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 from n $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and n $$ n $$ = 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.

Excess BMI in early adolescence adversely impacts maturating functional circuits supporting high-level cognition and their structural correlates.

BACKGROUND/OBJECTIVES: Adverse effects of excess BMI (affecting 1 in 5 children in the US) on brain circuits during neurodevelopmentally vulnerable periods are incompletely understood. This study investigated BMI-related alterations in maturating functional networks and their underlying brain structures, and high-level cognition in early adolescence. SUBJECTS/METHODS: Cross-sectional resting-state fMRI, structural sMRI, neurocognitive task scores, and BMI from 4922 youth [median (IQR) age = 120.0 (13.0) months, 2572 females (52.25%)] from the Adolescent Brain Cognitive Development (ABCD) cohort were analyzed. Comprehensive topological and morphometric network properties were estimated from fMRI and sMRI, respectively. Cross-validated linear regression models assessed correlations with BMI. Results were reproduced across multiple fMRI datasets. RESULTS: Almost 30% of youth had excess BMI, including 736 (15.0%) with overweight and 672 (13.7%) with obesity, and statistically more Black and Hispanic compared to white, Asian and non-Hispanic youth (p < 0.01). Those with obesity or overweight were less physically active, slept less than recommended, snored more frequently, and spent more time using an electronic device (p < 0.01). They also had lower topological efficiency, resilience, connectivity, connectedness and clustering in Default-Mode, dorsal attention, salience, control, limbic, and reward networks (p ≤ 0.04, Cohen's d: 0.07-0.39). Lower cortico-thalamic efficiency and connectivity were estimated only in youth with obesity (p < 0.01, Cohen's d: 0.09-0.19). Both groups had lower cortical thickness, volume and white matter intensity in these networks' constituent structures, particularly anterior cingulate, entorhinal, prefrontal, and lateral occipital cortices (p < 0.01, Cohen's d: 0.12-0.30), which also mediated inverse relationships between BMI and regional functional topologies. Youth with obesity or overweight had lower scores in a task measuring fluid reasoning - a core aspect of cognitive function, which were partially correlated with topological changes (p ≤ 0.04). CONCLUSIONS: Excess BMI in early adolescence may be associated with profound aberrant topological alterations in maturating functional circuits and underdeveloped brain structures that adversely impact core aspects of cognitive function.

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

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

GENERATIVE MODELS FOR LARGE-SCALE SIMULATIONS OF CONNECTOME DEVELOPMENT.

Functional interactions and anatomic connections between brain regions form the connectome. Its mathematical representation in terms of a graph reflects the inherent neuroanatomical organization into structures and regions (nodes) that are interconnected through neural fiber tracts and/or interact functionally (edges). Without knowledge of the ground truth topology of the connectome, functional (directional or nondirectional) graphs represent estimates of signal correlations, from which underlying mechanisms and processes, such as development and aging, or neuropathologies, are difficult to unravel. Biologically meaningful simulations using synthetic graphs with controllable parameters can complement real data analyses and provide critical insights into mechanisms underlying the organization of the connectome. Generative models can be highly valuable tools for creating large datasets of synthetic graphs with known topological characteristics. However, for these graphs to be meaningful, the variation of model parameters needs to be driven by real data. This paper presents a novel, data-driven approach for tuning the parameters of the generative Lancichinetti-Fortunato-Radicchi (LFR) model, using a large dataset of connectomes (n = 5566) estimated from resting-state fMRI from early adolescents in the historically large Adolescent Brain Cognitive Development Study (ABCD). It also presents an application, i.e., simulations using the LFR, to generate large datasets of synthetic graphs representing brains at different stages of neural maturation, and gain insights into developmental changes in their topological organization.

Shorter Duration and Lower Quality Sleep Have Widespread Detrimental Effects on Developing Functional Brain Networks in Early Adolescence.

Sleep is critical for cognitive health, especially during complex developmental periods such as adolescence. However, its effects on maturating brain networks that support cognitive function are only partially understood. We investigated the impact of shorter duration and reduced quality sleep, common stressors during development, on functional network properties in early adolescence-a period of significant neural maturation, using resting-state functional magnetic resonance imaging from 5566 children (median age = 120.0 months; 52.1% females) in the Adolescent Brain Cognitive Development cohort. Decreased sleep duration, increased sleep latency, frequent waking up at night, and sleep-disordered breathing symptoms were associated with lower topological efficiency, flexibility, and robustness of visual, sensorimotor, attention, fronto-parietal control, default-mode and/or limbic networks, and with aberrant changes in the thalamus, basal ganglia, hippocampus, and cerebellum (P < 0.05). These widespread effects, many of which were body mass index-independent, suggest that unhealthy sleep in early adolescence may impair neural information processing and integration across incompletely developed networks, potentially leading to deficits in their cognitive correlates, including attention, reward, emotion processing and regulation, memory, and executive control. Shorter sleep duration, frequent snoring, difficulty waking up, and daytime sleepiness had additional detrimental network effects in nonwhite participants, indicating racial disparities in the influence of sleep metrics.

