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Theis N, Bahuguna J, Rubin JE, Cape J, Iyengar S, Prasad KM. Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576937. [PMID: 38328170 PMCID: PMC10849576 DOI: 10.1101/2024.01.23.576937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Objective Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.
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Affiliation(s)
- Nicholas Theis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jyotika Bahuguna
- Department of Neuroscience, Laboratoire de Neurosciences Cognitive et Adaptive, University of Strasbourg, France
| | | | - Joshua Cape
- Department of Statistics, University of Wisconsin-Madison, WI, USA
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, PA, USA
| | - Konasale M. Prasad
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
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2
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Kora Y, Simon C. Coarse graining and criticality in the human connectome. Phys Rev E 2024; 109:044303. [PMID: 38755874 DOI: 10.1103/physreve.109.044303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 03/05/2024] [Indexed: 05/18/2024]
Abstract
In the face of the stupefying complexity of the human brain, network analysis is a most useful tool that allows one to greatly simplify the problem, typically by approximating the billions of neurons making up the brain by means of a coarse-grained picture with a practicable number of nodes. But even such relatively small and coarse networks, such as the human connectome with its 100-1000 nodes, may present challenges for some computationally demanding analyses that are incapable of handling networks with more than a handful of nodes. With such applications in mind, we set out to study the extent to which dynamical behavior and critical phenomena in the brain may be preserved following a severe coarse-graining procedure. Thus we proceeded to further coarse grain the human connectome by taking a modularity-based approach, the goal being to produce a network of a relatively small number of modules. After finding that the qualitative dynamical behavior of the coarse-grained networks reflected that of the original networks, albeit to a less pronounced extent, we then formulated a hypothesis based on the coarse-grained networks in the context of criticality in the Wilson-Cowan and Ising models, and we verified the hypothesis, which connected a transition value of the former with the critical temperature of the latter, using the original networks. This preservation of dynamical and critical behavior following a severe coarse-graining procedure, in principle, allows for the drawing of similar qualitative conclusions by analyzing much smaller networks, which opens the door for studying the human connectome in contexts typically regarded as computationally intractable, such as Integrated Information Theory and quantum models of the human brain.
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Affiliation(s)
- Youssef Kora
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, Calgary T2N 4N1, Canada
| | - Christoph Simon
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, Calgary T2N 4N1, Canada
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3
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Apablaza-Yevenes DE, Corsi-Cabrera M, Martinez-Guerrero A, Northoff G, Romaniello C, Farinelli M, Bertoletti E, Müller MF, Muñoz-Torres Z. Stationary stable cross-correlation pattern and task specific deviations in unresponsive wakefulness syndrome as well as clinically healthy subjects. PLoS One 2024; 19:e0300075. [PMID: 38489260 PMCID: PMC10942032 DOI: 10.1371/journal.pone.0300075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
Brain dynamics is highly non-stationary, permanently subject to ever-changing external conditions and continuously monitoring and adjusting internal control mechanisms. Finding stationary structures in this system, as has been done recently, is therefore of great importance for understanding fundamental dynamic trade relationships. Here we analyse electroencephalographic recordings (EEG) of 13 subjects with unresponsive wakefulness syndrome (UWS) during rest and while being influenced by different acoustic stimuli. We compare the results with a control group under the same experimental conditions and with clinically healthy subjects during overnight sleep. The main objective of this study is to investigate whether a stationary correlation pattern is also present in the UWS group, and if so, to what extent this structure resembles the one found in healthy subjects. Furthermore, we extract transient dynamical features via specific deviations from the stationary interrelation pattern. We find that (i) the UWS group is more heterogeneous than the two groups of healthy subjects, (ii) also the EEGs of the UWS group contain a stationary cross-correlation pattern, although it is less pronounced and shows less similarity to that found for healthy subjects and (iii) deviations from the stationary pattern are notably larger for the UWS than for the two groups of healthy subjects. The results suggest that the nervous system of subjects with UWS receive external stimuli but show an overreaching reaction to them, which may disturb opportune information processing.
