601
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Abstract
Computational and empirical neuroimaging studies have suggested that the anatomical connections between brain regions primarily constrain their functional interactions. Given that the large-scale organization of functional networks is determined by the temporal relationships between brain regions, the structural limitations may extend to the global characteristics of functional networks. Here, we explored the extent to which the functional network community structure is determined by the underlying anatomical architecture. We directly compared macaque (Macaca fascicularis) functional connectivity (FC) assessed using spontaneous blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) to directed anatomical connectivity derived from macaque axonal tract tracing studies. Consistent with previous reports, FC increased with increasing strength of anatomical connection, and FC was also present between regions that had no direct anatomical connection. We observed moderate similarity between the FC of each region and its anatomical connectivity. Notably, anatomical connectivity patterns, as described by structural motifs, were different within and across functional modules: partitioning of the functional network was supported by dense bidirectional anatomical connections within clusters and unidirectional connections between clusters. Together, our data directly demonstrate that the FC patterns observed in resting-state BOLD-fMRI are dictated by the underlying neuroanatomical architecture. Importantly, we show how this architecture contributes to the global organizational principles of both functional specialization and integration.
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602
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Jiang J, Egner T. Using neural pattern classifiers to quantify the modularity of conflict-control mechanisms in the human brain. Cereb Cortex 2013; 24:1793-805. [PMID: 23402762 DOI: 10.1093/cercor/bht029] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict-control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of "searchlight" classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict-control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict-control were not. Overall, these findings suggest a hybrid neural architecture of conflict-control that entails both modular (domain specific) and global (domain general) components.
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Affiliation(s)
- Jiefeng Jiang
- Center for Cognitive Neuroscience and Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Tobias Egner
- Center for Cognitive Neuroscience and Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
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603
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Sarkar S, Henderson JA, Robinson PA. Spectral characterization of hierarchical network modularity and limits of modularity detection. PLoS One 2013; 8:e54383. [PMID: 23382895 PMCID: PMC3557301 DOI: 10.1371/journal.pone.0054383] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2012] [Accepted: 12/11/2012] [Indexed: 11/24/2022] Open
Abstract
Many real world networks are reported to have hierarchically modular organization. However, there exists no algorithm-independent metric to characterize hierarchical modularity in a complex system. The main results of the paper are a set of methods to address this problem. First, classical results from random matrix theory are used to derive the spectrum of a typical stochastic block model hierarchical modular network form. Second, it is shown that hierarchical modularity can be fingerprinted using the spectrum of its largest eigenvalues and gaps between clusters of closely spaced eigenvalues that are well separated from the bulk distribution of eigenvalues around the origin. Third, some well-known results on fingerprinting non-hierarchical modularity in networks automatically follow as special cases, threreby unifying these previously fragmented results. Finally, using these spectral results, it is found that the limits of detection of modularity can be empirically established by studying the mean values of the largest eigenvalues and the limits of the bulk distribution of eigenvalues for an ensemble of networks. It is shown that even when modularity and hierarchical modularity are present in a weak form in the network, they are impossible to detect, because some of the leading eigenvalues fall within the bulk distribution. This provides a threshold for the detection of modularity. Eigenvalue distributions of some technological, social, and biological networks are studied, and the implications of detecting hierarchical modularity in real world networks are discussed.
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Affiliation(s)
- Somwrita Sarkar
- School of Physics, University of Sydney, Sydney, New South Wales, Australia.
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604
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Uehara T, Yamasaki T, Okamoto T, Koike T, Kan S, Miyauchi S, Kira JI, Tobimatsu S. Efficiency of a "small-world" brain network depends on consciousness level: a resting-state FMRI study. Cereb Cortex 2013; 24:1529-39. [PMID: 23349223 DOI: 10.1093/cercor/bht004] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
It has been revealed that spontaneous coherent brain activity during rest, measured by functional magnetic resonance imaging (fMRI), self-organizes a "small-world" network by which the human brain could sustain higher communication efficiency across global brain regions with lower energy consumption. However, the state-dependent dynamics of the network, especially the dependency on the conscious state, remain poorly understood. In this study, we conducted simultaneous electroencephalographic recording with resting-state fMRI to explore whether functional network organization reflects differences in the conscious state between an awake state and stage 1 sleep. We then evaluated whole-brain functional network properties with fine spatial resolution (3781 regions of interest) using graph theoretical analysis. We found that the efficiency of the functional network evaluated by path length decreased not only at the global level, but also in several specific regions depending on the conscious state. Furthermore, almost two-thirds of nodes that showed a significant decrease in nodal efficiency during stage 1 sleep were categorized as the default-mode network. These results suggest that brain functional network organizations are dynamically optimized for a higher level of information integration in the fully conscious awake state, and that the default-mode network plays a pivotal role in information integration for maintaining conscious awareness.
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Affiliation(s)
- Taira Uehara
- Faculty of Medicine, Department of Clinical Neurophysiology
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605
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Structure out of chaos: functional brain network analysis with EEG, MEG, and functional MRI. Eur Neuropsychopharmacol 2013; 23:7-18. [PMID: 23158686 DOI: 10.1016/j.euroneuro.2012.10.010] [Citation(s) in RCA: 80] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2011] [Revised: 09/10/2012] [Accepted: 10/18/2012] [Indexed: 01/21/2023]
Abstract
The brain is the characteristic of a complex structure. By representing brain function, measured with EEG, MEG, and fMRI, as an abstract network, methods for the study of complex systems can be applied. These network studies have revealed insights in the complex, yet organized, architecture that is evidently present in brain function. We will discuss some technical aspects of formation and assessment of the functional brain networks. Moreover, the results that have been reported in this respect in the last years, in healthy brains as well as in functional brain networks of subjects with a neurological or psychiatrical disease, will be reviewed.
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606
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Fingelkurts AA, Fingelkurts AA. Operational Architectonics Methodology for EEG Analysis: Theory and Results. MODERN ELECTROENCEPHALOGRAPHIC ASSESSMENT TECHNIQUES 2013. [DOI: 10.1007/7657_2013_60] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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607
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Alexander-Bloch AF, Vértes PE, Stidd R, Lalonde F, Clasen L, Rapoport J, Giedd J, Bullmore ET, Gogtay N. The anatomical distance of functional connections predicts brain network topology in health and schizophrenia. Cereb Cortex 2013; 23:127-38. [PMID: 22275481 PMCID: PMC3513955 DOI: 10.1093/cercor/bhr388] [Citation(s) in RCA: 219] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive "pruning" of short-distance functional connections in schizophrenia.
