problem solving or analyzing spatial information, these skills facilitate independence and autonomy while growing up. Despite promising results, only few studies investigate this relation between physical exercise and spatial abilities. To use this benefit and develop purposive physical exercise interventions, it is essential to summarize the current evidence. Objectives: This literature review aims to systematically summarize findings regarding the impact of physical exercise interventions on spatial abilities in healthy children and adolescents and identify knowledge gaps. Methods: A systematic search of the literature according to the PRISMA guidelines was conducted on the databases Pubmed, Web of Science, Cochrane Library, SportDiscus, and PsycInfo from their inception date till March 2021. Additionally, Google Scholar and refence lists of relevant publications were searched. A descriptive analysis of results was conducted. Results: The literature search identified a total of N = 1,215 records, 11 of which met the inclusion criteria and were analyzed in this review. A total of 621 participants aged 4 to 15 years participated in the studies. Exercise interventions included sport-specific activities, motor-coordinative exercises, high-intensity functional training or spatial orientation/navigation training. Five studies evaluated training effects on mental rotation performance (i.e., Mental Rotation Test), four studies investigated visuo-spatial working memory function/spatial memory (i.e., Corsi Block Test, Virtual Reality Morris Water Maze) and two studies tested spatial orientation capacity (i.e., Orientation-Running Test). Overall, results show a potential for improvement of spatial abilities through physical exercise interventions. However, keeping the diversity of study designs, populations and outcomes in mind, findings need to be interpreted with care. Conclusions: Despite growing interest on the effects of physical exercise interventions on spatial abilities and promising findings of available studies, evidence to date remains limited. Future research is needed to establish how spatial ability development of healthy children and adolescents can be positively supported.

Childhood exercise predicts response inhibition in later life via changes in brain connectivity and structure

Participation in exercise during early life (i.e., childhood through adolescence) enhances response inhibition; however, it is unclear whether participation in exercise during early life positively predicts response inhibition in later life. This historical cohort study was designed to clarify whether participation in exercise (e.g., structured sports participation) during early life predicts response inhibition in adulthood and if so, to reveal the brain connectivity and cortical structures contributing to this association. We analyzed data derived from 214 participants (women = 104, men = 110; age: 26‒69 years). Results indicated that participation in exercise during childhood (before entering junior high school; ≤ 12 years old) significantly predicted better response inhibition. No such association was found if exercise participation took place in early adolescence or later (junior high school or high school; ≥ 12 years old). The positive association of exercise participation during childhood with response inhibition was moderated by decreased structural and functional connectivity in the frontoparietal (FPN), cingulo-opercular (CON), and default mode networks (DMN), and increased inter-hemispheric structural networks. Greater cortical thickness and lower levels of dendritic arborization and density in the FPN, CON, and DMN also moderated this positive association. Our results suggest that participation in exercise during childhood positively predicts response inhibition later in life and that this association can be moderated by changes in neuronal circuitry, such as increased cortical thickness and efficiency, and strengthened inter-hemispheric connectivity.