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Affiliation(s)
- David E. Apablaza-Yevenes
- Instituto de Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Morelos, México
| | - María Corsi-Cabrera
- Unidad de Investigación en Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
| | | | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, People’s Republic of China
- Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China
| | | | | | | | - Markus F. Müller
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad de México, México
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Morelos, México
- Centro Internacional de Ciencias A.C., Morelos, México
| | - Zeidy Muñoz-Torres
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad de México, México
- Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, México
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4
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Papo D, Buldú JM. Does the brain behave like a (complex) network? I. Dynamics. Phys Life Rev 2024; 48:47-98. [PMID: 38145591 DOI: 10.1016/j.plrev.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/27/2023]
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.
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Affiliation(s)
- D Papo
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy; Center for Translational Neurophysiology, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy.
| | - J M Buldú
- Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, Madrid, Spain
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5
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Kirse HA, Bahrami M, Lyday RG, Simpson SL, Peterson-Sockwell H, Burdette JH, Laurienti PJ. Differences in Brain Network Topology Based on Alcohol Use History in Adolescents. Brain Sci 2023; 13:1676. [PMID: 38137124 PMCID: PMC10741456 DOI: 10.3390/brainsci13121676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/10/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Abstract
Approximately 6 million youth aged 12 to 20 consume alcohol monthly in the United States. The effect of alcohol consumption in adolescence on behavior and cognition is heavily researched; however, little is known about how alcohol consumption in adolescence may alter brain function, leading to long-term developmental detriments. In order to investigate differences in brain connectivity associated with alcohol use in adolescents, brain networks were constructed using resting-state functional magnetic resonance imaging data collected by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) from 698 youth (12-21 years; 117 hazardous drinkers and 581 no/low drinkers). Analyses assessed differences in brain network topology based on alcohol consumption in eight predefined brain networks, as well as in whole-brain connectivity. Within the central executive network (CEN), basal ganglia network (BGN), and sensorimotor network (SMN), no/low drinkers demonstrated stronger and more frequent connections between highly globally efficient nodes, with fewer and weaker connections between highly clustered nodes. Inverse results were observed within the dorsal attention network (DAN), visual network (VN), and frontotemporal network (FTN), with no/low drinkers demonstrating weaker connections between nodes with high efficiency and increased frequency of clustered nodes compared to hazardous drinkers. Cross-sectional results from this study show clear organizational differences between adolescents with no/low or hazardous alcohol use, suggesting that aberrant connectivity in these brain networks is associated with risky drinking behaviors.
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Affiliation(s)
- Haley A. Kirse
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Graduate Program, Wake Forest Graduate School of Arts and Sciences, Integrative Physiology and Pharmacology, Winston-Salem, NC 27101, USA
| | - Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Robert G. Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Sean L. Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Hope Peterson-Sockwell
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
| | - Jonathan H. Burdette
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.A.K.); (M.B.); (R.G.L.); (S.L.S.); (H.P.-S.); (J.H.B.)
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
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6
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Kardan O, Stier AJ, Layden EA, Choe KW, Lyu M, Zhang X, Beilock SL, Rosenberg MD, Berman MG. Improvements in task performance after practice are associated with scale-free dynamics of brain activity. Netw Neurosci 2023; 7:1129-1152. [PMID: 37781143 PMCID: PMC10473260 DOI: 10.1162/netn_a_00319] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 04/11/2023] [Indexed: 10/03/2023] Open
Abstract
Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.
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Affiliation(s)
- Omid Kardan
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Andrew J. Stier
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Elliot A. Layden
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Kyoung Whan Choe
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Muxuan Lyu
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Department of Management and Marketing, The Hong Kong Polytechnic University, Hong Kong
| | - Xihan Zhang
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Sian L. Beilock
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Barnard College, Columbia University, New York, NY, USA
| | | | - Marc G. Berman
- Department of Psychology, University of Chicago, Chicago, IL, USA
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7
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Janarek J, Drogosz Z, Grela J, Ochab JK, Oświęcimka P. Investigating structural and functional aspects of the brain's criticality in stroke. Sci Rep 2023; 13:12341. [PMID: 37524891 PMCID: PMC10390586 DOI: 10.1038/s41598-023-39467-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
This paper addresses the question of the brain's critical dynamics after an injury such as a stroke. It is hypothesized that the healthy brain operates near a phase transition (critical point), which provides optimal conditions for information transmission and responses to inputs. If structural damage could cause the critical point to disappear and thus make self-organized criticality unachievable, it would offer the theoretical explanation for the post-stroke impairment of brain function. In our contribution, however, we demonstrate using network models of the brain, that the dynamics remain critical even after a stroke. In cases where the average size of the second-largest cluster of active nodes, which is one of the commonly used indicators of criticality, shows an anomalous behavior, it results from the loss of integrity of the network, quantifiable within graph theory, and not from genuine non-critical dynamics. We propose a new simple model of an artificial stroke that explains this anomaly. The proposed interpretation of the results is confirmed by an analysis of real connectomes acquired from post-stroke patients and a control group. The results presented refer to neurobiological data; however, the conclusions reached apply to a broad class of complex systems that admit a critical state.
