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Rodriguez-Sabate C, Gonzalez A, Perez-Darias JC, Morales I, Sole-Sabater M, Rodriguez M. Causality methods to study the functional connectivity in brain networks: the basal ganglia - thalamus causal interactions. Brain Imaging Behav 2024; 18:1-18. [PMID: 37823962 PMCID: PMC10844145 DOI: 10.1007/s11682-023-00803-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2023] [Indexed: 10/13/2023]
Abstract
This study uses methods recently developed to study the complex evolution of atmospheric phenomena which have some similarities with the dynamics of the human brain. In both cases, it is possible to record the activity of particular centers (geographic regions or brain nuclei) but not to make an experimental modification of their state. The study of "causality", which is necessary to understand the dynamics of these complex systems and to develop robust models that can predict their evolution, is hampered by the experimental restrictions imposed by the nature of both systems. The study was performed with data obtained in the thalamus and basal ganglia of awake humans executing different tasks. This work studies the linear, non-linear and more complex relationships of these thalamic centers with the cortex and main BG nuclei, using three complementary techniques: the partial correlation regression method, the Gaussian process regression/distance correlation and a model-free method based on nearest-neighbor that computes the conditional mutual information. These causality methods indicated that the basal ganglia present a different functional relationship with the anterior-ventral (motor), intralaminar and medio-dorsal thalamic centers, and that more than 60% of these thalamus-basal ganglia relationships present a non-linear dynamic (35 of the 57 relationships found). These functional interactions were observed for basal ganglia nuclei with direct structural connections with the thalamus (primary somatosensory and motor cortex, striatum, internal globus pallidum and substantia nigra pars reticulata), but also for basal ganglia without structural connections with the thalamus (external globus pallidum and subthalamic nucleus). The motor tasks induced rapid modifications of the thalamus-basal ganglia interactions. These findings provide new perspectives of the thalamus - BG interactions, many of which may be supported by indirect functional relationships and not by direct excitatory/inhibitory interactions.
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Affiliation(s)
- Clara Rodriguez-Sabate
- Laboratory of Neurobiology and Experimental Neurology, Department of Physiology, Faculty of Medicine, University of La Laguna, Tenerife, Canary Islands, Spain
- Center for Networked Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Albano Gonzalez
- Department of Physics, University of La Laguna, Tenerife, Canary Islands, Spain
| | | | - Ingrid Morales
- Laboratory of Neurobiology and Experimental Neurology, Department of Physiology, Faculty of Medicine, University of La Laguna, Tenerife, Canary Islands, Spain
- Center for Networked Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Miguel Sole-Sabater
- Department of Neurology, La Candelaria University Hospital, Tenerife, Canary Islands, Spain
| | - Manuel Rodriguez
- Laboratory of Neurobiology and Experimental Neurology, Department of Physiology, Faculty of Medicine, University of La Laguna, Tenerife, Canary Islands, Spain.
- Center for Networked Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain.
