scholarly journals ADHD Symptoms are Associated with the Modular Structure of Intrinsic Brain Networks in a Representative Sample of Healthy Adults

2018 ◽  
Author(s):  
Kirsten Hilger ◽  
Christian J. Fiebach
Keyword(s):  
Representative Sample ◽  
Information Transfer ◽  
Adult Population ◽  
Brain Networks ◽  
Brain Regions ◽  
Adhd Symptoms ◽  
Functional Brain ◽  
Default Mode ◽  
Functional Brain Networks ◽  
And Task

AbstractAttention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal (i.e., clinically unaffected) adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only eight out of 291 participants scored in the clinical range. Subject-specific brain-network graphs were modeled from functional MRI resting-state data and revealed significant associations between (non-clinical) ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks.Author SummaryNeurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. We investigate whether these network-based theories of ADHD can be generalized to ADHD-related behaviors within the normal adult population. Subject-specific brain graphs were modeled from functional MRI resting-state data of a large and representative sample (N = 291). Significant associations between ADHD-related behaviors and region-specific profiles of between-module and within-module connectivity were observed in brain regions associated with multiple functional systems including the default-mode network, the salience network, and the central executive system. Our results support a dimensional conceptualization of ADHD and enforce network-based models of ADHD by providing further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions.

scholarly journals ADHD symptoms are associated with the modular structure of intrinsic brain networks in a representative sample of healthy adults

2019 ◽  
Vol 3 (2) ◽  
pp. 567-588 ◽  
Author(s):  
Kirsten Hilger ◽  
Christian J. Fiebach
Keyword(s):  
Representative Sample ◽  
Information Transfer ◽  
Adult Population ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Adhd Symptoms ◽  
Functional Brain ◽  
Default Mode ◽  
Functional Brain Networks

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal (i.e., clinically unaffected) adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only 8 out of 291 participants scored in the clinical range. Subject-specific brain network graphs were modeled from functional MRI resting-state data and revealed significant associations between (nonclinical) ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks.

scholarly journals Unifying the Notions of Modularity and Core–Periphery Structure in Functional Brain Networks during Youth

2019 ◽  
Vol 30 (3) ◽  
pp. 1087-1102
Author(s):  
Shi Gu ◽  
Cedric Huchuan Xia ◽  
Rastko Ciric ◽  
Tyler M Moore ◽  
Ruben C Gur ◽  
...  
Keyword(s):  
Individual Differences ◽  
Brain Function ◽  
Brain Networks ◽  
Brain Regions ◽  
Imaging Data ◽  
Modular Architecture ◽  
Functional Brain ◽  
Rich Club ◽  
Functional Brain Networks ◽  
Integrative Processing

AbstractAt rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core–periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core–periphery structure. Here, we leverage a recently-developed model-based approach—the weighted stochastic block model—that simultaneously uncovers modular and core–periphery structure, and we apply it to functional magnetic resonance imaging data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich mesoscale organization beyond that sought by modularity maximization techniques. Moreover, we show that this mesoscale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core–periphery structure in functional brain networks, offering novel insights into their development and implications for behavior.

scholarly journals Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242985
Author(s):  
Howard Muchen Hsu ◽  
Zai-Fu Yao ◽  
Kai Hwang ◽  
Shulan Hsieh
Keyword(s):  
Individual Differences ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Motor Inhibition ◽  
Attention Network ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Dorsal Attention Network ◽  
Functional Brain Networks

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.

scholarly journals Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle

2020 ◽  
Author(s):  
Joshua M. Mueller ◽  
Laura Pritschet ◽  
Tyler Santander ◽  
Caitlin M. Taylor ◽  
Scott T. Grafton ◽  
...  
Keyword(s):  
Menstrual Cycle ◽  
Community Detection ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Default Mode ◽  
Hormone Interactions ◽  
Dynamic Community Detection ◽  
Functional Brain Networks ◽  
Dynamic Community