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Affiliation(s)
- Jakub Janarek
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
| | - Zbigniew Drogosz
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
| | - Jacek Grela
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
- Mark Kac Center for Complex Systems Research, Jagiellonian University, 30-348, Kraków, Poland
| | - Jeremi K Ochab
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland.
- Mark Kac Center for Complex Systems Research, Jagiellonian University, 30-348, Kraków, Poland.
| | - Paweł Oświęcimka
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
- Mark Kac Center for Complex Systems Research, Jagiellonian University, 30-348, Kraków, Poland
- Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, 31-342, Kraków, Poland
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8
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Kora Y, Salhi S, Davidsen J, Simon C. Global excitability and network structure in the human brain. Phys Rev E 2023; 107:054308. [PMID: 37328981 DOI: 10.1103/physreve.107.054308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 04/07/2023] [Indexed: 06/18/2023]
Abstract
We utilize a model of Wilson-Cowan oscillators to investigate structure-function relationships in the human brain by means of simulations of the spontaneous dynamics of brain networks generated through human connectome data. This allows us to establish relationships between the global excitability of such networks and global structural network quantities for connectomes of two different sizes for a number of individual subjects. We compare the qualitative behavior of such correlations between biological networks and shuffled networks, the latter generated by shuffling the pairwise connectivities of the former while preserving their distribution. Our results point towards a remarkable propensity of the brain to achieve a trade-off between low network wiring cost and strong functionality, and highlight the unique capacity of brain network topologies to exhibit a strong transition from an inactive state to a globally excited one.
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Affiliation(s)
- Youssef Kora
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, T2N 4N1 Calgary, Canada
| | - Salma Salhi
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, T2N 4N1 Calgary, Canada
| | - Jörn Davidsen
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, T2N 4N1 Calgary, Canada
| | - Christoph Simon
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4, Canada and Hotchkiss Brain Institute, University of Calgary, T2N 4N1 Calgary, Canada
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9
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Ruffini G, Damiani G, Lozano-Soldevilla D, Deco N, Rosas FE, Kiani NA, Ponce-Alvarez A, Kringelbach ML, Carhart-Harris R, Deco G. LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics. PLoS Comput Biol 2023; 19:e1010811. [PMID: 36735751 PMCID: PMC9943020 DOI: 10.1371/journal.pcbi.1010811] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 02/21/2023] [Accepted: 12/11/2022] [Indexed: 02/04/2023] Open
Abstract
A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create "archetype" Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than in the placebo one, especially in homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than in the placebo condition (p = 9 × 10-5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity-especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state.
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Affiliation(s)
- Giulio Ruffini
- Neuroelectrics Barcelona, Barcelona, Spain
- Starlab Barcelona, Barcelona, Spain
- Haskins Laboratories, New Haven, Connecticut, United States of America
- * E-mail:
| | | | | | | | - Fernando E. Rosas
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Centre For Psychedelic Research (Department of Brain Science), Imperial College London, London, United Kingdom
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
| | - Narsis A. Kiani
- Algorithmic Dynamics Lab, Center of Molecular Medicine, Karolinksa Institutet, Stockholm, Sweden
- Oncology and Pathology Department, Karolinksa Institutet, Stockholm, Sweden
| | - Adrián Ponce-Alvarez
- Computational Neuroscience Group, Center for Brain and Cognition (Department of Information and Communication Technologies), Universitat Pompeu Fabra, Barcelona, Spain
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Robin Carhart-Harris
- Centre For Psychedelic Research (Department of Brain Science), Imperial College London, London, United Kingdom
- Psychedelics Division - Neuroscape, University of California San Francisco, San Francisco, California, United States of America
| | - Gustavo Deco
- The Catalan Institution for Research and Advanced Studies (ICREA), Universitat Pompeu Fabra, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
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10
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Shpurov I, Froese T. Evidence of Critical Dynamics in Movements of Bees inside a Hive. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1840. [PMID: 36554245 PMCID: PMC9777906 DOI: 10.3390/e24121840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Social insects such as honey bees exhibit complex behavioral patterns, and their distributed behavioral coordination enables decision-making at the colony level. It has, therefore, been proposed that a high-level description of their collective behavior might share commonalities with the dynamics of neural processes in brains. Here, we investigated this proposal by focusing on the possibility that brains are poised at the edge of a critical phase transition and that such a state is enabling increased computational power and adaptability. We applied mathematical tools developed in computational neuroscience to a dataset of bee movement trajectories that were recorded within the hive during the course of many days. We found that certain characteristics of the activity of the bee hive system are consistent with the Ising model when it operates at a critical temperature, and that the system's behavioral dynamics share features with the human brain in the resting state.