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2
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Seguin C, Sporns O, Zalesky A. Brain network communication: concepts, models and applications. Nat Rev Neurosci 2023; 24:557-574. [PMID: 37438433 DOI: 10.1038/s41583-023-00718-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2023] [Indexed: 07/14/2023]
Abstract
Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Program in Cognitive Science, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia
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Ono H, Sonoda M, Sakakura K, Kitazawa Y, Mitsuhashi T, Firestone E, Jeong JW, Luat AF, Marupudi NI, Sood S, Asano E. Dynamic cortical and tractography atlases of proactive and reactive alpha and high-gamma activities. Brain Commun 2023; 5:fcad111. [PMID: 37228850 PMCID: PMC10204271 DOI: 10.1093/braincomms/fcad111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/15/2022] [Accepted: 04/03/2023] [Indexed: 05/27/2023] Open
Abstract
Alpha waves-posterior dominant rhythms at 8-12 Hz reactive to eye opening and closure-are among the most fundamental EEG findings in clinical practice and research since Hans Berger first documented them in the early 20th century. Yet, the exact network dynamics of alpha waves in regard to eye movements remains unknown. High-gamma activity at 70-110 Hz is also reactive to eye movements and a summary measure of local cortical activation supporting sensorimotor or cognitive function. We aimed to build the first-ever brain atlases directly visualizing the network dynamics of eye movement-related alpha and high-gamma modulations, at cortical and white matter levels. We studied 28 patients (age: 5-20 years) who underwent intracranial EEG and electro-oculography recordings. We measured alpha and high-gamma modulations at 2167 electrode sites outside the seizure onset zone, interictal spike-generating areas and MRI-visible structural lesions. Dynamic tractography animated white matter streamlines modulated significantly and simultaneously beyond chance, on a millisecond scale. Before eye-closure onset, significant alpha augmentation occurred at the occipital and frontal cortices. After eye-closure onset, alpha-based functional connectivity was strengthened, while high gamma-based connectivity was weakened extensively in both intra-hemispheric and inter-hemispheric pathways involving the central visual areas. The inferior fronto-occipital fasciculus supported the strengthened alpha co-augmentation-based functional connectivity between occipital and frontal lobe regions, whereas the posterior corpus callosum supported the inter-hemispheric functional connectivity between the occipital lobes. After eye-opening offset, significant high-gamma augmentation and alpha attenuation occurred at occipital, fusiform and inferior parietal cortices. High gamma co-augmentation-based functional connectivity was strengthened, whereas alpha-based connectivity was weakened in the posterior inter-hemispheric and intra-hemispheric white matter pathways involving central and peripheral visual areas. Our results do not support the notion that eye closure-related alpha augmentation uniformly reflects feedforward or feedback rhythms propagating from lower to higher order visual cortex, or vice versa. Rather, proactive and reactive alpha waves involve extensive, distinct white matter networks that include the frontal lobe cortices, along with low- and high-order visual areas. High-gamma co-attenuation coupled to alpha co-augmentation in shared brain circuitry after eye closure supports the notion of an idling role for alpha waves during eye closure. These normative dynamic tractography atlases may improve understanding of the significance of EEG alpha waves in assessing the functional integrity of brain networks in clinical practice; they also may help elucidate the effects of eye movements on task-related brain network measures observed in cognitive neuroscience research.
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Affiliation(s)
- Hiroya Ono
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Pediatric Neurology, National Center of Neurology and Psychiatry, Joint Graduate School of Tohoku University, Tokyo 1878551, Japan
- Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Masaki Sonoda
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, Graduate School of Medicine, Yokohama City University, Yokohama 2360004, Japan
| | - Kazuki Sakakura
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, University of Tsukuba, Tsukuba 3058575, Japan
| | - Yu Kitazawa
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology and Stroke Medicine, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - Takumi Mitsuhashi
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, Juntendo University, School of Medicine, Tokyo 1138421, Japan
| | - Ethan Firestone
- Department of Physiology, Wayne State University, Detroit, MI 48201, USA
| | - Jeong-Won Jeong
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
| | - Aimee F Luat
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Pediatrics, Central Michigan University, Mount Pleasant, MI 48858, USA
| | - Neena I Marupudi
- Department of Neurosurgery, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
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Shin TJ, Kim PJ, Choi B. How general anesthetics work: from the perspective of reorganized connections within the brain. Korean J Anesthesiol 2022; 75:124-138. [PMID: 35130674 PMCID: PMC8980288 DOI: 10.4097/kja.22078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 02/06/2022] [Indexed: 11/24/2022] Open
Abstract
General anesthesia is critical for various procedures and surgeries. Despite the widespread use of anesthetics, their precise mechanisms remain poorly understood. Anesthetics inevitably act on the brain, primarily through the modulation of target receptors. Even if the action is specific to an individual neuron, however, long-range effects can occur due to the tremendous interconnectedness of neuronal activity. The strength of this connectivity can be understood using mathematical models that allow for the study of neuronal connectivity dynamics. These models also allow researchers to develop hypotheses on the candidate mechanisms of action of different types of anesthesia. This review highlights the theoretical background associated with the study of the mechanisms of action of anesthetics. We propose a candidate framework that describes how anesthetics act on the brain and consciousness in general.
