151
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Bettinger JS. Comparative approximations of criticality in a neural and quantum regime. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 131:445-462. [PMID: 29031703 DOI: 10.1016/j.pbiomolbio.2017.09.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 09/01/2017] [Accepted: 09/04/2017] [Indexed: 06/07/2023]
Abstract
Under a variety of conditions, stochastic and non-linear systems with many degrees of freedom tend to evolve towards complexity and criticality. Over the last decades, a steady proliferation of models re: far-from-equilibrium thermodynamics of metastable, many-valued systems arose, serving as attributes of a 'critical' attractor landscape. Building off recent data citing trademark aspects of criticality in the brain-including: power-laws, scale-free (1/f) behavior (scale invariance, or scale independence), critical slowing, and avalanches-it has been conjectured that operating at criticality entails functional advantages such as: optimized neural computation and information processing; boosted memory; large dynamical ranges; long-range communication; and an increased ability to react to highly diverse stimuli. In short, critical dynamics provide a necessary condition for neurobiologically significant elements of brain dynamics. Theoretical predictions have been verified in specific models such as Boolean networks, liquid state machines, and neural networks. These findings inspired the neural criticality hypothesis, proposing that the brain operates in a critical state because the associated optimal computational capabilities provide an evolutionarily advantage. This paper develops in three parts: after developing the critical landscape, we will then shift gears to rediscover another inroad to criticality via stochastic quantum field theory and dissipative dynamics. The existence of these two approaches deserves some consideration, given both neural and quantum criticality hypotheses propose specific mechanisms that leverage the same phenomena. This suggests that understanding the quantum approach could help to shed light on brain-based modeling. In the third part, we will turn to Whitehead's actual entities and modes of perception in order to demonstrate a concomitant logic underwriting both models. In the discussion, I briefly motivate a reading of criticality and its properties as responsive to the characterization of tenets from Eastern wisdom traditions.
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Affiliation(s)
- Jesse Sterling Bettinger
- Johns Hopkins University, Center for Talented Youth, Baltimore, MD, United States; Center for Process Studies, Claremont, CA, United States.
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152
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Xiao X, Liu B, Zhang J, Xiao X, Pan Y. An Optimized Method for Bayesian Connectivity Change Point Model. J Comput Biol 2017; 25:337-347. [PMID: 29185805 DOI: 10.1089/cmb.2017.0154] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The brain undergoes functional dynamic changes at all times. Investigating functional dynamics has been recently verified to be helpful for detecting psychological conditions and powerful for analyzing disease-related abnormalities of the brain. This article aims to detect functional dynamics. Specifically, we focus on how to effectively distinguish corresponding functional connectivity and change points from functional magnetic resonance imaging (fMRI) data. By combining Bayesian connectivity change point model (BCCPM), a modified genetic algorithm (GA) is presented to optimize the evolutionary procedure toward the most probable distributions of real change points in fMRI. We randomly initialize different binary indicator vectors to represent different distributions of change points. Each indicator vector represents an individual in GA, and together they form an initial population. Then we calculate Bayesian posterior probability and use it as the fitness of each individual. Finally, we evolve individuals of current generation toward the next higher fitness generation by a series of modified genetic operators. After several evolutionary procedures, individuals in the final generation may have outstanding fitness and the one with highest fitness can represent the most likely change point distribution in the corresponding fMRI data. Furthermore, the most probable change point distribution could be resolved. We test the optimized method for BCCPM on several synthesized data sets, and the experimental results verify that the proposed model produces higher accuracy results with lower time consumption. Also, we apply the new model to real block-designed task-based fMRI data set and excellent results are obtained.
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Affiliation(s)
- Xiuchun Xiao
- 1 College of Electronic and Information Engineering, Guangdong Ocean University , Zhanjiang, China .,2 Department of Computer Science, Georgia State University , Atlanta, Georgia
| | - Bing Liu
- 3 Department of Mathematics and Statistics, Georgia State University , Atlanta, Georgia
| | - Jing Zhang
- 3 Department of Mathematics and Statistics, Georgia State University , Atlanta, Georgia
| | - Xueli Xiao
- 2 Department of Computer Science, Georgia State University , Atlanta, Georgia
| | - Yi Pan
- 2 Department of Computer Science, Georgia State University , Atlanta, Georgia
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153
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Gratton C, Sun H, Petersen SE. Control networks and hubs. Psychophysiology 2017; 55. [PMID: 29193146 DOI: 10.1111/psyp.13032] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Revised: 09/28/2017] [Accepted: 10/28/2017] [Indexed: 01/06/2023]
Abstract
Executive control functions are associated with frontal, parietal, cingulate, and insular brain regions that interact through distributed large-scale networks. Here, we discuss how fMRI functional connectivity can shed light on the organization of control networks and how they interact with other parts of the brain. In the first section of our review, we present convergent evidence from fMRI functional connectivity, activation, and lesion studies that there are multiple dissociable control networks in the brain with distinct functional properties. In the second section, we discuss how graph theoretical concepts can help illuminate the mechanisms by which control networks interact with other brain regions to carry out goal-directed functions, focusing on the role of specialized hub regions for mediating cross-network interactions. Again, we use a combination of functional connectivity, lesion, and task activation studies to bolster this claim. We conclude that a large-scale network perspective provides important neurobiological constraints on the neural underpinnings of executive control, which will guide future basic and translational research into executive function and its disruption in disease.
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Affiliation(s)
- Caterina Gratton
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Haoxin Sun
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Steven E Petersen
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Psychology, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurological Surgery, Washington University in St. Louis, St. Louis, Missouri, USA
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154
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Biological conservation law as an emerging functionality in dynamical neuronal networks. Proc Natl Acad Sci U S A 2017; 114:11826-11831. [PMID: 29078286 DOI: 10.1073/pnas.1705704114] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.
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155
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Lam TK, Dawson DR, Honjo K, Ross B, Binns MA, Stuss DT, Black SE, Chen JJ, Levine BT, Fujioka T, Chen JL. Neural coupling between contralesional motor and frontoparietal networks correlates with motor ability in individuals with chronic stroke. J Neurol Sci 2017; 384:21-29. [PMID: 29249372 DOI: 10.1016/j.jns.2017.11.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 10/27/2017] [Accepted: 11/05/2017] [Indexed: 01/17/2023]
Abstract
Movement is traditionally viewed as a process that involves motor brain regions. However, movement also implicates non-motor regions such as prefrontal and parietal cortex, regions whose integrity may thus be important for motor recovery after stroke. Importantly, focal brain damage can affect neural functioning within and between distinct brain networks implicated in the damage. The aim of this study is to investigate how resting state connectivity (rs-connectivity) within and between motor and frontoparietal networks are affected post-stroke in correlation with motor outcome. Twenty-seven participants with chronic stroke with unilateral upper limb deficits underwent motor assessments and magnetic resonance imaging. Participants completed the Chedoke-McMaster Stroke Assessment as a measure of arm (CMSA-Arm) and hand (CMSA-Hand) impairment and the Action Research Arm Test (ARAT) as a measure of motor function. We used a seed-based rs-connectivity approach defining the motor (seed=contralesional primary motor cortex (M1)) and frontoparietal (seed=contralesional dorsolateral prefrontal cortex (DLPFC)) networks. We analyzed the rs-connectivity within each network (intra-network connectivity) and between both networks (inter-network connectivity), and performed correlations between: a) intra-network connectivity and motor assessment scores; b) inter-network connectivity and motor assessment scores. We found: a) Participants with high rs-connectivity within the motor network (between M1 and supplementary motor area) have higher CMSA-Hand stage (z=3.62, p=0.003) and higher ARAT score (z=3.41, p=0.02). Rs-connectivity within the motor network was not significantly correlated with CMSA-Arm stage (z=1.83, p>0.05); b) Participants with high rs-connectivity within the frontoparietal network (between DLPFC and mid-ventrolateral prefrontal cortex) have higher CMSA-Hand stage (z=3.64, p=0.01). Rs-connectivity within the frontoparietal network was not significantly correlated with CMSA-Arm stage (z=0.93, p=0.03) or ARAT score (z=2.53, p=0.05); and c) Participants with high rs-connectivity between motor and frontoparietal networks have higher CMSA-Hand stage (rs=0.54, p=0.01) and higher ARAT score (rs=0.54, p=0.009). Rs-connectivity between the motor and frontoparietal networks was not significantly correlated with CMSA-Arm stage (rs=0.34, p=0.13). Taken together, the connectivity within and between the motor and frontoparietal networks correlate with motor outcome post-stroke. The integrity of these regions may be important for an individual's motor outcome. Motor-frontoparietal connectivity may be a potential biomarker of motor recovery post-stroke.