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11
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O'Byrne J, Jerbi K. How critical is brain criticality? Trends Neurosci 2022; 45:820-837. [PMID: 36096888 DOI: 10.1016/j.tins.2022.08.007] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/27/2022] [Accepted: 08/10/2022] [Indexed: 10/31/2022]
Abstract
Criticality is the singular state of complex systems poised at the brink of a phase transition between order and randomness. Such systems display remarkable information-processing capabilities, evoking the compelling hypothesis that the brain may itself be critical. This foundational idea is now drawing renewed interest thanks to high-density data and converging cross-disciplinary knowledge. Together, these lines of inquiry have shed light on the intimate link between criticality, computation, and cognition. Here, we review these emerging trends in criticality neuroscience, highlighting new data pertaining to the edge of chaos and near-criticality, and making a case for the distance to criticality as a useful metric for probing cognitive states and mental illness. This unfolding progress in the field contributes to establishing criticality theory as a powerful mechanistic framework for studying emergent function and its efficiency in both biological and artificial neural networks.
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Affiliation(s)
- Jordan O'Byrne
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada; MILA (Quebec Artificial Intelligence Institute), Montreal, Quebec, Canada; UNIQUE Center (Quebec Neuro-AI Research Center), Montreal, Quebec, Canada.
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12
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Ivanov VA, Michmizos KP. Astrocytes Learn to Detect and Signal Deviations from Critical Brain Dynamics. Neural Comput 2022; 34:2047-2074. [PMID: 36027803 DOI: 10.1162/neco_a_01532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 06/03/2022] [Indexed: 11/04/2022]
Abstract
Astrocytes are nonneuronal brain cells that were recently shown to actively communicate with neurons and are implicated in memory, learning, and regulation of cognitive states. Interestingly, these information processing functions are also closely linked to the brain's ability to self-organize at a critical phase transition. Investigating the mechanistic link between astrocytes and critical brain dynamics remains beyond the reach of cellular experiments, but it becomes increasingly approachable through computational studies. We developed a biologically plausible computational model of astrocytes to analyze how astrocyte calcium waves can respond to changes in underlying network dynamics. Our results suggest that astrocytes detect synaptic activity and signal directional changes in neuronal network dynamics using the frequency of their calcium waves. We show that this function may be facilitated by receptor scaling plasticity by enabling astrocytes to learn the approximate information content of input synaptic activity. This resulted in a computationally simple, information-theoretic model, which we demonstrate replicating the signaling functionality of the biophysical astrocyte model with receptor scaling. Our findings provide several experimentally testable hypotheses that offer insight into the regulatory role of astrocytes in brain information processing.
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Affiliation(s)
- Vladimir A Ivanov
- Computational Brain Lab, Department of Computer Science, Rutgers University, Piscataway, NJ 08854, U.S.A.
| | - Konstantinos P Michmizos
- Computational Brain Lab, Department of Computer Science, Rutgers University, Piscataway, NJ 08854, U.S.A.