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The causal interaction in human basal ganglia. Sci Rep 2021; 11:12989. [PMID: 34155321 PMCID: PMC8217174 DOI: 10.1038/s41598-021-92490-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 06/03/2021] [Indexed: 02/05/2023] Open
Abstract
The experimental study of the human brain has important restrictions, particularly in the case of basal ganglia, subcortical centers whose activity can be recorded with fMRI methods but cannot be directly modified. Similar restrictions occur in other complex systems such as those studied by Earth system science. The present work studied the cause/effect relationships between human basal ganglia with recently introduced methods to study climate dynamics. Data showed an exhaustive (identifying basal ganglia interactions regardless of their linear, non-linear or complex nature) and selective (avoiding spurious relationships) view of basal ganglia activity, showing a fast functional reconfiguration of their main centers during the execution of voluntary motor tasks. The methodology used here offers a novel view of the human basal ganglia which expands the perspective provided by the classical basal ganglia model and may help to understand BG activity under normal and pathological conditions.
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Deco G, Vidaurre D, Kringelbach ML. Revisiting the global workspace orchestrating the hierarchical organization of the human brain. Nat Hum Behav 2021; 5:497-511. [PMID: 33398141 PMCID: PMC8060164 DOI: 10.1038/s41562-020-01003-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 10/12/2020] [Indexed: 12/31/2022]
Abstract
A central challenge in neuroscience is how the brain organizes the information necessary to orchestrate behaviour. Arguably, this whole-brain orchestration is carried out by a core subset of integrative brain regions, a 'global workspace', but its constitutive regions remain unclear. We quantified the global workspace as the common regions across seven tasks as well as rest, in a common 'functional rich club'. To identify this functional rich club, we determined the information flow between brain regions by means of a normalized directed transfer entropy framework applied to multimodal neuroimaging data from 1,003 healthy participants and validated in participants with retest data. This revealed a set of regions orchestrating information from perceptual, long-term memory, evaluative and attentional systems. We confirmed the causal significance and robustness of our results by systematically lesioning a generative whole-brain model. Overall, this framework describes a complex choreography of the functional hierarchical organization of the human brain.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia.
| | - Diego Vidaurre
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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7
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Großmann G, Bortolussi L, Wolf V. Efficient simulation of non-Markovian dynamics on complex networks. PLoS One 2020; 15:e0241394. [PMID: 33125408 PMCID: PMC7598478 DOI: 10.1371/journal.pone.0241394] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 10/07/2020] [Indexed: 11/19/2022] Open
Abstract
We study continuous-time multi-agent models, where agents interact according to a network topology. At any point in time, each agent occupies a specific local node state. Agents change their state at random through interactions with neighboring agents. The time until a transition happens can follow an arbitrary probability density. Stochastic (Monte-Carlo) simulations are often the preferred-sometimes the only feasible-approach to study the complex emerging dynamical patterns of such systems. However, each simulation run comes with high computational costs mostly due to updating the instantaneous rates of interconnected agents after each transition. This work proposes a stochastic rejection-based, event-driven simulation algorithm that scales extremely well with the size and connectivity of the underlying contact network and produces statistically correct samples. We demonstrate the effectiveness of our method on different information spreading models.
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Affiliation(s)
- Gerrit Großmann
- Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
| | - Luca Bortolussi
- Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
| | - Verena Wolf
- Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
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8
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Nuzzi D, Pellicoro M, Angelini L, Marinazzo D, Stramaglia S. Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age. Netw Neurosci 2020; 4:910-924. [PMID: 33615096 PMCID: PMC7888489 DOI: 10.1162/netn_a_00146] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 05/08/2020] [Indexed: 11/24/2022] Open
Abstract
We implement the dynamical Ising model on the large-scale architecture of white matter connections of healthy subjects in the age range 4-85 years, and analyze the dynamics in terms of the synergy, a quantity measuring the extent to which the joint state of pairs of variables is projected onto the dynamics of a target one. We find that the amount of synergy in explaining the dynamics of the hubs of the structural connectivity (in terms of degree strength) peaks before the critical temperature, and can thus be considered as a precursor of a critical transition. Conversely, the greatest amount of synergy goes into explaining the dynamics of more central nodes. We also find that the aging of structural connectivity is associated with significant changes in the simulated dynamics: There are brain regions whose synergy decreases with age, in particular the frontal pole, the subcallosal area, and the supplementary motor area; these areas could then be more likely to show a decline in terms of the capability to perform higher order computation (if structural connectivity was the sole variable). On the other hand, several regions in the temporal cortex show a positive correlation with age in the first 30 years of life, that is, during brain maturation.
