Abnormal Rich Club Organization and Functional Brain Dynamics in Schizophrenia
TL;DR: These findings provide novel biological evidence that schizophrenia is characterized by a selective disruption of brain connectivity among central hub regions of the brain, potentially leading to reduced communication capacity and altered functional brain dynamics.
Abstract: Importance The human brain forms a large-scale structural network of regions and interregional pathways. Recent studies have reported the existence of a selective set of highly central and interconnected hub regions that may play a crucial role in the brain’s integrative processes, together forming a central backbone for global brain communication. Abnormal brain connectivity may have a key role in the pathophysiology of schizophrenia. Objective To examine the structure of the rich club in schizophrenia and its role in global functional brain dynamics. Design Structural diffusion tensor imaging and resting-state functional magnetic resonance imaging were performed in patients with schizophrenia and matched healthy controls. Setting Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands. Participants Forty-eight patients and 45 healthy controls participated in the study. An independent replication data set of 41 patients and 51 healthy controls was included to replicate and validate significant findings. Main Outcome(s) and Measures Measures of rich club organization, connectivity density of rich club connections and connections linking peripheral regions to brain hubs, measures of global brain network efficiency, and measures of coupling between brain structure and functional dynamics. Results Rich club organization between high-degree hub nodes was significantly affected in patients, together with a reduced density of rich club connections predominantly comprising the white matter pathways that link the midline frontal, parietal, and insular hub regions. This reduction in rich club density was found to be associated with lower levels of global communication capacity, a relationship that was absent for other white matter pathways. In addition, patients had an increase in the strength of structural connectivity–functional connectivity coupling. Conclusions Our findings provide novel biological evidence that schizophrenia is characterized by a selective disruption of brain connectivity among central hub regions of the brain, potentially leading to reduced communication capacity and altered functional brain dynamics.
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TL;DR: Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function.
1,760 citations
TL;DR: It is concluded that the emergence of dynamic functional connectivity, from static structural connections, calls for formal (computational) approaches to neuronal information processing that may resolve the dialectic between structure and function.
Abstract: How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function.
1,612 citations
TL;DR: Empirical empathy is considered as an example of a complex social cognitive function that integrates several social processes and is impaired in schizophrenia, and interventions to improve social cognition in patients with this disorder are considered.
Abstract: Individuals with schizophrenia exhibit impaired social cognition, which manifests as difficulties in identifying emotions, feeing connected to others, inferring people's thoughts and reacting emotionally to others. These social cognitive impairments interfere with social connections and are strong determinants of the degree of impaired daily functioning in such individuals. Here, we review recent findings from the fields of social cognition and social neuroscience and identify the social processes that are impaired in schizophrenia. We also consider empathy as an example of a complex social cognitive function that integrates several social processes and is impaired in schizophrenia. This information may guide interventions to improve social cognition in patients with this disorder.
982 citations
TL;DR: The state of translational neuroimaging is reviewed, an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings is outlined and a program of broad exploration followed by increasingly rigorous assessment of generalizability is outlined.
Abstract: Despite its great promise, neuroimaging has yet to substantially impact clinical practice and public health. However, a developing synergy between emerging analysis techniques and data-sharing initiatives has the potential to transform the role of neuroimaging in clinical applications. We review the state of translational neuroimaging and outline an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings. The approach rests on three pillars: (i) the use of multivariate pattern-recognition techniques to develop brain signatures for clinical outcomes and relevant mental processes; (ii) assessment and optimization of their diagnostic value; and (iii) a program of broad exploration followed by increasingly rigorous assessment of generalizability across samples, research contexts and populations. Increasingly sophisticated models based on these principles will help to overcome some of the obstacles on the road from basic neuroscience to better health and will ultimately serve both basic and applied goals.
712 citations
TL;DR: Tools from control and network theories are used to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure and suggest that densely connected areas facilitate the movement of the brain to many easily reachable states.
Abstract: Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.
712 citations
References
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TL;DR: In this article, the modularity of a network is expressed in terms of the eigenvectors of a characteristic matrix for the network, which is then used for community detection.
Abstract: Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as “modularity” over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.
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TL;DR: Construction of brain networks from connectivity data is discussed and the most commonly used network measures of structural and functional connectivity are described, which variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, and test resilience of networks to insult.
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TL;DR: The results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.
6,411 citations
TL;DR: The spatial and topological centrality of the core within cortex suggests an important role in functional integration and a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants.
Abstract: Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.
4,035 citations
"Abnormal Rich Club Organization and..." refers background or methods in this paper
...d Parental educational level: principal data set: ranging from (unfinished) primary school education (1) to university (7); replication data set: ranging from no education (0) to university (8)....
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...Two networks were created: (1) a cortical network consisting of 68 cortical regions and (2) a wholebrain network consisting of the 68 cortical and 14 subcortical regions....
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TL;DR: A technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set is presented, comparable in accuracy to manual labeling.
Abstract: We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This procedure incorporates both geometric information derived from the cortical model, and neuroanatomical convention, as found in the training set. The result is a complete labeling of cortical sulci and gyri. Examples are given from two different training sets generated using different neuroanatomical conventions, illustrating the flexibility of the algorithm. The technique is shown to be comparable in accuracy to manual labeling.
3,880 citations