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13
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Mellor NG, Graham ES, Unsworth CP. Critical Spatial-Temporal Dynamics and Prominent Shape Collapse of Calcium Waves Observed in Human hNT Astrocytes in Vitro. Front Physiol 2022; 13:808730. [PMID: 35784870 PMCID: PMC9247335 DOI: 10.3389/fphys.2022.808730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 05/31/2022] [Indexed: 11/27/2022] Open
Abstract
Networks of neurons are typically studied in the field of Criticality. However, the study of astrocyte networks in the brain has been recently lauded to be of equal importance to that of the neural networks. To date criticality assessments have only been performed on networks astrocytes from healthy rats, and astrocytes from cultured dissociated resections of intractable epilepsy. This work, for the first time, presents studies of the critical dynamics and shape collapse of calcium waves observed in cultures of healthy human astrocyte networks in vitro, derived from the human hNT cell line. In this article, we demonstrate that avalanches of spontaneous calcium waves display strong critical dynamics, including power-laws in both the size and duration distributions. In addition, the temporal profiles of avalanches displayed self-similarity, leading to shape collapse of the temporal profiles. These findings are significant as they suggest that cultured networks of healthy human hNT astrocytes self-organize to a critical point, implying that healthy astrocytic networks operate at a critical point to process and transmit information. Furthermore, this work can serve as a point of reference to which other astrocyte criticality studies can be compared.
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Affiliation(s)
- Nicholas G. Mellor
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
- *Correspondence: Nicholas G. Mellor,
| | - E. Scott Graham
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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14
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Tomlinson CE, Laurienti PJ, Lyday RG, Simpson SL. A regression framework for brain network distance metrics. Netw Neurosci 2022; 6:49-68. [PMID: 35350586 PMCID: PMC8942614 DOI: 10.1162/netn_a_00214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: standard F test, F test with individual level effects (ILE), feasible generalized least squares (FGLS), and permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing multivariate distance matrix regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.
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Affiliation(s)
- Chal E. Tomlinson
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Robert G. Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sean L. Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
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15
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Daniel Arzate-Mena J, Abela E, Olguín-Rodríguez PV, Ríos-Herrera W, Alcauter S, Schindler K, Wiest R, Müller MF, Rummel C. Stationary EEG pattern relates to large-scale resting state networks - An EEG-fMRI study connecting brain networks across time-scales. Neuroimage 2021; 246:118763. [PMID: 34863961 DOI: 10.1016/j.neuroimage.2021.118763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 11/25/2022] Open
Abstract
Relating brain dynamics acting on time scales that differ by at least an order of magnitude is a fundamental issue in brain research. The same is true for the observation of stable dynamical structures in otherwise highly non-stationary signals. The present study addresses both problems by the analysis of simultaneous resting state EEG-fMRI recordings of 53 patients with epilepsy. Confirming previous findings, we observe a generic and temporally stable average correlation pattern in EEG recordings. We design a predictor for the General Linear Model describing fluctuations around the stationary EEG correlation pattern and detect resting state networks in fMRI data. The acquired statistical maps are contrasted to several surrogate tests and compared with maps derived by spatial Independent Component Analysis of the fMRI data. By means of the proposed EEG-predictor we observe core nodes of known fMRI resting state networks with high specificity in the default mode, the executive control and the salience network. Our results suggest that both, the stationary EEG pattern as well as resting state fMRI networks are different expressions of the same brain activity. This activity is interpreted as the dynamics on (or close to) a stable attractor in phase space that is necessary to maintain the brain in an efficient operational mode. We discuss that this interpretation is congruent with the theoretical framework of complex systems as well as with the brain's energy balance.
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Affiliation(s)
- J Daniel Arzate-Mena
- Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos,Cuernavaca Morelos, Mexico
| | - Eugenio Abela
- Center for Neuropsychiatrics, Psychiatric Services Aargau AG, Windisch, Switzerland
| | | | - Wady Ríos-Herrera
- Facultad de Psicología Universidad Nacional Autónoma de México, Mexico City, Mexico; Centro de Ciencias de la Complejidad (C3), Universisdad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus F Müller
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos (UAEM), Cuernavaca, Morelos, Mexico; Centro de Ciencias de la Complejidad (C3), Universisdad Nacional Autónoma de México, Mexico City 04510, Mexico; Centro Internacional de Ciencias A. C., Cuernavaca, México
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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16
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Wein S, Deco G, Tomé AM, Goldhacker M, Malloni WM, Greenlee MW, Lang EW. Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5573740. [PMID: 34135951 PMCID: PMC8177997 DOI: 10.1155/2021/5573740] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/06/2021] [Indexed: 12/12/2022]
Abstract
This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.