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Affiliation(s)
- Aaron F. Alexander-Bloch
- Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 3EB, UK,Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD 20892, USA,David Geffen School of Medicine at University of California—Los Angeles, Los Angeles, CA 90095, USA
| | - Petra E. Vértes
- Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 3EB, UK
| | - Reva Stidd
- Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - François Lalonde
- Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Liv Clasen
- Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Judith Rapoport
- Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Jay Giedd
- Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Edward T. Bullmore
- Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 3EB, UK
| | - Nitin Gogtay
- Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD 20892, USA
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608
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Zhang S. Hierarchical modular structure identification with its applications in gene coexpression networks. ScientificWorldJournal 2012; 2012:523706. [PMID: 23431250 PMCID: PMC3568690 DOI: 10.1100/2012/523706] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Accepted: 11/25/2012] [Indexed: 11/30/2022] Open
Abstract
Network module (community) structure has been a hot research topic in recent years. Many methods have been proposed for module detection and identification. Hierarchical structure of modules is shown to exist in many networks such as biological networks and social networks. Compared to the partitional module identification methods, less research is done on the inference of hierarchical modular structure. In this paper, we propose a method for constructing the hierarchical modular structure based on the stochastic block model. Statistical tests are applied to test the hierarchical relations between different modules. We give both artificial networks and real data examples to illustrate the performance of our approach. Application of the proposed method to yeast gene coexpression network shows that it does have a hierarchical modular structure with the modules on different levels corresponding to different gene functions.
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Affiliation(s)
- Shuqin Zhang
- Center for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China.
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609
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Yu K, Wang J, Deng B, Wei X. Synchronization of neuron population subject to steady DC electric field induced by magnetic stimulation. Cogn Neurodyn 2012; 7:237-52. [PMID: 24427204 DOI: 10.1007/s11571-012-9233-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2012] [Revised: 10/31/2012] [Accepted: 12/01/2012] [Indexed: 12/18/2022] Open
Abstract
Electric fields, which are ubiquitous in the context of neurons, are induced either by external electromagnetic fields or by endogenous electric activities. Clinical evidences point out that magnetic stimulation can induce an electric field that modulates rhythmic activity of special brain tissue, which are associated with most brain functions, including normal and pathological physiological mechanisms. Recently, the studies about the relationship between clinical treatment for psychiatric disorders and magnetic stimulation have been investigated extensively. However, further development of these techniques is limited due to the lack of understanding of the underlying mechanisms supporting the interaction between the electric field induced by magnetic stimulus and brain tissue. In this paper, the effects of steady DC electric field induced by magnetic stimulation on the coherence of an interneuronal network are investigated. Different behaviors have been observed in the network with different topologies (i.e., random and small-world network, modular network). It is found that the coherence displays a peak or a plateau when the induced electric field varies between the parameter range we defined. The coherence of the neuronal systems depends extensively on the network structure and parameters. All these parameters play a key role in determining the range for the induced electric field to synchronize network activities. The presented results could have important implications for the scientific theoretical studies regarding the effects of magnetic stimulation on human brain.
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Affiliation(s)
- Kai Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People's Republic of China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People's Republic of China
| | - Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People's Republic of China
| | - Xile Wei
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People's Republic of China
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610
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de Pasquale F, Sabatini U, Della Penna S, Sestieri C, Caravasso CF, Formisano R, Péran P. The connectivity of functional cores reveals different degrees of segregation and integration in the brain at rest. Neuroimage 2012; 69:51-61. [PMID: 23220493 DOI: 10.1016/j.neuroimage.2012.11.051] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2012] [Revised: 11/26/2012] [Accepted: 11/27/2012] [Indexed: 11/30/2022] Open
Abstract
The principles of functional specialization and integration in the resting brain are implemented in a complex system of specialized networks that share some degree of interaction. Recent studies have identified wider functional modules compared to previously defined networks and reported a small-world architecture of brain activity in which central nodes balance the pressure to evolve segregated pathways with the integration of local systems. The accurate identification of such central nodes is crucial but might be challenging for several reasons, e.g. inter-subject variability and physiological/pathological network plasticity, and recent works reported partially inconsistent results concerning the properties of these cortical hubs. Here, we applied a whole-brain data-driven approach to extract cortical functional cores and examined their connectivity from a resting state fMRI experiment on healthy subjects. Two main statistically significant cores, centered on the posterior cingulate cortex and the supplementary motor area, were extracted and their functional connectivity maps, thresholded at three statistical levels, revealed the presence of two complex systems. One system is consistent with the default mode network (DMN) and gradually connects to visual regions, the other centered on motor regions and gradually connects to more sensory-specific portions of cortex. These two large scale networks eventually converged to regions belonging to the medial aspect of the DMN, potentially allowing inter-network interactions.
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611
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Predicting errors from reconfiguration patterns in human brain networks. Proc Natl Acad Sci U S A 2012; 109:16714-9. [PMID: 23012417 DOI: 10.1073/pnas.1207523109] [Citation(s) in RCA: 109] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Task preparation is a complex cognitive process that implements anticipatory adjustments to facilitate future task performance. Little is known about quantitative network parameters governing this process in humans. Using functional magnetic resonance imaging (fMRI) and functional connectivity measurements, we show that the large-scale topology of the brain network involved in task preparation shows a pattern of dynamic reconfigurations that guides optimal behavior. This network could be decomposed into two distinct topological structures, an error-resilient core acting as a major hub that integrates most of the network's communication and a predominantly sensory periphery showing more flexible network adaptations. During task preparation, core-periphery interactions were dynamically adjusted. Task-relevant visual areas showed a higher topological proximity to the network core and an enhancement in their local centrality and interconnectivity. Failure to reconfigure the network topology was predictive for errors, indicating that anticipatory network reconfigurations are crucial for successful task performance. On the basis of a unique network decoding approach, we also develop a general framework for the identification of characteristic patterns in complex networks, which is applicable to other fields in neuroscience that relate dynamic network properties to behavior.
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612
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Niu H, Wang J, Zhao T, Shu N, He Y. Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy. PLoS One 2012; 7:e45771. [PMID: 23029235 PMCID: PMC3454388 DOI: 10.1371/journal.pone.0045771] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2012] [Accepted: 08/22/2012] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics. RESULTS We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques. CONCLUSIONS Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.
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Affiliation(s)
- Haijing Niu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People’s Republic of China
| | - Jinhui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People’s Republic of China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People’s Republic of China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People’s Republic of China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People’s Republic of China
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613
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Optimizing functional network representation of multivariate time series. Sci Rep 2012; 2:630. [PMID: 22953051 PMCID: PMC3433690 DOI: 10.1038/srep00630] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2012] [Accepted: 07/19/2012] [Indexed: 11/08/2022] Open
Abstract
By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.
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614
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Lord A, Horn D, Breakspear M, Walter M. Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One 2012; 7:e41282. [PMID: 22916105 PMCID: PMC3423402 DOI: 10.1371/journal.pone.0041282] [Citation(s) in RCA: 142] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2012] [Accepted: 06/19/2012] [Indexed: 11/28/2022] Open
Abstract
Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of “resting state” functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects. We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions. We characterised the hierarchical organization of these matrices using network-based matrics, with an emphasis on their mid-scale “modularity” arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index. In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets.
