101
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Erdeniz B, Serin E, İbadi Y, Taş C. Decreased functional connectivity in schizophrenia: The relationship between social functioning, social cognition and graph theoretical network measures. Psychiatry Res Neuroimaging 2017; 270:22-31. [PMID: 29017061 DOI: 10.1016/j.pscychresns.2017.09.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 09/14/2017] [Accepted: 09/16/2017] [Indexed: 12/21/2022]
Abstract
Schizophrenia is a complex disorder in which abnormalities in brain connectivity and social functioning play a central role. The aim of this study is to explore small-world network properties, and understand their relationship with social functioning and social cognition in the context of schizophrenia, by testing functional connectivity differences in network properties and its relation to clinical behavioral measures. Resting-state fMRI time series data were acquired from 23 patients diagnosed with schizophrenia and 23 healthy volunteers. The results revealed that patients with schizophrenia show significantly decreased connectivity between a range of brain regions, particularly involving connections among the right orbitofrontal cortex, bilateral putamen and left amygdala. Furthermore, topological properties of functional brain networks in patients with schizophrenia were characterized by reduced path length compared to healthy controls; however, no significant difference was found for clustering coefficient, local efficiency or global efficiency. Additionally, we found that nodal efficiency of the amygdala and the putamen were significantly correlated with the independence-performance subscale of social functioning scale (SFC), and Reading the Mind in the Eyes test; however, the correlations do not survive correction for multiple comparison. The current results help to clarify the relationship between social functioning deficits and topological brain measures in schizophrenia.
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Affiliation(s)
- Burak Erdeniz
- İzmir University of Economics, Faculty of Arts and Sciences, Department of Psychology, Turkey.
| | - Emin Serin
- Humboldt-Universitätzu Berlin, Berlin School of Mind and Brain, Berlin,Germany
| | - Yelda İbadi
- Üsküdar University, Faculty of Humanities and Social Sciences, Department of Psychology, İstanbul, Turkey
| | - Cumhur Taş
- Üsküdar University, Faculty of Humanities and Social Sciences, Department of Psychology, İstanbul, Turkey
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102
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Váša F, Bullmore ET, Patel AX. Probabilistic thresholding of functional connectomes: Application to schizophrenia. Neuroimage 2017; 172:326-340. [PMID: 29277403 DOI: 10.1016/j.neuroimage.2017.12.043] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 11/20/2017] [Accepted: 12/14/2017] [Indexed: 01/21/2023] Open
Abstract
Functional connectomes are commonly analysed as sparse graphs, constructed by thresholding cross-correlations between regional neurophysiological signals. Thresholding generally retains the strongest edges (correlations), either by retaining edges surpassing a given absolute weight, or by constraining the edge density. The latter (more widely used) method risks inclusion of false positive edges at high edge densities and exclusion of true positive edges at low edge densities. Here we apply new wavelet-based methods, which enable construction of probabilistically-thresholded graphs controlled for type I error, to a dataset of resting-state fMRI scans of 56 patients with schizophrenia and 71 healthy controls. By thresholding connectomes to fixed edge-specific P value, we found that functional connectomes of patients with schizophrenia were more dysconnected than those of healthy controls, exhibiting a lower edge density and a higher number of (dis)connected components. Furthermore, many participants' connectomes could not be built up to the fixed edge densities commonly studied in the literature (∼5-30%), while controlling for type I error. Additionally, we showed that the topological randomisation previously reported in the schizophrenia literature is likely attributable to "non-significant" edges added when thresholding connectomes to fixed density based on correlation. Finally, by explicitly comparing connectomes thresholded by increasing P value and decreasing correlation, we showed that probabilistically thresholded connectomes show decreased randomness and increased consistency across participants. Our results have implications for future analysis of functional connectivity using graph theory, especially within datasets exhibiting heterogenous distributions of edge weights (correlations), between groups or across participants.
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Affiliation(s)
- František Váša
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK.
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK; Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK; ImmunoPsychiatry, GlaxoSmithKline Research and Development, Stevenage, UK
| | - Ameera X Patel
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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103
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Gomez-Pilar J, Poza J, Bachiller A, Gómez C, Núñez P, Lubeiro A, Molina V, Hornero R. Quantification of Graph Complexity Based on the Edge Weight Distribution Balance: Application to Brain Networks. Int J Neural Syst 2017; 28:1750032. [DOI: 10.1142/s0129065717500320] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The aim of this study was to introduce a novel global measure of graph complexity: Shannon graph complexity (SGC). This measure was specifically developed for weighted graphs, but it can also be applied to binary graphs. The proposed complexity measure was designed to capture the interplay between two properties of a system: the ‘information’ (calculated by means of Shannon entropy) and the ‘order’ of the system (estimated by means of a disequilibrium measure). SGC is based on the concept that complex graphs should maintain an equilibrium between the aforementioned two properties, which can be measured by means of the edge weight distribution. In this study, SGC was assessed using four synthetic graph datasets and a real dataset, formed by electroencephalographic (EEG) recordings from controls and schizophrenia patients. SGC was compared with graph density (GD), a classical measure used to evaluate graph complexity. Our results showed that SGC is invariant with respect to GD and independent of node degree distribution. Furthermore, its variation with graph size [Formula: see text] is close to zero for [Formula: see text]. Results from the real dataset showed an increment in the weight distribution balance during the cognitive processing for both controls and schizophrenia patients, although these changes are more relevant for controls. Our findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process. In addition, SGC results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network.
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Affiliation(s)
- Javier Gomez-Pilar
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, 47011 Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, 47011 Valladolid, Spain
- IMUVA, Instituto de Investigación en Matemáticas, Universidad de Valladolid, Valladolid, Spain
- INCYL, Instituto de Neurociencias de Castilla y León, Universidad de Salamanca, Salamanca, Spain
| | - Alejandro Bachiller
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, 47011 Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, 47011 Valladolid, Spain
| | - Pablo Núñez
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, 47011 Valladolid, Spain
| | - Alba Lubeiro
- Psychiatry Department, Facultad de Medicina, Universidad de Valladolid, Valladolid, Spain
| | - Vicente Molina
- INCYL, Instituto de Neurociencias de Castilla y León, Universidad de Salamanca, Salamanca, Spain
- Psychiatry Department, Facultad de Medicina, Universidad de Valladolid, Valladolid, Spain
- Clinical University Hospital of Valladolid, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, 47011 Valladolid, Spain
- IMUVA, Instituto de Investigación en Matemáticas, Universidad de Valladolid, Valladolid, Spain
- INCYL, Instituto de Neurociencias de Castilla y León, Universidad de Salamanca, Salamanca, Spain
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104
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Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:4820935. [PMID: 29387141 PMCID: PMC5745775 DOI: 10.1155/2017/4820935] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 10/10/2017] [Accepted: 11/09/2017] [Indexed: 01/12/2023]
Abstract
High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.
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105
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Di Biase MA, Cropley VL, Baune BT, Olver J, Amminger GP, Phassouliotis C, Bousman C, McGorry PD, Everall I, Pantelis C, Zalesky A. White matter connectivity disruptions in early and chronic schizophrenia. Psychol Med 2017; 47:2797-2810. [PMID: 28528586 DOI: 10.1017/s0033291717001313] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND White matter disruptions in schizophrenia have been widely reported, but it remains unclear whether these abnormalities differ between illness stages. We mapped the connectome in patients with recently diagnosed and chronic schizophrenia and investigated the extent and overlap of white matter connectivity disruptions between these illness stages. METHODS Diffusion-weighted magnetic resonance images were acquired in recent-onset (n = 19) and chronic patients (n = 45) with schizophrenia, as well as age-matched controls (n = 87). Whole-brain fiber tracking was performed to quantify the strength of white matter connections. Connections were tested for significant streamline count reductions in recent-onset and chronic groups, relative to separate age-matched controls. Permutation tests were used to assess whether disrupted connections significantly overlapped between chronic and recent-onset patients. Linear regression was performed to test whether connectivity was strongest in controls, weakest in chronic patients, and midway between these extremities in recent-onset patients (controls > recent-onset > chronic). RESULTS Compared with controls, chronic patients displayed a widespread network of connectivity disruptions (p < 0.01). In contrast, connectivity reductions were circumscribed to the anterior fibers of the corpus callosum in recent-onset patients (p < 0.01). A significant proportion of disrupted connections in recent-onset patients (86%) coincided with disrupted connections in chronic patients (p < 0.01). Linear regression revealed that chronic patients displayed reduced connectivity relative to controls, while recent-onset patients showed an intermediate reduction compared with chronic patients (p < 0.01). CONCLUSIONS Connectome pathology in recent-onset patients with schizophrenia is confined to select tracts within a more extensive network of white matter connectivity disruptions found in chronic illness. These findings may suggest a trajectory of progressive deterioration of connectivity in schizophrenia.
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Affiliation(s)
- M A Di Biase
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - V L Cropley
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - B T Baune
- Discipline of Psychiatry,The University of Adelaide,SA,Australia
| | - J Olver
- Department of Psychiatry,The University of Melbourne,Parkville, VIC,Australia
| | - G P Amminger
- Orygen,The National Centre of Excellence in Youth Mental Health,VIC,Australia
| | - C Phassouliotis
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - C Bousman
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - P D McGorry
- North Western Mental Health,Melbourne Health,Parkville, VIC,Australia
| | - I Everall
- Department of Psychiatry,The University of Melbourne,Parkville, VIC,Australia
| | - C Pantelis
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - A Zalesky
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
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106
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Stahn K, Lehnertz K. Surrogate-assisted identification of influences of network construction on evolving weighted functional networks. CHAOS (WOODBURY, N.Y.) 2017; 27:123106. [PMID: 29289055 DOI: 10.1063/1.4996980] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
We aim at identifying factors that may affect the characteristics of evolving weighted networks derived from empirical observations. To this end, we employ various chains of analysis that are often used in field studies for a data-driven derivation and characterization of such networks. As an example, we consider fully connected, weighted functional brain networks before, during, and after epileptic seizures that we derive from multichannel electroencephalographic data recorded from epilepsy patients. For these evolving networks, we estimate clustering coefficient and average shortest path length in a time-resolved manner. Lastly, we make use of surrogate concepts that we apply at various levels of the chain of analysis to assess to what extent network characteristics are dominated by properties of the electroencephalographic recordings and/or the evolving weighted networks, which may be accessible more easily. We observe that characteristics are differently affected by the unavoidable referencing of the electroencephalographic recording, by the time-series-analysis technique used to derive the properties of network links, and whether or not networks were normalized. Importantly, for the majority of analysis settings, we observe temporal evolutions of network characteristics to merely reflect the temporal evolutions of mean interaction strengths. Such a property of the data may be accessible more easily, which would render the weighted network approach-as used here-as an overly complicated description of simple aspects of the data.