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Affiliation(s)
- Simon Wein
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Technology and Information, University Pompeu Fabra, Carrer Tanger, 122-140, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats, University Barcelona, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Ana Maria Tomé
- IEETA/DETI, University de Aveiro, Aveiro 3810-193, Portugal
| | - Markus Goldhacker
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Wilhelm M. Malloni
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Mark W. Greenlee
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Elmar W. Lang
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
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17
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Wason TD. A model integrating multiple processes of synchronization and coherence for information instantiation within a cortical area. Biosystems 2021; 205:104403. [PMID: 33746019 DOI: 10.1016/j.biosystems.2021.104403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 03/05/2021] [Indexed: 12/14/2022]
Abstract
What is the form of dynamic, e.g., sensory, information in the mammalian cortex? Information in the cortex is modeled as a coherence map of a mixed chimera state of synchronous, phasic, and disordered minicolumns. The theoretical model is built on neurophysiological evidence. Complex spatiotemporal information is instantiated through a system of interacting biological processes that generate a synchronized cortical area, a coherent aperture. Minicolumn elements are grouped in macrocolumns in an array analogous to a phased-array radar, modeled as an aperture, a "hole through which radiant energy flows." Coherence maps in a cortical area transform inputs from multiple sources into outputs to multiple targets, while reducing complexity and entropy. Coherent apertures can assume extremely large numbers of different information states as coherence maps, which can be communicated among apertures with corresponding very large bandwidths. The coherent aperture model incorporates considerable reported research, integrating five conceptually and mathematically independent processes: 1) a damped Kuramoto network model, 2) a pumped area field potential, 3) the gating of nearly coincident spikes, 4) the coherence of activity across cortical lamina, and 5) complex information formed through functions in macrocolumns. Biological processes and their interactions are described in equations and a functional circuit such that the mathematical pieces can be assembled the same way the neurophysiological ones are. The model can be conceptually convolved over the specifics of local cortical areas within and across species. A coherent aperture becomes a node in a graph of cortical areas with a corresponding distribution of information.
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Affiliation(s)
- Thomas D Wason
- North Carolina State University, Department of Biological Sciences, Meitzen Laboratory, Campus Box 7617, 128 David Clark Labs, Raleigh, NC 27695-7617, USA.
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18
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A comparison of diffusion tractography techniques in simulating the generalized Ising model to predict the intrinsic activity of the brain. Brain Struct Funct 2021; 226:817-832. [PMID: 33523294 DOI: 10.1007/s00429-020-02211-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 12/30/2020] [Indexed: 12/20/2022]
Abstract
Diffusion tractography is a non-invasive technique that is being used to estimate the location and direction of white matter tracts in the brain. Identifying the characteristics of white matter plays an important role in research as well as in clinical practice that relies on finding the relationship between the structure and function of the brain. An Ising model implemented on a structural connectivity (SC) has proven to explain the spontaneous fluctuations in the brain at criticality using brain's structure depicted by white matter tracts. Since the SC is the only input of the model, identifying the tractography technique which provides a SC that delivers the highest prediction of the brain's intrinsic activity via the generalized Ising model (GIM) is essential. Hence an Ising model is simulated on SCs generated using two different acquisition schemes (single and multi-shell) and two different tractography approaches (deterministic and probabilistic) and analyzed at criticality across 69 healthy subjects. Results showed that by introducing the GIM, predictability of the empirical correlation matrix increases on average from 0.2 to 0.6 compared to the predictability using the empirical connectivity matrix directly. It is also observed that the SC generated using deterministic tractography without fractional anisotropy resulted in the highest correlation coefficient value of 0.65 between the simulated and empirical correlation matrices. Additionally, calculated dimensionalities per simulation illustrated that the dimensionality depends upon the method of tractography that has been used to extract the SC.
