Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness.

PD46-02 LOOKING FOR YOUR OWN REFLECTION: ASSESSING BRAIN FUNCTIONAL STATE OF SURGICAL MENTOR DURING ROBOT-ASSISTED SURGERY

Assessment of driver drowsiness using electroencephalogram signals based on multiple functional brain networks.

Autism has been associated with differences in functional brain network organization. However, the exact nature of these differences across development compared to non-autistic individuals and their relationship to autism-related social cognition, remains unclear. This study first aimed to identify EEG resting-state network characteristics in autistic versus non-autistic children, adolescents, and adults. Second, we investigated associations with social cognition measures. Analyzing resting-state EEG data from the EU-AIMS Longitudinal European Autism Project, we compared network metrics (global efficiency, clustering coefficient, and small-worldness) between 344 autistic and non-autistic individuals within and across age groups in four frequency bands (delta, theta, alpha, and beta). If significant, we explored their relationships to measures of empathy (empathy quotient), complex emotion recognition [reading the mind in the eyes task (RMET)], and theory of mind (animated shapes task). Compared to their non-autistic peers, autistic adolescents showed lower alpha global efficiency, while autistic adults showed lower alpha clustering and small-worldness. No network differences were observed among children. In adolescents, higher long-range integration was tentatively associated with higher RMET scores; in those with high autistic traits, higher long-range integration related to fewer parent-reported empathic behaviors. No brain-behavior relationships were observed in adults. Our findings suggest subtle differences in network topology between autistic and non-autistic individuals, with less efficient long-range efficiency during adolescence, and less local and overall network efficiency in adulthood. Furthermore, long-range integration may play a role in complex emotion recognition and empathy difficulties associated with autism in adolescence.

Brain Connectivity: A Journal of Clinical Neurology, Neuroscience, and Neuroimaging Advancing the Field of Neurology.

Men and women can exhibit different pain sensitivities, and many chronic pain conditions are more prevalent in one sex. Although there is evidence of sex differences in the brain, it is not known whether there are sex differences in the organization of large-scale functional brain networks in chronic pain. Here, we used graph theory with modular analysis and machine-learning of resting-state-functional magnetic resonance imaging data from 220 participants: 155 healthy controls and 65 individuals with chronic low back pain due to ankylosing spondylitis, a form of arthritis. We found an extensive overlap in the graph partitions with the major brain intrinsic systems (ie, default mode, central, visual, and sensorimotor modules), but also sex-specific network topological characteristics in healthy people and those with chronic pain. People with chronic pain exhibited higher cross-network connectivity, and sex-specific nodal graph properties changes (ie, hub disruption), some of which were associated with the severity of the chronic pain condition. Females exhibited atypically higher functional segregation in the mid cingulate cortex and subgenual anterior cingulate cortex and lower connectivity in the network with the default mode and frontoparietal modules, whereas males exhibited stronger connectivity with the sensorimotor module. Classification models on nodal graph metrics could classify an individual's sex and whether they have chronic pain with high accuracies (77%-92%). These findings highlight the organizational abnormalities of resting-state-brain networks in people with chronic pain and provide a framework to consider sex-specific pain therapeutics.

The analysis of the topology and organization of brain networks is known to greatly benefit from network measures in graph theory. However, to evaluate dynamic changes of brain functional connectivity, more sophisticated quantitative metrics characterizing temporal evolution of brain topological features are required. To simplify conversion of time-varying brain connectivity to a static graph representation is straightforward but the procedure loses temporal information that could be critical in understanding the brain functions. To extend the understandings of functional segregation and integration to a dynamic fashion, we recommend dynamic graph metrics to characterise temporal changes of topological features of brain networks. This study investigated functional segregation and integration of brain networks over time by dynamic graph metrics derived from EEG signals during an experimental protocol: performance of complex flight simulation tasks with multiple levels of difficulty. We modelled time-varying brain functional connectivity as multi-layer networks, in which each layer models brain connectivity at time window t + Δt. Dynamic graph metrics were calculated to quantify temporal and topological properties of the network. Results show that brain networks under the performance of complex tasks reveal a dynamic small-world architecture with a number of frequently connected nodes or hubs, which supports the balance of information segregation and integration in brain over time. The results also show that greater cognitive workloads caused by more difficult tasks induced a more globally efficient but less clustered dynamic small-world functional network. Our study illustrates that task-related changes of functional brain network segregation and integration can be characterized by dynamic graph metrics.