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Affiliation(s)
- Anton Lord
- Division of Mental Health Research, Queensland Institute of Medical Research, Brisbane, Queensland, Australia.
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615
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Blumenfeld RS, Nomura EM, Gratton C, D'Esposito M. Lateral prefrontal cortex is organized into parallel dorsal and ventral streams along the rostro-caudal axis. Cereb Cortex 2012; 23:2457-66. [PMID: 22879354 DOI: 10.1093/cercor/bhs223] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Anatomical connectivity differences between the dorsal and ventral lateral prefrontal cortex (PFC) of the non-human primate strongly suggests that these regions support different functions. However, after years of study, it remains unclear whether these regions are functionally distinct. In contrast, there has been a groundswell of recent studies providing evidence for a rostro-caudal functional organization, along the lateral as well as dorsomedial frontal cortex. Thus, it is not known whether dorsal and ventral regions of lateral PFC form distinct functional networks and how to reconcile any dorso-ventral organization with the medio-lateral and rostro-caudal axes. Here, we used resting-state connectivity data to identify parallel dorsolateral and ventrolateral streams of intrinsic connectivity with the dorsomedial frontal cortex. Moreover, we show that this connectivity follows a rostro-caudal gradient. Our results provide evidence for a novel framework for the intrinsic organization of the frontal cortex that incorporates connections between medio-lateral, dorso-ventral, and rostro-caudal axes.
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Affiliation(s)
- Robert S Blumenfeld
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
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616
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Vaessen MJ, Braakman HMH, Heerink JS, Jansen JFA, Debeij-van Hall MHJA, Hofman PAM, Aldenkamp AP, Backes WH. Abnormal modular organization of functional networks in cognitively impaired children with frontal lobe epilepsy. ACTA ACUST UNITED AC 2012; 23:1997-2006. [PMID: 22772649 DOI: 10.1093/cercor/bhs186] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Many children with frontal lobe epilepsy (FLE) have significant cognitive comorbidity, for which the underlying mechanism has not yet been unraveled, but is likely related to disturbed cerebral network integrity. Using resting-state fMRI, we investigated whether cerebral network characteristics are associated with epilepsy and cognitive comorbidity. We included 37 children with FLE and 41 healthy age-matched controls. Cognitive performance was determined by means of a computerized visual searching task. A connectivity matrix for 82 cortical and subcortical brain regions was generated for each subject by calculating the inter-regional correlation of the fMRI time signals. From the connectivity matrix, graph metrics were calculated and the anatomical configuration of aberrant connections and modular organization was investigated. Both patients and controls displayed efficiently organized networks. However, FLE patients displayed a higher modularity, implying that subnetworks are less interconnected. Impaired cognition was associated with higher modularity scores and abnormal modular organization of the brain, which was mainly expressed as a decrease in long-range and an increase in interhemispheric connectivity in patients. We showed that network modularity analysis provides a sensitive marker for cognitive impairment in FLE and suggest that abnormally interconnected functional subnetworks of the brain might underlie the cognitive problems in children with FLE.
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Affiliation(s)
- M J Vaessen
- Department of Radiology, Maastricht University Medical Centre, Maastricht 6202 AZ, The Netherlands
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617
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Stam C, van Straaten E. The organization of physiological brain networks. Clin Neurophysiol 2012; 123:1067-87. [PMID: 22356937 DOI: 10.1016/j.clinph.2012.01.011] [Citation(s) in RCA: 346] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2011] [Revised: 01/12/2012] [Accepted: 01/15/2012] [Indexed: 01/08/2023]
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618
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Farmer MA, Baliki MN, Apkarian AV. A dynamic network perspective of chronic pain. Neurosci Lett 2012; 520:197-203. [PMID: 22579823 DOI: 10.1016/j.neulet.2012.05.001] [Citation(s) in RCA: 157] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2012] [Revised: 04/28/2012] [Accepted: 05/01/2012] [Indexed: 01/04/2023]
Abstract
We briefly summarize recent advances regarding brain functional representation of chronic pain, reorganization of resting state brain activity, and of brain anatomy with chronic pain. Based on these observations and recent theoretical advances regarding network architecture properties, we develop a general concept of the dynamic interplay between anatomy and function as the brain progresses into persistent pain, and outline the role of mesolimbic learning mechanisms that are likely involved in maintenance of chronic pain.
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Affiliation(s)
- Melissa A Farmer
- Department of Physiology, Northwestern University, Feinberg School of Medicine, 303 E. Chicago Avenue, Chicago, IL 60611, USA
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619
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Levy O, Ziv NE, Marom S. Enhancement of neural representation capacity by modular architecture in networks of cortical neurons. Eur J Neurosci 2012; 35:1753-60. [PMID: 22507055 DOI: 10.1111/j.1460-9568.2012.08094.x] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Biological networks are ubiquitously modular, a feature that is believed to be essential for the enhancement of their functional capacities. Here, we have used a simple modular in vitro design to examine the possibility that modularity enhances network functionality in the context of input representation. We cultured networks of cortical neurons obtained from newborn rats in vitro on substrate-integrated multi-electrode arrays, forcing the network to develop two well-defined modules of neural populations that are coupled by a narrow canal. We measured the neural activity, and examined the capacity of each module to individually classify (i.e. represent) spatially distinct electrical stimuli and propagate input-specific activity features to their downstream coupled counterpart. We show that, although each of the coupled modules maintains its autonomous functionality, a significant enhancement of representational capacity is achieved when the system is observed as a whole. We interpret our results in terms of a relative decorrelation effect imposed by weak coupling between modules.
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Affiliation(s)
- Ofri Levy
- Faculty of Medicine and Network Biology Laboratories, Technion, Haifa, Israel.
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620
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621
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Abstract
Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.
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622
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Worbe Y, Malherbe C, Hartmann A, Pélégrini-Issac M, Messé A, Vidailhet M, Lehéricy S, Benali H. Functional immaturity of cortico-basal ganglia networks in Gilles de la Tourette syndrome. ACTA ACUST UNITED AC 2012; 135:1937-46. [PMID: 22434213 DOI: 10.1093/brain/aws056] [Citation(s) in RCA: 140] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Gilles de la Tourette syndrome is a clinically heterogeneous disorder with poor known pathophysiology. Recent neuropathological and structural neuroimaging data pointed to the dysfunction of cortico-basal ganglia networks. Nonetheless, it is not clear how these structural changes alter the functional activity of the brain and lead to heterogeneous clinical expressions of the syndrome. The objective of this study was to evaluate global integrative state and organization of functional connections of sensori-motor, associative and limbic cortico-basal ganglia networks, which are likely involved in tics and behavioural expressions of Gilles de la Tourette syndrome. We also tested the hypothesis that specific regions and networks contribute to different symptoms. Data were acquired on 59 adult patients and 27 gender- and age-matched controls using a 3T magnetic resonance imaging scanner. Cortico-basal ganglia networks were constructed from 91 regions of interest. Functional connectivity was quantified using global integration and graph theory measures. We found a stronger functional integration (more interactions among anatomical regions) and a global functional disorganization of cortico-basal ganglia networks in patients with Gilles de la Tourette syndrome compared with controls. All networks were characterized by a shorter path length, a higher number of and stronger functional connections among the regions and by a loss of pivotal regions of information transfer (hubs). The functional abnormalities correlated to tic severity in all cortico-basal ganglia networks, namely in premotor, sensori-motor, parietal and cingulate cortices and medial thalamus. Tic complexity was correlated to functional abnormalities in sensori-motor and associative networks, namely in insula and putamen. Severity of obsessive-compulsive disorder was correlated with functional abnormalities in associative and limbic networks, namely in orbito-frontal and prefrontal dorsolateral cortices. The results suggest that the pattern of functional changes in cortico-basal ganglia networks in patients could reflect a defect in brain maturation. They also support the hypothesis that distinct regions of cortico-basal ganglia networks contribute to the clinical heterogeneity of this syndrome.