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Affiliation(s)
- Timothy K Lam
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada; Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
| | - Deirdre R Dawson
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada; Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada; Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada; Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada
| | - Kie Honjo
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Bernhard Ross
- Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Donald T Stuss
- Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada; Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada; Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada; Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada; Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
| | - J Jean Chen
- Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Brian T Levine
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada; Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada; Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
| | - Takako Fujioka
- Rotman Research Institute, Baycrest Centre, Toronto, ON, Canada; Center for Computer Research in Music and Acoustics, Department of Music, Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Joyce L Chen
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada; Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada; Department of Physical Therapy, University of Toronto, Toronto, ON, Canada.
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156
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Weber KA, Sentis AI, Bernadel-Huey ON, Chen Y, Wang X, Parrish TB, Mackey S. Thermal Stimulation Alters Cervical Spinal Cord Functional Connectivity in Humans. Neuroscience 2017; 369:40-50. [PMID: 29101078 DOI: 10.1016/j.neuroscience.2017.10.035] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 10/23/2017] [Accepted: 10/24/2017] [Indexed: 11/28/2022]
Abstract
The spinal cord has an active role in the modulation and transmission of the neural signals traveling between the body and the brain. Recent advancements in functional magnetic resonance imaging (fMRI) have made the in vivo examination of spinal cord function in humans now possible. This technology has been recently extended to the investigation of resting state functional networks in the spinal cord, leading to the identification of distinct patterns of spinal cord functional connectivity. In this study, we expand on the previous work and further investigate resting state cervical spinal cord functional connectivity in healthy participants (n = 15) using high resolution imaging coupled with both seed-based functional connectivity analyses and graph theory-based metrics. Within spinal cord segment functional connectivity was present between the left and right ventral horns (bilateral motor network), left and right dorsal horns (bilateral sensory network), and the ipsilateral ventral and dorsal horns (unilateral sensory-motor network). Functional connectivity between the spinal cord segments was less apparent with the connectivity centered at the region of interest and spanning <20 mm along the superior-inferior axis. In a subset of participants (n = 10), the cervical spinal cord functional network was demonstrated to be state-dependent as thermal stimulation of the right ventrolateral forearm resulted in significant disruption of the bilateral sensory network, increased network global efficiency, and decreased network modularity.
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Affiliation(s)
- Kenneth A Weber
- Systems Neuroscience and Pain Lab, Department of Anesthesia, Perioperative and Pain Medicine, Stanford University, Palo Alto 94304, CA, USA.
| | - Amy I Sentis
- Systems Neuroscience and Pain Lab, Department of Anesthesia, Perioperative and Pain Medicine, Stanford University, Palo Alto 94304, CA, USA
| | - Olivia N Bernadel-Huey
- Systems Neuroscience and Pain Lab, Department of Anesthesia, Perioperative and Pain Medicine, Stanford University, Palo Alto 94304, CA, USA
| | - Yufen Chen
- Department of Radiology, Northwestern University, Chicago, IL 60611, USA
| | - Xue Wang
- Department of Radiology, Northwestern University, Chicago, IL 60611, USA
| | - Todd B Parrish
- Department of Radiology, Northwestern University, Chicago, IL 60611, USA
| | - Sean Mackey
- Systems Neuroscience and Pain Lab, Department of Anesthesia, Perioperative and Pain Medicine, Stanford University, Palo Alto 94304, CA, USA
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157
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Tang E, Giusti C, Baum GL, Gu S, Pollock E, Kahn AE, Roalf DR, Moore TM, Ruparel K, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Developmental increases in white matter network controllability support a growing diversity of brain dynamics. Nat Commun 2017; 8:1252. [PMID: 29093441 PMCID: PMC5665937 DOI: 10.1038/s41467-017-01254-4] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 09/01/2017] [Indexed: 11/17/2022] Open
Abstract
As the human brain develops, it increasingly supports coordinated control of neural activity. The mechanism by which white matter evolves to support this coordination is not well understood. Here we use a network representation of diffusion imaging data from 882 youth ages 8-22 to show that white matter connectivity becomes increasingly optimized for a diverse range of predicted dynamics in development. Notably, stable controllers in subcortical areas are negatively related to cognitive performance. Investigating structural mechanisms supporting these changes, we simulate network evolution with a set of growth rules. We find that all brain networks are structured in a manner highly optimized for network control, with distinct control mechanisms predicted in child vs. older youth. We demonstrate that our results cannot be explained by changes in network modularity. This work reveals a possible mechanism of human brain development that preferentially optimizes dynamic network control over static network architecture.
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Affiliation(s)
- Evelyn Tang
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Chad Giusti
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Graham L Baum
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Shi Gu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Eli Pollock
- Department of Physics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ari E Kahn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David R Roalf
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Tyler M Moore
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kosha Ruparel
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Brain Behavior Laboratory, Department of Psychiatry, 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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158
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Silva Pereira S, Hindriks R, Mühlberg S, Maris E, van Ede F, Griffa A, Hagmann P, Deco G. Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study. Brain Connect 2017; 7:541-557. [PMID: 28875718 DOI: 10.1089/brain.2017.0525] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
A popular way to analyze resting-state electroencephalography (EEG) and magneto encephalography (MEG) data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time series and the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time series are mixtures of source activity. It is, therefore, of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks. Since these topological features are of high interest in experimental studies, we address the question of to what extent the network topology can be reconstructed from sensor-level functional connectivity (FC) measures in case of MEG data. Simple simulations that consider only a small number of regions do not allow to assess network properties; therefore, we use a diffusion magnetic resonance imaging-constrained whole-brain computational model of resting-state activity. Our motivation lies behind the fact that still many contributions found in the literature perform network analysis at sensor level, and we aim at showing the discrepancies between source- and sensor-level network topologies by using realistic simulations of resting-state cortical activity. Our main findings are that the effect of field spread on network topology depends on the type of interaction (instantaneous or lagged) and leads to an underestimation of lagged FC at sensor level due to instantaneous mixing of cortical signals, instantaneous interaction is more sensitive to field spread than lagged interaction, and discrepancies are reduced when using planar gradiometers rather than axial gradiometers. We, therefore, recommend using lagged interaction measures on planar gradiometer data when investigating network properties of resting-state sensor-level MEG data.
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Affiliation(s)
- Silvana Silva Pereira
- 1 Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Rikkert Hindriks
- 1 Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Stefanie Mühlberg
- 1 Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Eric Maris
- 2 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Freek van Ede
- 2 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Alessandra Griffa
- 3 Department of Radiology, Lausanne University Hospital (CHUV-UNIL), Lausanne, Switzerland .,4 Signal Processing Laboratory 5 , Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Patric Hagmann
- 3 Department of Radiology, Lausanne University Hospital (CHUV-UNIL), Lausanne, Switzerland
| | - Gustavo Deco
- 1 Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain .,5 Institució Catalana de la Recerca i Estudis Avanats (ICREA), Barcelona, Spain
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159
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De Asis-Cruz J, Donofrio MT, Vezina G, Limperopoulos C. Aberrant brain functional connectivity in newborns with congenital heart disease before cardiac surgery. NEUROIMAGE-CLINICAL 2017; 17:31-42. [PMID: 29034164 PMCID: PMC5635248 DOI: 10.1016/j.nicl.2017.09.020] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 09/13/2017] [Accepted: 09/25/2017] [Indexed: 01/27/2023]
Abstract
Newborns with congenital heart disease (CHD) requiring open heart surgery are at increased risk for neurodevelopmental disabilities. Recent quantitative MRI studies have reported disrupted growth, microstructure, and metabolism in fetuses and newborns with complex CHD. To date, no study has examined whether functional brain connectivity is altered in this high-risk population after birth, before surgery. Our objective was to compare whole-brain functional connectivity of resting state networks in healthy, term newborns (n = 82) and in term neonates with CHD before surgery (n = 30) using graph theory and network-based statistics. We report for the first time intact global network topology – efficient and economic small world networks – but reduced regional functional connectivity involving critical brain regions (i.e. network hubs and/or rich club nodes) in newborns with CHD before surgery. These findings suggest the presence of early-life brain dysfunction in CHD which may be associated with neurodevelopmental impairments in the years following cardiac surgery. Additional studies are needed to evaluate the prognostic, diagnostic and surveillance potential of these findings. We examined network topology of CHD resting state networks before cardiac surgery. Small world architecture was preserved in newborns with CHD. The density of connections among rich club nodes in CHD was diminished. Connectivity among some critical brain hubs was reduced in CHD.
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Affiliation(s)
- Josepheen De Asis-Cruz
- Division of Diagnostic Imaging and Radiology, Children's National Health System, Washington, D.C. 20010, USA
| | - Mary T Donofrio
- Division of Cardiology, Children's National Health System, Washington, D.C. 20010, USA
| | - Gilbert Vezina
- Division of Diagnostic Imaging and Radiology, Children's National Health System, Washington, D.C. 20010, USA
| | - Catherine Limperopoulos
- Division of Diagnostic Imaging and Radiology, Children's National Health System, Washington, D.C. 20010, USA.