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Affiliation(s)
- Davide Nuzzi
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
| | - Mario Pellicoro
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
| | - Leonardo Angelini
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
- Center of Innovative Technologies for Signal Detection and Processing (TIRES), Universitá degli Studi Aldo Moro, Bari, Italy
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9
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Achterberg MA, Dubbeldam JLA, Stam CJ, Van Mieghem P. Classification of link-breaking and link-creation updating rules in susceptible-infected-susceptible epidemics on adaptive networks. Phys Rev E 2020; 101:052302. [PMID: 32575241 DOI: 10.1103/physreve.101.052302] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 03/26/2020] [Indexed: 01/02/2023]
Abstract
In the classical susceptible-infected-susceptible (SIS) model, a disease or infection spreads over a given, mostly fixed graph. However, in many real complex networks, the topology of the underlying graph can change due to the influence of the dynamical process. In this paper, besides the spreading process, the network adaptively changes its topology based on the states of the nodes in the network. An entire class of link-breaking and link-creation mechanisms, which we name Generalized Adaptive SIS (G-ASIS), is presented and analyzed. For each instance of G-ASIS using the complete graph as initial network, the relation between the epidemic threshold and the effective link-breaking rate is determined to be linear, constant, or unknown. Additionally, we show that there exist link-breaking and link-creation mechanisms for which the metastable state does not exist. We confirm our theoretical results with several numerical simulations.
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Affiliation(s)
- Massimo A Achterberg
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
| | - Johan L A Dubbeldam
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
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10
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Surano FV, Bongiorno C, Zino L, Porfiri M, Rizzo A. Backbone reconstruction in temporal networks from epidemic data. Phys Rev E 2019; 100:042306. [PMID: 31770979 PMCID: PMC7217498 DOI: 10.1103/physreve.100.042306] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Indexed: 01/22/2023]
Abstract
Many complex systems are characterized by time-varying patterns of interactions. These interactions comprise strong ties, driven by dyadic relationships, and weak ties, based on node-specific attributes. The interplay between strong and weak ties plays an important role on dynamical processes that could unfold on complex systems. However, seldom do we have access to precise information about the time-varying topology of interaction patterns. A particularly elusive question is to distinguish strong from weak ties, on the basis of the sole node dynamics. Building upon analytical results, we propose a statistically-principled algorithm to reconstruct the backbone of strong ties from data of a spreading process, consisting of the time series of individuals' states. Our method is numerically validated over a range of synthetic datasets, encapsulating salient features of real-world systems. Motivated by compelling evidence, we propose the integration of our algorithm in a targeted immunization strategy that prioritizes influential nodes in the inferred backbone. Through Monte Carlo simulations on synthetic networks and a real-world case study, we demonstrate the viability of our approach.
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Affiliation(s)
- Francesco Vincenzo Surano
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
- Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, New York 11201, USA
| | - Christian Bongiorno
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
- Laboratoire de Mathématiques et Informatique pour les Systèmes Complexes, CentraleSupélec, Université Paris Saclay, 91190 Gif-sur-Yvette, France
| | - Lorenzo Zino
- Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, New York 11201, USA
| | - Maurizio Porfiri
- Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, New York 11201, USA
| | - Alessandro Rizzo
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
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11
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O'Neill GC, Tewarie P, Vidaurre D, Liuzzi L, Woolrich MW, Brookes MJ. Dynamics of large-scale electrophysiological networks: A technical review. Neuroimage 2018; 180:559-576. [PMID: 28988134 DOI: 10.1016/j.neuroimage.2017.10.003] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 09/23/2017] [Accepted: 10/02/2017] [Indexed: 12/12/2022] Open
Abstract
For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity.
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Affiliation(s)
- George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Lucrezia Liuzzi
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.