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Affiliation(s)
- Kirsten Stahn
- Department of Epileptology, University of Bonn Medical Centre, Sigmund-Freud-Straße 25, 53105 Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Sigmund-Freud-Straße 25, 53105 Bonn, Germany
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107
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Abstract
The present study evaluated brain connectivity using electroencephalography (EEG) data from 14 patients with schizophrenia and 14 healthy controls. Phase-Locking Value (PLV), Phase-Lag Index (PLI) and Directed Transfer Function (DTF) were calculated for the original EEG data and following current source density (CSD) transformation, re-referencing using the average reference electrode (AVERAGE) and reference electrode standardization techniques (REST). The statistical analysis of adjacency matrices was carried out using indices based on graph theory. Both CSD and REST reduced the influence of volume conducted currents. The largest group differences in connectivity were observed for the alpha band. Schizophrenic patients showed reduced connectivity strength, as well as a lower clustering coefficient and shorter characteristic path length for both measures of phase synchronization following CSD transformation or REST re-referencing. Reduced synchronization was accompanied by increased directional flow from the occipital region for the alpha band. Following the REST re-referencing, the sources of alpha activity were located at parietal rather than occipital derivations. The results of PLV and DTF demonstrated group differences in fronto-posterior asymmetry following CSD transformation, while for PLI the differences were significant only using REST. The only analysis that identified group differences in inter-hemispheric asymmetry was DTF calculated for REST. Our results suggest that a comparison of different connectivity measures using graph-based indices for each frequency band, separately, may be a useful tool in the study of disconnectivity disorders such as schizophrenia.
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108
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Graph-based analysis of brain connectivity in schizophrenia. PLoS One 2017; 12:e0188629. [PMID: 29190759 PMCID: PMC5708839 DOI: 10.1371/journal.pone.0188629] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 11/10/2017] [Indexed: 12/18/2022] Open
Abstract
The present study evaluated brain connectivity using electroencephalography (EEG) data from 14 patients with schizophrenia and 14 healthy controls. Phase-Locking Value (PLV), Phase-Lag Index (PLI) and Directed Transfer Function (DTF) were calculated for the original EEG data and following current source density (CSD) transformation, re-referencing using the average reference electrode (AVERAGE) and reference electrode standardization techniques (REST). The statistical analysis of adjacency matrices was carried out using indices based on graph theory. Both CSD and REST reduced the influence of volume conducted currents. The largest group differences in connectivity were observed for the alpha band. Schizophrenic patients showed reduced connectivity strength, as well as a lower clustering coefficient and shorter characteristic path length for both measures of phase synchronization following CSD transformation or REST re-referencing. Reduced synchronization was accompanied by increased directional flow from the occipital region for the alpha band. Following the REST re-referencing, the sources of alpha activity were located at parietal rather than occipital derivations. The results of PLV and DTF demonstrated group differences in fronto-posterior asymmetry following CSD transformation, while for PLI the differences were significant only using REST. The only analysis that identified group differences in inter-hemispheric asymmetry was DTF calculated for REST. Our results suggest that a comparison of different connectivity measures using graph-based indices for each frequency band, separately, may be a useful tool in the study of disconnectivity disorders such as schizophrenia.
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109
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Silva Pereira S, Hindriks R, Mühlberg S, Maris E, van Ede F, Griffa A, Hagmann P, Deco G. Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study. Brain Connect 2017; 7:541-557. [PMID: 28875718 DOI: 10.1089/brain.2017.0525] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
A popular way to analyze resting-state electroencephalography (EEG) and magneto encephalography (MEG) data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time series and the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time series are mixtures of source activity. It is, therefore, of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks. Since these topological features are of high interest in experimental studies, we address the question of to what extent the network topology can be reconstructed from sensor-level functional connectivity (FC) measures in case of MEG data. Simple simulations that consider only a small number of regions do not allow to assess network properties; therefore, we use a diffusion magnetic resonance imaging-constrained whole-brain computational model of resting-state activity. Our motivation lies behind the fact that still many contributions found in the literature perform network analysis at sensor level, and we aim at showing the discrepancies between source- and sensor-level network topologies by using realistic simulations of resting-state cortical activity. Our main findings are that the effect of field spread on network topology depends on the type of interaction (instantaneous or lagged) and leads to an underestimation of lagged FC at sensor level due to instantaneous mixing of cortical signals, instantaneous interaction is more sensitive to field spread than lagged interaction, and discrepancies are reduced when using planar gradiometers rather than axial gradiometers. We, therefore, recommend using lagged interaction measures on planar gradiometer data when investigating network properties of resting-state sensor-level MEG data.
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Affiliation(s)
- Silvana Silva Pereira
- 1 Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Rikkert Hindriks
- 1 Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Stefanie Mühlberg
- 1 Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Eric Maris
- 2 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Freek van Ede
- 2 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Alessandra Griffa
- 3 Department of Radiology, Lausanne University Hospital (CHUV-UNIL), Lausanne, Switzerland .,4 Signal Processing Laboratory 5 , Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Patric Hagmann
- 3 Department of Radiology, Lausanne University Hospital (CHUV-UNIL), Lausanne, Switzerland
| | - Gustavo Deco
- 1 Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain .,5 Institució Catalana de la Recerca i Estudis Avanats (ICREA), Barcelona, Spain
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110
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Yu M, Dai Z, Tang X, Wang X, Zhang X, Sha W, Yao S, Shu N, Wang X, Yang J, Zhang X, Zhang X, He Y, Zhang Z. Convergence and Divergence of Brain Network Dysfunction in Deficit and Non-deficit Schizophrenia. Schizophr Bull 2017; 43:1315-1328. [PMID: 29036672 PMCID: PMC5737538 DOI: 10.1093/schbul/sbx014] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Deficit schizophrenia (DS), characterized by primary and enduring negative symptoms, has been considered as a pathophysiologically distinct schizophrenic subgroup. Neuroimaging characteristics of DS, especially functional brain network architecture, remain largely unknown. Resting-state functional magnetic resonance imaging and graph theory approaches were employed to investigate the topological organization of whole-brain functional networks of 114 male participants including 33 DS, 41 non-deficit schizophrenia (NDS) and 40 healthy controls (HCs). At the whole-brain level, both the NDS and DS group exhibited lower local efficiency (Eloc) than the HC group, implying the reduction of local specialization of brain information processing (reduced functional segregation). The DS, but not NDS group, exhibited enhanced parallel information transfer (enhanced functional integration) as determined by smaller characteristic path length (Lp) and higher global efficiency (Eglob). The Lp and Eglob presented significant correlations with Brief Psychiatric Rating Scale (BPRS) total score in the DS group. At the nodal level, both the NDS and DS groups showed higher functional connectivity in the inferior frontal gyrus and hippocampus, and lower connectivity in the visual areas and striatum than the controls. The DS group exhibited higher nodal connectivity in the right inferior temporal gyrus than the NDS and HC group. The diminished expression of Scale for the Assessment of Negative Symptoms (SANS) subfactors negatively correlated with nodal connectivity of right putamen, while asociality/amotivation positively correlated with right hippocampus across whole patients. We highlighted the convergence and divergence of brain functional network dysfunctions in patients with DS and NDS, which provides crucial insights into pathophysiological mechanisms of the 2 schizophrenic subtypes.
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Affiliation(s)
- Miao Yu
- Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaowei Tang
- Department of Psychiatry, Wutaishan Hospital of Yangzhou, Yangzhou, China
| | - Xiang Wang
- Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiaobin Zhang
- Department of Psychiatry, Wutaishan Hospital of Yangzhou, Yangzhou, China
| | - Weiwei Sha
- Department of Psychiatry, Wutaishan Hospital of Yangzhou, Yangzhou, China
| | - Shuqiao Yao
- Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xindi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jiaying Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China
| | - Xiangyang Zhang
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX
| | - Xiangrong Zhang
- Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China,Department of Geriatric Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China,To whom correspondence should be addressed; Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, No.87 DingJiaQiao Road, Nanjing 210009, China; tel: 0086-25-822906586, fax:0086-25-83719457, e-mail:
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhijun Zhang
- Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China,Beijing Institute for Brain Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
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111
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Alloza C, Bastin ME, Cox SR, Gibson J, Duff B, Semple SI, Whalley HC, Lawrie SM. Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia. Hum Brain Mapp 2017; 38:5919-5930. [PMID: 28881417 DOI: 10.1002/hbm.23798] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 08/02/2017] [Accepted: 08/24/2017] [Indexed: 12/25/2022] Open
Abstract
Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network (pFDR < 0.05). All metrics across networks were significantly associated with intelligence (pFDR < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia (r = -0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp 38:5919-5930, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Clara Alloza
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Clinical Brain Sciences, Western General Hospital, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, United Kingdom
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, United Kingdom
| | - Jude Gibson
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Barbara Duff
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Scott I Semple
- Clinical Research Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
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112
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Chung MK, Lee H, Solo V, Davidson RJ, Pollak SD. Topological Distances Between Brain Networks. CONNECTOMICS IN NEUROIMAGING : FIRST INTERNATIONAL WORKSHOP, CNI 2017, HELD IN CONJUNCTION WITH MICCAI 2017, QUEBEC CITY, QC, CANADA, SEPTEMBER 14, 2017, PROCEEDINGS. CNI (WORKSHOP) (1ST : 2017 : QUEBEC, QUEBEC) 2017; 10511:161-170. [PMID: 29745383 DOI: 10.1007/978-3-319-67159-8_19] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Many existing brain network distances are based on matrix norms. The element-wise differences may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. Thus it is necessary to develop network distances that recognize topology. In this paper, we introduce Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances. GH-distance is often used in persistent homology based brain network models. The superior performance of KS-distance is contrasted against matrix norms and GH-distance in random network simulations with the ground truths. The KS-distance is then applied in characterizing the multimodal MRI and DTI study of maltreated children.