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Affiliation(s)
- Yulia Worbe
- Inserm, Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique–Hôpitaux de Paris, Centre d’Investigation Clinique CIC 9503, Pôle des Maladies du Système Nerveux, Paris, France.
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623
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Gratton C, Nomura EM, Pérez F, D'Esposito M. Focal brain lesions to critical locations cause widespread disruption of the modular organization of the brain. J Cogn Neurosci 2012; 24:1275-85. [PMID: 22401285 DOI: 10.1162/jocn_a_00222] [Citation(s) in RCA: 240] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Although it is generally assumed that brain damage predominantly affects only the function of the damaged region, here we show that focal damage to critical locations causes disruption of network organization throughout the brain. Using resting state fMRI, we assessed whole-brain network structure in patients with focal brain lesions. Only damage to those brain regions important for communication between subnetworks (e.g., "connectors")--but not to those brain regions important for communication within sub-networks (e.g., "hubs")--led to decreases in modularity, a measure of the integrity of network organization. Critically, this network dysfunction extended into the structurally intact hemisphere. Thus, focal brain damage can have a widespread, nonlocal impact on brain network organization when there is damage to regions important for the communication between networks. These findings fundamentally revise our understanding of the remote effects of focal brain damage and may explain numerous puzzling cases of functional deficits that are observed following brain injury.
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624
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Begega A, Cuesta M, Rubio S, Méndez M, Santín LJ, Arias JL. Functional networks involved in spatial learning strategies in middle-aged rats. Neurobiol Learn Mem 2012; 97:346-53. [PMID: 22406474 DOI: 10.1016/j.nlm.2012.02.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2011] [Revised: 02/08/2012] [Accepted: 02/23/2012] [Indexed: 11/29/2022]
Abstract
Our aim was to assess the way that middle-aged rats solve spatial learning tasks that can be performed using different strategies. We assessed the brain networks involved in these spatial learning processes using Principal Component Analysis. Two tasks were performed in a complex context, a four-arm radial maze, in which each group must use either an allocentric or an egocentric strategy. Another task was performed in a simple T-maze in which rats must use an egocentric strategy. Brain metabolic activity was quantified to evaluate neural changes related to spatial learning in the described tasks. Our findings revealed that two functional networks are involved in spatial learning in aged rats. One of the networks, spatial processing, is composed of brain regions involved in the integration of sensory and motivational information. The other network, context-dependent processing, mainly involves the dorsal hippocampus and is related to the processing of contextual information from the environment. Both networks work together to solve spatial tasks in a complex spatial environment.
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Affiliation(s)
- A Begega
- Laboratorio de Neurociencias, Departamento de Psicología, Universidad de Oviedo, Plaza Feijoo s/n, 33003 Oviedo, Spain.
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625
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Micheloyannis S. Graph-based network analysis in schizophrenia. World J Psychiatry 2012; 2:1-12. [PMID: 24175163 PMCID: PMC3782171 DOI: 10.5498/wjp.v2.i1.1] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2011] [Revised: 12/10/2011] [Accepted: 01/21/2012] [Indexed: 02/05/2023] Open
Abstract
Over the last few years, many studies have been published using modern network analysis of the brain. Researchers and practical doctors alike should understand this method and its results on the brain evaluation at rest, during activation and in brain disease. The studies are noninvasive and usually performed with elecroencephalographic, magnetoencephalographic, magnetic resonance imaging and diffusion tensor imaging brain recordings. Different tools for analysis have been developed, although the methods are in their early stages. The results of these analyses are of special value. Studies of these tools in schizophrenia are important because widespread and local network disturbances can be evaluated by assessing integration, segregation and several structural and functional properties. With the help of network analyses, the main findings in schizophrenia are lower optimum network organization, less efficiently wired networks, less local clustering, less hierarchical organization and signs of disconnection. There are only about twenty five relevant papers on the subject today. Only a few years of study of these methods have produced interesting results and it appears promising that the development of these methods will present important knowledge for both the preclinical signs of schizophrenia and the methods’ therapeutic effects.
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Affiliation(s)
- Sifis Micheloyannis
- Sifis Micheloyannis, Medical Division, Research Clinical Neurophysiological Laboratory (L. Widén Laboratory), University of Crete, Iraklion/Crete 71409, Greece
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626
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Alexander-Bloch A, Lambiotte R, Roberts B, Giedd J, Gogtay N, Bullmore E. The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia. Neuroimage 2012; 59:3889-900. [PMID: 22119652 PMCID: PMC3478383 DOI: 10.1016/j.neuroimage.2011.11.035] [Citation(s) in RCA: 131] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2011] [Revised: 10/27/2011] [Accepted: 11/08/2011] [Indexed: 02/02/2023] Open
Abstract
The modular organization of the brain network can vary in two fundamental ways. The amount of inter- versus intra-modular connections between network nodes can be altered, or the community structure itself can be perturbed, in terms of which nodes belong to which modules (or communities). Alterations have previously been reported in modularity, which is a function of the proportion of intra-modular edges over all modules in the network. For example, we have reported that modularity is decreased in functional brain networks in schizophrenia: There are proportionally more inter-modular edges and fewer intra-modular edges. However, despite numerous and increasing studies of brain modular organization, it is not known how to test for differences in the community structure, i.e., the assignment of regional nodes to specific modules. Here, we introduce a method based on the normalized mutual information between pairs of modular networks to show that the community structure of the brain network is significantly altered in schizophrenia, using resting-state fMRI in 19 participants with childhood-onset schizophrenia and 20 healthy participants. We also develop tools to show which specific nodes (or brain regions) have significantly different modular communities between groups, a subset that includes right insular and perisylvian cortical regions. The methods that we propose are broadly applicable to other experimental contexts, both in neuroimaging and other areas of network science.
