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Energy scaling of targeted optimal control of complex networks. Nat Commun 2017; 8:15145. [PMID: 28436417 PMCID: PMC5413984 DOI: 10.1038/ncomms15145] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 03/02/2017] [Indexed: 12/23/2022] Open
Abstract
Recently it has been shown that the control energy required to control a dynamical complex network is prohibitively large when there are only a few control inputs. Most methods to reduce the control energy have focused on where, in the network, to place additional control inputs. Here, in contrast, we show that by controlling the states of a subset of the nodes of a network, rather than the state of every node, while holding the number of control signals constant, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs as long as the target set is appropriately sized. We validate our conclusions in model and real networks to arrive at an energy scaling law to better design control objectives regardless of system size, energy restrictions, state restrictions, input node choices and target node choices. The energy required to control a dynamical complex network can be prohibitively large when there are only a few control inputs. Here the authors demonstrate that if only a subset of the network is targeted the energy requirements decrease exponentially.
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352
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Shirin A, Klickstein IS, Sorrentino F. Optimal control of complex networks: Balancing accuracy and energy of the control action. CHAOS (WOODBURY, N.Y.) 2017; 27:041103. [PMID: 28456155 DOI: 10.1063/1.4979647] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Recently, it has been shown that the control energy required to control a large dynamical complex network is prohibitively large when there are only a few control inputs. Most methods to reduce the control energy have focused on where, in the network, to place additional control inputs. We also have seen that by controlling the states of a subset of the nodes of a network, rather than the state of every node, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs as long as the target set is appropriately sized. Here, we see that the control energy can be reduced even more if the prescribed final states are not satisfied strictly. We introduce a new control strategy called balanced control for which we set our objective function as a convex combination of two competitive terms: (i) the distance between the output final states at a given final time and given prescribed states and (ii) the total control energy expenditure over the given time period. We also see that the required energy for the optimal balanced control problem approximates the required energy for the optimal target control problem when the coefficient of the second term is very small. We validate our conclusions in model and real networks regardless of system size, energy restrictions, state restrictions, input node choices, and target node choices.
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Affiliation(s)
- Afroza Shirin
- Mechanical Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - Isaac S Klickstein
- Mechanical Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - Francesco Sorrentino
- Mechanical Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131, USA
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353
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Sex differences in the influence of body mass index on anatomical architecture of brain networks. Int J Obes (Lond) 2017; 41:1185-1195. [PMID: 28360430 PMCID: PMC5548596 DOI: 10.1038/ijo.2017.86] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2016] [Revised: 03/02/2017] [Accepted: 03/06/2017] [Indexed: 12/18/2022]
Abstract
Background/Objective The brain plays a central role in regulating ingestive behavior in obesity. Analogous to addiction behaviors, an imbalance in the processing of rewarding and salient stimuli results in maladaptive eating behaviors that override homeostatic needs. We performed network analysis based on graph theory to examine the association between body mass index (BMI) and network measures of integrity, information flow, and global communication (centrality) in reward, salience and sensorimotor regions, and to identify sex-related differences in these parameters. Subjects/Methods Structural and diffusion tensor imaging were obtained in a sample of 124 individuals (61 males and 63 females). Graph theory was applied to calculate anatomical network properties (centrality) for regions of the reward, salience, and sensorimotor networks. General linear models with linear contrasts were performed to test for BMI and sex-related differences in measures of centrality, while controlling for age. Results In both males and females, individuals with high BMI (obese and overweight) had greater anatomical centrality (greater connectivity) of reward (putamen) and salience (anterior insula) network regions. Sex differences were observed both in individuals with normal and elevated BMI. In individuals with high BMI, females compared to males showed greater centrality in reward (amygdala, hippocampus, nucleus accumbens) and salience (anterior mid cingulate cortex) regions, while males compared to females had greater centrality in reward (putamen) and sensorimotor (posterior insula) regions. Conclusions In individuals with increased BMI, reward, salience, and sensorimotor network regions are susceptible to topological restructuring in a sex related manner. These findings highlight the influence of these regions on integrative processing of food-related stimuli and increased ingestive behavior in obesity, or in the influence of hedonic ingestion on brain topological restructuring. The observed sex differences emphasize the importance of considering sex differences in obesity pathophysiology.
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354
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Kozma R, Freeman WJ. Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems. Front Syst Neurosci 2017; 11:10. [PMID: 28352218 PMCID: PMC5348494 DOI: 10.3389/fnsys.2017.00010] [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: 10/07/2016] [Accepted: 02/16/2017] [Indexed: 11/10/2022] Open
Abstract
Measurements of local field potentials over the cortical surface and the scalp of animals and human subjects reveal intermittent bursts of beta and gamma oscillations. During the bursts, narrow-band metastable amplitude modulation (AM) patters emerge for a fraction of a second and ultimately dissolve to the broad-band random background activity. The burst process depends on previously learnt conditioned stimuli (CS), thus different AM patterns may emerge in response to different CS. This observation leads to our cinematic theory of cognition when perception happens in discrete steps manifested in the sequence of AM patterns. Our article summarizes findings in the past decades on experimental evidence of cinematic theory of cognition and relevant mathematical models. We treat cortices as dissipative systems that self-organize themselves near a critical level of activity that is a non-equilibrium metastable state. Criticality is arguably a key aspect of brains in their rapid adaptation, reconfiguration, high storage capacity, and sensitive response to external stimuli. Self-organized criticality (SOC) became an important concept to describe neural systems. We argue that transitions from one AM pattern to the other require the concept of phase transitions, extending beyond the dynamics described by SOC. We employ random graph theory (RGT) and percolation dynamics as fundamental mathematical approaches to model fluctuations in the cortical tissue. Our results indicate that perceptions are formed through a phase transition from a disorganized (high entropy) to a well-organized (low entropy) state, which explains the swiftness of the emergence of the perceptual experience in response to learned stimuli.
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Affiliation(s)
- Robert Kozma
- College of Information and Computer Sciences, University of MassachusettsAmherst, MA, USA; Department of Mathematical Sciences, University of MemphisMemphis, TN, USA
| | - Walter J Freeman
- Department of Molecular and Cell Biology, University of California at Berkeley Berkeley, CA, USA
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355
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Tozzi A, Peters JF, Ori O. Cracking the barcode of fullerene-like cortical microcolumns. Neurosci Lett 2017; 644:100-106. [PMID: 28242327 DOI: 10.1016/j.neulet.2017.02.064] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 02/05/2017] [Accepted: 02/22/2017] [Indexed: 11/27/2022]
Abstract
Artificial neural systems and nervous graph theoretical analysis rely upon the stance that the neural code is embodied in logic circuits, e.g., spatio-temporal sequences of ON/OFF spiking neurons. Nevertheless, this assumption does not fully explain complex brain functions. Here we show how nervous activity, other than logic circuits, could instead depend on topological transformations and symmetry constraints occurring at the micro-level of the cortical microcolumn, i.e., the embryological, anatomical and functional basic unit of the brain. Tubular microcolumns can be flattened in fullerene-like two-dimensional lattices, equipped with about 80 nodes standing for pyramidal neurons where neural computations take place. We show how the countless possible combinations of activated neurons embedded in the lattice resemble a barcode. Despite the fact that further experimental verification is required in order to validate our claim, different assemblies of firing neurons might have the appearance of diverse codes, each one responsible for a single mental activity. A two-dimensional fullerene-like lattice, grounded on simple topological changes standing for pyramidal neurons' activation, not just displays analogies with the real microcolumn's microcircuitry and the neural connectome, but also the potential for the manufacture of plastic, robust and fast artificial networks in robotic forms of full-fledged neural systems.
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Affiliation(s)
- Arturo Tozzi
- Center for Nonlinear Science, University of North Texas, 1155 Union Circle, #311427 Denton, TX 76203-5017, USA; Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada.
| | - James F Peters
- Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor's Circle, Winnipeg, MB R3T 5V6, Canada; Department of Mathematics, Adıyaman University, 02040 Adıyaman, Turkey; Department of Mathematics, Faculty of Arts and Sciences, Adıyaman University 02040 Adıyaman, Turkey; Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada.
| | - Ottorino Ori
- Actinium Chemical Research, Via Casilina 1626/A, 00133 Rome, Italy.