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160
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Chen GQ, Zhang X, Xing Y, Wen D, Cui GB, Han Y. Resting-state functional magnetic resonance imaging shows altered brain network topology in Type 2 diabetic patients without cognitive impairment. Oncotarget 2017; 8:104560-104570. [PMID: 29262661 PMCID: PMC5732827 DOI: 10.18632/oncotarget.21282] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 08/25/2017] [Indexed: 01/19/2023] Open
Abstract
We analyzed topology of brain functional networks in type 2 diabetes mellitus (T2DM) patients without mild cognitive impairment. We recruited T2DM patients without mild cognitive impairment (4 males and 8 females) and healthy control subjects (8 males and 16 females) to undergo cognitive testing and resting-state functional magnetic resonance imaging. Graph theoretical analysis of functional brain networks revealed abnormal small-world architecture in T2DM patients as compared to control subjects. The functional brain networks of T2DM patients showed increased path length, decreased global efficiency and disrupted long-distance connections. Moreover, reduced nodal characteristics were distributed in the frontal, parietal and temporal lobes, while increased nodal characteristics were distributed in the frontal, occipital lobes, and basal ganglia in the T2DM patients. The disrupted topological properties correlated with cognitive performance of T2DM patients. These findings demonstrate altered topological organization of functional brain networks in T2DM patients without mild cognitive impairment.
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Affiliation(s)
- Guan-Qun Chen
- Department of Neurology, XuanWu Hospital, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Xin Zhang
- Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China
| | - Yue Xing
- Radiological Sciences, Division of Clinical Neuroscience, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Dong Wen
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China.,The Key Laboratory of Software Engineering of Hebei Province, Yanshan University, Qinhuangdao, China
| | - Guang-Bin Cui
- Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China.,Beijing Institute of Geriatrics, Beijing, China.,PKUCare Rehabilitation Hospital, Beijing, China
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161
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Park J, Mori H, Okuyama Y, Asada M. Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks. PLoS One 2017; 12:e0182518. [PMID: 28796797 PMCID: PMC5552128 DOI: 10.1371/journal.pone.0182518] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Accepted: 07/07/2017] [Indexed: 11/19/2022] Open
Abstract
Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the “information networks” different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.
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Affiliation(s)
- Jihoon Park
- Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University, Suita, Osaka, Japan
- * E-mail:
| | - Hiroki Mori
- Department of Intermedia Art and Science, School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan
| | - Yuji Okuyama
- Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University, Suita, Osaka, Japan
| | - Minoru Asada
- Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University, Suita, Osaka, Japan
- Systems Intelligence Division, Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka, Japan
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162
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Ma C, Zhang HF, Lai YC. Reconstructing complex networks without time series. Phys Rev E 2017; 96:022320. [PMID: 28950596 DOI: 10.1103/physreve.96.022320] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Indexed: 06/07/2023]
Abstract
In the real world there are situations where the network dynamics are transient (e.g., various spreading processes) and the final nodal states represent the available data. Can the network topology be reconstructed based on data that are not time series? Assuming that an ensemble of the final nodal states resulting from statistically independent initial triggers (signals) of the spreading dynamics is available, we develop a maximum likelihood estimation-based framework to accurately infer the interaction topology. For dynamical processes that result in a binary final state, the framework enables network reconstruction based solely on the final nodal states. Additional information, such as the first arrival time of each signal at each node, can improve the reconstruction accuracy. For processes with a uniform final state, the first arrival times can be exploited to reconstruct the network. We derive a mathematical theory for our framework and validate its performance and robustness using various combinations of spreading dynamics and real-world network topologies.
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Affiliation(s)
- Chuang Ma
- School of Mathematical Science, Anhui University, Hefei 230601, China
| | - Hai-Feng Zhang
- School of Mathematical Science, Anhui University, Hefei 230601, China
- Center of Information Support &Assurance Technology, Anhui University, Hefei 230601, China
- Department of Communication Engineering, North University of China, Taiyuan, Shan'xi 030051, China
| | - Ying-Cheng Lai
- School of Electrical, Computer and Energy Engineering, Department of Physics, Arizona State University, Tempe, Arizona 85287, USA
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163
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Papadopoulos L, Kim JZ, Kurths J, Bassett DS. Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators. CHAOS (WOODBURY, N.Y.) 2017; 27:073115. [PMID: 28764402 PMCID: PMC5552408 DOI: 10.1063/1.4994819] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 07/07/2017] [Indexed: 05/06/2023]
Abstract
Synchronization of non-identical oscillators coupled through complex networks is an important example of collective behavior, and it is interesting to ask how the structural organization of network interactions influences this process. Several studies have explored and uncovered optimal topologies for synchronization by making purposeful alterations to a network. On the other hand, the connectivity patterns of many natural systems are often not static, but are rather modulated over time according to their dynamics. However, this co-evolution and the extent to which the dynamics of the individual units can shape the organization of the network itself are less well understood. Here, we study initially randomly connected but locally adaptive networks of Kuramoto oscillators. In particular, the system employs a co-evolutionary rewiring strategy that depends only on the instantaneous, pairwise phase differences of neighboring oscillators, and that conserves the total number of edges, allowing the effects of local reorganization to be isolated. We find that a simple rule-which preserves connections between more out-of-phase oscillators while rewiring connections between more in-phase oscillators-can cause initially disordered networks to organize into more structured topologies that support enhanced synchronization dynamics. We examine how this process unfolds over time, finding a dependence on the intrinsic frequencies of the oscillators, the global coupling, and the network density, in terms of how the adaptive mechanism reorganizes the network and influences the dynamics. Importantly, for large enough coupling and after sufficient adaptation, the resulting networks exhibit interesting characteristics, including degree-frequency and frequency-neighbor frequency correlations. These properties have previously been associated with optimal synchronization or explosive transitions in which the networks were constructed using global information. On the contrary, by considering a time-dependent interplay between structure and dynamics, this work offers a mechanism through which emergent phenomena and organization can arise in complex systems utilizing local rules.
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Affiliation(s)
- Lia Papadopoulos
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Jason Z Kim
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research - Telegraphenberg A 31, 14473 Potsdam, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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164
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Alavash M, Daube C, Wöstmann M, Brandmeyer A, Obleser J. Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making. Netw Neurosci 2017; 1:166-191. [PMID: 29911668 PMCID: PMC5988391 DOI: 10.1162/netn_a_00009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 03/01/2017] [Indexed: 11/24/2022] Open
Abstract
Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16-28 Hz) oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations.
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Affiliation(s)
- Mohsen Alavash
- Department of Psychology, University of Lübeck, Germany
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Christoph Daube
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Malte Wöstmann
- Department of Psychology, University of Lübeck, Germany
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alex Brandmeyer
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Germany
- Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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165
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Bassett DS, Khambhati AN. A network engineering perspective on probing and perturbing cognition with neurofeedback. Ann N Y Acad Sci 2017; 1396:126-143. [PMID: 28445589 PMCID: PMC5446287 DOI: 10.1111/nyas.13338] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition.
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Affiliation(s)
- Danielle S. Bassett
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of Electrical and Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Ankit N. Khambhati
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
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166
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De Domenico M. Multilayer modeling and analysis of human brain networks. Gigascience 2017; 6:1-8. [PMID: 28327916 PMCID: PMC5437946 DOI: 10.1093/gigascience/gix004] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 01/18/2017] [Accepted: 01/18/2017] [Indexed: 11/15/2022] Open
Abstract
Understanding how the human brain is structured, and how its architecture is related to function, is of paramount importance for a variety of applications, including but not limited to new ways to prevent, deal with, and cure brain diseases, such as Alzheimer's or Parkinson's, and psychiatric disorders, such as schizophrenia. The recent advances in structural and functional neuroimaging, together with the increasing attitude toward interdisciplinary approaches involving computer science, mathematics, and physics, are fostering interesting results from computational neuroscience that are quite often based on the analysis of complex network representation of the human brain. In recent years, this representation experienced a theoretical and computational revolution that is breaching neuroscience, allowing us to cope with the increasing complexity of the human brain across multiple scales and in multiple dimensions and to model structural and functional connectivity from new perspectives, often combined with each other. In this work, we will review the main achievements obtained from interdisciplinary research based on magnetic resonance imaging and establish de facto, the birth of multilayer network analysis and modeling of the human brain.
