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Abeyasinghe PM, Aiello M, Cavaliere C, Owen AM, Soddu A. Correction to: A comparison of diffusion tractography techniques in simulating the generalized Ising model to predict the intrinsic activity of the brain. Brain Struct Funct 2021; 226:1647. [PMID: 33904963 DOI: 10.1007/s00429-021-02239-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Pubuditha M Abeyasinghe
- Department of Physics and Astronomy, Western University, London, ON, Canada.
- Brain and Mind Institute, Western University, London, ON, Canada.
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia.
| | - Marco Aiello
- IRCCS SDN, Istituto Di Ricerca Diagnostica E Nucleare, Via E. Gianturco 113, 80143, Naples, Italy
| | - Carlo Cavaliere
- IRCCS SDN, Istituto Di Ricerca Diagnostica E Nucleare, Via E. Gianturco 113, 80143, Naples, Italy
| | - Adrian M Owen
- Brain and Mind Institute, Western University, London, ON, Canada
- Department of Psychology, Western University, London, ON, Canada
| | - Andrea Soddu
- Department of Physics and Astronomy, Western University, London, ON, Canada
- Brain and Mind Institute, Western University, London, ON, Canada
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Abeyasinghe PM, Aiello M, Nichols ES, Cavaliere C, Fiorenza S, Masotta O, Borrelli P, Owen AM, Estraneo A, Soddu A. Consciousness and the Dimensionality of DOC Patients via the Generalized Ising Model. J Clin Med 2020; 9:E1342. [PMID: 32375368 PMCID: PMC7290966 DOI: 10.3390/jcm9051342] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 04/29/2020] [Accepted: 04/29/2020] [Indexed: 02/06/2023] Open
Abstract
The data from patients with severe brain injuries show complex brain functions. Due to the difficulties associated with these complex data, computational modeling is an especially useful tool to examine the structure-function relationship in these populations. By using computational modeling for patients with a disorder of consciousness (DoC), not only we can understand the changes of information transfer, but we also can test changes to different states of consciousness by hypothetically changing the anatomical structure. The generalized Ising model (GIM), which specializes in using structural connectivity to simulate functional connectivity, has been proven to effectively capture the relationship between anatomical structures and the spontaneous fluctuations of healthy controls (HCs). In the present study we implemented the GIM in 25 HCs as well as in 13 DoC patients diagnosed at three different states of consciousness. Simulated data were analyzed and the criticality and dimensionality were calculated for both groups; together, those values capture the level of information transfer in the brain. Ratifying previous studies, criticality was observed in simulations of HCs. We were also able to observe criticality for DoC patients, concluding that the GIM is generalizable for DoC patients. Furthermore, dimensionality increased for the DoC group as compared to healthy controls, and could distinguish different diagnostic groups of DoC patients.
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Affiliation(s)
- Pubuditha M. Abeyasinghe
- Department of Physics and Astronomy, Western University, London ON N6G2V4, Canada; (E.S.N.); (A.S.)
- Brain and Mind Institute, Western University, London ON N6A57, Canada;
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC 3800, Australia
| | - Marco Aiello
- IRCCS SDN, Via E. Gianturco 113, 80143 Naples, Italy; (M.A.); (C.C.); (P.B.)
| | - Emily S. Nichols
- Department of Physics and Astronomy, Western University, London ON N6G2V4, Canada; (E.S.N.); (A.S.)
- Brain and Mind Institute, Western University, London ON N6A57, Canada;
| | - Carlo Cavaliere
- IRCCS SDN, Via E. Gianturco 113, 80143 Naples, Italy; (M.A.); (C.C.); (P.B.)
| | - Salvatore Fiorenza
- Clinical Scientific Institute Maugeri; Telese Terme Center; 82037 Telese Terme, Italy; (S.F.); (O.M.); (A.E.)
| | - Orsola Masotta
- Clinical Scientific Institute Maugeri; Telese Terme Center; 82037 Telese Terme, Italy; (S.F.); (O.M.); (A.E.)
| | - Pasquale Borrelli
- IRCCS SDN, Via E. Gianturco 113, 80143 Naples, Italy; (M.A.); (C.C.); (P.B.)
| | - Adrian M. Owen
- Brain and Mind Institute, Western University, London ON N6A57, Canada;
- Department of Psychology, Western University, London ON N6A5C2, Canada
- Department of Physiology and Pharmacology, Western University, London ON N6A5C1, Canada
| | - Anna Estraneo
- Clinical Scientific Institute Maugeri; Telese Terme Center; 82037 Telese Terme, Italy; (S.F.); (O.M.); (A.E.)
- Neurology Unit, SM della Pietà General Hospital, 80035 Nola, Italy
| | - Andrea Soddu
- Department of Physics and Astronomy, Western University, London ON N6G2V4, Canada; (E.S.N.); (A.S.)