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356
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van der Horn HJ, Kok JG, de Koning ME, Scheenen ME, Leemans A, Spikman JM, van der Naalt J. Altered Wiring of the Human Structural Connectome in Adults with Mild Traumatic Brain Injury. J Neurotrauma 2017; 34:1035-1044. [PMID: 27627836 DOI: 10.1089/neu.2016.4659] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Harm Jan van der Horn
- Department of Neurology, University Medical Center Groningen, Groningen, The Netherlands
| | - Jelmer G. Kok
- Department of Neurology, University Medical Center Groningen, Groningen, The Netherlands
| | - Myrthe E. de Koning
- Department of Neurology, University Medical Center Groningen, Groningen, The Netherlands
| | - Myrthe E. Scheenen
- Department of Neuropsychology of the University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jacoba M. Spikman
- Department of Neuropsychology of the University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Joukje van der Naalt
- Department of Neurology, University Medical Center Groningen, Groningen, The Netherlands
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357
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Gupta A, Mayer EA, Acosta JR, Hamadani K, Torgerson C, van Horn JD, Chang L, Naliboff B, Tillisch K, Labus JS. Early adverse life events are associated with altered brain network architecture in a sex- dependent manner. Neurobiol Stress 2017; 7:16-26. [PMID: 28239631 PMCID: PMC5318542 DOI: 10.1016/j.ynstr.2017.02.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 01/25/2017] [Accepted: 02/11/2017] [Indexed: 01/23/2023] Open
Abstract
INTRODUCTION Early adverse life events (EALs) increase the risk for chronic medical and psychiatric disorders by altering early neurodevelopment. The aim of this study was to examine associations between EALs and network properties of core brain regions in the emotion regulation and salience networks, and to test the influence of sex on these associations. METHODS Resting-state functional and diffusion tensor magnetic resonance imaging were obtained in healthy individuals (61 men, 63 women). Functional and anatomical network properties of centrality and segregation were calculated for the core regions of the two networks using graph theory. Moderator analyses were applied to test hypotheses. RESULTS The type of adversity experienced influences brain wiring differently, as higher general EALs were associated with decreased functional and anatomical centrality in salience and emotion regulation regions, while physical and emotional EALs were associated with increased anatomical centrality and segregation in emotion regulation regions. Sex moderated the associations between EALs and measures of centrality; with decreased centrality of salience and emotion regulation regions with increased general EALs in females, and increased centrality in salience regions with higher physical and emotional EALs in males. Increased segregation of salience regions was associated with increased general EALs in males. Centrality of the amygdala was associated with physical symptoms, and segregation of salience regions was correlated with higher somatization in men only. CONCLUSIONS Emotion regulation and salience regions are susceptible to topological brain restructuring associated with EALs. The male and female brains appear to be differently affected by specific types of EALs.
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Affiliation(s)
- Arpana Gupta
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, UCLA, Los Angeles, CA, United States; Department of Medicine, UCLA, Los Angeles, CA, United States; UCLA Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA, Los Angeles, CA, United States
| | - Emeran A Mayer
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, UCLA, Los Angeles, CA, United States; Department of Medicine, UCLA, Los Angeles, CA, United States; Department of Psychiatry, UCLA, Los Angeles, CA, United States; UCLA Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA, Los Angeles, CA, United States; Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, United States
| | - Jonathan R Acosta
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, UCLA, Los Angeles, CA, United States
| | - Kareem Hamadani
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, UCLA, Los Angeles, CA, United States
| | - Carinna Torgerson
- The Institute for Neuroimaging and Informatics (INI) and Laboratory of NeuroImaging (LONI), Keck School of Medicine at USC, Los Angeles, CA, United States
| | - John D van Horn
- The Institute for Neuroimaging and Informatics (INI) and Laboratory of NeuroImaging (LONI), Keck School of Medicine at USC, Los Angeles, CA, United States
| | - Lin Chang
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, UCLA, Los Angeles, CA, United States; Department of Medicine, UCLA, Los Angeles, CA, United States; UCLA Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA, Los Angeles, CA, United States
| | - Bruce Naliboff
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, UCLA, Los Angeles, CA, United States; Department of Medicine, UCLA, Los Angeles, CA, United States; Department of Psychiatry, UCLA, Los Angeles, CA, United States; UCLA Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA, Los Angeles, CA, United States; UCLA Brain Research Institute, Los Angeles, CA, United States
| | - Kirsten Tillisch
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, UCLA, Los Angeles, CA, United States; Department of Medicine, UCLA, Los Angeles, CA, United States; UCLA Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA, Los Angeles, CA, United States; Department of Integrative Medicine, GLA VHA, Los Angeles, CA, United States
| | - Jennifer S Labus
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, UCLA, Los Angeles, CA, United States; Department of Medicine, UCLA, Los Angeles, CA, United States; Department of Psychiatry, UCLA, Los Angeles, CA, United States; UCLA Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA, Los Angeles, CA, United States; UCLA Brain Research Institute, Los Angeles, CA, United States
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358
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Roy A. Examining dynamic functional relationships in a pathological brain using evolutionary computation. Soft comput 2017. [DOI: 10.1007/s00500-017-2496-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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359
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O'Donoghue S, Holleran L, Cannon DM, McDonald C. Anatomical dysconnectivity in bipolar disorder compared with schizophrenia: A selective review of structural network analyses using diffusion MRI. J Affect Disord 2017; 209:217-228. [PMID: 27930915 DOI: 10.1016/j.jad.2016.11.015] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 08/16/2016] [Accepted: 11/14/2016] [Indexed: 11/15/2022]
Abstract
BACKGROUND The dysconnectivity hypothesis suggests that psychotic illnesses arise not from regionally specific focal pathophysiology, but rather from impaired neuroanatomical integration across networks of brain regions. Decreased white matter organization has been hypothesized to be a feature of psychotic illnesses in general, which is supported by meta-analyses of DTI studies in bipolar disorder and schizophrenia. Although many diffusion MRI studies investigate bipolar disorder and schizophrenia alone, relatively few studies directly compare structural features in these psychotic illnesses. Recently, the application of graph theory analyses to DTI data has supported the dysconnectivity hypothesis in bipolar disorder and schizophrenia, employing topological properties to assess neuroanatomical dysconnectivity. METHODS This selective review evaluates white matter alterations using Diffusion Tensor Imaging (DTI) in bipolar disorder and schizophrenia, with a focus upon direct comparison DTI studies in both psychotic illnesses. We then expand in more detail on the development of network analyses and the application of these techniques in bipolar disorder and schizophrenia. RESULTS Converging evidence indicates that frontal connectivity alterations are common to both disorders, with prominent fronto-temporal deficits identified in schizophrenia and inter-hemispheric and limbic alterations reported in bipolar disorder. LIMITATIONS In bipolar disorder, most connectome reports use cortical maps alone, which given the importance of the limbic system in emotional regulation may limit the scope of network approaches in mood disorders. CONCLUSIONS Further direct connectivity comparisons between these psychotic illnesses may assist in unravelling the neuroanatomical deviations underpinning the overlapping features of psychosis and cognitive impairment, and the more diagnostically distinctive features of affective disturbance in bipolar disorder and deficit syndrome in schizophrenia.
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Affiliation(s)
- Stefani O'Donoghue
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland.
| | - Laurena Holleran
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Dara M Cannon
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Colm McDonald
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
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360
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van der Horn HJ, Liemburg EJ, Scheenen ME, de Koning ME, Spikman JM, van der Naalt J. Graph Analysis of Functional Brain Networks in Patients with Mild Traumatic Brain Injury. PLoS One 2017; 12:e0171031. [PMID: 28129397 PMCID: PMC5271400 DOI: 10.1371/journal.pone.0171031] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Accepted: 01/13/2017] [Indexed: 12/21/2022] Open
Abstract
Mild traumatic brain injury (mTBI) is one of the most common neurological disorders worldwide. Posttraumatic complaints are frequently reported, interfering with outcome. However, a consistent neural substrate has not yet been found. We used graph analysis to further unravel the complex interactions between functional brain networks, complaints, anxiety and depression in the sub-acute stage after mTBI. This study included 54 patients with uncomplicated mTBI and 20 matched healthy controls. Posttraumatic complaints, anxiety and depression were measured at two weeks post-injury. Patients were selected based on presence (n = 34) or absence (n = 20) of complaints. Resting-state fMRI scans were made approximately four weeks post-injury. High order independent component analysis resulted in 89 neural components that were included in subsequent graph analyses. No differences in graph measures were found between patients with mTBI and healthy controls. Regarding the two patient subgroups, degree, strength, local efficiency and eigenvector centrality of the bilateral posterior cingulate/precuneus and bilateral parahippocampal gyrus were higher, and eigenvector centrality of the frontal pole/ bilateral middle & superior frontal gyrus was lower in patients with complaints compared to patients without complaints. In patients with mTBI, higher degree, strength and eigenvector centrality of default mode network components were related to higher depression scores, and higher degree and eigenvector centrality of executive network components were related to lower depression scores. In patients without complaints, one extra module was found compared to patients with complaints and healthy controls, consisting of the cingulate areas. In conclusion, this research extends the knowledge of functional network connectivity after mTBI. Specifically, our results suggest that an imbalance in the function of the default mode- and executive network plays a central role in the interaction between emotion regulation and the persistence of posttraumatic complaints.