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Affiliation(s)
- Aaron Alexander-Bloch
- Behavioural & Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | | | - Ben Roberts
- Statistical Laboratory, University of Cambridge, Cambridge UK
| | - Jay Giedd
- Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Nitin Gogtay
- Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Ed Bullmore
- Behavioural & Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge UK,Corresponding authors at: Herchel Smith Building for Brain and Mind Sciences, Cambridge Biomedical Campus, Cambridge CB2 0SZ, UK. Fax: +44 1223 336581
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627
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A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks. Proc Natl Acad Sci U S A 2012; 109:2825-30. [PMID: 22308319 DOI: 10.1073/pnas.1106612109] [Citation(s) in RCA: 204] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are "large-world" self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the "strength of weak ties" crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain.
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628
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Kim EY, Hwang DU, Ko TW. Multiscale ensemble clustering for finding modules in complex networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2012; 85:026119. [PMID: 22463291 DOI: 10.1103/physreve.85.026119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2011] [Indexed: 05/31/2023]
Abstract
The identification of modules in complex networks is important for the understanding of systems. Here, we propose an ensemble clustering method incorporating node groupings in various sizes and the sequential removal of weak ties between nodes which are rarely grouped together. This method successfully detects modules in various networks, such as hierarchical random networks and the American college football network, with known modular structures. Some of the results are compared with those obtained by modularity optimization and K-means clustering.
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Affiliation(s)
- Eun-Youn Kim
- Computational Neuroscience Team, National Institute for Mathematical Sciences, Daejeon 305-811, Republic of Korea
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629
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Yu Q, Plis SM, Erhardt EB, Allen EA, Sui J, Kiehl KA, Pearlson G, Calhoun VD. Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State. Front Syst Neurosci 2012; 5:103. [PMID: 22275887 PMCID: PMC3257855 DOI: 10.3389/fnsys.2011.00103] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2011] [Accepted: 12/19/2011] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging studies have shown that functional brain networks composed from select regions of interest have a modular community structure. However, the organization of functional network connectivity (FNC), comprising a purely data-driven network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain networks in this mental illness.
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Affiliation(s)
- Qingbao Yu
- The Mind Research Network Albuquerque, NM, USA
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630
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Banerjee A, Pillai AS, Horwitz B. Using large-scale neural models to interpret connectivity measures of cortico-cortical dynamics at millisecond temporal resolution. Front Syst Neurosci 2012; 5:102. [PMID: 22291621 PMCID: PMC3258667 DOI: 10.3389/fnsys.2011.00102] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2011] [Accepted: 12/16/2011] [Indexed: 12/20/2022] Open
Abstract
Over the last two decades numerous functional imaging studies have shown that higher order cognitive functions are crucially dependent on the formation of distributed, large-scale neuronal assemblies (neurocognitive networks), often for very short durations. This has fueled the development of a vast number of functional connectivity measures that attempt to capture the spatiotemporal evolution of neurocognitive networks. Unfortunately, interpreting the neural basis of goal directed behavior using connectivity measures on neuroimaging data are highly dependent on the assumptions underlying the development of the measure, the nature of the task, and the modality of the neuroimaging technique that was used. This paper has two main purposes. The first is to provide an overview of some of the different measures of functional/effective connectivity that deal with high temporal resolution neuroimaging data. We will include some results that come from a recent approach that we have developed to identify the formation and extinction of task-specific, large-scale neuronal assemblies from electrophysiological recordings at a ms-by-ms temporal resolution. The second purpose of this paper is to indicate how to partially validate the interpretations drawn from this (or any other) connectivity technique by using simulated data from large-scale, neurobiologically realistic models. Specifically, we applied our recently developed method to realistic simulations of MEG data during a delayed match-to-sample (DMS) task condition and a passive viewing of stimuli condition using a large-scale neural model of the ventral visual processing pathway. Simulated MEG data using simple head models were generated from sources placed in V1, V4, IT, and prefrontal cortex (PFC) for the passive viewing condition. The results show how closely the conclusions obtained from the functional connectivity method match with what actually occurred at the neuronal network level.
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Affiliation(s)
- Arpan Banerjee
- Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health (NIH) Bethesda, MD, USA
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631
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Zhang Z, Liao W, Zuo XN, Wang Z, Yuan C, Jiao Q, Chen H, Biswal BB, Lu G, Liu Y. Resting-state brain organization revealed by functional covariance networks. PLoS One 2011; 6:e28817. [PMID: 22174905 PMCID: PMC3236756 DOI: 10.1371/journal.pone.0028817] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2011] [Accepted: 11/15/2011] [Indexed: 01/03/2023] Open
Abstract
Background Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN) and structural covariance network (SCN) have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization. Methodology and Principal Findings We proposed a functional covariance network (FCN) method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF) in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network. Conclusion The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.
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Affiliation(s)
- Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu Province, China
| | - Wei Liao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
| | - Xi-Nian Zuo
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences. Beijing, China
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, New York, United States of America
| | - Zhengge Wang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu Province, China
| | - Cuiping Yuan
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu Province, China
| | - Qing Jiao
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu Province, China
| | - Huafu Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
| | - Bharat B. Biswal
- Department of Radiology, UMDNJ New Jersey Medical School, Newark, New Jersey, United States of America
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu Province, China
- * E-mail:
| | - Yijun Liu
- Department of Psychiatry, University of Florida, Gainesville, Florida, United States of America
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632
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Brügger M, Lutz K, Brönnimann B, Meier M, Luechinger R, Barlow A, Jäncke L, Ettlin D. Tracing Toothache Intensity in the Brain. J Dent Res 2011; 91:156-60. [DOI: 10.1177/0022034511431253] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Identification of brain regions that differentially respond to pain intensity may improve our understanding of trigeminally mediated nociception. This report analyzed cortical responses to painless and painful electrical stimulation of a right human maxillary canine tooth. Functional magnetic resonance images were obtained during the application of five graded stimulus strengths, from below, at, and above the individually determined pain thresholds. Study participants reported each stimulus on a visual rating scale with respect to evoked sensation. Based on hemodynamic responses of all pooled stimuli, a cerebral network was identified that largely corresponds to the known lateral and medial nociceptive system. Further analysis of the five graded stimulus strengths revealed positive linear correlations for the anterior insula bilaterally, the contralateral (left) anterior mid-cingulate, as well as contralateral (left) pregenual cingulate cortices. Cerebral toothache intensity coding on a group level can thus be attributed to specific subregions within the cortical pain network.