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12
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Sami S, Williams N, Hughes LE, Cope TE, Rittman T, Coyle-Gilchrist ITS, Henson RN, Rowe JB. Neurophysiological signatures of Alzheimer's disease and frontotemporal lobar degeneration: pathology versus phenotype. Brain 2018; 141:2500-2510. [PMID: 30060017 PMCID: PMC6061803 DOI: 10.1093/brain/awy180] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 04/27/2018] [Accepted: 05/17/2018] [Indexed: 01/21/2023] Open
Abstract
The disruption of brain networks is characteristic of neurodegenerative dementias. However, it is controversial whether changes in connectivity reflect only the functional anatomy of disease, with selective vulnerability of brain networks, or the specific neurophysiological consequences of different neuropathologies within brain networks. We proposed that the oscillatory dynamics of cortical circuits reflect the tuning of local neural interactions, such that different pathologies are selective in their impact on the frequency spectrum of oscillations, whereas clinical syndromes reflect the anatomical distribution of pathology and physiological change. To test this hypothesis, we used magnetoencephalography from five patient groups, representing dissociated pathological subtypes and distributions across frontal, parietal and temporal lobes: amnestic Alzheimer's disease, posterior cortical atrophy, and three syndromes associated with frontotemporal lobar degeneration. We measured effective connectivity with graph theory-based measures of local efficiency, using partial directed coherence between sensors. As expected, each disease caused large-scale changes of neurophysiological brain networks, with reductions in local efficiency compared to controls. Critically however, the frequency range of altered connectivity was consistent across clinical syndromes that shared a likely underlying pathology, whilst the localization of changes differed between clinical syndromes. Multivariate pattern analysis of the frequency-specific topographies of local efficiency separated the disorders from each other and from controls (accuracy 62% to 100%, according to the groups' differences in likely pathology and clinical syndrome). The data indicate that magnetoencephalography has the potential to reveal specific changes in neurophysiology resulting from neurodegenerative disease. Our findings confirm that while clinical syndromes have characteristic anatomical patterns of abnormal connectivity that may be identified with other methods like structural brain imaging, the different mechanisms of neurodegeneration also cause characteristic spectral signatures of physiological coupling that are not accessible with structural imaging nor confounded by the neurovascular signalling of functional MRI. We suggest that these spectral characteristics of altered connectivity are the result of differential disruption of neuronal microstructure and synaptic physiology by Alzheimer's disease versus frontotemporal lobar degeneration.
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Affiliation(s)
- Saber Sami
- Department of Clinical Neurosciences, University of Cambridge, UK
| | | | - Laura E Hughes
- Department of Clinical Neurosciences, University of Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
| | - Thomas E Cope
- Department of Clinical Neurosciences, University of Cambridge, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, UK
| | | | - Richard N Henson
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
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Liu Q, Van Mieghem P. Autocorrelation of the susceptible-infected-susceptible process on networks. Phys Rev E 2018; 97:062309. [PMID: 30011514 PMCID: PMC7217534 DOI: 10.1103/physreve.97.062309] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Indexed: 11/24/2022]
Abstract
In this paper, we focus on the autocorrelation of the susceptible-infected-susceptible (SIS) process on networks. The N-intertwined mean-field approximation (NIMFA) is applied to calculate the autocorrelation properties of the exact SIS process. We derive the autocorrelation of the infection state of each node and the fraction of infected nodes both in the steady and transient states as functions of the infection probabilities of nodes. Moreover, we show that the autocorrelation can be used to estimate the infection and curing rates of the SIS process. The theoretical results are compared with the simulation of the exact SIS process. Our work fully utilizes the potential of the mean-field method and shows that NIMFA can indeed capture the autocorrelation properties of the exact SIS process.