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Affiliation(s)
| | | | - Victor Solo
- University of New South Wales, Sydney, Australia
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113
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Mijalkov M, Kakaei E, Pereira JB, Westman E, Volpe G. BRAPH: A graph theory software for the analysis of brain connectivity. PLoS One 2017; 12:e0178798. [PMID: 28763447 PMCID: PMC5538719 DOI: 10.1371/journal.pone.0178798] [Citation(s) in RCA: 150] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 05/18/2017] [Indexed: 11/19/2022] Open
Abstract
The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH-BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.
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Affiliation(s)
- Mite Mijalkov
- UNAM—National Nanotechnology Research Center & Department of Physics, Bilkent University, Ankara, Turkey
| | - Ehsan Kakaei
- UNAM—National Nanotechnology Research Center & Department of Physics, Bilkent University, Ankara, Turkey
| | - Joana B. Pereira
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- UNAM—National Nanotechnology Research Center & Department of Physics, Bilkent University, Ankara, Turkey
- Department of Physics, Goteborg University, Goteborg, Sweden
- * E-mail:
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114
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Racz FS, Mukli P, Nagy Z, Eke A. Increased prefrontal cortex connectivity during cognitive challenge assessed by fNIRS imaging. BIOMEDICAL OPTICS EXPRESS 2017; 8:3842-3855. [PMID: 28856054 PMCID: PMC5560845 DOI: 10.1364/boe.8.003842] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 05/05/2017] [Accepted: 05/30/2017] [Indexed: 05/24/2023]
Abstract
In this study, functional near-infrared spectroscopy (fNIRS) and the graph theory approach were used to access the functional connectivity (FC) of the prefrontal cortex (PFC) in a resting state and during increased mental workload. For this very purpose, a pattern recognition-based test was developed, which elicited a strong response throughout the PFC during the test condition. FC parameters obtained during stimulation were found increased compared to those in a resting state after correlation based signal improvement (CBSI), which can attenuate those components of fNIRS signals which are unrelated to neural activity. These results indicate that the cognitive challenge increased the FC in the PFC and suggests a great potential in investigating FC in various cognitive states.
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Affiliation(s)
- Frigyes Samuel Racz
- Institute of Clinical Experimental Research, Semmelweis University, 37-43 Tűzoltó Street, Budapest 1094, Hungary
- Department of Physiology, 37-43 Tűzoltó Street, Budapest 1094, Hungary
| | - Peter Mukli
- Institute of Clinical Experimental Research, Semmelweis University, 37-43 Tűzoltó Street, Budapest 1094, Hungary
- Department of Physiology, 37-43 Tűzoltó Street, Budapest 1094, Hungary
| | - Zoltan Nagy
- Institute of Clinical Experimental Research, Semmelweis University, 37-43 Tűzoltó Street, Budapest 1094, Hungary
| | - Andras Eke
- Institute of Clinical Experimental Research, Semmelweis University, 37-43 Tűzoltó Street, Budapest 1094, Hungary
- Department of Physiology, 37-43 Tűzoltó Street, Budapest 1094, Hungary
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115
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Tao S, Way SF, Garland J, Chrispin J, Ciuffo LA, Balouch MA, Nazarian S, Spragg DD, Marine JE, Berger RD, Calkins H, Ashikaga H. Ablation as targeted perturbation to rewire communication network of persistent atrial fibrillation. PLoS One 2017; 12:e0179459. [PMID: 28678805 PMCID: PMC5497967 DOI: 10.1371/journal.pone.0179459] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 05/29/2017] [Indexed: 11/24/2022] Open
Abstract
Persistent atrial fibrillation (AF) can be viewed as disintegrated patterns of information transmission by action potential across the communication network consisting of nodes linked by functional connectivity. To test the hypothesis that ablation of persistent AF is associated with improvement in both local and global connectivity within the communication networks, we analyzed multi-electrode basket catheter electrograms of 22 consecutive patients (63.5 ± 9.7 years, 78% male) during persistent AF before and after the focal impulse and rotor modulation-guided ablation. Eight patients (36%) developed recurrence within 6 months after ablation. We defined communication networks of AF by nodes (cardiac tissue adjacent to each electrode) and edges (mutual information between pairs of nodes). To evaluate patient-specific parameters of communication, thresholds of mutual information were applied to preserve 10% to 30% of the strongest edges. There was no significant difference in network parameters between both atria at baseline. Ablation effectively rewired the communication network of persistent AF to improve the overall connectivity. In addition, successful ablation improved local connectivity by increasing the average clustering coefficient, and also improved global connectivity by decreasing the characteristic path length. As a result, successful ablation improved the efficiency and robustness of the communication network by increasing the small-world index. These changes were not observed in patients with AF recurrence. Furthermore, a significant increase in the small-world index after ablation was associated with synchronization of the rhythm by acute AF termination. In conclusion, successful ablation rewires communication networks during persistent AF, making it more robust, efficient, and easier to synchronize. Quantitative analysis of communication networks provides not only a mechanistic insight that AF may be sustained by spatially localized sources and global connectivity, but also patient-specific metrics that could serve as a valid endpoint for therapeutic interventions.
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Affiliation(s)
- Susumu Tao
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Samuel F. Way
- Department of Computer Science, University of Colorado, Boulder, Colorado, United States of America
| | - Joshua Garland
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Jonathan Chrispin
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Luisa A. Ciuffo
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Muhammad A. Balouch
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Saman Nazarian
- Section for Cardiac Electrophysiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - David D. Spragg
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Joseph E. Marine
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Ronald D. Berger
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Hugh Calkins
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Hiroshi Ashikaga
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
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116
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Murugesan S, Bouchard K, Brown JA, Hamann B, Seeley WW, Trujillo A, Weber GH. Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:805-818. [PMID: 28113724 PMCID: PMC5585064 DOI: 10.1109/tcbb.2016.2564970] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views-such as heat maps, node link diagrams and anatomical views-using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parameters gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. To demonstrate the utility of our tool, we present two case studies-exploring progressive supranuclear palsy, as well as memory encoding and retrieval.
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117
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Functional EEG network analysis in schizophrenia: Evidence of larger segregation and deficit of modulation. Prog Neuropsychopharmacol Biol Psychiatry 2017; 76:116-123. [PMID: 28336496 DOI: 10.1016/j.pnpbp.2017.03.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 03/17/2017] [Accepted: 03/17/2017] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Higher mental functions depend on global cerebral functional coordination. Our aim was to study fast modulation of functional networks in schizophrenia that has not been previously assessed. METHODS Graph-theory was used to analyze the electroencephalographic (EEG) activity during an odd-ball task in 57 schizophrenia patients (18 first episode patients, FEPs) and 59 healthy controls. Clustering coefficient (CLC), characteristic path length (PL) and small-worldness (SW) were computed at baseline ([-300 0] ms prior to stimulus delivery) and response ([150 450] ms post-stimulus) windows. Clinical and cognitive assessments were performed. RESULTS CLC, PL and SW showed a significant modulation between baseline and response in controls but not in patients. Patients obtained higher CLC and SW at baseline, lower CLC and higher PL at response, and diminished modulation of CLC and SW as compared to controls. In patients, CLC and SW modulation were inversely associated to cognitive performance in executive tasks and directly associated to working memory. Similar patterns were observed in FEPs. CLC and SW during the baseline were inversely associated to their respective modulation magnitudes. CONCLUSIONS Our results are coherent with a hyper-segregated network at baseline (higher CLC) and a decreased modulation of the functional connectivity during cognition in schizophrenia.
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118
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Sharma G, Gramann K, Chandra S, Singh V, Mittal AP. Brain connectivity during encoding and retrieval of spatial information: individual differences in navigation skills. Brain Inform 2017; 4:207-217. [PMID: 28510210 PMCID: PMC5563302 DOI: 10.1007/s40708-017-0066-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 04/27/2017] [Indexed: 11/28/2022] Open
Abstract
Emerging evidence suggests that the variations in the ability to navigate through any real or virtual environment are accompanied by distinct underlying cortical activations in multiple regions of the brain. These activations may appear due to the use of different frame of reference (FOR) for representing an environment. The present study investigated the brain dynamics in the good and bad navigators using Graph Theoretical analysis applied to low-density electroencephalography (EEG) data. Individual navigation skills were rated according to the performance in a virtual reality (VR)-based navigation task and the effect of navigator's proclivity towards a particular FOR on the navigation performance was explored. Participants were introduced to a novel virtual environment that they learned from a first-person or an aerial perspective and were subsequently assessed on the basis of efficiency with which they learnt and recalled. The graph theoretical parameters, path length (PL), global efficiency (GE), and clustering coefficient (CC) were computed for the functional connectivity network in the theta and alpha frequency bands. During acquisition of the spatial information, good navigators were distinguished by a lower degree of dispersion in the functional connectivity compared to the bad navigators. Within the groups of good and bad navigators, better performers were characterised by the formation of multiple hubs at various sites and the percentage of connectivity or small world index. The proclivity towards a specific FOR during exploration of a new environment was not found to have any bearing on the spatial learning. These findings may have wider implications for how the functional connectivity in the good and bad navigators differs during spatial information acquisition and retrieval in the domains of rescue operations and defence systems.
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Affiliation(s)
- Greeshma Sharma
- Biomedical Engineering Department, INMAS, DRDO, Delhi, 110054, India.
| | - Klaus Gramann
- Biological Psychology and Neuroergonomics, Institute of Technology, University of Berlin, 10587, Berlin, Germany
| | - Sushil Chandra
- Biomedical Engineering Department, INMAS, DRDO, Delhi, 110054, India
| | - Vijander Singh
- Instrumentation and Control Engineering Department, NSIT, Dwarka, Delhi, 110078, India
| | - Alok Prakash Mittal
- Instrumentation and Control Engineering Department, NSIT, Dwarka, Delhi, 110078, India
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119
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Lord AR, Li M, Demenescu LR, van den Meer J, Borchardt V, Krause AL, Heinze HJ, Breakspear M, Walter M. Richness in Functional Connectivity Depends on the Neuronal Integrity within the Posterior Cingulate Cortex. Front Neurosci 2017; 11:184. [PMID: 28439224 PMCID: PMC5384321 DOI: 10.3389/fnins.2017.00184] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 03/20/2017] [Indexed: 12/19/2022] Open
Abstract
The brain's connectivity skeleton-a rich club of strongly interconnected members-was initially shown to exist in human structural networks, but recent evidence suggests a functional counterpart. This rich club typically includes key regions (or hubs) from multiple canonical networks, reducing the cost of inter-network communication. The posterior cingulate cortex (PCC), a hub node embedded within the default mode network, is known to facilitate communication between brain networks and is a key member of the "rich club." Here, we assessed how metabolic signatures of neuronal integrity and cortical thickness influence the global extent of a functional rich club as measured using the functional rich club coefficient (fRCC). Rich club estimation was performed on functional connectivity of resting state brain signals acquired at 3T in 48 healthy adult subjects. Magnetic resonance spectroscopy was measured in the same session using a point resolved spectroscopy sequence. We confirmed convergence of functional rich club with a previously established structural rich club. N-acetyl aspartate (NAA) in the PCC is significantly correlated with age (p = 0.001), while the rich club coefficient showed no effect of age (p = 0.106). In addition, we found a significant quadratic relationship between fRCC and NAA concentration in PCC (p = 0.009). Furthermore, cortical thinning in the PCC was correlated with a reduced rich club coefficient after accounting for age and NAA. In conclusion, we found that the fRCC is related to a marker of neuronal integrity in a key region of the cingulate cortex. Furthermore, cortical thinning in the same area was observed, suggesting that both cortical thinning and neuronal integrity in the hub regions influence functional integration of at a whole brain level.