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Affiliation(s)
- M. Brügger
- University of Zürich, Center of Dental Medicine, Clinic for Removable Prosthodontics, Masticatory Disorders and Special Care Dentistry, Plattenstrasse 11, Zürich 8032, Switzerland
- Swiss Federal Institute of Technology and the University of Zürich, Institute of Biomedical Engineering, Zürich, Switzerland
| | - K. Lutz
- University of Zürich, Department of Psychology, Neuro-psychology, Zürich, Switzerland
| | - B. Brönnimann
- University of Zürich, Department of Psychology, Neuro-psychology, Zürich, Switzerland
| | - M.L. Meier
- University of Zürich, Department of Psychology, Neuro-psychology, Zürich, Switzerland
| | - R. Luechinger
- Swiss Federal Institute of Technology and the University of Zürich, Institute of Biomedical Engineering, Zürich, Switzerland
| | - A. Barlow
- Consumer Healthcare, GlaxoSmithKline, Weybridge, UK
| | - L. Jäncke
- University of Zürich, Department of Psychology, Neuro-psychology, Zürich, Switzerland
| | - D.A. Ettlin
- University of Zürich, Center of Dental Medicine, Clinic for Removable Prosthodontics, Masticatory Disorders and Special Care Dentistry, Plattenstrasse 11, Zürich 8032, Switzerland
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633
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Gutiérrez R, Amann A, Assenza S, Gómez-Gardeñes J, Latora V, Boccaletti S. Emerging meso- and macroscales from synchronization of adaptive networks. PHYSICAL REVIEW LETTERS 2011; 107:234103. [PMID: 22182093 DOI: 10.1103/physrevlett.107.234103] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2011] [Indexed: 05/07/2023]
Abstract
We consider a set of interacting phase oscillators, with a coupling between synchronized nodes adaptively reinforced, and the constraint of a limited resource for a node to establish connections with the other units of the network. We show that such a competitive mechanism leads to the emergence of a rich modular structure underlying cluster synchronization, and to a scale-free distribution for the connection strengths of the units.
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Affiliation(s)
- R Gutiérrez
- Centre for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain
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634
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Yu H, Wang J, Liu C, Deng B, Wei X. Vibrational resonance in excitable neuronal systems. CHAOS (WOODBURY, N.Y.) 2011; 21:043101. [PMID: 22225338 DOI: 10.1063/1.3644390] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In this paper, we investigate the effect of a high-frequency driving on the dynamical response of excitable neuronal systems to a subthreshold low-frequency signal by numerical simulation. We demonstrate the occurrence of vibrational resonance in spatially extended neuronal networks. Different network topologies from single small-world networks to modular networks of small-world subnetworks are considered. It is shown that an optimal amplitude of high-frequency driving enhances the response of neuron populations to a low-frequency signal. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, such as the coupling strength between neurons, network size, and rewiring probability of single small-world networks, as well as the number of links between different subnetworks and the number of subnetworks in the modular networks. All these parameters play a key role in determining the ability of the network to enhance the outreach of the localized subthreshold low-frequency signal. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for the weak signal detection and information propagation across neuronal systems.
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Affiliation(s)
- Haitao Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China
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635
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Yu H, Wang J, Liu Q, Wen J, Deng B, Wei X. Chaotic phase synchronization in a modular neuronal network of small-world subnetworks. CHAOS (WOODBURY, N.Y.) 2011; 21:043125. [PMID: 22225362 DOI: 10.1063/1.3660327] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
We investigate the onset of chaotic phase synchronization of bursting oscillators in a modular neuronal network of small-world subnetworks. A transition to mutual phase synchronization takes place on the bursting time scale of coupled oscillators, while on the spiking time scale, they behave asynchronously. It is shown that this bursting synchronization transition can be induced not only by the variations of inter- and intra-coupling strengths but also by changing the probability of random links between different subnetworks. We also analyze the effect of external chaotic phase synchronization of bursting behavior in this clustered network by an external time-periodic signal applied to a single neuron. Simulation results demonstrate a frequency locking tongue in the driving parameter plane, where bursting synchronization is maintained, even with the external driving. The width of this synchronization region increases with the signal amplitude and the number of driven neurons but decreases rapidly with the network size. Considering that the synchronization of bursting neurons is thought to play a key role in some pathological conditions, the presented results could have important implications for the role of externally applied driving signal in controlling bursting activity in neuronal ensembles.
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Affiliation(s)
- Haitao Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China
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636
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Yu H, Wang J, Liu C, Deng B, Wei X. Stochastic resonance on a modular neuronal network of small-world subnetworks with a subthreshold pacemaker. CHAOS (WOODBURY, N.Y.) 2011; 21:047502. [PMID: 22225376 DOI: 10.1063/1.3620401] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
We study the phenomenon of stochastic resonance on a modular neuronal network consisting of several small-world subnetworks with a subthreshold periodic pacemaker. Numerical results show that the correlation between the pacemaker frequency and the dynamical response of the network is resonantly dependent on the intensity of additive spatiotemporal noise. This effect of pacemaker-driven stochastic resonance of the system depends extensively on the local and the global network structure, such as the intra- and inter-coupling strengths, rewiring probability of individual small-world subnetwork, the number of links between different subnetworks, and the number of subnetworks. All these parameters play a key role in determining the ability of the network to enhance the noise-induced outreach of the localized subthreshold pacemaker, and only they bounded to a rather sharp interval of values warrant the emergence of the pronounced stochastic resonance phenomenon. Considering the rather important role of pacemakers in real-life, the presented results could have important implications for many biological processes that rely on an effective pacemaker for their proper functioning.
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Affiliation(s)
- Haitao Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China
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637
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Alexander-Bloch A, Lambiotte R, Roberts B, Giedd J, Gogtay N, Bullmore E. The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia. Neuroimage 2011. [PMID: 22119652 DOI: 10.1016/jneuroimage.2011.11.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The modular organization of the brain network can vary in two fundamental ways. The amount of inter- versus intra-modular connections between network nodes can be altered, or the community structure itself can be perturbed, in terms of which nodes belong to which modules (or communities). Alterations have previously been reported in modularity, which is a function of the proportion of intra-modular edges over all modules in the network. For example, we have reported that modularity is decreased in functional brain networks in schizophrenia: There are proportionally more inter-modular edges and fewer intra-modular edges. However, despite numerous and increasing studies of brain modular organization, it is not known how to test for differences in the community structure, i.e., the assignment of regional nodes to specific modules. Here, we introduce a method based on the normalized mutual information between pairs of modular networks to show that the community structure of the brain network is significantly altered in schizophrenia, using resting-state fMRI in 19 participants with childhood-onset schizophrenia and 20 healthy participants. We also develop tools to show which specific nodes (or brain regions) have significantly different modular communities between groups, a subset that includes right insular and perisylvian cortical regions. The methods that we propose are broadly applicable to other experimental contexts, both in neuroimaging and other areas of network science.
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Affiliation(s)
- Aaron Alexander-Bloch
- Behavioural & Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, UK.