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Affiliation(s)
- Manlio De Domenico
- Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Av.da Països Catalans, 26, 43004 Tarragona, Spain
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167
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Hillary FG, Grafman JH. Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity. Trends Cogn Sci 2017; 21:385-401. [PMID: 28372878 PMCID: PMC6664441 DOI: 10.1016/j.tics.2017.03.003] [Citation(s) in RCA: 184] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 03/01/2017] [Accepted: 03/03/2017] [Indexed: 01/15/2023]
Abstract
A common finding in human functional brain-imaging studies is that damage to neural systems paradoxically results in enhanced functional connectivity between network regions, a phenomenon commonly referred to as 'hyperconnectivity'. Here, we describe the various ways that hyperconnectivity operates to benefit a neural network following injury while simultaneously negotiating the trade-off between metabolic cost and communication efficiency. Hyperconnectivity may be optimally expressed by increasing connections through the most central and metabolically efficient regions (i.e., hubs). While adaptive in the short term, we propose that chronic hyperconnectivity may leave network hubs vulnerable to secondary pathological processes over the life span due to chronically elevated metabolic stress. We conclude by offering novel, testable hypotheses for advancing our understanding of the role of hyperconnectivity in systems-level brain plasticity in neurological disorders.
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Affiliation(s)
- Frank G Hillary
- Pennsylvania State University, University Park, PA, USA; Social Life and Engineering Sciences Imaging Center, University Park, PA, USA; Department of Neurology, Hershey Medical Center, Hershey, PA, USA.
| | - Jordan H Grafman
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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168
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Liao X, Vasilakos AV, He Y. Small-world human brain networks: Perspectives and challenges. Neurosci Biobehav Rev 2017; 77:286-300. [PMID: 28389343 DOI: 10.1016/j.neubiorev.2017.03.018] [Citation(s) in RCA: 221] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 01/19/2017] [Accepted: 03/31/2017] [Indexed: 12/15/2022]
Abstract
Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.
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Affiliation(s)
- Xuhong Liao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
| | - Athanasios V Vasilakos
- Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.
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169
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Zanin M, Papo D. Detecting switching and intermittent causalities in time series. CHAOS (WOODBURY, N.Y.) 2017; 27:047403. [PMID: 28456157 DOI: 10.1063/1.4979046] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
During the last decade, complex network representations have emerged as a powerful instrument for describing the cross-talk between different brain regions both at rest and as subjects are carrying out cognitive tasks, in healthy brains and neurological pathologies. The transient nature of such cross-talk has nevertheless by and large been neglected, mainly due to the inherent limitations of some metrics, e.g., causality ones, which require a long time series in order to yield statistically significant results. Here, we present a methodology to account for intermittent causal coupling in neural activity, based on the identification of non-overlapping windows within the original time series in which the causality is strongest. The result is a less coarse-grained assessment of the time-varying properties of brain interactions, which can be used to create a high temporal resolution time-varying network. We apply the proposed methodology to the analysis of the brain activity of control subjects and alcoholic patients performing an image recognition task. Our results show that short-lived, intermittent, local-scale causality is better at discriminating both groups than global network metrics. These results highlight the importance of the transient nature of brain activity, at least under some pathological conditions.
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Affiliation(s)
| | - David Papo
- SCALab, UMR CNRS 9193, University of Lille, Villeneuve d'Ascq, France
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170
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Bassett DS, Mattar MG. A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior. Trends Cogn Sci 2017; 21:250-264. [PMID: 28259554 PMCID: PMC5366087 DOI: 10.1016/j.tics.2017.01.010] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Revised: 01/15/2017] [Accepted: 01/19/2017] [Indexed: 01/21/2023]
Abstract
Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior.
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Affiliation(s)
- 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.
| | - Marcelo G Mattar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
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171
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Abstract
The brain is one of the largest and most complex organs in the human body and EEG is a noninvasive electrophysiological monitoring method that is used to record the electrical activity of the brain. Lately, the functional connectivity in human brain has been regarded and studied as a complex network using EEG signals. This means that the brain is studied as a connected system where nodes, or units, represent different specialized brain regions and links, or connections, represent communication pathways between the nodes. Graph theory and theory of complex networks provide a variety of measures, methods, and tools that can be useful to efficiently model, analyze, and study EEG networks. This article is addressed to computer scientists who wish to be acquainted and deal with the study of EEG data and also to neuroscientists who would like to become familiar with graph theoretic approaches and tools to analyze EEG data.
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Affiliation(s)
- Nantia D Iakovidou
- Data Engineering Laboratory, Department of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece
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172
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De Ridder D, Perera S, Vanneste S. State of the Art: Novel Applications for Cortical Stimulation. Neuromodulation 2017; 20:206-214. [PMID: 28371170 DOI: 10.1111/ner.12593] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 01/13/2017] [Accepted: 01/30/2017] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Electrical stimulation via implanted electrodes that overlie the cortex of the brain is an upcoming neurosurgical technique that was hindered for a long time by insufficient knowledge of how the brain functions in a dynamic, physiological, and pathological way, as well as by technological limitations of the implantable stimulation devices. METHODS This paper provides an overview of cortex stimulation via implantable devices and introduces future possibilities to improve cortex stimulation. RESULTS Cortex stimulation was initially used preoperatively as a technique to localize functions in the brain and only later evolved into a treatment technique. It was first used for pain, but more recently a multitude of pathologies are being targeted by cortex stimulation. These disorders are being treated by stimulating different cortical areas of the brain. Risks and complications are essentially similar to those related to deep brain stimulation and predominantly include haemorrhage, seizures, infection, and hardware failures. For cortex stimulation to fully mature, further technological development is required to predict its outcomes and improve stimulation designs. This includes the development of network science-based functional connectivity approaches, genetic analyses, development of navigated high definition transcranial alternating current stimulation, and development of pseudorandom stimulation designs for preventing habituation. CONCLUSION In conclusion, cortex stimulation is a nascent but very promising approach to treating a variety of diseases, but requires further technological development for predicting outcomes, such as network science based functional connectivity approaches, genetic analyses, development of navigated transcranial electrical stimulation, and development of pseudorandom stimulation designs for preventing habituation.
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Affiliation(s)
- Dirk De Ridder
- Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | | | - Sven Vanneste
- Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand.,The University of Texas at Dallas, Richardson, TX, USA
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173
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Mehta-Pandejee G, Robinson PA, Henderson JA, Aquino KM, Sarkar S. Inference of direct and multistep effective connectivities from functional connectivity of the brain and of relationships to cortical geometry. J Neurosci Methods 2017; 283:42-54. [PMID: 28342831 DOI: 10.1016/j.jneumeth.2017.03.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 03/15/2017] [Accepted: 03/18/2017] [Indexed: 01/26/2023]
Abstract
BACKGROUND The problem of inferring effective brain connectivity from functional connectivity is under active investigation, and connectivity via multistep paths is poorly understood. NEW METHOD A method is presented to calculate the direct effective connection matrix (deCM), which embodies direct connection strengths between brain regions, from functional CMs (fCMs) by minimizing the difference between an experimental fCM and one calculated via neural field theory from an ansatz deCM based on an experimental anatomical CM. RESULTS The best match between fCMs occurs close to a critical point, consistent with independent published stability estimates. Residual mismatch between fCMs is identified to be largely due to interhemispheric connections that are poorly estimated in an initial ansatz deCM due to experimental limitations; improved ansatzes substantially reduce the mismatch and enable interhemispheric connections to be estimated. Various levels of significant multistep connections are then imaged via the neural field theory (NFT) result that these correspond to powers of the deCM; these are shown to be predictable from geometric distances between regions. COMPARISON WITH EXISTING METHODS This method gives insight into direct and multistep effective connectivity from fCMs and relating to physiology and brain geometry. This contrasts with other methods, which progressively adjust connections without an overarching physiologically based framework to deal with multistep or poorly estimated connections. CONCLUSIONS deCMs can be usefully estimated using this method and the results enable multistep connections to be investigated systematically.