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Affiliation(s)
- Anton R Lord
- Department of Behavioral Neurology, Leibniz Institute for NeurobiologyMagdeburg, Germany.,Clinical Affective Neuroimaging Laboratory, Otto-von-Guericke UniversityMagdeburg, Germany.,QIMR Berghofer Medical Research InstituteBrisbane, QLD, Australia
| | - Meng Li
- Clinical Affective Neuroimaging Laboratory, Otto-von-Guericke UniversityMagdeburg, Germany.,Department of Neurology, Otto-von-Guericke UniversityMagdeburg, Germany
| | - Liliana R Demenescu
- Department of Behavioral Neurology, Leibniz Institute for NeurobiologyMagdeburg, Germany.,Clinical Affective Neuroimaging Laboratory, Otto-von-Guericke UniversityMagdeburg, Germany
| | - Johan van den Meer
- Clinical Affective Neuroimaging Laboratory, Otto-von-Guericke UniversityMagdeburg, Germany.,Department of Neurology, Otto-von-Guericke UniversityMagdeburg, Germany.,Department of Cognition and Emotion, Netherlands Institute for Neuroscience, An Institute of the Royal Academy of Arts and SciencesAmsterdam, Netherlands
| | - Viola Borchardt
- Clinical Affective Neuroimaging Laboratory, Otto-von-Guericke UniversityMagdeburg, Germany
| | - Anna Linda Krause
- Clinical Affective Neuroimaging Laboratory, Otto-von-Guericke UniversityMagdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto-von-Guericke UniversityMagdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Behavioral Neurology, Leibniz Institute for NeurobiologyMagdeburg, Germany.,Department of Neurology, Otto-von-Guericke UniversityMagdeburg, Germany.,Center of Behavioral Brain Sciences, Otto-von-Guericke UniversityMagdeburg, Germany
| | - Michael Breakspear
- QIMR Berghofer Medical Research InstituteBrisbane, QLD, Australia.,Metro North Mental Health Service, Royal Brisbane and Women's HospitalBrisbane, QLD, Australia
| | - Martin Walter
- Department of Behavioral Neurology, Leibniz Institute for NeurobiologyMagdeburg, Germany.,Clinical Affective Neuroimaging Laboratory, Otto-von-Guericke UniversityMagdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto-von-Guericke UniversityMagdeburg, Germany.,Center of Behavioral Brain Sciences, Otto-von-Guericke UniversityMagdeburg, Germany.,Department of Psychiatry, Eberhad Karls University TuebingenTuebingen, Germany
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120
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Olejarczyk E, Marzetti L, Pizzella V, Zappasodi F. Comparison of connectivity analyses for resting state EEG data. J Neural Eng 2017; 14:036017. [PMID: 28378705 DOI: 10.1088/1741-2552/aa6401] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
OBJECTIVE In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. APPROACH The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. MAIN RESULTS The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. SIGNIFICANCE Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
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121
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Altered Effective Connectivity Network in Childhood Absence Epilepsy: A Multi-frequency MEG Study. Brain Topogr 2017; 30:673-684. [PMID: 28286918 DOI: 10.1007/s10548-017-0555-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2016] [Accepted: 02/07/2017] [Indexed: 12/11/2022]
Abstract
Using multi-frequency magnetoencephalography (MEG) data, we investigated whether the effective connectivity (EC) network of patients with childhood absence epilepsy (CAE) is altered during the inter-ictal period in comparison with healthy controls. MEG data from 13 untreated CAE patients and 10 healthy controls were recorded. Correlation analysis and Granger causality analysis were used to construct an EC network at the source level in eight frequency bands. Alterations in the spatial pattern and topology of the network in CAE were investigated by comparing the patients with the controls. The network pattern was altered mainly in 1-4 Hz, showing strong connections within the frontal cortex and weak connections in the anterior-posterior pathways. The EC involving the precuneus/posterior cingulate cortex (PC/PCC) significantly decreased in low-frequency bands. In addition, the parameters of graph theory were significantly altered in several low- and high-frequency bands. CAE patients display frequency-specific abnormalities in the network pattern even during the inter-ictal period, and the frontal cortex and PC/PCC might play crucial roles in the pathophysiology of CAE. The EC network of CAE patients was over-connective and random during the inter-ictal period. This study is the first to reveal the frequency-specific alteration in the EC network during the inter-ictal period in CAE patients. Multiple-frequency MEG data are useful in investigating the pathophysiology of CAE, which can serve as new biomarkers of this disorder.
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122
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Gomez-Pilar J, Poza J, Bachiller A, Nunez P, Gomez C, Lubeiro A, Molina V, Hornero R. Novel measure of the weigh distribution balance on the brain network: graph complexity applied to schizophrenia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:700-703. [PMID: 28268424 DOI: 10.1109/embc.2016.7590798] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The aim of this study was to assess brain complexity dynamics in schizophrenia (SCH) patients during an auditory oddball task. For this task, we applied a novel graph measure based on the balance of the node weights distribution. Previous studies applied complexity parameters that were strongly dependent on network topology. This could bias the results, as well as making correction techniques, such as surrogating process, necessary. In the present study, we applied a novel graph complexity measure derived from the information theory: Shannon Graph Complexity (SGC). Complexity patterns from electroencephalographic recordings of 20 healthy controls and 20 SCH patients during an auditory oddball task were analyzed. Results showed a significantly more pronounced decrease of SGC for controls than for SCH patients during the cognitive task. These findings suggest an important change in the brain configuration towards a more balanced network, mainly in the connections related to long-range interactions. Since these changes are significantly more pronounced in controls, a deficit in the neural network reorganization can be associated with SCH. In addition, an accuracy of 72.5% was obtained using a receiver operating characteristic curve with a leave-one-out cross-validation procedure. The independence of network topology has been demonstrated by the novel complexity measure proposed in this study, therefore, it complements traditional graph measures as a means to characterize brain networks.
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123
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Yin Z, Li J, Zhang Y, Ren A, Von Meneen KM, Huang L. Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.08.013] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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124
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Gkigkitzis I, Haranas I, Kotsireas I. Biological Relevance of Network Architecture. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 988:1-29. [PMID: 28971385 DOI: 10.1007/978-3-319-56246-9_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.
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Affiliation(s)
- Ioannis Gkigkitzis
- Department of Mathematics, East Carolina University, 124 Austin Building, East Fifth Street, Greenville, NC, 27858-4353, USA.
| | - Ioannis Haranas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
| | - Ilias Kotsireas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
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125
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Lo PC, Tian WJM, Liu FL. Macrostate and Microstate of EEG Spatio-Temporal Nonlinear Dynamics in Zen Meditation. ACTA ACUST UNITED AC 2017. [DOI: 10.4236/jbbs.2017.713046] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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126
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Park CH, Chun JW, Cho H, Jung YC, Choi J, Kim DJ. Is the Internet gaming-addicted brain close to be in a pathological state? Addict Biol 2017; 22:196-205. [PMID: 26135331 DOI: 10.1111/adb.12282] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Internet gaming addiction (IGA) is becoming a common and widespread mental health concern. Although IGA induces a variety of negative psychosocial consequences, it is yet ambiguous whether the brain addicted to Internet gaming is considered to be in a pathological state. We investigated IGA-induced abnormalities of the brain specifically from the network perspective and qualitatively assessed whether the Internet gaming-addicted brain is in a state similar to the pathological brain. Topological properties of brain functional networks were examined by applying a graph-theoretical approach to analyzing functional magnetic resonance imaging data acquired during a resting state in 19 IGA adolescents and 20 age-matched healthy controls. We compared functional distance-based measures, global and local efficiency of resting state brain functional networks between the two groups to assess how the IGA subjects' brain was topologically altered from the controls' brain. The IGA subjects had severer impulsiveness and their brain functional networks showed higher global efficiency and lower local efficiency relative to the controls. These topological differences suggest that IGA induced brain functional networks to shift toward the random topological architecture, as exhibited in other pathological states. Furthermore, for the IGA subjects, the topological alterations were specifically attributable to interregional connections incident on the frontal region, and the degree of impulsiveness was associated with the topological alterations over the frontolimbic connections. The current findings lend support to the proposition that the Internet gaming-addicted brain could be in the state similar to pathological states in terms of topological characteristics of brain functional networks.