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Affiliation(s)
- Qiang Liu
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2600 GA Delft, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2600 GA Delft, The Netherlands
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Neurophysiologic Correlates of Ketamine Sedation and Anesthesia: A High-density Electroencephalography Study in Healthy Volunteers. Anesthesiology 2017; 127:58-69. [PMID: 28486269 DOI: 10.1097/aln.0000000000001671] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Previous studies have demonstrated inconsistent neurophysiologic effects of ketamine, although discrepant findings might relate to differences in doses studied, brain regions analyzed, coadministration of other anesthetic medications, and resolution of the electroencephalograph. The objective of this study was to characterize the dose-dependent effects of ketamine on cortical oscillations and functional connectivity. METHODS Ten healthy human volunteers were recruited for study participation. The data were recorded using a 128-channel electroencephalograph during baseline consciousness, subanesthetic dosing (0.5 mg/kg over 40 min), anesthetic dosing (1.5 mg/kg bolus), and recovery. No other sedative or anesthetic medications were administered. Spectrograms, topomaps, and functional connectivity (weighted and directed phase lag index) were computed and analyzed. RESULTS Frontal theta bandwidth power increased most dramatically during ketamine anesthesia (mean power ± SD, 4.25 ± 1.90 dB) compared to the baseline (0.64 ± 0.28 dB), subanesthetic (0.60 ± 0.30 dB), and recovery (0.68 ± 0.41 dB) states; P < 0.001. Gamma power also increased during ketamine anesthesia. Weighted phase lag index demonstrated theta phase locking within anterior regions (0.2349 ± 0.1170, P < 0.001) and between anterior and posterior regions (0.2159 ± 0.1538, P < 0.01) during ketamine anesthesia. Alpha power gradually decreased with subanesthetic ketamine, and anterior-to-posterior directed connectivity was maximally reduced (0.0282 ± 0.0772) during ketamine anesthesia compared to all other states (P < 0.05). CONCLUSIONS Ketamine anesthesia correlates most clearly with distinct changes in the theta bandwidth, including increased power and functional connectivity. Anterior-to-posterior connectivity in the alpha bandwidth becomes maximally depressed with anesthetic ketamine administration, suggesting a dose-dependent effect.
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Märtens M, Meier J, Hillebrand A, Tewarie P, Van Mieghem P. Brain network clustering with information flow motifs. APPLIED NETWORK SCIENCE 2017; 2:25. [PMID: 30443580 PMCID: PMC6214277 DOI: 10.1007/s41109-017-0046-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 07/26/2017] [Indexed: 06/09/2023]
Abstract
Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands. Motifs are the building blocks of networks on this level and have previously been identified as important features for healthy and abnormal brain function. In this study, we present a network construction that enables us to search and analyze motifs in different frequency bands. We give evidence that the bi-directional two-hop path is the most important motif for the information flow in functional brain networks. A clustering based on this motif exposes a spatially coherent yet frequency-dependent sub-division between the posterior, occipital and frontal brain regions.
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Affiliation(s)
- Marcus Märtens
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, P.O Box 5031, Delft, The Netherlands
| | - Jil Meier
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, P.O Box 5031, Delft, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Piet Van Mieghem
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, P.O Box 5031, Delft, The Netherlands
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Engels MMA, van der Flier WM, Stam CJ, Hillebrand A, Scheltens P, van Straaten ECW. Alzheimer's disease: The state of the art in resting-state magnetoencephalography. Clin Neurophysiol 2017. [PMID: 28622527 DOI: 10.1016/j.clinph.2017.05.012] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Alzheimer's disease (AD) is accompanied by functional brain changes that can be detected in imaging studies, including electromagnetic activity recorded with magnetoencephalography (MEG). Here, we systematically review the studies that have examined resting-state MEG changes in AD and identify areas that lack scientific or clinical progress. Three levels of MEG analysis will be covered: (i) single-channel signal analysis, (ii) pairwise analyses over time series, which includes the study of interdependencies between two time series and (iii) global network analyses. We discuss the findings in the light of other functional modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Overall, single-channel MEG results show consistent changes in AD that are in line with EEG studies, but the full potential of the high spatial resolution of MEG and advanced functional connectivity and network analysis has yet to be fully exploited. Adding these features to the current knowledge will potentially aid in uncovering organizational patterns of brain function in AD and thereby aid the understanding of neuronal mechanisms leading to cognitive deficits.
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Affiliation(s)
- M M A Engels
- Alzheimer Centrum and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
| | - W M van der Flier
- Alzheimer Centrum 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
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - A Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Ph Scheltens
- Alzheimer Centrum and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - E C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
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