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Affiliation(s)
- Harm J. van der Horn
- Department of Neurology of the University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Edith J. Liemburg
- BCN NeuroImaging Center and Department of Neuroscience of the University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Myrthe E. Scheenen
- Department of Neuropsychology of the University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Myrthe E. de Koning
- Department of Neurology of the University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jacoba M. Spikman
- Department of Neuropsychology of the University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Joukje van der Naalt
- Department of Neurology of the University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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361
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Scheinost D, Sinha R, Cross SN, Kwon SH, Sze G, Constable RT, Ment LR. Does prenatal stress alter the developing connectome? Pediatr Res 2017; 81:214-226. [PMID: 27673421 PMCID: PMC5313513 DOI: 10.1038/pr.2016.197] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Accepted: 08/30/2016] [Indexed: 12/22/2022]
Abstract
Human neurodevelopment requires the organization of neural elements into complex structural and functional networks called the connectome. Emerging data suggest that prenatal exposure to maternal stress plays a role in the wiring, or miswiring, of the developing connectome. Stress-related symptoms are common in women during pregnancy and are risk factors for neurobehavioral disorders ranging from autism spectrum disorder, attention deficit hyperactivity disorder, and addiction, to major depression and schizophrenia. This review focuses on structural and functional connectivity imaging to assess the impact of changes in women's stress-based physiology on the dynamic development of the human connectome in the fetal brain.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Rajita Sinha
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut,Department of Child Study, Yale School of Medicine, New Haven, Connecticut,Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut
| | - Sarah N. Cross
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, Connecticut
| | - Soo Hyun Kwon
- Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut
| | - Gordon Sze
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut,Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut
| | - Laura R. Ment
- Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut,Department of Neurology, Yale School of Medicine, New Haven, Connecticut,()
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362
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Sarubbo S, De Benedictis A, Merler S, Mandonnet E, Barbareschi M, Dallabona M, Chioffi F, Duffau H. Structural and functional integration between dorsal and ventral language streams as revealed by blunt dissection and direct electrical stimulation. Hum Brain Mapp 2016; 37:3858-3872. [PMID: 27258125 PMCID: PMC6867442 DOI: 10.1002/hbm.23281] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Revised: 05/07/2016] [Accepted: 05/24/2016] [Indexed: 01/24/2023] Open
Abstract
The most accepted framework of language processing includes a dorsal phonological and a ventral semantic pathway, connecting a wide network of distributed cortical hubs. However, the cortico-subcortical connectivity and the reciprocal anatomical relationships of this dual-stream system are not completely clarified. We performed an original blunt microdissection of 10 hemispheres with the exposition of locoregional short fibers and six long-range fascicles involved in language elaboration. Special attention was addressed to the analysis of termination sites and anatomical relationships between long- and short-range fascicles. We correlated these anatomical findings with a topographical analysis of 93 functional responses located at the terminal sites of the language bundles, collected by direct electrical stimulation in 108 right-handers. The locations of phonological and semantic paraphasias, verbal apraxia, speech arrest, pure anomia, and alexia were statistically analyzed, and the respective barycenters were computed in the MNI space. We found that terminations of main language bundles and functional responses have a wider distribution in respect to the classical definition of language territories. Our analysis showed that dorsal and ventral streams have a similar anatomical layer organization. These pathways are parallel and relatively segregated over their subcortical course while their terminal fibers are strictly overlapped at the cortical level. Finally, the anatomical features of the U-fibers suggested a role of locoregional integration between the phonological, semantic, and executive subnetworks of language, in particular within the inferoventral frontal lobe and the temporoparietal junction, which revealed to be the main criss-cross regions between the dorsal and ventral pathways. Hum Brain Mapp 37:3858-3872, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Silvio Sarubbo
- Division of Neurosurgery, Department of Neurosciences, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy.
- Structural and Functional Connectivity Lab, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy.
| | - Alessandro De Benedictis
- Department of Neuroscience and Neurorehabilitation, Neurosurgery Unit, Bambino Gesù Children's Hospital - IRCCS, 4 Piazza Sant'Onofrio, Roma, 00165, Italy
| | - Stefano Merler
- Bruno Kessler Foundation (FBK), 18 via Sommarive, Trento, 38123, Italy
| | - Emmanuel Mandonnet
- Department of Neurosurgery, Lariboisiere Hospital, 2 Rue Ambroise Pare, Paris, 75010, France
| | - Mattia Barbareschi
- Department of Histopathology, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Monica Dallabona
- Division of Neurosurgery, Department of Neurosciences, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Franco Chioffi
- Division of Neurosurgery, Department of Neurosciences, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
- Structural and Functional Connectivity Lab, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Hugues Duffau
- Department of Neurosurgery, Hôpital Gui De Chauliac, Montpellier University Medical Center, 80 Av Augustin Fliche, Montpellier, 34295, France
- Institute for Neuroscience of Montpellier, INSERM U1051, Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors," Saint Eloi Hospital, Montpellier, France
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363
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Gleichgerrcht E, Kocher M, Nesland T, Rorden C, Fridriksson J, Bonilha L. Preservation of structural brain network hubs is associated with less severe post-stroke aphasia. Restor Neurol Neurosci 2016; 34:19-28. [PMID: 26599472 DOI: 10.3233/rnn-150511] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE Post-stroke aphasia is typically associated with ischemic damage to cortical areas or with loss of connectivity among spared brain regions. It remains unclear whether the participation of spared brain regions as networks hubs affects the severity of aphasia. METHODS We evaluated language performance and magnetic resonance imaging from 44 participants with chronic aphasia post-stroke. The individual structural brain connectomes were constructed from diffusion tensor. Hub regions were defined in accordance with the rich club classification and studied in relation with language performance. RESULTS Number of remaining left hemisphere rich club nodes was associated with aphasia, including comprehension, repetition and naming sub-scores. Importantly, among participants with relative preservation of regions of interest for language, aphasia severity was lessened if the region was not only spared, but also participated in the remaining network as a rich club node: Brodmann area (BA) 44/45 - repetition (p = 0.009), BA 39 - repetition (p = 0.045) and naming (p < 0.01), BA 37 - fluency (p < 0.001), comprehension (p = 0.025), repetition (p < 0.001) and naming (p < 0.001). CONCLUSIONS Disruption of language network structural hubs is directly associated with aphasia severity after stroke.
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Affiliation(s)
| | | | | | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Julius Fridriksson
- Department of Communications Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
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364
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Liu C, Li Y, Edwards TJ, Kurniawan ND, Richards LJ, Jiang T. Altered structural connectome in adolescent socially isolated mice. Neuroimage 2016; 139:259-270. [DOI: 10.1016/j.neuroimage.2016.06.037] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 06/11/2016] [Accepted: 06/18/2016] [Indexed: 12/18/2022] Open
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365
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Storti SF, Formaggio E, Manganotti P, Menegaz G. Brain Network Connectivity and Topological Analysis During Voluntary Arm Movements. Clin EEG Neurosci 2016; 47:276-290. [PMID: 26251456 DOI: 10.1177/1550059415598905] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Accepted: 07/08/2015] [Indexed: 11/16/2022]
Abstract
Functional connectivity estimates the temporal synchrony among functionally homogeneous brain regions based on the assessment of the dynamics of topologically localized neurophysiological responses. The aim of this study was to investigate task-related changes in brain activity and functional connectivity by applying different methods namely event-related desynchronization (ERD), coherence, and graph-theoretical analysis to electroencephalographic (EEG) recordings, for comparing their respective descriptive power and complementarity. As it is well known, ERD provides an estimate of differences in power spectral densities between active (or task) and rest conditions, functional connectivity allows assessing the level of synchronization between the signals recorded at different scalp locations and graph analysis enables the estimation of the functional network features and topology. EEG activity was recorded on 10 subjects during left/right arm movements. The theta, alpha, and beta bands were considered. Conventional analysis showed a significant ERD in both alpha and beta bands over the sensorimotor cortex during the left arm movement and in beta band during the right arm movement, besides identifying the regions involved in the task, as it was expected. On the other hand, connectivity assessment highlighted that stronger connections are those that involved the motor regions for which graph analysis revealed reduced accessibility and an increased centrality during the movement. Jointly, the last two methods allow identifying the cortical areas that are functionally related in the active condition as well as the topological organization of the functional network. Results support the hypothesis that network analysis brings complementary knowledge with respect to established approaches for modeling motor-induced functional connectivity and could be profitably exploited in clinical contexts.
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Affiliation(s)
| | - Emanuela Formaggio
- Department of Neurophysiology, Foundation IRCCS San Camillo Hospital, Venice, Italy
| | - Paolo Manganotti
- Department of Medical, Surgical and Health Sciences, Clinical Neurology Unit, Cattinara University Hospital, Trieste, Italy
| | - Gloria Menegaz
- Department of Computer Science, University of Verona, Verona, Italy
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366
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Maren AJ. The Cluster Variation Method: A Primer for Neuroscientists. Brain Sci 2016; 6:E44. [PMID: 27706022 PMCID: PMC5187558 DOI: 10.3390/brainsci6040044] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 09/14/2016] [Accepted: 09/15/2016] [Indexed: 11/24/2022] Open
Abstract
Effective Brain-Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.
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Affiliation(s)
- Alianna J Maren
- Northwestern University School of Professional Studies, Master of Science in Predictive Analytics Program, 405 Church St, Evanston, IL 60201, USA.
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367
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Papadopoulos L, Puckett JG, Daniels KE, Bassett DS. Evolution of network architecture in a granular material under compression. Phys Rev E 2016; 94:032908. [PMID: 27739788 DOI: 10.1103/physreve.94.032908] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2016] [Indexed: 01/26/2023]
Abstract
As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. Force chains are a prime example of such structures, and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, capturing and characterizing the evolving nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend the current approaches by utilizing multilayer networks as a framework for directly quantifying the progression of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and interparticle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the changes in this structure throughout the compression process. We separately consider the network of normal and tangential forces, and find that they display a different progression throughout compression. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be achieved by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than a purely local measure of interparticle forces alone. The results discussed throughout this study suggest that these network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup.