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Affiliation(s)
- Chang-hyun Park
- Department of Neurology; Ewha Medical Research Institute; Ewha Womans University School of Medicine; Seoul Korea
| | - Ji-Won Chun
- Department of Psychiatry; Seoul St. Mary's Hospital; The Catholic University of Korea School of Medicine; Seoul Korea
| | - Huyn Cho
- Department of Psychiatry; Seoul St. Mary's Hospital; The Catholic University of Korea School of Medicine; Seoul Korea
| | - Young-Chul Jung
- Department of Psychiatry; Yonsei University College of Medicine; Seoul Korea
| | - Jihye Choi
- Department of Psychiatry; Seoul St. Mary's Hospital; The Catholic University of Korea School of Medicine; Seoul Korea
| | - Dai Jin Kim
- Department of Psychiatry; Seoul St. Mary's Hospital; The Catholic University of Korea School of Medicine; Seoul Korea
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127
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Sun Y, Dai Z, Li J, Collinson SL, Sim K. Modular-level alterations of structure-function coupling in schizophrenia connectome. Hum Brain Mapp 2016; 38:2008-2025. [PMID: 28032370 DOI: 10.1002/hbm.23501] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Revised: 12/07/2016] [Accepted: 12/14/2016] [Indexed: 12/29/2022] Open
Abstract
Convergent evidences have revealed that schizophrenia is associated with brain dysconnectivity, which leads to abnormal network organization. However, discrepancies were apparent between the structural connectivity (SC) and functional connectivity (FC) studies, and the relationship between structural and functional deficits in schizophrenia remains largely unknown. In this study, resting-state functional magnetic resonance imaging and structural diffusion tensor imaging were performed in 20 patients with schizophrenia and 20 matched healthy volunteers (patients/controls = 19/17 after head motion rejection). Functional and structural brain networks were obtained for each participant. Graph theoretical approaches were employed to parcellate the FC networks into functional modules. The relationships between the entries of SC and FC were estimated within each module to identify group differences and their correlations with clinical symptoms. Although five common functional modules (including the default mode, occipital, subcortical, frontoparietal, and central modules) were identified in both groups, the patients showed a significantly reduced modularity in comparison with healthy participants. Furthermore, we found that schizophrenia-related aberrations of SC-FC coupling exhibited complex patterns among modules. Compared with controls, patients showed an increased SC-FC coupling in the default mode and the central modules. Moreover, significant SC-FC decoupling was demonstrated in the occipital and the subcortical modules, which was associated with longer duration of illness and more severe clinical manifestations of schizophrenia. Taken together, these findings demonstrated that altered module-dependent SC-FC coupling may underlie abnormal brain function and clinical symptoms observed in schizophrenia and highlighted the potential for using new multimodal neuroimaging biomarkers for diagnosis and severity evaluation of schizophrenia. Hum Brain Mapp 38:2008-2025, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Yu Sun
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Science, National University of Singapore, Singapore
| | - Zhongxiang Dai
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Science, National University of Singapore, Singapore
| | - Junhua Li
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Science, National University of Singapore, Singapore
| | - Simon L Collinson
- Department of Psychology, National University of Singapore, Singapore
| | - Kang Sim
- Department of General Psychiatry, Institute of Mental Health (IMH), Singapore.,Department of Research, Institute of Mental Health (IMH), Singapore
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128
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Friston K, Brown HR, Siemerkus J, Stephan KE. The dysconnection hypothesis (2016). Schizophr Res 2016; 176:83-94. [PMID: 27450778 PMCID: PMC5147460 DOI: 10.1016/j.schres.2016.07.014] [Citation(s) in RCA: 358] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 07/06/2016] [Accepted: 07/15/2016] [Indexed: 02/06/2023]
Abstract
Twenty years have passed since the dysconnection hypothesis was first proposed (Friston and Frith, 1995; Weinberger, 1993). In that time, neuroscience has witnessed tremendous advances: we now live in a world of non-invasive neuroanatomy, computational neuroimaging and the Bayesian brain. The genomics era has come and gone. Connectomics and large-scale neuroinformatics initiatives are emerging everywhere. So where is the dysconnection hypothesis now? This article considers how the notion of schizophrenia as a dysconnection syndrome has developed - and how it has been enriched by recent advances in clinical neuroscience. In particular, we examine the dysconnection hypothesis in the context of (i) theoretical neurobiology and computational psychiatry; (ii) the empirical insights afforded by neuroimaging and associated connectomics - and (iii) how bottom-up (molecular biology and genetics) and top-down (systems biology) perspectives are converging on the mechanisms and nature of dysconnections in schizophrenia.
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Affiliation(s)
- Karl Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK.
| | - Harriet R. Brown
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK,Oxford Centre for Human Brain Activity, University of Oxford, UK
| | - Jakob Siemerkus
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland,Department of Psychiatry, Psychotherapy and Psychosomatics, Zurich, Switzerland
| | - Klaas E. Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland
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129
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Yu Q, Wu L, Bridwell DA, Erhardt EB, Du Y, He H, Chen J, Liu P, Sui J, Pearlson G, Calhoun VD. Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study. Front Hum Neurosci 2016; 10:476. [PMID: 27733821 PMCID: PMC5039193 DOI: 10.3389/fnhum.2016.00476] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 09/08/2016] [Indexed: 12/21/2022] Open
Abstract
The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.
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Affiliation(s)
- Qingbao Yu
- The Mind Research Network Albuquerque, NM, USA
| | - Lei Wu
- The Mind Research Network Albuquerque, NM, USA
| | | | - Erik B Erhardt
- Department of Mathematics and Statistics, University of New Mexico Albuquerque, NM, USA
| | - Yuhui Du
- The Mind Research NetworkAlbuquerque, NM, USA; School of Information and Communication Engineering, North University of ChinaTaiyuan, China
| | - Hao He
- Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA
| | - Jiayu Chen
- The Mind Research Network Albuquerque, NM, USA
| | - Peng Liu
- The Mind Research NetworkAlbuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA; Life Science Research Center, School of Life Sciences and Technology, Xidian UniversityShanxi, China
| | - Jing Sui
- The Mind Research NetworkAlbuquerque, NM, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research CenterHartford, CT, USA; Department of Psychiatry, Yale UniversityNew Haven, CT, USA; Department of Neurobiology, Yale UniversityNew Haven, CT, USA
| | - Vince D Calhoun
- The Mind Research NetworkAlbuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA; Department of Psychiatry, Yale UniversityNew Haven, CT, USA
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130
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Peeters SCT, Gronenschild EHBM, van Amelsvoort T, van Os J, Marcelis M, Kahn R, Wiersma D, Bruggeman R, Cahn W, de Haan L, Meijer C, Myin-Germeys I. Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity. Brain Behav 2016; 6:e00508. [PMID: 27688938 PMCID: PMC5036431 DOI: 10.1002/brb3.508] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Revised: 04/24/2016] [Accepted: 04/29/2016] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Previous research has shown that the human brain can be represented as a complex functional network that is characterized by specific topological properties, such as clustering coefficient, characteristic path length, and global/local efficiency. Patients with psychotic disorder may have alterations in these properties with respect to controls, indicating altered efficiency of network organization. This study examined graph theoretical changes in relation to differential genetic risk for the disorder and aimed to identify clinical correlates. METHODS Anatomical and resting-state MRI brain scans were obtained from 73 patients with psychotic disorder, 83 unaffected siblings, and 72 controls. Topological measures (i.e., clustering coefficient, characteristic path length, and small-worldness) were used as dependent variables in a multilevel random regression analysis to investigate group differences. In addition, associations with (subclinical) psychotic/cognitive symptoms were examined. RESULTS Patients had a significantly lower clustering coefficient compared to siblings and controls, with no difference between the latter groups. No group differences were observed for characteristic path length and small-worldness. None of the topological properties were associated with (sub)clinical psychotic and cognitive symptoms. CONCLUSIONS The reduced ability for specialized processing (reflected by a lower clustering coefficient) within highly interconnected brain regions observed in the patient group may indicate state-related network alterations. There was no evidence for an intermediate phenotype and no evidence for psychopathology-related alterations.
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Affiliation(s)
- Sanne C T Peeters
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands; Faculty of Psychology and Educational Sciences Open University of the Netherlands Heerlen The Netherlands
| | - Ed H B M Gronenschild
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands
| | - Therese van Amelsvoort
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands
| | - Jim van Os
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands; Department of Psychosis Studies Institute of Psychiatry King's Health Partners King's College London London UK
| | - Machteld Marcelis
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands; Institute for Mental Health Care Eindhoven (GGzE) Eindhoven The Netherlands
| | | | - Rene Kahn
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands
| | - Durk Wiersma
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands
| | - Richard Bruggeman
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands
| | - Wiepke Cahn
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands
| | - Lieuwe de Haan
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands
| | - Carin Meijer
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands
| | - Inez Myin-Germeys
- Department of Psychiatry & Neuropsychology School for Mental Health and Neuroscience EURON Maastricht University Medical Center PO Box 616 6200 MD Maastricht The Netherlands
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131
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Kambeitz J, Kambeitz-Ilankovic L, Cabral C, Dwyer DB, Calhoun VD, van den Heuvel MP, Falkai P, Koutsouleris N, Malchow B. Aberrant Functional Whole-Brain Network Architecture in Patients With Schizophrenia: A Meta-analysis. Schizophr Bull 2016; 42 Suppl 1:S13-21. [PMID: 27460615 PMCID: PMC4960431 DOI: 10.1093/schbul/sbv174] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Findings from multiple lines of research provide evidence of aberrant functional brain connectivity in schizophrenia. By using graph-analytical measures, recent studies indicate that patients with schizophrenia exhibit changes in the organizational principles of whole-brain networks and that these changes relate to cognitive symptoms. However, there has not been a systematic investigation of functional brain network changes in schizophrenia to test the consistency of these changes across multiple studies. A comprehensive literature search was conducted to identify all available functional graph-analytical studies in patients with schizophrenia. Effect size measures were derived from each study and entered in a random-effects meta-analytical model. All models were tested for effects of potential moderator variables as well as for the presence of publication bias. The results of a total of n = 13 functional neuroimaging studies indicated that brain networks in patients with schizophrenia exhibit significant decreases in measures of local organization (g = -0.56, P = .02) and significant decreases in small-worldness (g = -0.65, P = .01) whereas global short communication paths seemed to be preserved (g = 0.26, P = .32). There was no evidence for a publication bias or moderator effects. The present meta- analysis demonstrates significant changes in whole brain network architecture associated with schizophrenia across studies.