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Affiliation(s)
- Grishma Mehta-Pandejee
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia.
| | - P A Robinson
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia
| | - James A Henderson
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia; School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, Queensland 4072, Australia
| | - K M Aquino
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia; Sir Peter Mansfield Imaging Center, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Somwrita Sarkar
- Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia; Design Lab, University of Sydney, Sydney, New South Wales 2006, Australia
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174
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Yu M, Engels MMA, Hillebrand A, van Straaten ECW, Gouw AA, Teunissen C, van der Flier WM, Scheltens P, Stam CJ. Selective impairment of hippocampus and posterior hub areas in Alzheimer’s disease: an MEG-based multiplex network study. Brain 2017; 140:1466-1485. [PMID: 28334883 DOI: 10.1093/brain/awx050] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 01/14/2017] [Indexed: 12/11/2022] Open
Affiliation(s)
- Meichen Yu
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Marjolein M A Engels
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
- Nutricia Advanced Medical Nutrition, Nutricia Research, Utrecht, The Netherlands
| | - Alida A Gouw
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Charlotte Teunissen
- Neurochemistry lab and Biobank, Department of Clinical Chemistry, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
- Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
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175
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Naro A, Leo A, Manuli A, Cannavò A, Bramanti A, Bramanti P, Calabrò RS. How far can we go in chronic disorders of consciousness differential diagnosis? The use of neuromodulation in detecting internal and external awareness. Neuroscience 2017; 349:165-173. [PMID: 28285941 DOI: 10.1016/j.neuroscience.2017.02.053] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 02/23/2017] [Accepted: 02/24/2017] [Indexed: 12/12/2022]
Abstract
Awareness generation and modulation may depend on a balanced information integration and differentiation across default mode network (DMN) and external awareness networks (EAN). Neuromodulation approaches, capable of shaping information processing, may highlight residual network activities supporting awareness, which are not detectable through active paradigms, thus allowing to differentiate chronic disorders of consciousness (DoC). We studied aftereffects of repetitive transcranial magnetic stimulation (rTMS) by applying graph theory within canonical frequency bands to compare the markers of these networks in the electroencephalographic data from 20 patients with DoC. We found that patients' high-frequency networks suffered from a large-scale connectivity breakdown, paralleled by a local hyperconnectivity, whereas low-frequency networks showed a preserved but dysfunctional large-scale connectivity. There was a correlation between metrics and the behavioral awareness. Interestingly, two persons with UWS showed a residual rTMS-induced modulation of the functional correlations between the DMN and the EAN, as observed in patients with MCS. Hence, we may hypothesize that the patients with UWS who demonstrate evidence of residual DMN-EAN functional correlation may be misdiagnosed, given that such residual network correlations could support covert consciousness.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi "Bonino-Pulejo", Messina, Italy
| | - Antonino Leo
- IRCCS Centro Neurolesi "Bonino-Pulejo", Messina, Italy
| | | | | | - Alessia Bramanti
- Institute of Applied Sciences and Intelligent Systems "Edoardo Caianello", National Research Council of Italy, Messina, Italy
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176
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Anderson JAE, Sarraf S, Amer T, Bellana B, Man V, Campbell KL, Hasher L, Grady CL. Task-linked Diurnal Brain Network Reorganization in Older Adults: A Graph Theoretical Approach. J Cogn Neurosci 2017; 29:560-572. [DOI: 10.1162/jocn_a_01060] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Abstract
Testing older adults in the morning generally improves behavioral performance relative to afternoon testing. Morning testing is also associated with brain activity similar to that of young adults. Here, we used graph theory to explore how time of day (TOD) affects the organization of brain networks in older adults across rest and task states. We used nodes from the automated anatomical labeling atlas to construct participant-specific correlation matrices of fMRI data obtained during 1-back tasks with interference and rest. We computed pairwise group differences for key graph metrics, including small-worldness and modularity. We found that older adults tested in the morning and young adults did not differ on any graph metric. Both of these groups differed from older adults tested in the afternoon during the tasks—but not rest. Specifically, the latter group had lower modularity and small-worldness (indices of more efficient network organization). Across all groups, higher modularity and small-worldness strongly correlated with reduced distractibility on an implicit priming task. Increasingly, TOD is seen as important for interpreting and reproducing neuroimaging results. Our study emphasizes how TOD affects brain network organization and executive control in older adults.
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Affiliation(s)
| | | | - Tarek Amer
- 1University of Toronto
- 2Rotman Research Institute, Toronto, Canada
| | - Buddhika Bellana
- 1University of Toronto
- 2Rotman Research Institute, Toronto, Canada
| | | | | | - Lynn Hasher
- 1University of Toronto
- 2Rotman Research Institute, Toronto, Canada
| | - Cheryl L. Grady
- 1University of Toronto
- 2Rotman Research Institute, Toronto, Canada
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177
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Sun Y, Lim J, Dai Z, Wong K, Taya F, Chen Y, Li J, Thakor N, Bezerianos A. The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study. Neuroimage 2017; 152:19-30. [PMID: 28257928 DOI: 10.1016/j.neuroimage.2017.02.084] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Revised: 02/09/2017] [Accepted: 02/27/2017] [Indexed: 12/11/2022] Open
Abstract
Although rest breaks are commonly administered as a countermeasure to reduce mental fatigue and boost cognitive performance, the effects of taking a break on behavior are not consistent. Moreover, our understanding of the underlying neural mechanisms of rest breaks and how they modulate mental fatigue is still rudimentary. In this study, we investigated the effects of receiving a rest break on the topological properties of brain connectivity networks via a two-session experimental paradigm, in which one session comprised four successive blocks of a mentally demanding visual selective attention task (No-rest session), whereas the other contained a rest break between the second and third task blocks (Rest session). Functional brain networks were constructed using resting-state functional MRI data recorded from 20 healthy adults before and after the performance of the task blocks. Behaviorally, subjects displayed robust time-on-task (TOT) declines, as reflected by increasingly slower reaction time as the test progressed and lower post-task self-reported ratings of engagement. However, we did not find a significant effect on task performance due to administering a mid-task break. Compared to pre-task measurements, post-task functional brain networks demonstrated an overall decrease of optimal small-world properties together with lower global efficiency. Specifically, we found TOT-related reduced nodal efficiency in brain regions that mainly resided in the subcortical areas. More interestingly, a significant block-by-session interaction was revealed in local efficiency, attributing to a significant post-task decline in No-rest session and a preserved local efficiency when a mid-task break opportunity was introduced in the Rest session. Taken together, these findings augment our understanding of how the resting brain reorganizes following the accumulation of prolonged task, suggest dissociable processes between the neural mechanisms of fatigue and recovery, and provide some of the first quantitative insights into the cognitive neuroscience of work and rest.
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Affiliation(s)
- Yu Sun
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Science, National University of Singapore, Singapore.
| | - Julian Lim
- Center of Cognitive Neuroscience, Neuroscience & Behavioral Disorders Program, Duke-NUS Graduate Medical School, Singapore
| | - Zhongxiang Dai
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Science, National University of Singapore, Singapore
| | - KianFoong Wong
- Center of Cognitive Neuroscience, Neuroscience & Behavioral Disorders Program, Duke-NUS Graduate Medical School, Singapore
| | - Fumihiko Taya
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Science, National University of Singapore, Singapore
| | - Yu Chen
- School of Computer Engineering, Nanyang Technological University, Singapore
| | - Junhua Li
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Science, National University of Singapore, Singapore
| | - Nitish Thakor
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Science, National University of Singapore, Singapore
| | - Anastasios Bezerianos
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Science, National University of Singapore, Singapore
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178
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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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179
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Yadav SK, Gupta RK, Garg RK, Venkatesh V, Gupta PK, Singh AK, Hashem S, Al-Sulaiti A, Kaura D, Wang E, Marincola FM, Haris M. Altered structural brain changes and neurocognitive performance in pediatric HIV. NEUROIMAGE-CLINICAL 2017; 14:316-322. [PMID: 28224079 PMCID: PMC5304232 DOI: 10.1016/j.nicl.2017.01.032] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2016] [Revised: 01/11/2017] [Accepted: 01/29/2017] [Indexed: 11/23/2022]
Abstract
Pediatric HIV patients often suffer with neurodevelopmental delay and subsequently cognitive impairment. While tissue injury in cortical and subcortical regions in the brain of adult HIV patients has been well reported there is sparse knowledge about these changes in perinatally HIV infected pediatric patients. We analyzed cortical thickness, subcortical volume, structural connectivity, and neurocognitive functions in pediatric HIV patients and compared with those of pediatric healthy controls. With informed consent, 34 perinatally infected pediatric HIV patients and 32 age and gender matched pediatric healthy controls underwent neurocognitive assessment and brain magnetic resonance imaging (MRI) on a 3 T clinical scanner. Altered cortical thickness, subcortical volumes, and abnormal neuropsychological test scores were observed in pediatric HIV patients. The structural network connectivity analysis depicted lower connection strengths, lower clustering coefficients, and higher path length in pediatric HIV patients than healthy controls. The network betweenness and network hubs in cortico-limbic regions were distorted in pediatric HIV patients. The findings suggest that altered cortical and subcortical structures and regional brain connectivity in pediatric HIV patients may contribute to deficits in their neurocognitive functions. Further, longitudinal studies are required for better understanding of the effect of HIV pathogenesis on brain structural changes throughout the brain development process under standard ART treatment. Structural brain MRI and cognitive assessments were performed in pediatric HIV. Pediatric HIV showed altered cortical thickness and subcortical volumes. Disrupted structural connectivity was observed in pediatric HIV. Altered brain structures and connectivity contribute to deficits in neurocognition.