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Affiliation(s)
- Lia Papadopoulos
- Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - James G Puckett
- Department of Physics, Gettysburg College, Gettysburg, Pennsylvania 17325, USA
| | - Karen E Daniels
- Department of Physics, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Danielle S Bassett
- Departments of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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368
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Abstract
There is a paucity of accurate and reliable biomarkers to detect traumatic brain injury, grade its severity, and model post-traumatic brain injury (TBI) recovery. This gap could be addressed via advances in brain mapping which define injury signatures and enable tracking of post-injury trajectories at the individual level. Mapping of molecular and anatomical changes and of modifications in functional activation supports the conceptual paradigm of TBI as a disorder of large-scale neural connectivity. Imaging approaches with particular relevance are magnetic resonance techniques (diffusion weighted imaging, diffusion tensor imaging, susceptibility weighted imaging, magnetic resonance spectroscopy, functional magnetic resonance imaging, and positron emission tomographic methods including molecular neuroimaging). Inferences from mapping represent unique endophenotypes which have the potential to transform classification and treatment of patients with TBI. Limitations of these methods, as well as future research directions, are highlighted.
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369
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Lee A, Tan M, Qiu A. Distinct Aging Effects on Functional Networks in Good and Poor Cognitive Performers. Front Aging Neurosci 2016; 8:215. [PMID: 27667972 PMCID: PMC5016512 DOI: 10.3389/fnagi.2016.00215] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Accepted: 08/26/2016] [Indexed: 12/13/2022] Open
Abstract
Brain network hubs are susceptible to normal aging processes and disruptions of their functional connectivity are detrimental to decline in cognitive functions in older adults. However, it remains unclear how the functional connectivity of network hubs cope with cognitive heterogeneity in an aging population. This study utilized cognitive and resting-state functional magnetic resonance imaging data, cluster analysis, and graph network analysis to examine age-related alterations in the network hubs’ functional connectivity of good and poor cognitive performers. Our results revealed that poor cognitive performers showed age-dependent disruptions in the functional connectivity of the right insula and posterior cingulate cortex (PCC), while good cognitive performers showed age-related disruptions in the functional connectivity of the left insula and PCC. Additionally, the left PCC had age-related declines in the functional connectivity with the left medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC). Most interestingly, good cognitive performers showed age-related declines in the functional connectivity of the left insula and PCC with their right homotopic structures. These results may provide insights of neuronal correlates for understanding individual differences in aging. In particular, our study suggests prominent protection roles of the left insula and PCC and bilateral ACC in good performers.
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Affiliation(s)
- Annie Lee
- Department of Biomedical Engineering, National University of Singapore Singapore, Singapore
| | - Mingzhen Tan
- Department of Biomedical Engineering, National University of Singapore Singapore, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of SingaporeSingapore, Singapore; Clinical Imaging Research Center, National University of SingaporeSingapore, Singapore; Singapore Institute for Clinical Sciences, the Agency for Science, Technology and ResearchSingapore, Singapore
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370
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Giusti C, Papadopoulos L, Owens ET, Daniels KE, Bassett DS. Topological and geometric measurements of force-chain structure. Phys Rev E 2016; 94:032909. [PMID: 27739731 DOI: 10.1103/physreve.94.032909] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Indexed: 06/06/2023]
Abstract
Developing quantitative methods for characterizing structural properties of force chains in densely packed granular media is an important step toward understanding or predicting large-scale physical properties of a packing. A promising framework in which to develop such methods is network science, which can be used to translate particle locations and force contacts into a graph in which particles are represented by nodes and forces between particles are represented by weighted edges. Recent work applying network-based community-detection techniques to extract force chains opens the door to developing statistics of force-chain structure, with the goal of identifying geometric and topological differences across packings, and providing a foundation on which to build predictions of bulk material properties from mesoscale network features. Here we discuss a trio of related but fundamentally distinct measurements of the mesoscale structure of force chains in two-dimensional (2D) packings, including a statistic derived using tools from algebraic topology, which together provide a tool set for the analysis of force chain architecture. We demonstrate the utility of this tool set by detecting variations in force-chain architecture with pressure. Collectively, these techniques can be generalized to 3D packings, and to the assessment of continuous deformations of packings under stress or strain.
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Affiliation(s)
- Chad Giusti
- Warren Center for Network and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Lia Papadopoulos
- Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Eli T Owens
- Department of Physics, Presbyterian College, Clinton, South Carolina, USA
| | - Karen E Daniels
- Department of Physics, North Carolina State University, Raleigh, North Carolina, USA
| | - Danielle S Bassett
- Departments of Bioengineering and Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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371
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Mevel K, Fransson P. The functional brain connectome of the child and autism spectrum disorders. Acta Paediatr 2016; 105:1024-35. [PMID: 27228241 DOI: 10.1111/apa.13484] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Revised: 04/05/2016] [Accepted: 05/24/2016] [Indexed: 11/30/2022]
Abstract
Brain connectomics is a relatively new field of research that maps the brain's large-scale structural and functional networks at rest. The connectome of the human brain develops progressively from early infancy to late adolescence, and this review describes the theory behind the concept and its applicability to studying the development and dynamics of brain networks through graph theoretical metrics. We also describe how the brain connectome concept could further our understanding of autism spectrum disorders (ASD) CONCLUSION: Further research into the functional child brain connectome concept could enhance our understanding of atypical brain connectivity patterns presumed to be linked to ASD.
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Affiliation(s)
- Katell Mevel
- Laboratory for the Psychology of Child Development and Education (LaPsyDÉ); CNRS UMR 8240; Sorbonne Paris Cité; GIP Cyceron; Université de Caen Normandie; Université Paris Descartes; Paris France
| | - Peter Fransson
- Department of Clinical Neuroscience; Karolinska Institutet; Stockholm Sweden
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372
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De Benedictis A, Petit L, Descoteaux M, Marras CE, Barbareschi M, Corsini F, Dallabona M, Chioffi F, Sarubbo S. New insights in the homotopic and heterotopic connectivity of the frontal portion of the human corpus callosum revealed by microdissection and diffusion tractography. Hum Brain Mapp 2016; 37:4718-4735. [PMID: 27500966 DOI: 10.1002/hbm.23339] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2016] [Revised: 06/12/2016] [Accepted: 07/26/2016] [Indexed: 12/16/2022] Open
Abstract
Extensive studies revealed that the human corpus callosum (CC) plays a crucial role in providing large-scale bi-hemispheric integration of sensory, motor and cognitive processing, especially within the frontal lobe. However, the literature lacks of conclusive data regarding the structural macroscopic connectivity of the frontal CC. In this study, a novel microdissection approach was adopted, to expose the frontal fibers of CC from the dorsum to the lateral cortex in eight hemispheres and in one entire brain. Post-mortem results were then combined with data from advanced constrained spherical deconvolution in 130 healthy subjects. We demonstrated as the frontal CC provides dense inter-hemispheric connections. In particular, we found three types of fronto-callosal fibers, having a dorso-ventral organization. First, the dorso-medial CC fibers subserve homotopic connections between the homologous medial cortices of the superior frontal gyrus. Second, the ventro-lateral CC fibers subserve homotopic connections between lateral frontal cortices, including both the middle frontal gyrus and the inferior frontal gyrus, as well as heterotopic connections between the medial and lateral frontal cortices. Third, the ventro-striatal CC fibers connect the medial and lateral frontal cortices with the contralateral putamen and caudate nucleus. We also highlighted an intricate crossing of CC fibers with the main association pathways terminating in the lateral regions of the frontal lobes. This combined approach of ex vivo microdissection and in vivo diffusion tractography allowed demonstrating a previously unappreciated three-dimensional architecture of the anterior frontal CC, thus clarifying the functional role of the CC in mediating the inter-hemispheric connectivity. Hum Brain Mapp 37:4718-4735, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Alessandro De Benedictis
- Department of Neuroscience and Neurorehabilitation, Neurosurgery Unit, Bambino Gesù Children's Hospital - IRCCS, 4 Piazza Sant'Onofrio, Roma, 00165, Italy
| | - Laurent Petit
- Groupe D'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives - UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, University of Sherbrooke, Sherbrooke, Québec, Canada
| | - Carlo Efisio Marras
- Department of Neuroscience and Neurorehabilitation, Neurosurgery Unit, Bambino Gesù Children's Hospital - IRCCS, 4 Piazza Sant'Onofrio, Roma, 00165, Italy
| | - Mattia Barbareschi
- Department of Histopathology, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Francesco Corsini
- Department of Neurosciences, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy.,Structural and Functional Connectivity Lab, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Monica Dallabona
- Department of Neurosciences, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy.,Structural and Functional Connectivity Lab, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Franco Chioffi
- Department of Neurosciences, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy.,Structural and Functional Connectivity Lab, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
| | - Silvio Sarubbo
- Department of Neurosciences, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy.,Structural and Functional Connectivity Lab, Division of Neurosurgery, "S. Chiara" Hospital, Trento APSS - 9 Largo Medaglie D'Oro, Trento, 38122, Italy
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373
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Thilaga M, Vijayalakshmi R, Nadarajan R, Nandagopal D. A novel pattern mining approach for identifying cognitive activity in EEG based functional brain networks. J Integr Neurosci 2016; 15:223-45. [PMID: 27401999 DOI: 10.1142/s0219635216500151] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The complex nature of neuronal interactions of the human brain has posed many challenges to the research community. To explore the underlying mechanisms of neuronal activity of cohesive brain regions during different cognitive activities, many innovative mathematical and computational models are required. This paper presents a novel Common Functional Pattern Mining approach to demonstrate the similar patterns of interactions due to common behavior of certain brain regions. The electrode sites of EEG-based functional brain network are modeled as a set of transactions and node-based complex network measures as itemsets. These itemsets are transformed into a graph data structure called Functional Pattern Graph. By mining this Functional Pattern Graph, the common functional patterns due to specific brain functioning can be identified. The empirical analyses show the efficiency of the proposed approach in identifying the extent to which the electrode sites (transactions) are similar during various cognitive load states.