- Brain and Mind Institute, Western University, London ON N6A57, Canada;
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Abeyasinghe PM, de Paula DR, Khajehabdollahi S, Valluri SR, Owen AM, Soddu A. Role of Dimensionality in Predicting the Spontaneous Behavior of the Brain Using the Classical Ising Model and the Ising Model Implemented on a Structural Connectome. Brain Connect 2018; 8:444-455. [PMID: 29936876 PMCID: PMC6152861 DOI: 10.1089/brain.2017.0516] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
There is accumulating evidence that spontaneous fluctuations of the brain are sustained by a structural architecture of axonal fiber bundles. Various models have been used to investigate this structure–function relationship. In this work, we implemented the Ising model using the number of fibers between each pair of brain regions as input. The output of the Ising model simulations on a structural connectome was then compared with empirical functional connectivity data. A simpler two-dimensional classical Ising model was used as the baseline model for comparison purpose. Thermodynamic properties, such as the magnetic susceptibility and the specific heat, illustrated a phase transition from an ordered phase to a disordered phase at the critical temperature. Despite the differences between the two models, the lattice Ising model and the Ising model implemented on a structural connectome (the generalized Ising model) exhibited similar patterns of global properties. To study the behavior of the generalized Ising model around criticality, calculation of the dimensionality and critical exponents was performed for the first time, by introducing a new concept of distance based on structural connectivity. Same value inside the fitting error was found for the dimensionality in both models suggesting similar behavior of the models around criticality.
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Affiliation(s)
- Pubuditha M Abeyasinghe
- Department of Physics and Astronomy, Western University, London, Ontario, Canada.,The Brain and Mind Institute, Western University, London, Ontario, Canada
| | - Demetrius Ribeiro de Paula
- Department of Physics and Astronomy, Western University, London, Ontario, Canada.,The Brain and Mind Institute, Western University, London, Ontario, Canada
| | - Sina Khajehabdollahi
- Department of Physics and Astronomy, Western University, London, Ontario, Canada.,The Brain and Mind Institute, Western University, London, Ontario, Canada
| | - Sree Ram Valluri
- Department of Physics and Astronomy, Western University, London, Ontario, Canada
| | - Adrian M Owen
- The Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Psychology, Western University, London, Ontario, Canada
| | - Andrea Soddu
- Department of Physics and Astronomy, Western University, London, Ontario, Canada.,The Brain and Mind Institute, Western University, London, Ontario, Canada
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Ribeiro de Paula D, Ziegler E, Abeyasinghe PM, Das TK, Cavaliere C, Aiello M, Heine L, di Perri C, Demertzi A, Noirhomme Q, Charland-Verville V, Vanhaudenhuyse A, Stender J, Gomez F, Tshibanda JFL, Laureys S, Owen AM, Soddu A. A method for independent component graph analysis of resting-state fMRI. Brain Behav 2017; 7:e00626. [PMID: 28293468 PMCID: PMC5346515 DOI: 10.1002/brb3.626] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 10/28/2016] [Accepted: 11/18/2016] [Indexed: 01/02/2023] Open
Abstract
INTRODUCTION Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. OBJECTIVE Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. METHODS First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network. RESULTS Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness. CONCLUSIONS This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.
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Affiliation(s)
| | - Erik Ziegler
- Coma Science Group GIGA Research Université et Centre Hospitalier Universitaire de Liège Liège Belgium
| | - Pubuditha M Abeyasinghe
- Department of Physics & Astronomy Brain & Mind Institute Western University London ON Canada
| | - Tushar K Das
- Department of Physics & Astronomy Brain & Mind Institute Western University London ON Canada
| | - Carlo Cavaliere
- Coma Science Group GIGA Research Université et Centre Hospitalier Universitaire de Liège Liège Belgium; IRCCS SDN, Istituto di Ricerca Diagnostica e Nucleare Naples Italy
| | - Marco Aiello
- IRCCS SDN, Istituto di Ricerca Diagnostica e Nucleare Naples Italy
| | - Lizette Heine
- Coma Science Group GIGA Research Université et Centre Hospitalier Universitaire de Liège Liège Belgium
| | - Carol di Perri
- Coma Science Group GIGA Research Université et Centre Hospitalier Universitaire de Liège Liège Belgium
| | - Athena Demertzi
- Coma Science Group GIGA Research Université et Centre Hospitalier Universitaire de Liège Liège Belgium; Brain and Spine Institute (ICM) Hôpital Pitié-Salpêtrière Paris France
| | - Quentin Noirhomme
- Coma Science Group GIGA Research Université et Centre Hospitalier Universitaire de Liège Liège Belgium
| | - Vanessa Charland-Verville
- Coma Science Group GIGA Research Université et Centre Hospitalier Universitaire de Liège Liège Belgium
| | | | - Johan Stender
- Department of Neuroscience and Pharmacology University of Copenhagen Copenhagen Denmark
| | - Francisco Gomez
- Department of Mathematics Universidad Nacional de Colombia sede Bogotá Bogotá Colombia
| | | | - Steven Laureys
- Coma Science Group GIGA Research Université et Centre Hospitalier Universitaire de Liège Liège Belgium; Department of Neurology Université de Liège Liège Belgium
| | - Adrian M Owen
- Department of Psychology Brain & Mind Institute Western University London ON Canada
| | - Andrea Soddu
- Department of Physics & Astronomy Brain & Mind Institute Western University London ON Canada
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