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Key Words
- AIDS, acquired immunodeficiency syndrome
- C, clustering coefficient
- Cortical thickness
- ELISA, enzyme-linked immunosorbent assay
- FA, flip angel
- FLAIR, fluid attenuation inversion recovery
- FOV, field of view
- FSPGR, fast spoiled gradient echo
- GAT, graph-theoretical analysis toolbox
- HIV, human immunodeficiency virus
- Human immunodeficiency virus
- L, characteristic path length
- MRI, magnetic resonance imaging
- Magnetic resonance imaging
- Neurocognitive functions
- RAKIT, revised Amsterdamse kinder intelligence
- ROIs, regions of interest
- SW, small-world index
- Structural connectivity
- Subcortical volume
- TBM, tensor based morphometry
- TE, echo time
- TR, repetition time
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Affiliation(s)
- Santosh K Yadav
- Division of Translational Medicine, Research Branch, Sidra Medical and Research Center, Doha, Qatar
| | - Rakesh K Gupta
- Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, Delhi, India
| | - Ravindra K Garg
- Department of Neurology, King George Medical University, Lucknow, India
| | - Vimala Venkatesh
- Department of Microbiology, King George Medical University, Lucknow, India
| | - Pradeep K Gupta
- Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, Delhi, India
| | - Alok K Singh
- Department of Neurology, King George Medical University, Lucknow, India
| | - Sheema Hashem
- Division of Translational Medicine, Research Branch, Sidra Medical and Research Center, Doha, Qatar
| | - Asma Al-Sulaiti
- Division of Translational Medicine, Research Branch, Sidra Medical and Research Center, Doha, Qatar
| | - Deepak Kaura
- Department of Radiology, Sidra Medical and Research Center, Doha, Qatar
| | - Ena Wang
- Division of Translational Medicine, Research Branch, Sidra Medical and Research Center, Doha, Qatar
| | - Francesco M Marincola
- Division of Translational Medicine, Research Branch, Sidra Medical and Research Center, Doha, Qatar
| | - Mohammad Haris
- Division of Translational Medicine, Research Branch, Sidra Medical and Research Center, Doha, Qatar
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180
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Chen JH, Yao ZJ, Qin JL, Yan R, Hua LL, Lu Q. Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder. Chin Med J (Engl) 2017; 129:679-89. [PMID: 26960371 PMCID: PMC4804414 DOI: 10.4103/0366-6999.178002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.
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Affiliation(s)
| | - Zhi-Jian Yao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu 210029, China
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181
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Comparison of Functional Connectivity Estimated from Concatenated Task-State Data from Block-Design Paradigm with That of Continuous Task. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:4198430. [PMID: 28191030 PMCID: PMC5278200 DOI: 10.1155/2017/4198430] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 12/05/2016] [Accepted: 12/19/2016] [Indexed: 11/17/2022]
Abstract
Functional connectivity (FC) analysis with data collected as continuous tasks and activation analysis using data from block-design paradigms are two main methods to investigate the task-induced brain activation. If the concatenated data of task blocks extracted from the block-design paradigm could provide equivalent FC information to that derived from continuous task data, it would shorten the data collection time and simplify experimental procedures, and the already collected data of block-design paradigms could be reanalyzed from the perspective of FC. Despite being used in many studies, such a hypothesis of equivalence has not yet been tested from multiple perspectives. In this study, we collected fMRI blood-oxygen-level-dependent signals from 24 healthy subjects during a continuous task session as well as in block-design task sessions. We compared concatenated task blocks and continuous task data in terms of region of interest- (ROI-) based FC, seed-based FC, and brain network topology during a short motor task. According to our results, the concatenated data was not significantly different from the continuous data in multiple aspects, indicating the potential of using concatenated data to estimate task-state FC in short motor tasks. However, even under appropriate experimental conditions, the interpretation of FC results based on concatenated data should be cautious and take the influence due to inherent information loss during concatenation into account.
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182
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Khambhati AN, Bassett DS, Oommen BS, Chen SH, Lucas TH, Davis KA, Litt B. Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy. eNeuro 2017; 4:ENEURO.0091-16.2017. [PMID: 28303256 PMCID: PMC5343278 DOI: 10.1523/eneuro.0091-16.2017] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 01/10/2023] Open
Abstract
Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network.
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Affiliation(s)
- Ankit N. Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
| | - Brian S. Oommen
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Stephanie H. Chen
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Timothy H. Lucas
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Kathryn A. Davis
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
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183
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Cannon J. Analytical Calculation of Mutual Information between Weakly Coupled Poisson-Spiking Neurons in Models of Dynamically Gated Communication. Neural Comput 2016; 29:118-145. [PMID: 27870617 DOI: 10.1162/neco_a_00915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Mutual information is a commonly used measure of communication between neurons, but little theory exists describing the relationship between mutual information and the parameters of the underlying neuronal interaction. Such a theory could help us understand how specific physiological changes affect the capacity of neurons to synaptically communicate, and, in particular, they could help us characterize the mechanisms by which neuronal dynamics gate the flow of information in the brain. Here we study a pair of linear-nonlinear-Poisson neurons coupled by a weak synapse. We derive an analytical expression describing the mutual information between their spike trains in terms of synapse strength, neuronal activation function, the time course of postsynaptic currents, and the time course of the background input received by the two neurons. This expression allows mutual information calculations that would otherwise be computationally intractable. We use this expression to analytically explore the interaction of excitation, information transmission, and the convexity of the activation function. Then, using this expression to quantify mutual information in simulations, we illustrate the information-gating effects of neural oscillations and oscillatory coherence, which may either increase or decrease the mutual information across the synapse depending on parameters. Finally, we show analytically that our results can quantitatively describe the selection of one information pathway over another when multiple sending neurons project weakly to a single receiving neuron.
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Affiliation(s)
- Jonathan Cannon
- Department of Biology, Brandeis University, Waltham, MA 02453, U.S.A.
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184
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Trpevski I, Dimitrova T, Boshkovski T, Stikov N, Kocarev L. Graphlet characteristics in directed networks. Sci Rep 2016; 6:37057. [PMID: 27830769 PMCID: PMC5103263 DOI: 10.1038/srep37057] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 10/17/2016] [Indexed: 11/28/2022] Open
Abstract
Graphlet analysis is part of network theory that does not depend on the choice of the network null model and can provide comprehensive description of the local network structure. Here, we propose a novel method for graphlet-based analysis of directed networks by computing first the signature vector for every vertex in the network and then the graphlet correlation matrix of the network. This analysis has been applied to brain effective connectivity networks by considering both direction and sign (inhibitory or excitatory) of the underlying directed (effective) connectivity. In particular, the signature vectors for brain regions and the graphlet correlation matrices of the brain effective network are computed for 40 healthy subjects and common dependencies are revealed. We found that the signature vectors (node, wedge, and triangle degrees) are dominant for the excitatory effective brain networks. Moreover, by considering only those correlations (or anti correlations) in the correlation matrix that are significant (>0.7 or <−0.7) and are presented in more than 60% of the subjects, we found that excitatory effective brain networks show stronger causal (measured with Granger causality) patterns (G-causes and G-effects) than inhibitory effective brain networks.
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Affiliation(s)
- Igor Trpevski
- Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia
| | - Tamara Dimitrova
- Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia
| | - Tommy Boshkovski
- Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia
| | - Nikola Stikov
- Institute for Biomedical Engineering, Ecole Polytechnique, Montreal, QC, Canada.,Montreal Heart Institute, Montreal, QC, Canada
| | - Ljupcho Kocarev
- Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia.,Faculty of Computer Science and Engineering, UKIM, Skopje, Republic of Macedonia.,BioCircuits Institute, UC San Diego, La Jolla, CA 92093-0402, USA
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185
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Characterising brain network topologies: A dynamic analysis approach using heat kernels. Neuroimage 2016; 141:490-501. [DOI: 10.1016/j.neuroimage.2016.07.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 05/27/2016] [Accepted: 07/03/2016] [Indexed: 12/13/2022] Open
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186
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Andric M, Goldin-Meadow S, Small SL, Hasson U. Repeated movie viewings produce similar local activity patterns but different network configurations. Neuroimage 2016; 142:613-627. [DOI: 10.1016/j.neuroimage.2016.07.061] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 07/17/2016] [Accepted: 07/29/2016] [Indexed: 11/30/2022] Open
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187
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Abstract
It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.
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Affiliation(s)
- Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Danielle S. Bassett, Department of Bioengineering, University of Pennsylvania, 210 S. 33rd Street, 240 Skirkanich Hall, Philadelphia, PA, 19104, USA.