Aging is typically associated with substantial declines in motor functioning as well as robust changes in the functional organization of brain networks. Previous research has investigated the link between these 2 age-varying factors but examinations were predominantly limited to the functional organization within motor-related brain networks. Little is known about the relationship between age-related behavioral impairments and changes in functional organization at the whole brain (i.e., multiple network) level. This knowledge gap is surprising given that the decreased segregation of brain networks (i.e., increased internetwork connectivity) can be considered a hallmark of the aging process. Accordingly, we investigated the association between declines in motor performance across the adult lifespan (20-75 years) and age-related modulations of functional connectivity within and between resting state networks. Results indicated that stronger internetwork resting state connectivity observed as a function of age was significantly related to worse motor performance. Moreover, performance had a significantly stronger association with the strength of internetwork as compared with intranetwork connectivity, including connectivity within motor networks. These findings suggest that age-related declines in motor performance may be attributed to a breakdown in the functional organization of large-scale brain networks rather than simply age-related connectivity changes within motor-related networks.

Effects of familiarity on child brain networks when listening to a storybook reading: A magneto-encephalographic study

Preterm birth disrupts critical phases of brain maturation, placing individuals at increased risk for long-term cognitive and functional impairments. This study investigated how very preterm birth affects the spatial and temporal organization of functional brain networks in school-aged children born very preterm using a spatiotemporal connectome framework. Multimodal MRI, including diffusion-weighted imaging and resting-state fMRI, was acquired from 25 children born before 30 gestational weeks and 25 age- and sex-matched full-term controls (8-9years). We characterized the structure-function coupling of dynamic brain activity through Connected Components (CCs) defined as structurally constrained sets of functionally co-active regions identified on a multilayer graph. Three different metrics were computed: CC number (count of distinct co-activation patterns), CC height (peak number of regions within a CC, representing the spatial extent) and CC width (temporal span across consecutive time repetitions (TRs)). In addition, we quantified System Diversity (SD) and Spatiotemporal Diversity (STD), indices reflecting integrative richness and temporal variability of functional network dynamics. CC number decreased with age across groups, reflecting typical developmental patterns, while CC height was significantly greater in preterm children and positively associated with processing speed, suggesting altered or compensatory network co-activation. No significant group differences were observed for SD metrics. However, network-level analyses revealed significantly lower STD values in all functional networks in the preterm group, indicating possible heightened temporal stability and reduced functional flexibility. These findings suggest that very preterm birth selectively alters the dynamic engagement of functional systems, with potential implications for cognitive vulnerabilities. (243 mots; 250 max).

Obesity is a major public health issue affecting nearly 40% of American adults and is associated with increased mortality and elevated risk for a number of physical and psychological illnesses. Obesity is associated with impairments in executive functions such as decision making and inhibitory control, as well as in reward valuation, which is thought to contribute to difficulty sustaining healthy lifestyle behaviors, including adhering to a healthy diet. Growing evidence indicates that these impairments are accompanied by disruptions in functional brain networks, particularly those that support self-regulation, reward valuation, self-directed thinking and homeostatic control. Weight-related differences in task-evoked and resting-state connectivity have most frequently been noted in the executive control network (ECN), salience network (SN) and default mode network (DMN), with obesity generally being associated with weakened connectivity in the ECN and enhanced connectivity in the SN and DMN. Similar disruptions have been observed in the much smaller literature examining the relationship between diet and disordered eating behaviors on functional network organization. The purpose of this narrative review was to summarize what is currently known about how obesity and eating behavior relate to functional brain networks, describe common patterns and provide recommendations for future research based on the identified gaps in knowledge.

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

Characterization of EEG-based functional brain networks in myotonic dystrophy type 1

P.553 5-HT4R binding is positively correlated with obsessive-compulsive features: a preliminary analysis in healthy volunteers

Review for "Testing nonlinearity in topological organization of functional brain networks"

AIMTo increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug (AED) treatment.METHODSThe relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients with epilepsy with a different risk profile for developing cognitive side effects were included: A “low risk” category (lamotrigine or levetiracetam, n = 16), an “intermediate risk” category (carbamazepine, oxcarbazepine, phenytoin, or valproate, n = 34) and a “high risk” category (topiramate, n = 5). Brain connectivity was assessed using resting state functional magnetic resonance imaging and graph theoretical network analysis. The Computerized Visual Searching Task was used to measure central information processing speed, a common cognitive side effect of AED treatment.RESULTSCentral information processing speed was lower in patients taking AEDs from the intermediate and high risk categories, compared with patients from the low risk category. The effect of risk category on global efficiency was significant (P < 0.05, ANCOVA), with a significantly higher global efficiency for patient from the low category compared with the high risk category (P < 0.05, post-hoc test). Risk category had no significant effect on the clustering coefficient (ANCOVA, P > 0.2). Also no significant associations between information processing speed and global efficiency or the clustering coefficient (linear regression analysis, P > 0.15) were observed.CONCLUSIONOnly the four patients taking topiramate show aberrant network measures, suggesting that alterations in functional brain network organization may be only subtle and measureable in patients with more severe cognitive side effects.