Parental religiosity is associated with changes in youth functional network organization and cognitive performance in early adolescence.

Parental religious beliefs and practices (religiosity) may have profound effects on youth, especially in neurodevelopmentally complex periods such as adolescence. In n = 5566 children (median age = 120.0 months; 52.1% females; 71.2% with religious affiliation) from the Adolescent Brain Cognitive Development study, relationships between parental religiosity and non-religious beliefs on family values (data on youth beliefs were not available), topological properties of youth resting-state brain networks, and executive function, inhibitory control, and cognitive flexibility were investigated. Lower caregiver education and family income were associated with stronger parental beliefs (p < 0.01). Strength of both belief types was correlated with lower efficiency, community structure, and robustness of frontoparietal control, temporoparietal, and dorsal attention networks (p < 0.05), and lower Matrix Reasoning scores. Stronger religious beliefs were negatively associated (directly and indirectly) with multiscale properties of salience and default-mode networks, and lower Flanker and Dimensional Card Sort scores, but positively associated with properties of the precuneus. Overall, these effects were small (Cohen's d ~ 0.2 to ~ 0.4). Overlapping neuromodulatory and cognitive effects of parental beliefs suggest that early adolescents may perceive religious beliefs partly as context-independent rules on expected behavior. However, religious beliefs may also differentially affect cognitive flexibility, attention, and inhibitory control and their neural substrates.

A Novel Mobile Phone App for Optimizing Dynamic Discrete Data Collection in Pediatric Epilepsy Studies.

Mobile technologies, including applications (apps) and wearable devices, are playing an increasingly important role in health monitoring. In particular, apps are becoming a critical component of m-health, which promises to transform personalized care management, optimize clinical outcomes, and improve patient-provider communication. They may also play a central role in research, to facilitate rapid and inexpensive collection of repeated data, such as momentary clinical, physiological, and/or behavioral assessments and optimize their sampling. This is particularly important for measuring systems/processes with characteristic temporal patterns, e.g., circadian rhythms, which need to be adequately sampled in order to be accurately estimated from discrete measurements. Temporal sampling of these patterns may also be critical for elucidating their modulation by pathological events. This paper presents a novel app, developed with the overarching goal to optimize repeated salivary hormone collection in pediatric patients with epilepsy through improved patient-investigator communication and enhanced alerts. The ultimate goal of the app is to maximize regularity of the data collection (up to 8 samples/day for ~4-5 days of hospitalization) while minimizing intrusion on patients during clinical monitoring. In addition, the app facilitates flexible collection of data on stress and seizure symptoms at the time of saliva sampling, which can then be correlated with hormone levels and physiological changes indicating impending seizures.

Big Data-Driven Brain Parcellation from fMRI: Impact of Cohort Heterogeneity on Functional Connectivity Maps.

Ongoing large-scale human brain studies are generating complex neuroimaging data from thousands of individuals that can be leveraged to derive data-driven, anatomically accurate brain parcellations. However, despite their promise and many strengths, these data are highly heterogeneous, a characteristic that may affect the anatomical accuracy and generalization of the template but has received relatively little attention. Using multiple similarity measures and thresholding approaches, this study investigated the topological intra- and inter-individual variability of restingstate (rs) functional edge maps (often used for brain parcellation), estimated from rs-fMRI connectivity in n = 5878 children from the Adolescent Brain Cognitive Development (ABCD) study. Findings from this initial investigation indicate that choosing a subject- vs cohort-based threshold for estimating edge maps from connectivity matrices does not significantly impact the map topology. In contrast, the choice of similarity measure and non-linear relationship between similarity and edge map sparsity may have a significant impact on map classification and the generation of parcellation atlases. Multi-level classification revealed multiple clusters with a potentially complex mapping onto biological variables beyond simple demographics.Clinical Relevance- Case-control neuroimaging studies should use domain-specific (e.g., demographics-specific) atlases for parcellating the brain, to improve accuracy and rigor of cohort comparisons. To be generalizable, such atlases need to be derived from large datasets, which are inherently heterogeneous. In a cohort of 5878 children (age ~9-10 years), this study systematically assessed the impact of heterogeneity and similarity of edge maps, which are derived from rs-fMRI connectivity and typically used to generate parcellation atlases.