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Affiliation(s)
- Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany;
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Carlos Cabral
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Vince D Calhoun
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM
| | | | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Berend Malchow
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
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132
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Cui Y, Li SF, Gu H, Hu YZ, Liang X, Lu CQ, Cai Y, Wang CX, Yang Y, Teng GJ. Disrupted Brain Connectivity Patterns in Patients with Type 2 Diabetes. AJNR Am J Neuroradiol 2016; 37:2115-2122. [PMID: 27365332 DOI: 10.3174/ajnr.a4858] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 05/11/2016] [Indexed: 01/12/2023]
Abstract
BACKGROUND AND PURPOSE Type 2 diabetes is associated with an increased risk of dementia. This study investigated the global connectivity patterns in the brains of patients with type 2 diabetes by using a functional MR imaging technique. MATERIALS AND METHODS Forty patients and 43 age-, sex-, and education-matched healthy controls underwent resting-state functional imaging in a 3T MR imaging unit. Degree centrality, a commonly used measurement of global connectivity, was computed for a full-brain exploration of the regions influenced by type 2 diabetes. We then examined the functional connectivity of each region by using the seed-based approach. Finally, voxelwise correlation analyses were performed to explore the relationship among the connectivity changes, cognitive performance, and diabetes-related variables. RESULTS Patients exhibited decreased degree centrality in the left lingual gyrus and increased centrality in the right insula and dorsal anterior cingulate cortex (corrected P < .05). The occipital network anchored in the lingual gyrus showed extensively reduced connectivity, while the network connectivity of the insula and cingulate cortex (mostly included in the salience network) was significantly elevated (corrected P < .05). Correlational analyses revealed that in the diabetic group, impaired visual memory and executive function performance were correlated with occipital hypoconnectivity, while higher fasting plasma glucose levels and better executive functioning were related to anterior cingulate cortex hyperconnectivity (all corrected P values < .05). Similar effects were not detected in the controls. CONCLUSIONS This preliminary study shows that network connectivity is altered in patients with type 2 diabetes, which may provide critical insight into the neural substrate of diabetes-related cognitive decline.
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Affiliation(s)
- Y Cui
- From the Department of Radiology (Y.Cui, C.-Q.L., Y.Cai, C-X.W., G.-J.T.), Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.,Neuroimaging Research Branch (Y.Cui, S.-F.L., H.G., Y.-Z.H, X.L., Y.Y.), National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - S-F Li
- Neuroimaging Research Branch (Y.Cui, S.-F.L., H.G., Y.-Z.H, X.L., Y.Y.), National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - H Gu
- Neuroimaging Research Branch (Y.Cui, S.-F.L., H.G., Y.-Z.H, X.L., Y.Y.), National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Y-Z Hu
- Neuroimaging Research Branch (Y.Cui, S.-F.L., H.G., Y.-Z.H, X.L., Y.Y.), National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - X Liang
- Neuroimaging Research Branch (Y.Cui, S.-F.L., H.G., Y.-Z.H, X.L., Y.Y.), National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - C-Q Lu
- From the Department of Radiology (Y.Cui, C.-Q.L., Y.Cai, C-X.W., G.-J.T.), Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Y Cai
- From the Department of Radiology (Y.Cui, C.-Q.L., Y.Cai, C-X.W., G.-J.T.), Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - C-X Wang
- From the Department of Radiology (Y.Cui, C.-Q.L., Y.Cai, C-X.W., G.-J.T.), Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Y Yang
- Neuroimaging Research Branch (Y.Cui, S.-F.L., H.G., Y.-Z.H, X.L., Y.Y.), National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - G-J Teng
- From the Department of Radiology (Y.Cui, C.-Q.L., Y.Cai, C-X.W., G.-J.T.), Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
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133
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Szedlak A, Smith N, Liu L, Paternostro G, Piermarocchi C. Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks. PLoS Comput Biol 2016; 12:e1005009. [PMID: 27359334 PMCID: PMC4928929 DOI: 10.1371/journal.pcbi.1005009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2016] [Accepted: 05/25/2016] [Indexed: 01/26/2023] Open
Abstract
The diverse, specialized genes present in today's lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins' binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes' evolutionary properties. Slowly evolving ("cold"), old genes tend to interact with each other, as do rapidly evolving ("hot"), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN's community structures and its genes' evolutionary properties provide new perspectives for understanding evolutionary genetics.
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Affiliation(s)
- Anthony Szedlak
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America
| | - Nicholas Smith
- Salgomed Inc., Del Mar, California, United States of America
| | - Li Liu
- College of Health Solutions, Arizona State University, Tempe, Arizona, United States of America
| | - Giovanni Paternostro
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, United States of America
| | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America
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134
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Hong X, Liu Y, Sun J, Tong S. Age-Related Differences in the Modulation of Small-World Brain Networks during a Go/NoGo Task. Front Aging Neurosci 2016; 8:100. [PMID: 27242512 PMCID: PMC4869596 DOI: 10.3389/fnagi.2016.00100] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 04/18/2016] [Indexed: 11/30/2022] Open
Abstract
Although inter-regional phase synchrony of neural oscillations has been proposed as a plausible mechanism for response control, little is known about the possible effects due to normal aging. We recorded multi-channel electroencephalography (EEG) from healthy younger and older adults in a Go/NoGo task, and examined the aging effects on synchronous brain networks with graph theoretical analysis. We found that in both age groups, brain networks in theta, alpha or beta band for either response execution (Go) or response inhibition (NoGo) condition showed prominent small-world property. Furthermore, small-world property of brain networks showed significant differences between different task conditions. Further analyses of node degree suggested that frontal-central theta band phase synchrony was enhanced during response inhibition, whereas during response execution, increased phase synchrony was observed in beta band over central-parietal regions. More interestingly, these task-related modulations on brain networks were well preserved and even more robust in older adults compared with younger adults. Taken together, our findings not only suggest that response control involves synchronous brain networks in functionally-distinct frequency bands, but also indicate an increase in the recruitment of brain network resources due to normal aging.
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Affiliation(s)
- Xiangfei Hong
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineShanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong UniversityShanghai, China
| | - Yuelu Liu
- Center for Mind and Brain, University of California Davis, CA, USA
| | - Junfeng Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai, China
| | - Shanbao Tong
- School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai, China
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135
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Mapping the small-world properties of brain networks in deception with functional near-infrared spectroscopy. Sci Rep 2016; 6:25297. [PMID: 27126145 PMCID: PMC4850450 DOI: 10.1038/srep25297] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 04/14/2016] [Indexed: 11/08/2022] Open
Abstract
Deception is not a rare occurrence among human behaviors; however, the present brain mapping techniques are insufficient to reveal the neural mechanism of deception under spontaneous or controlled conditions. Interestingly, functional near-infrared spectroscopy (fNIRS) has emerged as a highly promising neuroimaging technique that enables continuous and noninvasive monitoring of changes in blood oxygenation and blood volume in the human brain. In this study, fNIRS was used in combination with complex network theory to extract the attribute features of the functional brain networks underling deception in subjects exhibiting spontaneous or controlled behaviors. Our findings revealed that the small-world networks of the subjects engaged in spontaneous behaviors exhibited greater clustering coefficients, shorter average path lengths, greater average node degrees, and stronger randomness compared with those of subjects engaged in control behaviors. Consequently, we suggest that small-world network topology is capable of distinguishing well between spontaneous and controlled deceptions.
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136
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Change in brain network topology as a function of treatment response in schizophrenia: a longitudinal resting-state fMRI study using graph theory. NPJ SCHIZOPHRENIA 2016; 2:16014. [PMID: 27336056 PMCID: PMC4898893 DOI: 10.1038/npjschz.2016.14] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 01/21/2016] [Accepted: 01/22/2016] [Indexed: 01/17/2023]
Abstract
A number of neuroimaging studies have provided evidence in support of the hypothesis that faulty interactions between spatially disparate brain regions underlie the pathophysiology of schizophrenia, but it remains unclear to what degree antipsychotic medications affect these. We hypothesized that the balance between functional integration and segregation of brain networks is impaired in unmedicated patients with schizophrenia, but that it can be partially restored by antipsychotic medications. We included 32 unmedicated patients with schizophrenia (SZ) and 32 matched healthy controls (HC) in this study. We obtained resting-state scans while unmedicated, and again after 6 weeks of treatment with risperidone to assess functional integration and functional segregation of brain networks using graph theoretical measures. Compared with HC, unmedicated SZ showed reduced global efficiency and increased clustering coefficients. This pattern of aberrant functional network integration and segregation was modulated with antipsychotic medications, but only in those who responded to treatment. Our work lends support to the concept of schizophrenia as a dysconnectivity syndrome, and suggests that faulty brain network topology in schizophrenia is modulated by antipsychotic medication as a function of treatment response.
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137
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Distinct disruptions of resting-state functional brain networks in familial and sporadic schizophrenia. Sci Rep 2016; 6:23577. [PMID: 27032817 PMCID: PMC4817042 DOI: 10.1038/srep23577] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 03/08/2016] [Indexed: 01/24/2023] Open
Abstract
Clinical and brain structural differences have been reported between patients with familial and sporadic schizophrenia; however, little is known about the brain functional differences between the two subtypes of schizophrenia. Twenty-six patients with familial schizophrenia (PFS), 26 patients with sporadic schizophrenia (PSS) and 26 healthy controls (HC) underwent a resting-state functional magnetic resonance imaging. The whole-brain functional network was constructed and analyzed using graph theoretical approaches. Topological properties (including global, nodal and edge measures) were compared among the three groups. We found that PFS, PSS and HC exhibited common small-world architecture of the functional brain networks. However, at a global level, only PFS showed significantly lower normalized clustering coefficient, small-worldness, and local efficiency, indicating a randomization shift of their brain networks. At a regional level, PFS and PSS disrupted different neural circuits, consisting of abnormal nodes (increased or decreased nodal centrality) and edges (decreased functional connectivity strength), which were widely distributed throughout the entire brain. Furthermore, some of these altered network measures were significantly correlated with severity of psychotic symptoms. These results suggest that familial and sporadic schizophrenia had segregated disruptions in the topological organization of the intrinsic functional brain network, which may be due to different etiological contributions.
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138
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de Beaurepaire R. Imagerie cérébrale et déconstruction de l’esprit. EVOLUTION PSYCHIATRIQUE 2016. [DOI: 10.1016/j.evopsy.2016.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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139
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Sun Y, Chen Y, Lee R, Bezerianos A, Collinson SL, Sim K. Disruption of brain anatomical networks in schizophrenia: A longitudinal, diffusion tensor imaging based study. Schizophr Res 2016; 171:149-57. [PMID: 26811255 DOI: 10.1016/j.schres.2016.01.025] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2015] [Revised: 01/08/2016] [Accepted: 01/12/2016] [Indexed: 01/12/2023]
Abstract
Despite convergent neuroimaging evidence indicating a wide range of brain abnormalities in schizophrenia, our understanding of alterations in the topological architecture of brain anatomical networks and how they are modulated over time, is still rudimentary. Here, we employed graph theoretical analysis of longitudinal diffusion tensor imaging data (DTI) over a 5-year period to investigate brain network topology in schizophrenia and its relationship with clinical manifestations of the illness. Using deterministic tractography, weighted brain anatomical networks were constructed from 31 patients experiencing schizophrenia and 28 age- and gender-matched healthy control subjects. Although the overall small-world characteristics were observed at both baseline and follow-up, a scan-point independent significant deficit of global integration was found in patients compared to controls, suggesting dysfunctional integration of the brain and supporting the notion of schizophrenia as a disconnection syndrome. Specifically, several brain regions (e.g., the inferior frontal gyrus and the bilateral insula) that are crucial for cognitive and emotional integration were aberrant. Furthermore, a significant group-by-longitudinal scan interaction was revealed in the characteristic path length and global efficiency, attributing to a progressive aberration of global integration in patients compared to healthy controls. Moreover, the progressive disruptions of the brain anatomical network topology were associated with the clinical symptoms of the patients. Together, our findings provide insights into the substrates of anatomical dysconnectivity patterns for schizophrenia and highlight the potential for connectome-based metrics as neural markers of illness progression and clinical change with treatment.
