In neuroimaging studies, the brains of children with autism spectrum disorder (ASD) show distinct functional mapping of specific brain nodes, with nodes in normal developing children (without autism spectrum disorder) and shown to play different roles. Our findings suggest that these changes to brain node topology may begin to occur at a very early age in children with autism. This research autism research.
Recent neuroimaging studies of human brain function using magnetic resonance imaging and novel graph-theoretic methodological approaches have shown that the human brain possesses a hierarchical modular organization. Larger functional communities of brain cells (nodes) are divided into smaller communities to create this hierarchy.
Recent studies of persons with autism spectrum disorders using functional magnetic resonance imaging show atypical functional connectivity and functional brain network organization associated with the social and cognitive abilities of these individuals. it was done. ASD is a neurodevelopmental disorder characterized by restricted interests, repetitive behaviors, and difficulties in social communication. Many studies have linked these features to differences in brain function. However, findings regarding the precise nature of these functional changes have been mixed.
“Our interest in investigating atypical nodal processing in ASD began with the question of whether individual node-level information could reveal important insights during atypical neurodevelopment in ASD. Principal Investigator, Cognitive Brain Dynamics Lab.
“According to the existing literature, brain network topology and early developmental (3–5 years) functional organization exhibit global modular organization. , plays an important role in late developmental processes and behavior.Previous neuroimaging studies have shown discrepancies in functional brain modular organization studies in ASD.These discrepancies have been explored across different age groups. It is the result of
“Furthermore, recent findings indicate that while much attention has been paid to deriving atlases and measuring connectivity between nodes, information within nodes may be altered in ASD compared to typical developments. suggesting that it may be important in determining modular organization, although altered modular organization resulting from systematic node changes has not yet been investigated in younger children with ASD. To fill this knowledge gap, we used innovative graph-theoretical means to deduce node mapping, inter-node communication, and hyper-synchronization.”
Researchers analyzed functional magnetic resonance imaging data from multiple sources, males with an IQ >75, and children with ASD aged 5 to 10 years. They also collected data from the Autism Brain Imaging Exchange (ABIDE) for normally developing children with the same age, gender, and IQ requirements for comparison.
Researchers used specific algorithms to investigate the modular organization of functional brain networks in individual participants. The global modularity Q, the number of modules, and the size of the modules were calculated.
Results showed that global network structure and functional connectivity were well preserved in both ASD and typically developing children. Their brain network structures were also very similar on average. However, Roy and his colleagues found much greater differences between brain network partitions in the ASD group. This indicates that the network structure of the brains of children with autism is much different than that of a group of normally developing children.
“The role of altered lymph nodes in ASD has not been fully elucidated in other studies so far,” Roy told PsyPost. The proportion of different types of nodes further influences the global flow of information, the efficient integration/separation of functional brain networks, as it affects the flow of information within and between modules and cognitive performance. .”
“Therefore, the proportion of nodes with different topological roles is also considered a global property of modular network organization. To facilitate the flow of information between and within modules, different node types They also have different ability to switch, so different types of nodes have different consistency of modular membership (for example, connector hubs are more flexible to switch modules and play an important role in information exchange). within and across modules).
“Our results show that children with ASD have a relatively high proportion of non-hub connectors (responsible for connections between modules) compared to normal developmental stages, and peripheral non-hub and local hub ( maintain connections within modules), indicating a lower proportion of children.Follow-up analysis showed that peripheral nodes in typical development transformed into non-hub connectors in ASD, whereas local hubs has been transformed into a connector hub for ASD.”
“These findings explain the increase in inter-module connectivity observed in ASD by increasing the proportion of non-hub connector nodes and decreasing intra-module connectivity due to the decreasing proportion of local hubs and peripheral non-hubs. We are doing it,” explained Roy. “While the main common connector hubs were from sensorimotor, saliency and default mode networks, as reported in previous literature, children with ASD have sensorimotor and saliency networks. There were many connectors from
“These nodes, which are connectors, also have greater flexibility to switch between modules and improved connectivity between modules. In addition, another node type showing increased modular connectivity in ASD is default mode networks and dorsal attention.” and non-connector nodes from the limbic network.These results suggest that major large-scale network-level changes in children with ASD involve brain regions of the sensorimotor, saliency, and default mode networks. It reflects that there is
Altered topological roles and modular cohesiveness of brain cell nodes were primarily associated with social and sensory deficits in children with autism.
“Given the lack of developmental consistency in the previous literature investigating global and nodal modular network organization in individuals with ASD, our results suggest that functional brain modularity in children with ASD may be “It provides evidence for the atypicality of the trait,” Roy told PsyPost. Further exploration of the biomarkers may help facilitate advances in intervention strategies for early developmental social cognitive and communication deficits.”
The results of this study highlight the importance of early brain development for understanding ASD. However, it should be considered that the data used in the study came from a variety of sources and did not take into account differences due to differences in scanning protocols and scanners themselves. bottom.
“The child sample size in this study was particularly small because fMRI scans provided by the ABIDE consortium are limited to children in the age group of 3–5 years. We await replication with larger sample sizes using data from non-II sites.”
“Overall, the results suggest that an increase in inter-module connectivity between the default mode network, the salience network, and the central management network, and a decrease in global modularity in the ASD group, may indicate a less robust modular configuration for the system. Functional brain network synchronicity reported in individuals with ASD causes core deficits increase.
“However, the key roles exhibited by the DMN, salience, and central executive network nodes contribute to the development of atypical modular organization and functional organization of core and peripheral brain networks in ASD, and what proportion of peripheral hubs are involved.” Brain regions and non-hub peripheral brain regions and core regions need to be fully explored to shed insights into how they change in cognitive flexibility, social cognition deficits, speech and communication deficits, emotional It affects perception and accommodation.”
The study “Altered global modular organization of endogenous functional connectivity in autism results from atypical node-level processing” was authored by Priyanka Sigar, Lucina Q. Uddin, and Dipanjan Roy.