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Affiliation(s)
- M Thilaga
- * Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India
| | - R Vijayalakshmi
- * Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India
| | - R Nadarajan
- * Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India
| | - D Nandagopal
- † Cognitive NeuroEngineering Laboratory, Division of Information Technology, Engineering and the Environment, University of South Australia, Adelaide, South Australia 5001, Australia
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374
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Karuza EA, Thompson-Schill SL, Bassett DS. Local Patterns to Global Architectures: Influences of Network Topology on Human Learning. Trends Cogn Sci 2016; 20:629-640. [PMID: 27373349 DOI: 10.1016/j.tics.2016.06.003] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2016] [Revised: 06/03/2016] [Accepted: 06/03/2016] [Indexed: 01/01/2023]
Abstract
A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels.
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Affiliation(s)
- Elisabeth A Karuza
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Sharon L Thompson-Schill
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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375
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Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks. Sci Rep 2016; 6:28384. [PMID: 27328705 PMCID: PMC4916598 DOI: 10.1038/srep28384] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 05/25/2016] [Indexed: 01/12/2023] Open
Abstract
The central nervous system is a dense, layered, 3D interconnected network of populations of neurons, and thus recapitulating that complexity for in vitro CNS models requires methods that can create defined topologically-complex neuronal networks. Several three-dimensional patterning approaches have been developed but none have demonstrated the ability to control the connections between populations of neurons. Here we report a method using AC electrokinetic forces that can guide, accelerate, slow down and push up neurites in un-modified collagen scaffolds. We present a means to create in vitro neural networks of arbitrary complexity by using such forces to create 3D intersections of primary neuronal populations that are plated in a 2D plane. We report for the first time in vitro basic brain motifs that have been previously observed in vivo and show that their functional network is highly decorrelated to their structure. This platform can provide building blocks to reproduce in vitro the complexity of neural circuits and provide a minimalistic environment to study the structure-function relationship of the brain circuitry.
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376
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Schiller CE, Johnson SL, Abate AC, Schmidt PJ, Rubinow DR. Reproductive Steroid Regulation of Mood and Behavior. Compr Physiol 2016; 6:1135-60. [PMID: 27347888 PMCID: PMC6309888 DOI: 10.1002/cphy.c150014] [Citation(s) in RCA: 108] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In this article, we examine evidence supporting the role of reproductive steroids in the regulation of mood and behavior in women and the nature of that role. In the first half of the article, we review evidence for the following: (i) the reproductive system is designed to regulate behavior; (ii) from the subcellular to cellular to circuit to behavior, reproductive steroids are powerful neuroregulators; (iii) affective disorders are disorders of behavioral state; and (iv) reproductive steroids affect virtually every system implicated in the pathophysiology of depression. In the second half of the article, we discuss the diagnosis of the three reproductive endocrine-related mood disorders (premenstrual dysphoric disorder, postpartum depression, and perimenopausal depression) and present evidence supporting the relevance of reproductive steroids to these conditions. Existing evidence suggests that changes in reproductive steroid levels during specific reproductive states (i.e., the premenstrual phase of the menstrual cycle, pregnancy, parturition, and the menopause transition) trigger affective dysregulation in susceptible women, thus suggesting the etiopathogenic relevance of these hormonal changes in reproductive mood disorders. Understanding the source of individual susceptibility is critical to both preventing the onset of illness and developing novel, individualized treatments for reproductive-related affective dysregulation. © 2016 American Physiological Society. Compr Physiol 6:1135-1160, 2016e.
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Affiliation(s)
- Crystal Edler Schiller
- Psychiatry Department, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sarah L. Johnson
- Psychiatry Department, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anna C. Abate
- Psychiatry Department, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Peter J. Schmidt
- Section on Behavioral Endocrinology, National Institute of Mental Health, Department of Health and Human Services, Bethesda, Maryland, USA
| | - David R. Rubinow
- Psychiatry Department, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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377
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Telesford QK, Lynall ME, Vettel J, Miller MB, Grafton ST, Bassett DS. Detection of functional brain network reconfiguration during task-driven cognitive states. Neuroimage 2016; 142:198-210. [PMID: 27261162 DOI: 10.1016/j.neuroimage.2016.05.078] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 05/25/2016] [Accepted: 05/29/2016] [Indexed: 12/23/2022] Open
Abstract
Network science offers computational tools to elucidate the complex patterns of interactions evident in neuroimaging data. Recently, these tools have been used to detect dynamic changes in network connectivity that may occur at short time scales. The dynamics of fMRI connectivity, and how they differ across time scales, are far from understood. A simple way to interrogate dynamics at different time scales is to alter the size of the time window used to extract sequential (or rolling) measures of functional connectivity. Here, in n=82 participants performing three distinct cognitive visual tasks in recognition memory and strategic attention, we subdivided regional BOLD time series into variable sized time windows and determined the impact of time window size on observed dynamics. Specifically, we applied a multilayer community detection algorithm to identify temporal communities and we calculated network flexibility to quantify changes in these communities over time. Within our frequency band of interest, large and small windows were associated with a narrow range of network flexibility values across the brain, while medium time windows were associated with a broad range of network flexibility values. Using medium time windows of size 75-100s, we uncovered brain regions with low flexibility (considered core regions, and observed in visual and attention areas) and brain regions with high flexibility (considered periphery regions, and observed in subcortical and temporal lobe regions) via comparison to appropriate dynamic network null models. Generally, this work demonstrates the impact of time window length on observed network dynamics during task performance, offering pragmatic considerations in the choice of time window in dynamic network analysis. More broadly, this work reveals organizational principles of brain functional connectivity that are not accessible with static network approaches.
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Affiliation(s)
- Qawi K Telesford
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Army Research Laboratory, Aberdeen Proving Ground, MD 21001, USA
| | - Mary-Ellen Lynall
- Department of Psychiatry, University of Cambridge, Cambridge, UK; Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Jean Vettel
- Army Research Laboratory, Aberdeen Proving Ground, MD 21001, USA; Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Michael B Miller
- Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Scott T Grafton
- Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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378
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Wirsich J, Perry A, Ridley B, Proix T, Golos M, Bénar C, Ranjeva JP, Bartolomei F, Breakspear M, Jirsa V, Guye M. Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy. NEUROIMAGE-CLINICAL 2016; 11:707-718. [PMID: 27330970 PMCID: PMC4909094 DOI: 10.1016/j.nicl.2016.05.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Revised: 03/15/2016] [Accepted: 05/18/2016] [Indexed: 12/13/2022]
Abstract
The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.
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Key Words
- CSD, constrained spherical deconvolution
- CSF, cerebrospinal fluid
- FA, fractional anisotropy
- FCA, analytic functional connectivity
- FCD, functional connectivity dynamics
- FOD, fiber orientation distribution
- Functional connectivity
- NBS, network based statistics
- Network based statistics
- Network communication
- Rich club
- Structural connectivity
- Temporal lobe epilepsy
- dMRI, diffusion magnetic resonance imaging
- rTLE, right temporal lobe epilepsy
- rsfMRI, resting state functional magnetic resonance imaging
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Affiliation(s)
- Jonathan Wirsich
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France; Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Alistair Perry
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia.
| | - Ben Ridley
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
| | - Timothée Proix
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Mathieu Golos
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Christian Bénar
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Jean-Philippe Ranjeva
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
| | - Fabrice Bartolomei
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle de Neurosciences Cliniques, Service de Neurophysiologie Clinique, 13005 Marseille, France.
| | - Michael Breakspear
- School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Metro North Mental Health Services, Brisbane, QLD 4006, Australia.
| | - Viktor Jirsa
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Maxime Guye
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
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379
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Jung WH, Chang KJ, Kim NH. Disrupted topological organization in the whole-brain functional network of trauma-exposed firefighters: A preliminary study. Psychiatry Res Neuroimaging 2016; 250:15-23. [PMID: 27107156 DOI: 10.1016/j.pscychresns.2016.03.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Revised: 02/29/2016] [Accepted: 03/10/2016] [Indexed: 01/19/2023]
Abstract
Given that partial posttraumatic stress disorder (pPTSD) may be a specific risk factor for the development of posttraumatic stress disorder (PTSD), it is important to understand the neurobiology of pPTSD. However, there are few extant studies in this domain. Using resting-state functional magnetic resonance imaging (rs-fMRI) and a graph theoretical approach, we compared the topological organization of the whole-brain functional network in trauma-exposed firefighters with pPTSD (pPTSD group, n=9) with those without pPTSD (PC group, n=8) and non-traumatized healthy controls (HC group, n=11). We also examined changes in the network topology of five individuals with pPTSD before and after eye movement desensitization and reprocessing (EMDR) therapy. Individuals with pPTSD exhibited altered global properties, including a reduction in values of a normalized clustering coefficient, normalized local efficiency, and small-worldness. We also observed altered local properties, particularly in the association cortex, including the temporal and parietal cortices, across groups. These disruptive global and local network properties presented in pPTSD before treatment were ameliorated after treatment. Our preliminary results suggest that subthreshold manifestation of PTSD may be due to a disruption in the optimal balance in the functional brain networks and that this disruption can be ameliorated by psychotherapy.