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- ImmunoPsychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage, UK
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188
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Filho E, Bertollo M, Tamburro G, Schinaia L, Chatel-Goldman J, di Fronso S, Robazza C, Comani S. Hyperbrain features of team mental models within a juggling paradigm: a proof of concept. PeerJ 2016; 4:e2457. [PMID: 27688968 PMCID: PMC5036110 DOI: 10.7717/peerj.2457] [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: 06/01/2016] [Accepted: 08/17/2016] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Research on cooperative behavior and the social brain exists, but little research has focused on real-time motor cooperative behavior and its neural correlates. In this proof of concept study, we explored the conceptual notion of shared and complementary mental models through EEG mapping of two brains performing a real-world interactive motor task of increasing difficulty. We used the recently introduced participative "juggling paradigm," and collected neuro-physiological and psycho-social data. We were interested in analyzing the between-brains coupling during a dyadic juggling task, and in exploring the relationship between the motor task execution, the jugglers'skill level and the task difficulty. We also investigated how this relationship could be mirrored in the coupled functional organization of the interacting brains. METHODS To capture the neural schemas underlying the notion of shared and complementary mental models, we examined the functional connectivity patterns and hyperbrain features of a juggling dyad involved in cooperative motor tasks of increasing difficulty. Jugglers' cortical activity was measured using two synchronized 32-channel EEG systems during dyadic juggling performed with 3, 4, 5 and 6 balls. Individual and hyperbrain functional connections were quantified through coherence maps calculated across all electrode pairs in the theta and alpha bands (4-8 and 8-12 Hz). Graph metrics were used to typify the global topology and efficiency of the functional networks for the four difficulty levels in the theta and alpha bands. RESULTS Results indicated that, as task difficulty increased, the cortical functional organization of the more skilled juggler became progressively more segregated in both frequency bands, with a small-world organization in the theta band during easier tasks, indicative of a flow-like state in line with the neural efficiency hypothesis. Conversely, more integrated functional patterns were observed for the less skilled juggler in both frequency bands, possibly related to cognitive overload due to the difficulty of the task at hand (reinvestment hypothesis). At the hyperbrain level, a segregated functional organization involving areas of the visuo-attentional networks of both jugglers was observed in both frequency bands and for the easier task only. DISCUSSION These results suggest that cooperative juggling is supported by integrated activity of specialized cortical areas from both brains only during easier tasks, whereas it relies on individual skills, mirrored in uncorrelated individual brain activations, during more difficult tasks. These findings suggest that task difficulty and jugglers' personal skills may influence the features of the hyperbrain network in its shared/integrative and complementary/segregative tendencies.
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Affiliation(s)
- Edson Filho
- BIND-Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy; School of Psychology, University of Central Lancashire, Preston, Lancashire, United Kingdom
| | - Maurizio Bertollo
- BIND-Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy; Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Gabriella Tamburro
- BIND-Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy; Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Lorenzo Schinaia
- BIND-Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy; Department of Neurology, Casa di Cura Privata Villa Serena, Città Sant'Angelo, Italy
| | - Jonas Chatel-Goldman
- BIND-Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy; Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Selenia di Fronso
- BIND-Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy; Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Claudio Robazza
- BIND-Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy; Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Silvia Comani
- BIND-Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy; Department of Neurology, Casa di Cura Privata Villa Serena, Città Sant'Angelo, Italy; Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" Chieti-Pescara, Chieti, Italy
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189
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Khambhati AN, Davis KA, Lucas TH, Litt B, Bassett DS. Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution. Neuron 2016; 91:1170-1182. [PMID: 27568515 PMCID: PMC5017915 DOI: 10.1016/j.neuron.2016.07.039] [Citation(s) in RCA: 131] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 05/31/2016] [Accepted: 07/22/2016] [Indexed: 12/17/2022]
Abstract
In ∼20 million people with drug-resistant epilepsy, focal seizures originating in dysfunctional brain networks will often evolve and spread to surrounding tissue, disrupting function in otherwise normal brain regions. To identify network control mechanisms that regulate seizure spread, we developed a novel tool for pinpointing brain regions that facilitate synchronization in the epileptic network. Our method measures the impact of virtually resecting putative control regions on synchronization in a validated model of the human epileptic network. By applying our technique to time-varying functional networks, we identified brain regions whose topological role is to synchronize or desynchronize the epileptic network. Our results suggest that greater antagonistic push-pull interaction between synchronizing and desynchronizing brain regions better constrains seizure spread. These methods, while applied here to epilepsy, are generalizable to other brain networks and have wide applicability in isolating and mapping functional drivers of brain dynamics in health and disease.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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190
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Grandchamp R, Delorme A. The Brainarium: An Interactive Immersive Tool for Brain Education, Art, and Neurotherapy. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016; 2016:4204385. [PMID: 27698660 PMCID: PMC5028849 DOI: 10.1155/2016/4204385] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 06/30/2016] [Indexed: 11/17/2022]
Abstract
Recent theoretical and technological advances in neuroimaging techniques now allow brain electrical activity to be recorded using affordable and user-friendly equipment for nonscientist end-users. An increasing number of educators and artists have begun using electroencephalogram (EEG) to control multimedia and live artistic contents. In this paper, we introduce a new concept based on brain computer interface (BCI) technologies: the Brainarium. The Brainarium is a new pedagogical and artistic tool, which can deliver and illustrate scientific knowledge, as well as a new framework for scientific exploration. The Brainarium consists of a portable planetarium device that is being used as brain metaphor. This is done by projecting multimedia content on the planetarium dome and displaying EEG data recorded from a subject in real time using Brain Machine Interface (BMI) technologies. The system has been demonstrated through several performances involving an interaction between the subject controlling the BMI, a musician, and the audience during series of exhibitions and workshops in schools. We report here feedback from 134 participants who filled questionnaires to rate their experiences. Our results show improved subjective learning compared to conventional methods, improved entertainment value, improved absorption into the material being presented, and little discomfort.
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Affiliation(s)
- Romain Grandchamp
- Laboratoire de Psychologie et NeuroCognition, Université de Grenoble, Grenoble, BSHM, 1251 av Centrale CS40700, 38058 Grenoble Cedex 9, France
- CNRS, UMR 5105, Grenoble, France
| | - Arnaud Delorme
- Centre de Recherche Cerveau et Cognition (CerCo), Université Paul Sabatier, Pavillon Baudot, Hopital Purpan, BP 25202, 31052 Toulouse Cedex 3, France
- CNRS, UMR 5549, Toulouse, France
- Swartz Center for Computational Neuroscience, Institute of Neural Computation (INC), University of San Diego California, La Jolla, CA 92093-0559, USA
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191
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Yadav SK, Kathiresan N, Mohan S, Vasileiou G, Singh A, Kaura D, Melhem ER, Gupta RK, Wang E, Marincola FM, Borthakur A, Haris M. Gender-based analysis of cortical thickness and structural connectivity in Parkinson's disease. J Neurol 2016; 263:2308-2318. [PMID: 27544505 DOI: 10.1007/s00415-016-8265-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 08/08/2016] [Accepted: 08/09/2016] [Indexed: 01/08/2023]
Abstract
Parkinson's disease (PD) is a progressive neurological disorder and appears to have gender-specific symptoms. Studies have observed a higher frequency for development of PD in male than in female. In the current study, we evaluated the gender-based changes in cortical thickness and structural connectivity in PD patients. With informed consent, 64 PD (43 males and 21 females) patients, and 46 (12 males and 34 females) age-matched controls underwent clinical assessment including Mini-Mental State Examination (MMSE) and magnetic resonance imaging on a 1.5 Tesla clinical MR scanner. Whole brain high-resolution T1-weighted images were acquired from all subjects and used to measure cortical thickness and structural network connectivity. No significant difference in MMSE score was observed between male and female both in control and PD subjects. Male PD patients showed significantly reduced cortical thickness in multiple brain regions including frontal, parietal, temporal, and occipital lobes as compared with those in female PD patients. The graph theory-based network analysis depicted lower connection strengths, lower clustering coefficients, and altered network hubs in PD male than in PD female. Male-specific cortical thickness changes and altered connectivity in PD patients may derive from behavioral, physiological, environmental, and genetical differences between male and female, and may have significant implications in diagnosing and treating PD among genders.
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Affiliation(s)
- Santosh K Yadav
- Translational Medicine Research Branch, Sidra Medical and Research Center, P.O. Box 26999, Doha, Qatar
| | - Nagarajan Kathiresan
- Translational Medicine Research Branch, Sidra Medical and Research Center, P.O. Box 26999, Doha, Qatar
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA, USA
| | - Georgia Vasileiou
- Department of Medical Physics, University College of London, London, UK
| | - Anup Singh
- Center for Biomedical Engineering, Indian Institute of Technology, New Delhi, India
| | - Deepak Kaura
- Department of Radiology, Sidra Medical and Research Center, Doha, Qatar
| | - Elias R Melhem
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rakesh K Gupta
- Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, India
| | - Ena Wang
- Translational Medicine Research Branch, Sidra Medical and Research Center, P.O. Box 26999, Doha, Qatar
| | - Francesco M Marincola
- Translational Medicine Research Branch, Sidra Medical and Research Center, P.O. Box 26999, Doha, Qatar
| | - Arijitt Borthakur
- Department of Radiology, Center for Magnetic Resonance and Optical Imaging, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA, USA
| | - Mohammad Haris
- Translational Medicine Research Branch, Sidra Medical and Research Center, P.O. Box 26999, Doha, Qatar.