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638
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Sendina-Nadal I, Buldu JM, Leyva I, Bajo R, Almendral JA, del-Pozo F. Integration Versus Segregation in Functional Brain Networks. IEEE Trans Biomed Eng 2011; 58:3004-7. [DOI: 10.1109/tbme.2011.2161084] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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639
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Shein-Idelson M, Ben-Jacob E, Hanein Y. Engineered neuronal circuits: a new platform for studying the role of modular topology. FRONTIERS IN NEUROENGINEERING 2011; 4:10. [PMID: 21991254 PMCID: PMC3180629 DOI: 10.3389/fneng.2011.00010] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2011] [Accepted: 08/23/2011] [Indexed: 12/05/2022]
Abstract
Neuron–glia cultures serve as a valuable model system for exploring the bio-molecular activity of single cells. Since neurons in culture can be conveniently recorded with great fidelity from many sites simultaneously, it has long been suggested that uniform cultured neurons may also be used to investigate network-level mechanisms pertinent to information processing, activity propagation, memory, and learning. But how much of the functionality of neural circuits can be retained in vitro remains an open question. Recent studies utilizing patterned networks suggest that they provide a most useful platform to address fundamental questions in neuroscience. Here we review recent efforts in the realm of patterned networks’ activity investigations. We give a brief overview of the patterning methods and experimental approaches commonly employed in the field, and summarize the main results reported in the literature. The general picture that emerges from these reports indicates that patterned networks with uniform connectivity do not exhibit unique activity patterns. Rather, their activity is very similar to that of unpatterned uniform networks. However, by breaking the connectivity homogeneity, using a modular architecture, it is possible to introduce pronounced topology-related gating and delay effects. These findings suggest that patterned cultured networks may serve as a new platform for studying the role of modularity in neuronal circuits.
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640
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Gorbach NS, Schütte C, Melzer C, Goldau M, Sujazow O, Jitsev J, Douglas T, Tittgemeyer M. Hierarchical information-based clustering for connectivity-based cortex parcellation. Front Neuroinform 2011; 5:18. [PMID: 21977015 PMCID: PMC3178812 DOI: 10.3389/fninf.2011.00018] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2011] [Accepted: 08/26/2011] [Indexed: 11/25/2022] Open
Abstract
One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (“the functional fingerprint”) is closely related to its anatomical connections (“the connectional fingerprint”) and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as premotoric areas and those associated with pre-frontal areas.
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Affiliation(s)
- Nico S Gorbach
- Cortical Networks Group, Max Planck Institute for Neurological Research Cologne, Germany
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641
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Sporns O. From simple graphs to the connectome: networks in neuroimaging. Neuroimage 2011; 62:881-6. [PMID: 21964480 DOI: 10.1016/j.neuroimage.2011.08.085] [Citation(s) in RCA: 109] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2011] [Accepted: 08/26/2011] [Indexed: 11/17/2022] Open
Abstract
Connectivity is fundamental for understanding the nature of brain function. The intricate web of synaptic connections among neurons is critically important for shaping neural responses, representing statistical features of the sensory environment, coordinating distributed resources for brain-wide processing, and retaining a structural record of the past in order to anticipate future events and infer their relations. The importance of brain connectivity naturally leads to the adoption of the theoretical framework of networks and graphs. Network science approaches have been productively deployed in other domains of science and technology and are now beginning to make contributions across many areas of neuroscience. This article offers a personal perspective on the confluence of networks and neuroimaging, charting the origins of some of its major intellectual themes.
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Affiliation(s)
- Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.
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642
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Adachi Y, Osada T, Sporns O, Watanabe T, Matsui T, Miyamoto K, Miyashita Y. Functional Connectivity between Anatomically Unconnected Areas Is Shaped by Collective Network-Level Effects in the Macaque Cortex. Cereb Cortex 2011; 22:1586-92. [PMID: 21893683 DOI: 10.1093/cercor/bhr234] [Citation(s) in RCA: 161] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Yusuke Adachi
- Department of Physiology, The University of Tokyo School of Medicine, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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643
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Hutchison RM, Womelsdorf T, Gati JS, Leung LS, Menon RS, Everling S. Resting-state connectivity identifies distinct functional networks in macaque cingulate cortex. ACTA ACUST UNITED AC 2011; 22:1294-308. [PMID: 21840845 DOI: 10.1093/cercor/bhr181] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Subregions of the cingulate cortex represent prominent intersections in the structural networks of the primate brain. The relevance of the cingulate to the structure and dynamics of large-scale networks ultimately requires a link to functional connectivity. Here, we map fine-grained functional connectivity across the complete extent of the macaque (Macaca fascicularis) cingulate cortex and delineate subdivisions pertaining to distinct identifiable networks. In particular, we identified 4 primary networks representing the functional spectrum of the cingulate: somatomotor, attention-orienting, executive, and limbic. The cingulate nodes of these networks originated from separable subfields along the rostral-to-caudal axis and were characterized by positive and negative correlations of spontaneous blood oxygen level-dependent activity. These findings represent a critical component for understanding how the anterior and midcingulate cortices integrate and shape information processing during task performance. The connectivity patterns also suggest future electrophysiological targets that may reveal new functional representations including those involved in conflict monitoring.
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Affiliation(s)
- R Matthew Hutchison
- Graduate Program in Neuroscience, University of Western Ontario, London, Ontario N6A 5K8, Canada
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644
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Kaiser M. A tutorial in connectome analysis: Topological and spatial features of brain networks. Neuroimage 2011; 57:892-907. [PMID: 21605688 DOI: 10.1016/j.neuroimage.2011.05.025] [Citation(s) in RCA: 206] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2011] [Revised: 05/06/2011] [Accepted: 05/07/2011] [Indexed: 01/07/2023] Open
Affiliation(s)
- Marcus Kaiser
- School of Computing Science, Newcastle University, Newcastle upon Tyne, UK.
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645
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Knösche TR, Tittgemeyer M. The role of long-range connectivity for the characterization of the functional-anatomical organization of the cortex. Front Syst Neurosci 2011; 5:58. [PMID: 21779237 PMCID: PMC3133730 DOI: 10.3389/fnsys.2011.00058] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2011] [Accepted: 06/25/2011] [Indexed: 11/23/2022] Open
Abstract
This review focuses on the role of long-range connectivity as one element of brain structure that is of key importance for the functional–anatomical organization of the cortex. In this context, we discuss the putative guiding principles for mapping brain function and structure onto the cortical surface. Such mappings reveal a high degree of functional–anatomical segregation. Given that brain regions frequently maintain characteristic connectivity profiles and the functional repertoire of a cortical area is closely related to its anatomical connections, long-range connectivity may be used to define segregated cortical areas. This methodology is called connectivity-based parcellation. Within this framework, we investigate different techniques to estimate connectivity profiles with emphasis given to non-invasive methods based on diffusion magnetic resonance imaging (dMRI) and diffusion tractography. Cortical parcellation is then defined based on similarity between diffusion tractograms, and different clustering approaches are discussed. We conclude that the use of non-invasively acquired connectivity estimates to characterize the functional–anatomical organization of the brain is a valid, relevant, and necessary endeavor. Current and future developments in dMRI technology, tractography algorithms, and models of the similarity structure hold great potential for a substantial improvement and enrichment of the results of the technique.