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Affiliation(s)
- Yu Sun
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.
| | - Yu Chen
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Renick Lee
- Department of Bioengineering, National University of Singapore, Singapore
| | - Anastasios Bezerianos
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Simon L Collinson
- Department of Psychology, National University of Singapore, Singapore
| | - Kang Sim
- Department of General Psychiatry, Institute of Mental Health (IMH), Singapore; Department of Research, Institute of Mental Health (IMH), Singapore
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140
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Borchardt V, Lord AR, Li M, van der Meer J, Heinze HJ, Bogerts B, Breakspear M, Walter M. Preprocessing strategy influences graph-based exploration of altered functional networks in major depression. Hum Brain Mapp 2016; 37:1422-42. [PMID: 26888761 DOI: 10.1002/hbm.23111] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 12/11/2015] [Accepted: 12/23/2015] [Indexed: 12/16/2022] Open
Abstract
Resting-state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease-related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting-state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph-theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean-signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field.
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Affiliation(s)
- Viola Borchardt
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.,Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany
| | - Anton Richard Lord
- Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,University of Queensland, St Lucia, Queensland, Australia
| | - Meng Li
- Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,Department of Neurology, Otto Von Guericke University, Magdeburg, Germany
| | - Johan van der Meer
- Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto Von Guericke University, Magdeburg, Germany.,Department of Cognition and Emotion, Netherlands Institute for Neuroscience, an Institute of the Royal Academy of Arts and Sciences, Amsterdam, Netherlands
| | - Hans-Jochen Heinze
- Department of Neurology, Otto Von Guericke University, Magdeburg, Germany.,Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
| | - Bernhard Bogerts
- Department of Psychiatry and Psychotherapy, Otto Von Guericke University, Magdeburg, Germany
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Metro North Mental Health Service, Brisbane, Queensland, Australia
| | - Martin Walter
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.,Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto Von Guericke University, Magdeburg, Germany.,Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany.,Department of Psychiatry, University Tübingen
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141
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Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents. Neural Plast 2015; 2016:9849087. [PMID: 26819781 PMCID: PMC4706957 DOI: 10.1155/2016/9849087] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 07/22/2015] [Indexed: 12/14/2022] Open
Abstract
Previous neuroimaging studies suggested structural or functional brain reorganizations occurred in prelingually deaf subjects. However, little is known about the reorganizations of brain network architectures in prelingually deaf adolescents. The present study aims to investigate alterations of whole-brain functional network using resting-state fMRI and graph theory analysis. We recruited 16 prelingually deaf adolescents (10~18 years) and 16 normal controls matched in age and gender. Brain networks were constructed from mean time courses of 90 regions. Widely distributed network was observed in deaf subjects, with increased connectivity between the limbic system and regions involved in visual and language processing, suggesting reinforcement of the processing for the visual and verbal information in deaf adolescents. Decreased connectivity was detected between the visual regions and language regions possibly due to inferior reading or speaking skills in deaf subjects. Using graph theory analysis, we demonstrated small-worldness property did not change in prelingually deaf adolescents relative to normal controls. However, compared with healthy adolescents, eight regions involved in visual, language, and auditory processing were identified as hubs only present in prelingually deaf adolescents. These findings revealed reorganization of brain functional networks occurred in prelingually deaf adolescents to adapt to deficient auditory input.
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142
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Yun JY, Kim SN, Lee TY, Chon MW, Kwon JS. Individualized covariance profile of cortical morphology for auditory hallucinations in first-episode psychosis. Hum Brain Mapp 2015; 37:1051-65. [PMID: 26678706 DOI: 10.1002/hbm.23083] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 10/08/2015] [Accepted: 12/02/2015] [Indexed: 12/29/2022] Open
Abstract
Neocortical phenotype of cortical surface area (CSA) and thickness (CT) are influenced by distinctive genetic factors and undergo differential developmental trajectories, which could be captured using the individualized cortical structural covariance (ISC). Disturbed patterns of neocortical development and maturation underlie the perceptual disturbance of psychosis including auditory hallucination (AH). To demonstrate the utility of selected ISC features as primal biomarker of AH in first-episode psychosis (FEP) subjects experiencing AH (FEP-AH), we employed herein a support vector machine (SVM). A total of 147 subjects (FEP-AH, n = 27; FEP-NAH, n = 24; HC, n = 96) underwent T1 -weighted magnetic resonance imaging at 3T. The FreeSurfer software suite was used for cortical parcellation, with the CSA-ISC and CT-ISC then calculated. The most informative ISCs showing statistical significance (P < 0.001) across every run of leave-one-out group-comparison were aligned according to the absolute value of averaged t-statistics and were packaged into candidate feature sets for classification analysis using the SVM. An optimal feature set comprising three CSA-ISCs, including the intraparietal sulcus, Broca's complex, and the anterior insula, distinguished FEP-AH from FEP-NAH subjects with 83.6% accuracy (sensitivity = 82.8%; specificity = 85.7%). Furthermore, six CT-ISCs encompassing the executive control network and Wernicke's module classified FEP-AH from FEP-NAH subjects with 82.3% accuracy (sensitivity = 79.5%; specificity = 88.6%). Finally, extended sets of ISCs related to the default-mode network distinguished FEP-AH or FEP-NAH from HC subjects with 89.0-93.0% accuracy (sensitivity = 88.4-93.4%; specificity = 89.0-94.1%). This study established a distinctive intermediate phenotype of biological proneness for AH in FEP using CSA-ISCs as well as a state marker of disease progression using CT-ISCs.
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Affiliation(s)
- Je-Yeon Yun
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sung Nyun Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Tae Young Lee
- SNU-MRC, Institute of Human Behavioral Medicine, Seoul, Republic of Korea
| | - Myong-Wuk Chon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,SNU-MRC, Institute of Human Behavioral Medicine, Seoul, Republic of Korea.,Department of Brain and Cognitive Sciences, College of Natural Science, Seoul National University, Seoul, Republic of Korea
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143
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Váša F, Griffa A, Scariati E, Schaer M, Urben S, Eliez S, Hagmann P. An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome. NEUROIMAGE-CLINICAL 2015; 10:239-49. [PMID: 26870660 PMCID: PMC4711395 DOI: 10.1016/j.nicl.2015.11.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 11/06/2015] [Accepted: 11/24/2015] [Indexed: 01/01/2023]
Abstract
Chromosome 22q11.2 deletion syndrome (22q11DS) is a genetic disease known to lead to cerebral structural alterations, which we study using the framework of the macroscopic white-matter connectome. We create weighted connectomes of 44 patients with 22q11DS and 44 healthy controls using diffusion tensor magnetic resonance imaging, and perform a weighted graph theoretical analysis. After confirming global network integration deficits in 22q11DS (previously identified using binary connectomes), we identify the spatial distribution of regions responsible for global deficits. Next, we further characterize the dysconnectivity of the deficient regions in terms of sub-network properties, and investigate their relevance with respect to clinical profiles. We define the subset of regions with decreased nodal integration (evaluated using the closeness centrality measure) as the affected core (A-core) of the 22q11DS structural connectome. A-core regions are broadly bilaterally symmetric and consist of numerous network hubs — chiefly parietal and frontal cortical, as well as subcortical regions. Using a simulated lesion approach, we demonstrate that these core regions and their connections are particularly important to efficient network communication. Moreover, these regions are generally densely connected, but less so in 22q11DS. These specific disturbances are associated to a rerouting of shortest network paths that circumvent the A-core in 22q11DS, “de-centralizing” the network. Finally, the efficiency and mean connectivity strength of an orbito-frontal/cingulate circuit, included in the affected regions, correlate negatively with the extent of negative symptoms in 22q11DS patients, revealing the clinical relevance of present findings. The identified A-core overlaps numerous regions previously identified as affected in 22q11DS as well as in schizophrenia, which approximately 30–40% of 22q11DS patients develop. Graph theory confirms reduced integration in 22q11.2 deletion syndrome (22q11DS). An “affected core” (A-core) of hub regions drives global integration deficits. The A-core is generally densely connected, but less so in 22q11DS. Shortest network paths are rerouted around the A-core in 22q11DS. Connectivity of a subset of A-core regions correlates with negative symptoms.