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Affiliation(s)
- Wi Hoon Jung
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ki Jung Chang
- Department of Psychiatry and Behavioral Sciences, Ajou University School of Medicine, Suwon 16499, South Korea
| | - Nam Hee Kim
- Department of Psychiatry and Behavioral Sciences, Ajou University School of Medicine, Suwon 16499, South Korea.
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380
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Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network. Neuroimage 2016; 134:475-485. [PMID: 27103137 DOI: 10.1016/j.neuroimage.2016.04.030] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 03/04/2016] [Accepted: 04/13/2016] [Indexed: 11/21/2022] Open
Abstract
Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for rehabilitation of motor abilities and prosthesis control for patients with motor impairments. However, MI-BCI performance exhibits a wide variability across subjects, and the underlying neural mechanism remains unclear. Several studies have demonstrated that both the fronto-parietal attention network (FPAN) and MI are involved in high-level cognitive processes that are crucial for the control of BCIs. Therefore, we hypothesized that the FPAN may play an important role in MI-BCI performance. In our study, we recorded multi-modal datasets consisting of MI electroencephalography (EEG) signals, T1-weighted structural and resting-state functional MRI data for each subject. MI-BCI performance was evaluated using the common spatial pattern to extract the MI features from EEG signals. One cortical structural feature (cortical thickness (CT)) and two measurements (degree centrality (DC) and eigenvector centrality (EC)) of node centrality were derived from the structural and functional MRI data, respectively. Based on the information extracted from the EEG and MRI, a correlation analysis was used to elucidate the relationships between the FPAN and MI-BCI performance. Our results show that the DC of the right ventral intraparietal sulcus, the EC and CT of the left inferior parietal lobe, and the CT of the right dorsolateral prefrontal cortex were significantly associated with MI-BCI performance. Moreover, the receiver operating characteristic analysis and machine learning classification revealed that the EC and CT of the left IPL could effectively predict the low-aptitude BCI users from the high-aptitude BCI users with 83.3% accuracy. Those findings consistently reveal that the individuals who have efficient FPAN would perform better on MI-BCI. Our findings may deepen the understanding of individual variability in MI-BCI performance, and also may provide a new biomarker to predict individual MI-BCI performance.
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381
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Abstract
The brain uses 20% of the body’s energy. The processes delivering that energy to neurons can fail in numerous ways. The neuroenergetics theory draws out the implications of failure in the supply chain between blood capillaries and neurons. The theory is implemented as a diffusion model that yields response-latency distributions, error rates, and other predictions for typical individuals engaged in focused activities and for special populations such as those with neurodevelopmental disorders. It predicts the effects of stimulants, trial spacing, and fatigue. Here, the implications of energetic insufficiency are explored in the context of the positive manifold of abilities, disabilities, and psychiatric comorbidities.
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Affiliation(s)
| | | | - Rosemary Tannock
- Applied Psychology and Human Development Department, University of Toronto
- Neurosciences & Mental Health Research Program, The Hospital for Sick Children, Toronto, Canada
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382
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Pastore VP, Poli D, Godjoski A, Martinoia S, Massobrio P. ToolConnect: A Functional Connectivity Toolbox for In vitro Networks. Front Neuroinform 2016; 10:13. [PMID: 27065841 PMCID: PMC4811958 DOI: 10.3389/fninf.2016.00013] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Accepted: 03/14/2016] [Indexed: 11/13/2022] Open
Abstract
Nowadays, the use of in vitro reduced models of neuronal networks to investigate the interplay between structural-functional connectivity and the emerging collective dynamics is a widely accepted approach. In this respect, a relevant advance for this kind of studies has been given by the recent introduction of high-density large-scale Micro-Electrode Arrays (MEAs) which have favored the mapping of functional connections and the recordings of the neuronal electrical activity. Although, several toolboxes have been implemented to characterize network dynamics and derive functional links, no specifically dedicated software for the management of huge amount of data and direct estimation of functional connectivity maps has been developed. toolconnect offers the implementation of up to date algorithms and a user-friendly Graphical User Interface (GUI) to analyze recorded data from large scale networks. It has been specifically conceived as a computationally efficient open-source software tailored to infer functional connectivity by analyzing the spike trains acquired from in vitro networks coupled to MEAs. In the current version, toolconnect implements correlation- (cross-correlation, partial-correlation) and information theory (joint entropy, transfer entropy) based core algorithms, as well as useful and practical add-ons to visualize functional connectivity graphs and extract some topological features. In this work, we present the software, its main features and capabilities together with some demonstrative applications on hippocampal recordings.
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Affiliation(s)
- Vito Paolo Pastore
- Neuroengineering and Bio-Nano Technology Lab, Department of Informatics, Bioengineering, Robotics, System Engineering, University of Genoa Genoa, Italy
| | - Daniele Poli
- Neuroengineering and Bio-Nano Technology Lab, Department of Informatics, Bioengineering, Robotics, System Engineering, University of Genoa Genoa, Italy
| | - Aleksandar Godjoski
- Neuroengineering and Bio-Nano Technology Lab, Department of Informatics, Bioengineering, Robotics, System Engineering, University of Genoa Genoa, Italy
| | - Sergio Martinoia
- Neuroengineering and Bio-Nano Technology Lab, Department of Informatics, Bioengineering, Robotics, System Engineering, University of GenoaGenoa, Italy; Institute of Biophysics, National Research CouncilGenova, Italy
| | - Paolo Massobrio
- Neuroengineering and Bio-Nano Technology Lab, Department of Informatics, Bioengineering, Robotics, System Engineering, University of Genoa Genoa, Italy
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383
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Cortical connectivity and memory performance in cognitive decline: A study via graph theory from EEG data. Neuroscience 2016; 316:143-50. [DOI: 10.1016/j.neuroscience.2015.12.036] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Revised: 12/18/2015] [Accepted: 12/18/2015] [Indexed: 11/21/2022]
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384
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Poli D, Pastore VP, Martinoia S, Massobrio P. From functional to structural connectivity using partial correlation in neuronal assemblies. J Neural Eng 2016; 13:026023. [PMID: 26912115 DOI: 10.1088/1741-2560/13/2/026023] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Our goal is to re-introduce an optimized version of the partial correlation to infer structural connections from functional-effective ones in dissociated neuronal cultures coupled to microelectrode arrays. APPROACH We first validate our partialization procedure on in silico networks, mimicking different experimental conditions (i.e., different connectivity degrees and number of nodes) and comparing the partial correlation's performance with two gold-standard methods: cross-correlation and transfer entropy. Afterwards, to infer the structural connections in in vitro neuronal networks where the ground truth is unknown, we propose a thresholding heuristic approach. Then, to validate whether the partialization process correctly reconstructs macroscopic features of the network structure, we extract a modularity index from segregated in silico and in vitro models. Finally, as a case study, we apply our partialization procedure to analyze connectivity and topology on spontaneous developing and electrically stimulated in vitro cultures. MAIN RESULTS In simulated networks, partial correlation outperforms cross-correlation and transfer entropy at low and medium connectivity degrees, not only in relatively small (60 nodes) but also in larger (120-240 nodes) assemblies. Furthermore, partial correlation correctly identifies interconnected neuronal sub-populations and allows one to derive network topology in in vitro cortical networks. SIGNIFICANCE Our results support the idea that partial correlation is a good method for connectivity studies and can be applied to derive topological and structural features of neuronal assemblies.
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Affiliation(s)
- Daniele Poli
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genova, Genova, Italy
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385
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Otal B, Dutta A, Foerster Á, Ripolles O, Kuceyeski A, Miranda PC, Edwards DJ, Ilić TV, Nitsche MA, Ruffini G. Opportunities for Guided Multichannel Non-invasive Transcranial Current Stimulation in Poststroke Rehabilitation. Front Neurol 2016; 7:21. [PMID: 26941708 PMCID: PMC4764713 DOI: 10.3389/fneur.2016.00021] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 02/09/2016] [Indexed: 12/21/2022] Open
Abstract
Stroke is a leading cause of serious long-term disability worldwide. Functional outcome depends on stroke location, severity, and early intervention. Conventional rehabilitation strategies have limited effectiveness, and new treatments still fail to keep pace, in part due to a lack of understanding of the different stages in brain recovery and the vast heterogeneity in the poststroke population. Innovative methodologies for restorative neurorehabilitation are required to reduce long-term disability and socioeconomic burden. Neuroplasticity is involved in poststroke functional disturbances and also during rehabilitation. Tackling poststroke neuroplasticity by non-invasive brain stimulation is regarded as promising, but efficacy might be limited because of rather uniform application across patients despite individual heterogeneity of lesions, symptoms, and other factors. Transcranial direct current stimulation (tDCS) induces and modulates neuroplasticity, and has been shown to be able to improve motor and cognitive functions. tDCS is suited to improve poststroke rehabilitation outcomes, but effect sizes are often moderate and suffer from variability. Indeed, the location, extent, and pattern of functional network connectivity disruption should be considered when determining the optimal location sites for tDCS therapies. Here, we present potential opportunities for neuroimaging-guided tDCS-based rehabilitation strategies after stroke that could be personalized. We introduce innovative multimodal intervention protocols based on multichannel tDCS montages, neuroimaging methods, and real-time closed-loop systems to guide therapy. This might help to overcome current treatment limitations in poststroke rehabilitation and increase our general understanding of adaptive neuroplasticity leading to neural reorganization after stroke.