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Affiliation(s)
- Thomas R Knösche
- Cortical Networks and Cognitive Functions, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany
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646
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Yuan WJ, Zhou C. Interplay between structure and dynamics in adaptive complex networks: emergence and amplification of modularity by adaptive dynamics. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2011; 84:016116. [PMID: 21867266 DOI: 10.1103/physreve.84.016116] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2010] [Revised: 05/27/2011] [Indexed: 05/23/2023]
Abstract
Many real networks display modular organization, which can influence dynamical clustering on the networks. Therefore, there have been proposals put forth recently to detect network communities by using dynamical clustering. In this paper, we study how the feedback from dynamical clusters can shape the network connection weights with a weight-adaptation scheme motivated from Hebbian learning in neural systems. We show that such a scheme generically leads to the formation of community structure in globally coupled chaotic oscillators. The number of communities in the adaptive network depends on coupling strength c and adaptation strength r. In a modular network, the adaptation scheme will enhance the intramodule connection weights and weaken the intermodule connection strengths, generating effectively separated dynamical clusters that coincide with the communities of the network. In this sense, the modularity of the network is amplified by the adaptation. Thus, for a network with a strong community structure, the adaptation scheme can evidently reflect its community structure by the resulting amplified weighted network. For a network with a weak community structure, the statistical properties of synchronization clusters from different realizations can be used to amplify the modularity of the communities so that they can be detected reliably by the other traditional algorithms.
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Affiliation(s)
- Wu-Jie Yuan
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
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647
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Zhao M, Zhou C, Lü J, Lai CH. Competition between intra-community and inter-community synchronization and relevance in brain cortical networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2011; 84:016109. [PMID: 21867259 DOI: 10.1103/physreve.84.016109] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2011] [Revised: 05/25/2011] [Indexed: 05/31/2023]
Abstract
In this paper the effects of inter-community links on the synchronization performance of community networks, especially on the competition between individual community and the whole network, are studied in detail. The study is organized from two aspects: the number or portion of inter-community links and the connection strategy of inter-community links between different communities. A critical point is found in the competition of global network and individual communities. Increasing the number of inter-community links will enhance the global synchronizability but degrade the synchronization performance of individual community before this point. After that the individual community will synchronize better and better as part of the whole network because the community structure is not so prominent. The critical point represents a balance region where the individual community is maximally independent while the information transmission remains effective between different communities. Among various connection strategies, connecting nodes belonging to different communities randomly rather than connecting nodes with larger degrees are the most efficient way to enhance global synchronization of the network. However, the dynamical modularity is the reverse case. A preferential connection scheme linking most of the hubs from the communities will allow rather efficient global synchronization while maintaining strong dynamical clustering of the communities. Interestingly, the observations are found to be relevant in a realistic network of cat cortex. The synchronization state is just at the critical point, which shows a reasonable combination of segregated function in individual communities and coordination among them. Our work sheds light on principles underlying the emergence of modular architectures in real network systems and provides guidance for the manipulation of synchronization in community networks.
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Affiliation(s)
- Ming Zhao
- Department of Physics, National University of Singapore, Singapore.
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648
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Hermundstad AM, Brown KS, Bassett DS, Carlson JM. Learning, memory, and the role of neural network architecture. PLoS Comput Biol 2011; 7:e1002063. [PMID: 21738455 PMCID: PMC3127797 DOI: 10.1371/journal.pcbi.1002063] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2010] [Accepted: 04/06/2011] [Indexed: 11/18/2022] Open
Abstract
The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems. Information processing systems, such as natural biological networks and artificial computational networks, exhibit a strong interdependence between structural organization and functional performance. However, the extent to which variations in structure impact performance is not well understood, particularly in systems whose functionality must be simultaneously flexible and stable. By statistically analyzing the behavior of network systems during flexible learning and stable memory processes, we quantify the impact of structural variations on the ability of the network to learn, modify, and retain representations of information. Across a range of architectures drawn from both natural and artificial systems, we show that these networks face tradeoffs between the ability to learn and retain information, and the observed behavior varies depending on the initial network state and the time given to process information. Furthermore, we analyze the difficulty with which different network architectures produce accurate versus generalizable representations of information, thereby identifying the structural mechanisms that give rise to functional tradeoffs between learning and memory.
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Affiliation(s)
- Ann M Hermundstad
- Physics Department, University of California, Santa Barbara, Santa Barbara, California, United States of America.
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649
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Zamora-López G, Zhou C, Kurths J. Exploring brain function from anatomical connectivity. Front Neurosci 2011; 5:83. [PMID: 21734863 PMCID: PMC3124130 DOI: 10.3389/fnins.2011.00083] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2010] [Accepted: 06/09/2011] [Indexed: 01/12/2023] Open
Abstract
The intrinsic relationship between the architecture of the brain and the range of sensory and behavioral phenomena it produces is a relevant question in neuroscience. Here, we review recent knowledge gained on the architecture of the anatomical connectivity by means of complex network analysis. It has been found that cortico-cortical networks display a few prominent characteristics: (i) modular organization, (ii) abundant alternative processing paths, and (iii) the presence of highly connected hubs. Additionally, we present a novel classification of cortical areas of the cat according to the role they play in multisensory connectivity. All these properties represent an ideal anatomical substrate supporting rich dynamical behaviors, facilitating the capacity of the brain to process sensory information of different modalities segregated and to integrate them toward a comprehensive perception of the real world. The results here exposed are mainly based on anatomical data of cats’ brain, but further observations suggest that, from worms to humans, the nervous system of all animals might share these fundamental principles of organization.
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650
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Kitzbichler MG, Henson RNA, Smith ML, Nathan PJ, Bullmore ET. Cognitive effort drives workspace configuration of human brain functional networks. J Neurosci 2011; 31:8259-70. [PMID: 21632947 PMCID: PMC6622866 DOI: 10.1523/jneurosci.0440-11.2011] [Citation(s) in RCA: 270] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2011] [Revised: 03/29/2011] [Accepted: 04/19/2011] [Indexed: 12/23/2022] Open
Abstract
Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N = 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the β-band (16-32 Hz) and γ-band (32-63 Hz) networks, compared with both lower (α-band; 8-16 Hz) and higher (high γ-band; 63-125 Hz) frequency intervals. Workspace configuration of β-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting <2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.
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Affiliation(s)
- Manfred G. Kitzbichler
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 0SZ, United Kingdom
| | - Richard N. A. Henson
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
| | - Marie L. Smith
- Cognition and Brain Sciences Unit, Medical Research Council, Cambridge CB2 7EF, United Kingdom
| | - Pradeep J. Nathan
- Clinical Unit Cambridge, GlaxoSmithKline, Addenbrooke's Centre for Clinical Investigations, Cambridge CB2 0QQ, United Kingdom
| | - Edward T. Bullmore
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 0SZ, United Kingdom
- Clinical Unit Cambridge, GlaxoSmithKline, Addenbrooke's Centre for Clinical Investigations, Cambridge CB2 0QQ, United Kingdom
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