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Affiliation(s)
- František Váša
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Alessandra Griffa
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Elisa Scariati
- Office Médico-Pédagogique, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Marie Schaer
- Office Médico-Pédagogique, Department of Psychiatry, University of Geneva, Geneva, Switzerland; Stanford Cognitive and Systems Neuroscience Laboratory, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Sébastien Urben
- Service Universitaire de Psychiatrie de l'Enfant et de l'Adolescent (SUPEA), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Stephan Eliez
- Office Médico-Pédagogique, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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144
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Peeters S, Simas T, Suckling J, Gronenschild E, Patel A, Habets P, van Os J, Marcelis M. Semi-metric analysis of the functional brain network: Relationship with familial risk for psychotic disorder. NEUROIMAGE-CLINICAL 2015; 9:607-16. [PMID: 26740914 PMCID: PMC4644247 DOI: 10.1016/j.nicl.2015.10.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Revised: 09/21/2015] [Accepted: 10/02/2015] [Indexed: 12/26/2022]
Abstract
Background Dysconnectivity in schizophrenia can be understood in terms of dysfunctional integration of a distributed network of brain regions. Here we propose a new methodology to analyze complex networks based on semi-metric behavior, whereby higher levels of semi-metricity may represent a higher level of redundancy and dispersed communication. It was hypothesized that individuals with (increased risk for) psychotic disorder would have more semi-metric paths compared to controls and that this would be associated with symptoms. Methods Resting-state functional MRI scans were obtained from 73 patients with psychotic disorder, 83 unaffected siblings and 72 controls. Semi-metric percentages (SMP) at the whole brain, hemispheric and lobar level were the dependent variables in a multilevel random regression analysis to investigate group differences. SMP was further examined in relation to symptomatology (i.e., psychotic/cognitive symptoms). Results At the whole brain and hemispheric level, patients had a significantly higher SMP compared to siblings and controls, with no difference between the latter. In the combined sibling and control group, individuals with high schizotypy had intermediate SMP values in the left hemisphere with respect to patients and individuals with low schizotypy. Exploratory analyses in patients revealed higher SMP in 12 out of 42 lobar divisions compared to controls, of which some were associated with worse PANSS symptomatology (i.e., positive symptoms, excitement and emotional distress) and worse cognitive performance on attention and emotion processing tasks. In the combined group of patients and controls, working memory, attention and social cognition were associated with higher SMP. Discussion The results are suggestive of more dispersed network communication in patients with psychotic disorder, with some evidence for trait-based network alterations in high-schizotypy individuals. Dispersed communication may contribute to the clinical phenotype in psychotic disorder. In addition, higher SMP may contribute to neuro- and social cognition, independent of psychosis risk. Higher SMP was observed at whole brain and hemispheric level in psychotic disorder. In patients, lobar SMP was associated with psychotic and cognitive symptoms. Trait-based SMP alterations were observed in high schizotypy individuals.
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Affiliation(s)
- Sanne Peeters
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616 (Vijv1), 6200 MD Maastricht, The Netherlands; Faculty of Psychology and Educational Sciences, Open University of the Netherlands, Heerlen, The Netherlands
| | - Tiago Simas
- Behavioral and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, United Kingdom
| | - John Suckling
- Behavioral and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, United Kingdom; Cambridge and Peterborough Foundation NHS Trust. Cambridge, United Kingdom
| | - Ed Gronenschild
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616 (Vijv1), 6200 MD Maastricht, The Netherlands
| | - Ameera Patel
- Behavioral and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, United Kingdom
| | - Petra Habets
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616 (Vijv1), 6200 MD Maastricht, The Netherlands
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616 (Vijv1), 6200 MD Maastricht, The Netherlands; King's College London, King's Health Partners, Department of Psychosis Studies Institute of Psychiatry, London, United Kingdom
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616 (Vijv1), 6200 MD Maastricht, The Netherlands; Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, The Netherlands
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145
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Lee J, Lee M, Kim DS, Kim YH. Functional reorganization and prediction of motor recovery after a stroke: A graph theoretical analysis of functional networks. Restor Neurol Neurosci 2015; 33:785-93. [DOI: 10.3233/rnn-140467] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Jungsoo Lee
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Minji Lee
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Irwon-ro, Gangnam-gu, Seoul, Republic of Korea
| | - Dae-Shik Kim
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Yun-Hee Kim
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Irwon-ro, Gangnam-gu, Seoul, Republic of Korea
- Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular and Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-ro, Gangnam-gu, Seoul, Republic of Korea
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146
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Dasari NM, Nandagopal ND, Ramasamy V, Cocks B, Thomas BH, Dahal N, Gaertner P. Moment to moment variability in functional brain networks during cognitive activity in EEG data. J Integr Neurosci 2015; 14:383-402. [PMID: 26365114 DOI: 10.1142/s0219635215500211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Functional brain networks (FBNs) are gaining increasing attention in computational neuroscience due to their ability to reveal dynamic interdependencies between brain regions. The dynamics of such networks during cognitive activity between stimulus and response using multi-channel electroencephalogram (EEG), recorded from 16 healthy human participants are explored in this research. Successive EEG segments of 500[Formula: see text]ms duration starting from the onset of cognitive stimulation have been used to analyze and understand the cognitive dynamics. The approach employs a combination of signal processing techniques, nonlinear statistical measures and graph-theoretical analysis. The efficacy of this approach in detecting and tracking cognitive load induced changes in EEG data is clearly demonstrated using graph metrics. It is revealed that most cognitive activity occurs within approximately 500[Formula: see text]ms of the stimulus presentation in addition to temporal variability in the FBNs. It is shown that mutual information (MI), a nonlinear measure, produces good correlations between the EEG channels thus enabling the construction of FBNs which are sensitive to cognitive load induced changes in EEG. Analyses of the dynamics of FBNs and the visualization approach reveal hard to detect subtle changes in cognitive function and hence may lead to a better understanding of cognitive processing in the brain. The techniques exploited have the potential to detect human cognitive dysfunction (impairments).
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Affiliation(s)
- Naga M Dasari
- * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
| | - Nanda D Nandagopal
- * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
| | - Vijayalaxmi Ramasamy
- † Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Tamil Nadu, India
| | - Bernadine Cocks
- * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
| | - Bruce H Thomas
- † Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Tamil Nadu, India
| | - Nabaraj Dahal
- * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
| | - Paul Gaertner
- ‡ Defence Science and Technology Group, Edinburgh, South Australia, Australia
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147
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Sun Y, Lee R, Shen K, Bezerianos A, Thakor N, Sim K. Structural connectivity analysis reveals topological aberrations in patients with schizophrenia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:1386-9. [PMID: 24109955 DOI: 10.1109/embc.2013.6609768] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Topological analysis and the associated parameters allow elucidation of brain networks in health and illness. Evidently useful measures for defining network competency such as small-worldness can potentially improve understanding of brain connectivity and their disruptions underlying neuropsychiatric conditions such as schizophrenia. In the current study, we assessed the structural differences of brain networks in schizophrenia patients as compared with healthy controls. As proof of concept investigation, diffusion tensor imaging recordings from 2 schizophrenia patients and 2, gender and age matched, control subjects were subjected to analysis using several graph network distance metrics. Among them, those that appeared to have the ability to encode and highest sensitivity in shedding light about anatomical changes in neuron deficiency were the shortest path length and clustering coefficient parameters. Schizophrenia patients displayed comparatively lower clustering coefficient, longer path lengths and hence reduced small-worldness. These results suggest aberrant topological architecture in the structural brain networks of patients with schizophrenia, which may impact the psychopathological and cognitive manifestations of this potentially crippling illness.
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148
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Moseley R, Ypma R, Holt R, Floris D, Chura L, Spencer M, Baron-Cohen S, Suckling J, Bullmore E, Rubinov M. Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents. Neuroimage Clin 2015; 9:140-52. [PMID: 26413477 PMCID: PMC4556734 DOI: 10.1016/j.nicl.2015.07.015] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Revised: 07/30/2015] [Accepted: 07/30/2015] [Indexed: 11/04/2022]
Abstract
Endophenotypes are heritable and quantifiable markers that may assist in the identification of the complex genetic underpinnings of psychiatric conditions. Here we examined global hypoconnectivity as an endophenotype of autism spectrum conditions (ASCs). We studied well-matched groups of adolescent males with autism, genetically-related siblings of individuals with autism, and typically-developing control participants. We parcellated the brain into 258 regions and used complex-network analysis to detect a robust hypoconnectivity endophenotype in our participant group. We observed that whole-brain functional connectivity was highest in controls, intermediate in siblings, and lowest in ASC, in task and rest conditions. We identified additional, local endophenotype effects in specific networks including the visual processing and default mode networks. Our analyses are the first to show that whole-brain functional hypoconnectivity is an endophenotype of autism in adolescence, and may thus underlie the heritable similarities seen in adolescents with ASC and their relatives.
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Affiliation(s)
- R.L. Moseley
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, UK
| | - R.J.F. Ypma
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, UK
- University of Cambridge, Hughes Hall, Cambridge, UK
| | - R.J. Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - D. Floris
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - L.R. Chura
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - M.D. Spencer
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - S. Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridge Lifespan Asperger Syndrome Service (CLASS) Clinic, Cambridgeshire and Peterborough National Health Service Foundation Trust, Cambridge, UK
| | - J. Suckling
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, UK
- Department of Experimental Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough National Health Service Foundation Trust, Cambridge, UK
| | - E. Bullmore
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, UK
- Department of Experimental Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough National Health Service Foundation Trust, Cambridge, UK
- ImmunoPsychiatry, Alternative Discovery & Development, GlaxoSmithKline, Stevenage, UK
| | - M. Rubinov
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, UK
- Churchill College, University of Cambridge, Cambridge, UK
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149
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Elton A, Gao W. Task-positive Functional Connectivity of the Default Mode Network Transcends Task Domain. J Cogn Neurosci 2015; 27:2369-81. [PMID: 26244722 DOI: 10.1162/jocn_a_00859] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The default mode network (DMN) was first recognized as a set of brain regions demonstrating consistently greater activity during rest than during a multitude of tasks. Originally, this network was believed to interfere with goal-directed behavior based on its decreased activity during many such tasks. More recently, however, the role of the DMN during goal-directed behavior was established for internally oriented tasks, in which the DMN demonstrated increased activity. However, the well-documented hub position and information-bridging potential of midline DMN regions indicate that there is more to uncover regarding its functional contributions to goal-directed tasks, which may be based on its functional interactions rather than its level of activation. An investigation of task-related changes in DMN functional connectivity during a series of both internal and external tasks would provide the requisite investigation for examining the role of the DMN during goal-directed task performance. In this study, 20 participants underwent fMRI while performing six tasks spanning diverse internal and external domains in addition to a resting-state scan. We hypothesized that the DMN would demonstrate "task-positive" (i.e., positively contributing to task performance) changes in functional connectivity relative to rest regardless of the direction of task-related changes in activity. Indeed, our results demonstrate significant increases in DMN connectivity with task-promoting regions (e.g., anterior insula, inferior frontal gyrus, middle frontal gyrus) across all six tasks. Furthermore, canonical correlation analyses indicated that the observed task-related connectivity changes were significantly associated with individual differences in task performance. Our results indicate that the DMN may not only support a "default" mode but may play a greater role in both internal and external tasks through flexible coupling with task-relevant brain regions.
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Affiliation(s)
| | - Wei Gao
- University of North Carolina at Chapel Hill
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Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia. Proc Natl Acad Sci U S A 2015; 112:9123-8. [PMID: 26150519 DOI: 10.1073/pnas.1502052112] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.
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