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Affiliation(s)
| | - Anirban Dutta
- INRIA (Sophia Antipolis), Université Montpellier, Montpellier, France
| | | | | | - Amy Kuceyeski
- Department of Radiology, Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY, USA
| | - Pedro C. Miranda
- Institute of Biophysics and Biomedical Engineering (IBEB), Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Dylan J. Edwards
- Non-Invasive Brain Stimulation and Human Motor Control Laboratory, Burke-Cornell Medical Research Institute, White Plains, NY, USA
| | - Tihomir V. Ilić
- Department of Clinical Neurophysiology, Medical Faculty of Military Medical Academy, University of Defense, Belgrade, Serbia
| | - Michael A. Nitsche
- Leibniz Research Centre for Working Environment and Human Factors, Technical University of Dortmund, Dortmund, Germany
- Department of Neurology, University Medical Hospital Bergmannsheil, Bochum, Germany
| | - Giulio Ruffini
- Neuroelectrics Barcelona, Barcelona, Spain
- Starlab Barcelona, Barcelona, Spain
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386
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Exploring the topological sources of robustness against invasion in biological and technological networks. Sci Rep 2016; 6:20666. [PMID: 26861189 PMCID: PMC4748249 DOI: 10.1038/srep20666] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 01/11/2016] [Indexed: 11/29/2022] Open
Abstract
For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent’s rule are applied to show a subtle trade-off between topological and wiring complexity.
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387
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Mears D, Pollard HB. Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease. J Neurosci Res 2016; 94:590-605. [DOI: 10.1002/jnr.23705] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 11/24/2015] [Accepted: 12/04/2015] [Indexed: 11/06/2022]
Affiliation(s)
- David Mears
- Department of Anatomy, Physiology, and Genetics; Uniformed Services University of the Health Sciences; Bethesda Maryland
| | - Harvey B. Pollard
- Department of Anatomy, Physiology, and Genetics; Uniformed Services University of the Health Sciences; Bethesda Maryland
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388
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Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm. Neural Plast 2015; 2016:4890497. [PMID: 26819768 PMCID: PMC4706973 DOI: 10.1155/2016/4890497] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Revised: 08/13/2015] [Accepted: 08/18/2015] [Indexed: 12/29/2022] Open
Abstract
This paper proposed a prewhitening invariance of noise space (PW-INN) as a new magnetoencephalography (MEG) source analysis method, which is particularly suitable for localizing closely spaced and highly correlated cortical sources under real MEG noise. Conventional source localization methods, such as sLORETA and beamformer, cannot distinguish closely spaced cortical sources, especially under strong intersource correlation. Our previous work proposed an invariance of noise space (INN) method to resolve closely spaced sources, but its performance is seriously degraded under correlated noise between MEG sensors. The proposed PW-INN method largely mitigates the adverse influence of correlated MEG noise by projecting MEG data to a new space defined by the orthogonal complement of dominant eigenvectors of correlated MEG noise. Simulation results showed that PW-INN is superior to INN, sLORETA, and beamformer in terms of localization accuracy for closely spaced and highly correlated sources. Lastly, source connectivity between closely spaced sources can be satisfactorily constructed from source time courses estimated by PW-INN but not from results of other conventional methods. Therefore, the proposed PW-INN method is a promising MEG source analysis to provide a high spatial-temporal characterization of cortical activity and connectivity, which is crucial for basic and clinical research of neural plasticity.
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389
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Neuroscience of drug craving for addiction medicine: From circuits to therapies. PROGRESS IN BRAIN RESEARCH 2015; 223:115-41. [PMID: 26806774 DOI: 10.1016/bs.pbr.2015.10.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Drug craving is a dynamic neurocognitive emotional-motivational response to a wide range of cues, from internal to external environments and from drug-related to stressful or affective events. The subjective feeling of craving, as an appetitive or compulsive state, could be considered a part of this multidimensional process, with modules in different levels of consciousness and embodiment. The neural correspondence of this dynamic and complex phenomenon may be productively investigated in relation to regional, small-scale networks, large-scale networks, and brain states. Within cognitive neuroscience, this approach has provided a long list of neural and cognitive targets for craving modulations with different cognitive, electrical, or pharmacological interventions. There are new opportunities to integrate different approaches for carving management from environmental, behavioral, psychosocial, cognitive, and neural perspectives. By using cognitive neuroscience models that treat drug craving as a dynamic and multidimensional process, these approaches may yield more effective interventions for addiction medicine.
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390
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Abstract
One of the most remarkable features of the human brain is its ability to adapt rapidly and efficiently to external task demands. Novel and non-routine tasks, for example, are implemented faster than structural connections can be formed. The neural underpinnings of these dynamics are far from understood. Here we develop and apply novel methods in network science to quantify how patterns of functional connectivity between brain regions reconfigure as human subjects perform 64 different tasks. By applying dynamic community detection algorithms, we identify groups of brain regions that form putative functional communities, and we uncover changes in these groups across the 64-task battery. We summarize these reconfiguration patterns by quantifying the probability that two brain regions engage in the same network community (or putative functional module) across tasks. These tools enable us to demonstrate that classically defined cognitive systems-including visual, sensorimotor, auditory, default mode, fronto-parietal, cingulo-opercular and salience systems-engage dynamically in cohesive network communities across tasks. We define the network role that a cognitive system plays in these dynamics along the following two dimensions: (i) stability vs. flexibility and (ii) connected vs. isolated. The role of each system is therefore summarized by how stably that system is recruited over the 64 tasks, and how consistently that system interacts with other systems. Using this cartography, classically defined cognitive systems can be categorized as ephemeral integrators, stable loners, and anything in between. Our results provide a new conceptual framework for understanding the dynamic integration and recruitment of cognitive systems in enabling behavioral adaptability across both task and rest conditions. This work has important implications for understanding cognitive network reconfiguration during different task sets and its relationship to cognitive effort, individual variation in cognitive performance, and fatigue.
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391
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392
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Forde NJ, O'Donoghue S, Scanlon C, Emsell L, Chaddock C, Leemans A, Jeurissen B, Barker GJ, Cannon DM, Murray RM, McDonald C. Structural brain network analysis in families multiply affected with bipolar I disorder. Psychiatry Res 2015; 234:44-51. [PMID: 26382105 DOI: 10.1016/j.pscychresns.2015.08.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 07/17/2015] [Accepted: 08/19/2015] [Indexed: 01/06/2023]
Abstract
Disrupted structural connectivity is associated with psychiatric illnesses including bipolar disorder (BP). Here we use structural brain network analysis to investigate connectivity abnormalities in multiply affected BP type I families, to assess the utility of dysconnectivity as a biomarker and its endophenotypic potential. Magnetic resonance diffusion images for 19 BP type I patients in remission, 21 of their first degree unaffected relatives, and 18 unrelated healthy controls underwent tractography. With the automated anatomical labelling atlas being used to define nodes, a connectivity matrix was generated for each subject. Network metrics were extracted with the Brain Connectivity Toolbox and then analysed for group differences, accounting for potential confounding effects of age, gender and familial association. Whole brain analysis revealed no differences between groups. Analysis of specific mainly frontal regions, previously implicated as potentially endophenotypic by functional magnetic resonance imaging analysis of the same cohort, revealed a significant effect of group in the right medial superior frontal gyrus and left middle frontal gyrus driven by reduced organisation in patients compared with controls. The organisation of whole brain networks of those affected with BP I does not differ from their unaffected relatives or healthy controls. In discreet frontal regions, however, anatomical connectivity is disrupted in patients but not in their unaffected relatives.
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Affiliation(s)
- Natalie J Forde
- Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, School of Medicine, College of Medicine, Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland; Department of Psychiatry, University Medical Centre Groningen, The Netherlands.
| | - Stefani O'Donoghue
- Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, School of Medicine, College of Medicine, Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Cathy Scanlon
- Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, School of Medicine, College of Medicine, Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland; Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Louise Emsell
- Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, School of Medicine, College of Medicine, Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland; Translational MRI, Department of Imaging & Pathology, KU Leuven & Radiology, University Hospitals Leuven, Belgium
| | - Chris Chaddock
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands
| | | | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Dara M Cannon
- Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, School of Medicine, College of Medicine, Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Robin M Murray
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, School of Medicine, College of Medicine, Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
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393
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Lacasa L, Nicosia V, Latora V. Network structure of multivariate time series. Sci Rep 2015; 5:15508. [PMID: 26487040 PMCID: PMC4614448 DOI: 10.1038/srep15508] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 09/28/2015] [Indexed: 11/09/2022] Open
Abstract
Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.
