Intrinsic and Task-Evoked Network Architectures of the Human Brain

The brain's mature functional network architecture has been extensively studied but the early emergence of the brain's network organization remains largely unknown. In this study, leveraging a large sample (143 subjects) with longitudinal rsfMRI scans (333 datasets), we aimed to characterize the important developmental process of the brain's functional network architecture during the first 2years of life. Based on spatial independent component analysis and longitudinal linear mixed effect modeling, our results unveiled the detailed topology and growth trajectories of nine cortical functional networks. Within networks, our findings clearly separated the brains networks into two categories: primary networks were topologically adult-like in neonates while higher-order networks were topologically incomplete and isolated in neonates but demonstrated consistent synchronization during the first 2years of life (connectivity increases 0.13-0.35). Between networks, our results demonstrated both network-level connectivity decreases (-0.02 to -0.64) and increases (0.05-0.18) but decreasing connections (n=14) dominated increasing ones (n=5). Finally, significant sex differences were observed with boys demonstrating faster network-level connectivity increases among the two frontoparietal networks (growth rate was 1.63e-4 per day for girls and 2.69e-4 per day for boys, p<1e-4). Overall, our study delineated the development of the whole brain functional architecture during the first 2years of life featuring significant changes of both within- and between-network interactions.

The presence of both apolipoprotein E (APOE) ε4 allele and amnestic mild cognitive impairment (aMCI) are considered to be risk factors for Alzheimer’s disease (AD). Numerous neuroimaging studies have suggested that the modulation of APOE ε4 affects intrinsic functional brain networks, both in healthy populations and in AD patients. However, it remains largely unclear whether and how ε4 allele modulates the brain’s functional network architecture in subjects with aMCI. Using resting-state functional magnetic resonance imaging (fMRI) and graph-theory approaches-functional connectivity strength (FCS), we investigate the topological organization of the whole-brain functional network in 28 aMCI ε4 carriers and 38 aMCI ε3ε3 carriers. In the present study, we first observe that ε4-related FCS increases in the right hippocampus/parahippocampal gyrus (HIP/PHG). Subsequent seed-based resting-state functional connectivity (RSFC) analysis revealed that, compared with the ε3ε3 carriers, the ε4 carriers had lower or higher RSFCs between the right HIP/PHG seed and the bilateral medial prefrontal cortex (MPFC) or the occipital cortex, respectively. Further correlation analyses have revealed that the FCS values in the right HIP/PHG and lower HIP/PHG-RSFCs with the bilateral MPFC were significantly correlated with the impairment of episodic memory and executive function in the aMCI ε4 carriers. Importantly, the logistic regression analysis showed that the HIP/PHG-RSFC with the bilateral MPFC predicted aMCI-conversion to AD. These findings suggest that the APOE ε4 allele may modulate the large-scale brain network in aMCI subjects, facilitating our understanding of how the entire assembly of the brain network reorganizes in response to APOE variants in aMCI. Further longitudinal studies need to be conducted, in order to examine whether these network measures could serve as primary predictors of conversion from aMCI ε4 carriers to AD.

Clinical Concepts Emerging from fMRI Functional Connectomics

A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks.

Schizophrenia, a mental disorder that is characterized by abnormal social behaviour and failure to distinguish one’s own thoughts and ideas from reality, has been associated with structural abnormalities in the architecture of functional brain networks. In this article, we (1) investigate whether mesoscale network properties give relevant information to distinguish groups of patients from controls in different scenarios and (2) use this lens to examine network effects of different antipsychotic treatments. Using various methods of network analysis, we examine the effect of two classical therapeutic antipsychotics—Aripiprazole and Sulpiride—on the architecture of functional brain networks of both controls (i.e., a set of people who were deemed to be healthy) and patients (who were diagnosed with schizophrenia). We compare community structures of functional brain networks of different individuals using mesoscopic response functions, which measure how community structure changes across different scales of a network. Our approach does a reasonably good job of distinguishing patients from controls, and the distinction is sharper for patients and controls who have been treated with Aripiprazole. Unexpectedly, we find that this increased separation between patients and controls is associated with a change in the control group, as the functional brain networks of the patient group appear to be predominantly unaffected by this drug. This suggests that Aripiprazole has a significant and measurable effect on community structure in healthy individuals but not in individuals who are diagnosed with schizophrenia, something that conflicts with the naive assumption that the drug alters the mesoscale network properties of the patients (rather than the controls). By contrast, we are less successful at separating the networks of patients from those of controls when the subjects have been given the drug Sulpiride. Taken together, in our results, we observe differences in the effects of the drugs (and a placebo) on community structure in patients and controls and also that this effect differs across groups. From a network-science perspective, we thereby demonstrate that different types of antipsychotic drugs selectively affect mesoscale properties of brain networks, providing support that structures such as communities are meaningful functional units in the brain.