AbstractSex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intra-network increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that ovulation results in a temporary, localized patterns of brain network reorganization.Author SummarySex steroid hormones influence the central nervous system across multiple spatiotemporal scales. Estrogen and progesterone concentrations rise and fall throughout the menstrual cycle, but it remains poorly understood how day-to-day fluctuations in hormones shape human brain dynamics. Here, we assessed the structure and stability of resting-state brain network activity in concordance with serum hormone levels from a female who underwent fMRI and venipuncture for 30 consecutive days. Our results reveal that while network structure is largely stable over the menstrual cycle, there is temporary reorganization of several largescale functional brain networks during the ovulatory window. In particular, a default mode subnetwork exhibits increased connectivity with itself and with regions from temporoparietal and limbic networks, providing novel perspective into brain-hormone interactions.

scholarly journals Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

2019 ◽  
Author(s):  
Amir Hossein Ghaderi ◽  
Bianca R. Baltaretu ◽  
Masood Nemati Andevari ◽  
Vishal Bharmauria ◽  
Fuat Balci
Keyword(s):  
Graph Theory ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Spectral Graph Theory ◽  
Theory Approach ◽  
Topological Properties ◽  
Functional Brain ◽  
Spectral Graph ◽  
Functional Brain Networks

AbstractTo characterize differences between different state-related brain networks, statistical graph theory approaches have been employed to identify informative, topological properties. However, dynamical properties have been studied little in this regard. Our goal here was to introduce spectral graph theory as a reliable approach to determine dynamic properties of functional brain networks and to find how topological versus dynamical features differentiate between such networks. To this goal, 45 participants performed no task with eyes open (EO) or closed (EC) while electroencephalography data were recorded. These data were used to create weighted adjacency matrices for each condition (rest state EO and rest state EC). Then, using the spectral graph theory approach and Shannon entropy, we identified dynamical properties for weighted graphs, and we compared these features with topological aspects of graphs. The results showed that spectral graph theory can distinguish different state-dependent neural networks with different synchronies. On the other hand, correlation analysis indicated that although dynamical and topological properties of random networks are completely independent, these network features can be related in the case of brain generated graphs. In conclusion, the spectral graph theory approach can be used to make inferences about various state-related brain networks, for healthy and clinical populations.Author SummeryBy considering functional communications across different brain regions, a complex network is achieved that is known as functional brain network. Topologically, this network is constructed by different nodes (activity of brain regions or signals over recording electrodes) and different edges (similarity, correlation or phase difference between nodes). Paths, clusters, hubs, and centrality of nodes are examples of topological properties of these networks. However, synchrony and stability of functional brain networks can not be revealed by consideration of topological properties. Alternatively, spectral graph theory (SGT) can demonstrate the dynamic, synchrony and stability of graphs. But this approach has been studied little in brain network analysis. Here, we employed SGT, as well as topological methods, to investigate which approaches are more reliable to find differences between distinct state-related brain networks. On the other hand, we investigated correlations between topology and dynamic in different type of networks (brain generated and random networks). We found that SGT measures can clearly distinguish between distinct state-related brain networks and it can reveal synchrony and complexity of these networks. Also, results show that although dynamic and topology of random-generated graph are completely independent, these properties exhibit several correlations in the case of functional brain networks.

scholarly journals Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle

2020 ◽  
pp. 1-20 ◽  
Author(s):  
Joshua M. Mueller ◽  
Laura Pritschet ◽  
Tyler Santander ◽  
Caitlin M. Taylor ◽  
Scott T. Grafton ◽  
...  
Keyword(s):  
Menstrual Cycle ◽  
Community Detection ◽  
Large Scale ◽  
Brain Networks ◽  
Functional Brain ◽  
Default Mode ◽  
Detection Techniques ◽  
Dynamic Community Detection ◽  
Functional Brain Networks ◽  
Dynamic Community

Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization.

scholarly journals Cerebral Energy Metabolism and the Brain’s Functional Network Architecture: An Integrative Review

2013 ◽  
Vol 33 (9) ◽  
pp. 1347-1354 ◽  
Author(s):  
Louis-David Lord ◽  
Paul Expert ◽  
Jeremy F Huckins ◽  
Federico E Turkheimer
Keyword(s):  
Functional Connectivity ◽  
High Frequency ◽  
Network Architecture ◽  
Large Scale ◽  
Brain Networks ◽  
Glucose Consumption ◽  
Brain Regions ◽  
Functional Brain ◽  
Neurobiological Substrates ◽  
Functional Brain Networks

Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain’s ‘functional connectivity’ is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks.

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