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Affiliation(s)
- Lucas Lacasa
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E14NS London, UK
| | - Vincenzo Nicosia
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E14NS London, UK
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E14NS London, UK
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394
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Petri G, Expert P, Turkheimer F, Carhart-Harris R, Nutt D, Hellyer PJ, Vaccarino F. Homological scaffolds of brain functional networks. J R Soc Interface 2015; 11:20140873. [PMID: 25401177 PMCID: PMC4223908 DOI: 10.1098/rsif.2014.0873] [Citation(s) in RCA: 235] [Impact Index Per Article: 26.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10, 186-198. (doi:10.1038/nrn2618)). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects-homological cycles-associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds, a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle,we apply these tools to compare resting state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin-the main psychoactive component of magic mushrooms. The results show that the homological structure of the brain's functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo.
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Affiliation(s)
- G. Petri
- ISI Foundation, Via Alassio 11/c, 10126 Torino, Italy
| | - P. Expert
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Kings College London, De Crespigny Park, London SE5 8AF, UK
- e-mail:
| | - F. Turkheimer
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Kings College London, De Crespigny Park, London SE5 8AF, UK
| | - R. Carhart-Harris
- Centre for Neuropsychopharmacology, Imperial College London, London W12 0NN, UK
| | - D. Nutt
- Centre for Neuropsychopharmacology, Imperial College London, London W12 0NN, UK
| | - P. J. Hellyer
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London W12 0NN, UK
| | - F. Vaccarino
- ISI Foundation, Via Alassio 11/c, 10126 Torino, Italy
- Dipartimento di Scienze Matematiche, Politecnico di Torino, C.so Duca degli Abruzzi no 24, Torino 10129, Italy
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395
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Poli D, Pastore VP, Massobrio P. Functional connectivity in in vitro neuronal assemblies. Front Neural Circuits 2015; 9:57. [PMID: 26500505 PMCID: PMC4595785 DOI: 10.3389/fncir.2015.00057] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 09/22/2015] [Indexed: 01/21/2023] Open
Abstract
Complex network topologies represent the necessary substrate to support complex brain functions. In this work, we reviewed in vitro neuronal networks coupled to Micro-Electrode Arrays (MEAs) as biological substrate. Networks of dissociated neurons developing in vitro and coupled to MEAs, represent a valid experimental model for studying the mechanisms governing the formation, organization and conservation of neuronal cell assemblies. In this review, we present some examples of the use of statistical Cluster Coefficients and Small World indices to infer topological rules underlying the dynamics exhibited by homogeneous and engineered neuronal networks.
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Affiliation(s)
- Daniele Poli
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova Genova, Italy
| | - Vito P Pastore
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova Genova, Italy
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova Genova, Italy
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396
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García-García I, Jurado MÁ, Garolera M, Marqués-Iturria I, Horstmann A, Segura B, Pueyo R, Sender-Palacios MJ, Vernet-Vernet M, Villringer A, Junqué C, Margulies DS, Neumann J. Functional network centrality in obesity: A resting-state and task fMRI study. Psychiatry Res 2015; 233:331-8. [PMID: 26145769 DOI: 10.1016/j.pscychresns.2015.05.017] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 02/03/2015] [Accepted: 05/28/2015] [Indexed: 01/04/2023]
Abstract
Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention.
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Affiliation(s)
- Isabel García-García
- Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035 Barcelona, Spain; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - María Ángeles Jurado
- Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035 Barcelona, Spain; Institute for Brain, Cognition and Behaviour (IR3C), University of Barcelona, Barcelona, Spain; Grup de Recerca Consolidat en Neuropsicologia (2014 SGR 98), Barcelona, Spain.
| | - Maite Garolera
- Grup de Recerca Consolidat en Neuropsicologia (2014 SGR 98), Barcelona, Spain; Neuropsychology Unit, Hospital de Terrassa, Consorci Sanitari de Terrassa, Terrassa, Spain
| | - Idoia Marqués-Iturria
- Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035 Barcelona, Spain; Institute for Brain, Cognition and Behaviour (IR3C), University of Barcelona, Barcelona, Spain
| | - Annette Horstmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; IFB Adiposity Diseases, Leipzig University Medical Center, Leipzig, Germany
| | - Bàrbara Segura
- Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035 Barcelona, Spain; Grup de Recerca Consolidat en Neuropsicologia (2014 SGR 98), Barcelona, Spain
| | - Roser Pueyo
- Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035 Barcelona, Spain; Institute for Brain, Cognition and Behaviour (IR3C), University of Barcelona, Barcelona, Spain; Grup de Recerca Consolidat en Neuropsicologia (2014 SGR 98), Barcelona, Spain
| | | | | | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Carme Junqué
- Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035 Barcelona, Spain; Grup de Recerca Consolidat en Neuropsicologia (2014 SGR 98), Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Daniel S Margulies
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jane Neumann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; IFB Adiposity Diseases, Leipzig University Medical Center, Leipzig, Germany
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397
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Langen CD, White T, Ikram MA, Vernooij MW, Niessen WJ. Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia. PLoS One 2015; 10:e0137484. [PMID: 26331844 PMCID: PMC4557994 DOI: 10.1371/journal.pone.0137484] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 08/16/2015] [Indexed: 11/18/2022] Open
Abstract
Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, “bi-modal comparison plots” show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using “worm plots”. Group differences in connections are examined with an existing visualization, the “connectogram”. These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the “Statistical Analysis of Minimum cost path based Structural Connectivity” method and the average fractional anisotropy along the fiber. The functional measures were Pearson’s correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.
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Affiliation(s)
- Carolyn D. Langen
- Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- * E-mail:
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus Medical Centre, Rotterdam, Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Wiro J. Niessen
- Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
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398
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Aberrant white matter networks mediate cognitive impairment in patients with silent lacunar infarcts in basal ganglia territory. J Cereb Blood Flow Metab 2015; 35:1426-34. [PMID: 25873426 PMCID: PMC4640338 DOI: 10.1038/jcbfm.2015.67] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Revised: 02/16/2015] [Accepted: 03/16/2015] [Indexed: 01/12/2023]
Abstract
Silent lacunar infarcts, which are present in over 20% of healthy elderly individuals, are associated with subtle deficits in cognitive functions. However, it remains largely unclear how these silent brain infarcts lead to cognitive deficits and even dementia. Here, we used diffusion tensor imaging tractography and graph theory to examine the topological organization of white matter networks in 27 patients with silent lacunar infarcts in the basal ganglia territory and 30 healthy controls. A whole-brain white matter network was constructed for each subject, where the graph nodes represented brain regions and the edges represented interregional white matter tracts. Compared with the controls, the patients exhibited a significant reduction in local efficiency and global efficiency. In addition, a total of eighteen brain regions showed significantly reduced nodal efficiency in patients. Intriguingly, nodal efficiency-behavior associations were significantly different between the two groups. The present findings provide new aspects into our understanding of silent infarcts that even small lesions in subcortical brain regions may affect large-scale cortical white matter network, as such may be the link between subcortical silent infarcts and the associated cognitive impairments. Our findings highlight the need for network-level neuroimaging assessment and more medical care for individuals with silent subcortical infarcts.
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399
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Chen JE, Glover GH. Functional Magnetic Resonance Imaging Methods. Neuropsychol Rev 2015; 25:289-313. [PMID: 26248581 PMCID: PMC4565730 DOI: 10.1007/s11065-015-9294-9] [Citation(s) in RCA: 88] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2015] [Accepted: 07/28/2015] [Indexed: 12/11/2022]
Abstract
Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.
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Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA,
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400
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Resting-State fMRI in MS: General Concepts and Brief Overview of Its Application. BIOMED RESEARCH INTERNATIONAL 2015; 2015:212693. [PMID: 26413509 PMCID: PMC4564590 DOI: 10.1155/2015/212693] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Revised: 01/15/2015] [Accepted: 01/28/2015] [Indexed: 01/30/2023]
Abstract
Brain functional connectivity (FC) is defined as the coherence in the activity between cerebral areas under a task or in the resting-state (RS). By applying functional magnetic resonance imaging (fMRI), RS FC shows several patterns which define RS brain networks (RSNs) involved in specific functions, because brain function is known to depend not only on the activity within individual regions, but also on the functional interaction of different areas across the whole brain. Region-of-interest analysis and independent component analysis are the two most commonly applied methods for RS investigation. Multiple sclerosis (MS) is characterized by multiple lesions mainly affecting the white matter, determining both structural and functional disconnection between various areas of the central nervous system. The study of RS FC in MS is mainly aimed at understanding alterations in the intrinsic functional architecture of the brain and their role in disease progression and clinical impairment. In this paper, we will examine the results obtained by the application of RS fMRI in different multiple sclerosis (MS) phenotypes and the correlations of FC changes with clinical features in this pathology. The knowledge of RS FC changes may represent a substantial step forward in the MS research field, both for clinical and therapeutic purposes.
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