Neighborhood poverty during childhood prospectively predicts adolescent functional brain network architecture

Investigating brain network properties in BD patients across mood states can offer insights into the underlying mechanisms of the disorder. This study aimed to explore the topological architecture of functional brain networks in BD and its relationship with clinical variables and genetic/transcriptomic variations. The study involved 100 BD patients and 95 healthy controls. Researchers used graph theory-based methods to analyze whole-brain functional networks and explore their relationship with clinical variables. We also conducted a neuroimaging-transcription association analysis using the Allen Human Brain Atlas. Depressive and manic BD patients exhibited increased local efficiency and decreased global efficiency at the global network level compared to healthy controls. Nodal-level analysis revealed disrupted nodal parameters within specific brain networks, including the fronto-parietal, default mode, and somatomotor networks. Significant correlations were found between nodal properties and cognitive function. All BD groups showed enhanced connectivity strength in rich-club and feeder connections compared to controls. Neuroimaging-transcription analysis identified potential genetic factors related to BD. Our investigation unveiled shared impairments in the overall topological architecture of functional brain networks across depressive, manic, and euthymic BD. These observed abnormalities were associated with cognitive deficits in BD patients across three mood states. These common deficits, possibly stemming from the segregated changes in structural and functional rich-club connections, might represent trait-like pathophysiological mechanisms inherent to BD. Furthermore, our neuroimaging-transcription association analysis indicates the potential use of brain functional anomalies as endophenotypes in BD.

Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG). Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach. Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers. This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.

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.

APOE ε4 and resting-state functional connectivity in racially/ethnically diverse older adults.

Event Abstract Back to Event Uncovering intrinsic connectional architecture of functional networks in awake rat brain Zhifeng Liang1*, Jean King1 and Nanyin Zhang1 1 University of Massachusetts Medical School, United States Intrinsic connectional architecture of the brain is a crucial element in understanding the governing principle of brain organization. To date, enormous effort has been focused on addressing this issue in humans by resting-state functional magnetic resonance imaging (rsfMRI). rsfMRI noninvasively measures functional connectivity without external stimulation, based on spontaneous low frequency fluctuations of the blood oxygenation level dependent (BOLD) signal. Using this technique, resting-state functional connectivity (RSFC) has been consistently revealed in multiple networks of the human brain, and has been shown to be altered by various factors such as sleep, anesthesia, as well as neurological and psychiatric disorders. Although much progress has been made in understanding connectional architecture of the human brain, this research area is significantly underexplored in animals, perhaps because of confounding effects of anesthetic agents used in most animal experiments on rsfMRI. To bridge this gap, we have systematically investigated the intrinsic connectional architecture by using a previously established awake-animal imaging model. The following results have been published recently (Liang et al., 2011). First, group independent component analysis (ICA) was applied to the rsfMRI data to extract elementary functional clusters of the brain. Most ICA components identified were located in specific anatomical regions, including bilateral caudate putamen, hippocampus, somatosensory cortex, thalamus and other cortical and subcortical regions. Therefore, this step provided a global layout of functional clusters in the awake rat brain. To our knowledge, this is currently the first study utilizing group ICA to study RSFC in rat brain, with only one previous rat ICA study at individual level (Hutchison et al., 2010). Subsequently, the connectional relationships between these clusters were evaluated by partial correlation analysis. Partial correlation coefficients between time courses of any two ICA components were calculated, controlling the rest of ICA components. Compared to widely used Pearson’s correlation in RSFC analysis, partial correlation could eliminate a large portion of connectivity that is mediated by other components, leaving largely direct connectivity. The partial correlation coefficients matrices were then used to construct whole-brain functional neural network. Graph theoretical analysis was carried out for this network to examine two network characteristics: small-worldness and modular structure. This global network exhibited typical features of small-worldness, characterized by relatively high clustering coefficient and almost the same average shortest path length compared to random networks. Thus this result suggests that small-worldness is conserved in awake rat functional neural network, similar to results seen in human studies. In addition, this network also exhibited strong community structure as seen in many biological and social networks. Modularity based community detection algorithm revealed significantly higher modularity (Q) value (Q =0.414) compared to random networks (p value<0.01), suggesting a significant modular structure. The whole-brain functional network was first partitioned into three modules. The first module predominantly extended across the cortical ribbon, indicating a strong intercortical communication across the cortex. The second module highlighted the olfactory pathway and its interaction with prefrontal cortex (PFC), and the integration of other sensory input, cognitive processing, and output in cortical and subcortical regions. Regions in the third module, including PFC, insular cortex, hypothalamus, and amygdala, are all key components subserving emotional and autonomic regulations. To address degeneracy issue of the modularity function, distributions of Q values and community structures were obtained for 20 repetitions. The result showed that the later two of the three modules previously identified were highly consistent across all repetitions with little variation, whereas the community structure of cortical regions was further divided into two sub-modules in the majority of repetitions. Overall, the results of this work provided a functional atlas of intrinsic connectional architecture of the rat brain at both intraregional and interregional levels. More importantly, the current work revealed that functional networks in rats are organized in a nontrivial manner and conserve fundamental topological properties that are also seen in the human brain. Given the high psychopathological relevance of network organization of the brain, this study demonstrated the feasibility of studying mechanisms and therapies of multiple neurological and psychiatric diseases through translational research. Keywords: Neuroimaging Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: Neuroimaging Citation: Liang Z, King J and Zhang N (2011). Uncovering intrinsic connectional architecture of functional networks in awake rat brain. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00145 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Zhifeng Liang, University of Massachusetts Medical School, Worcester, United States, Zhifeng.Liang@umassmed.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Zhifeng Liang Jean King Nanyin Zhang Google Zhifeng Liang Jean King Nanyin Zhang Google Scholar Zhifeng Liang Jean King Nanyin Zhang PubMed Zhifeng Liang Jean King Nanyin Zhang Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