- Department of Radiology, Center for Magnetic Resonance and Optical Imaging, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA, USA.
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192
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Aerts H, Fias W, Caeyenberghs K, Marinazzo D. Brain networks under attack: robustness properties and the impact of lesions. Brain 2016; 139:3063-3083. [PMID: 27497487 DOI: 10.1093/brain/aww194] [Citation(s) in RCA: 178] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 05/13/2016] [Accepted: 06/08/2016] [Indexed: 12/30/2022] Open
Abstract
A growing number of studies approach the brain as a complex network, the so-called 'connectome'. Adopting this framework, we examine what types or extent of damage the brain can withstand-referred to as network 'robustness'-and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer's disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions-and especially those connecting different subnetworks-was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research.
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Affiliation(s)
- Hannelore Aerts
- 1 Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
| | - Wim Fias
- 2 Department of Experimental Psychology, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
| | - Karen Caeyenberghs
- 3 School of Psychology, Faculty of Health Sciences, Australian Catholic University, Australia
| | - Daniele Marinazzo
- 1 Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
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193
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Petersen A, Zhao J, Carmichael O, Müller HG. Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks. Brain Connect 2016; 6:540-7. [PMID: 27267074 DOI: 10.1089/brain.2016.0420] [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: 01/08/2023] Open
Abstract
In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.
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Affiliation(s)
- Alexander Petersen
- 1 Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, California
| | - Jianyang Zhao
- 1 Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, California
| | - Owen Carmichael
- 2 Pennington Biomedical Research Center, Louisiana State University , Baton Rouge, Louisiana
| | - Hans-Georg Müller
- 1 Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, California
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194
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Soto FA, Bassett DS, Ashby FG. Dissociable changes in functional network topology underlie early category learning and development of automaticity. Neuroimage 2016; 141:220-241. [PMID: 27453156 DOI: 10.1016/j.neuroimage.2016.07.032] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Revised: 06/01/2016] [Accepted: 07/14/2016] [Indexed: 11/30/2022] Open
Abstract
Recent work has shown that multimodal association areas-including frontal, temporal, and parietal cortex-are focal points of functional network reconfiguration during human learning and performance of cognitive tasks. On the other hand, neurocomputational theories of category learning suggest that the basal ganglia and related subcortical structures are focal points of functional network reconfiguration during early learning of some categorization tasks but become less so with the development of automatic categorization performance. Using a combination of network science and multilevel regression, we explore how changes in the connectivity of small brain regions can predict behavioral changes during training in a visual categorization task. We find that initial category learning, as indexed by changes in accuracy, is predicted by increasingly efficient integrative processing in subcortical areas, with higher functional specialization, more efficient integration across modules, but a lower cost in terms of redundancy of information processing. The development of automaticity, as indexed by changes in the speed of correct responses, was predicted by lower clustering (particularly in subcortical areas), higher strength (highest in cortical areas), and higher betweenness centrality. By combining neurocomputational theories and network scientific methods, these results synthesize the dissociative roles of multimodal association areas and subcortical structures in the development of automaticity during category learning.
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Affiliation(s)
- Fabian A Soto
- Department of Psychology, Florida International University, Miami, FL 33199, 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
| | - F Gregory Ashby
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA 93106, USA
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195
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De Domenico M, Sasai S, Arenas A. Mapping Multiplex Hubs in Human Functional Brain Networks. Front Neurosci 2016; 10:326. [PMID: 27471443 PMCID: PMC4945645 DOI: 10.3389/fnins.2016.00326] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 06/28/2016] [Indexed: 01/22/2023] Open
Abstract
Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches.
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Affiliation(s)
- Manlio De Domenico
- Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i VirgiliTarragona, Spain
| | - Shuntaro Sasai
- Department of Psychiatry, University of Wisconsin-MadisonMadison, WI, USA
| | - Alex Arenas
- Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i VirgiliTarragona, Spain
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196
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Gaiteri C, Mostafavi S, Honey CJ, De Jager PL, Bennett DA. Genetic variants in Alzheimer disease - molecular and brain network approaches. Nat Rev Neurol 2016; 12:413-27. [PMID: 27282653 PMCID: PMC5017598 DOI: 10.1038/nrneurol.2016.84] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.
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Affiliation(s)
- Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, 600 S Paulina Street, Chicago, Illinois 60612, USA
| | - Sara Mostafavi
- Department of Statistics, and Medical Genetics; Centre for Molecular and Medicine and Therapeutics, University of British Columbia, 950 West 28th Avenue, Vancouver, British Columbia V5Z 4H4, Canada
| | - Christopher J Honey
- Department of Psychology, University of Toronto, 100 St. George Street, 4th Floor Sidney Smith Hall, Toronto, Ontario M5S 3G3, Canada
| | - Philip L De Jager
- Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, 600 S Paulina Street, Chicago, Illinois 60612, USA
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197
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Novi SL, Rodrigues RBML, Mesquita RC. Resting state connectivity patterns with near-infrared spectroscopy data of the whole head. BIOMEDICAL OPTICS EXPRESS 2016; 7:2524-37. [PMID: 27446687 PMCID: PMC4948611 DOI: 10.1364/boe.7.002524] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Revised: 05/27/2016] [Accepted: 05/31/2016] [Indexed: 05/15/2023]
Abstract
Resting state cerebral dynamics has been a useful approach to explore the brain's functional organization. In this study, we employed graph theory to deeply investigate resting state functional connectivity (rs-FC) as measured by near-infrared spectroscopy (NIRS). Our results suggest that network parameters are very similar across time and subjects. We also identified the most frequent connections between brain regions and the main hubs that participate in the spontaneous activity of brain hemodynamics. Similar to previous findings, we verified that symmetrically located brain areas are highly connected. Overall, our results introduce new insights in NIRS-based functional connectivity at rest.
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198
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Hatz F, Hardmeier M, Bousleiman H, Rüegg S, Schindler C, Fuhr P. Reliability of Functional Connectivity of Electroencephalography Applying Microstate-Segmented Versus Classical Calculation of Phase Lag Index. Brain Connect 2016; 6:461-9. [DOI: 10.1089/brain.2015.0368] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Florian Hatz
- Department of Neurology, Hospital of the University of Basel, Basel, Switzerland
| | - Martin Hardmeier
- Department of Neurology, Hospital of the University of Basel, Basel, Switzerland
| | - Habib Bousleiman
- Department of Neurology, Hospital of the University of Basel, Basel, Switzerland
- Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
| | - Stephan Rüegg
- Department of Neurology, Hospital of the University of Basel, Basel, Switzerland
| | - Christian Schindler
- Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
| | - Peter Fuhr
- Department of Neurology, Hospital of the University of Basel, Basel, Switzerland
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199
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Brain connectivity in normally developing children and adolescents. Neuroimage 2016; 134:192-203. [DOI: 10.1016/j.neuroimage.2016.03.062] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 02/02/2016] [Accepted: 03/23/2016] [Indexed: 11/21/2022] Open
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200
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Hong X, Liu Y, Sun J, Tong S. Age-Related Differences in the Modulation of Small-World Brain Networks during a Go/NoGo Task. Front Aging Neurosci 2016; 8:100. [PMID: 27242512 PMCID: PMC4869596 DOI: 10.3389/fnagi.2016.00100] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 04/18/2016] [Indexed: 11/30/2022] Open
Abstract
Although inter-regional phase synchrony of neural oscillations has been proposed as a plausible mechanism for response control, little is known about the possible effects due to normal aging. We recorded multi-channel electroencephalography (EEG) from healthy younger and older adults in a Go/NoGo task, and examined the aging effects on synchronous brain networks with graph theoretical analysis. We found that in both age groups, brain networks in theta, alpha or beta band for either response execution (Go) or response inhibition (NoGo) condition showed prominent small-world property. Furthermore, small-world property of brain networks showed significant differences between different task conditions. Further analyses of node degree suggested that frontal-central theta band phase synchrony was enhanced during response inhibition, whereas during response execution, increased phase synchrony was observed in beta band over central-parietal regions. More interestingly, these task-related modulations on brain networks were well preserved and even more robust in older adults compared with younger adults. Taken together, our findings not only suggest that response control involves synchronous brain networks in functionally-distinct frequency bands, but also indicate an increase in the recruitment of brain network resources due to normal aging.
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Affiliation(s)
- Xiangfei Hong
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineShanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong UniversityShanghai, China
| | - Yuelu Liu
- Center for Mind and Brain, University of California Davis, CA, USA
| | - Junfeng Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai, China
| | - Shanbao Tong
- School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai, China
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