We investigated changes in functional network architecture in amnestic mild cognitive impairment using graph-based analysis of task-free functional magnetic resonance imaging and fine cortical parcellation. Widespread disconnection was observed primarily in cortical hubs known to manifest early Alzheimer's disease pathology, namely precuneus, parietal lobules, supramarginal and angular gyri, and cuneus, with additional involvement of subcortical regions, sensorimotor cortex and insula. The connectivity changes determined using graph-based analysis significantly exceed those detected using independent component analysis both in amplitude and topographical extent, and are largely decoupled from the presence of overt atrophy. This superior ability of graph-based analysis to detect disease-related disconnection highlights its potential use in the determination of biomarkers of early dementia. Graph-based analysis source code is provided as supplementary material.

The apolipoprotein E (APOE) ɛ4 allele is a well-established genetic risk factor for Alzheimer's disease (AD). Recent research has demonstrated an APOE ɛ4-mediated modulation of intrinsic functional brain networks in cognitively normal individuals. However, it remains largely unknown whether and how APOE ɛ4 affects the brain's functional network architecture in patients with AD. Using resting-state functional MRI and graph-theory approaches, we systematically investigated the topological organization of whole-brain functional networks in 16 APOE ɛ4 carriers and 26 matched noncarriers with AD at three levels: global whole-brain, intermediate module, and regional node/connection. Neuropsychological analysis showed that the APOE ɛ4 carriers performed worse on delayed memory but better on a late item generation of a verbal fluency task (associated with executive function) than noncarriers. Whole-brain graph analyses revealed that APOE ɛ4 significantly disrupted whole-brain topological organization as characterized by (i) reduced parallel information transformation efficiency; (ii) decreased intramodular connectivity within the posterior default mode network (pDMN) and intermodular connectivity of the pDMN and executive control network (ECN) with other neuroanatomical systems; and (iii) impaired functional hubs and their rich-club connectivities that primarily involve the pDMN, ECN, and sensorimotor systems. Further simulation analysis indicated that these altered connectivity profiles of the pDMN and ECN largely accounted for the abnormal global network topology. Finally, the changes in network topology exhibited significant correlations with the patients' cognitive performances. Together, our findings suggest that the APOE genotype modulates large-scale brain networks in AD and shed new light on the gene-connectome interaction in this disease.

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the ability of the human brain to adaptively alter its functionality via rapid changes in inter-regional relationships. We used activity flow mapping-an approach for building empirically derived network models-to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.SIGNIFICANCE STATEMENT Human cognition is highly dynamic, yet the functional network organization of the human brain is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the intrinsic (resting-state) network organization of the brain strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.

Community detection algorithms have been widely used to study the organization of complex networks like the brain. These techniques provide a partition of brain regions (or nodes) into clusters (or communities), where nodes within a community are densely interconnected with one another. In their simplest application, community detection algorithms are agnostic to the presence of community hierarchies: clusters embedded within clusters of other clusters. To address this limitation, we exercise a multi-scale extension of a common community detection technique, and we apply the tool to synthetic graphs and to graphs derived from human neuroimaging data, including structural and functional imaging data. Our multi-scale community detection algorithm links a graph to copies of itself across neighboring topological scales, thereby becoming sensitive to conserved community organization across levels of the hierarchy. We demonstrate that this method is sensitive to topological inhomogeneities of the graph’s hierarchy by providing a local measure of community stability and inter-scale reliability across topological scales. We compare the brain’s structural and functional network architectures, and we demonstrate that structural graphs display a more prominent hierarchical community organization than functional graphs. Finally, we build an explicitly multimodal multiplex graph that combines both structural and functional connectivity in a single model, and we identify the topological scales where resting state functional connectivity and underlying structural connectivity show similar versus unique hierarchical community architecture. Together, our results demonstrate the advantages of the multi-scale community detection algorithm in studying hierarchical community structure in brain graphs, and they illustrate its utility in modeling multimodal neuroimaging data.