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19
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Kandeepan S, Rudas J, Gomez F, Stojanoski B, Valluri S, Owen AM, Naci L, Nichols ES, Soddu A. Modeling an auditory stimulated brain under altered states of consciousness using the generalized Ising model. Neuroimage 2020; 223:117367. [PMID: 32931944 DOI: 10.1016/j.neuroimage.2020.117367] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 08/08/2020] [Accepted: 09/08/2020] [Indexed: 10/23/2022] Open
Abstract
Propofol is a short-acting medication that results in decreased levels of consciousness and is used for general anesthesia. Although it is the most commonly used anesthetic in the world, much remains unknown about the mechanisms by which it induces a loss of consciousness. Characterizing anesthesia-induced alterations to brain network activity might provide a powerful framework for understanding the neural mechanisms of unconsciousness. The aim of this work was to model brain activity in healthy brains during various stages of consciousness, as induced by propofol, in the auditory paradigm. We used the generalized Ising model (GIM) to fit the empirical fMRI data of healthy subjects while they listened to an audio clip from a movie. The external stimulus (audio clip) is believed to be at least partially driving a synchronization process of the brain activity and provides a similar conscious experience in different subjects. In order to observe the common synchronization among the subjects, a novel technique called the inter subject correlation (ISC) was implemented. We showed that the GIM-modified to incorporate the naturalistic external field-was able to fit the empirical task fMRI data in the awake state, in mild sedation, in deep sedation, and in recovery, at a temperature T* which is well above the critical temperature. To our knowledge this is the first study that captures human brain activity in response to real-life external stimuli at different levels of conscious awareness using mathematical modeling. This study might be helpful in the future to assess the level of consciousness of patients with disorders of consciousness and help in regaining their consciousness.
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Affiliation(s)
- Sivayini Kandeepan
- Department of Physics and Astronomy and the Brain and Mind Institute, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada; Department of Physics, Faculty of Applied Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
| | - Jorge Rudas
- Department of Mathematics, Universidad Nacional de Colombia, Cra 45, Bogotá, Colombia
| | - Francisco Gomez
- Department of Mathematics, Universidad Nacional de Colombia, Cra 45, Bogotá, Colombia
| | - Bobby Stojanoski
- Brain and Mind Institute, University of Western Ontario, 1151 Richmond St, London, Ontario, N6A 3K7, Canada
| | - Sreeram Valluri
- Department of Physics and Astronomy and the Brain and Mind Institute, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
| | - Adrian Mark Owen
- Brain and Mind Institute, University of Western Ontario, 1151 Richmond St, London, Ontario, N6A 3K7, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, Trinity College Dublin, College Green, Dublin 2, Ireland
| | - Emily Sophia Nichols
- Brain and Mind Institute, University of Western Ontario, 1151 Richmond St, London, Ontario, N6A 3K7, Canada
| | - Andrea Soddu
- Department of Physics and Astronomy and the Brain and Mind Institute, University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada
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20
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Zimmern V. Why Brain Criticality Is Clinically Relevant: A Scoping Review. Front Neural Circuits 2020; 14:54. [PMID: 32982698 PMCID: PMC7479292 DOI: 10.3389/fncir.2020.00054] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 07/23/2020] [Indexed: 12/13/2022] Open
Abstract
The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains - including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry - are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered.
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Affiliation(s)
- Vincent Zimmern
- Division of Child Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
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21
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The Emergence of Integrated Information, Complexity, and 'Consciousness' at Criticality. ENTROPY 2020; 22:e22030339. [PMID: 33286113 PMCID: PMC7516800 DOI: 10.3390/e22030339] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/06/2020] [Accepted: 03/12/2020] [Indexed: 11/16/2022]
Abstract
Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte-Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model.
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22
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Ezaki T, Fonseca Dos Reis E, Watanabe T, Sakaki M, Masuda N. Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence. Commun Biol 2020; 3:52. [PMID: 32015402 PMCID: PMC6997374 DOI: 10.1038/s42003-020-0774-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 01/13/2020] [Indexed: 01/05/2023] Open
Abstract
According to the critical brain hypothesis, the brain is considered to operate near criticality and realize efficient neural computations. Despite the prior theoretical and empirical evidence in favor of the hypothesis, no direct link has been provided between human cognitive performance and the neural criticality. Here we provide such a key link by analyzing resting-state dynamics of functional magnetic resonance imaging (fMRI) networks at a whole-brain level. We develop a data-driven analysis method, inspired from statistical physics theory of spin systems, to map out the whole-brain neural dynamics onto a phase diagram. Using this tool, we show evidence that neural dynamics of human participants with higher fluid intelligence quotient scores are closer to a critical state, i.e., the boundary between the paramagnetic phase and the spin-glass (SG) phase. The present results are consistent with the notion of "edge-of-chaos" neural computation.
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Affiliation(s)
- Takahiro Ezaki
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Meguro-ku, Tokyo, Japan
| | | | - Takamitsu Watanabe
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, WC1N 3AZ, UK
- RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Michiko Sakaki
- School of Psychology and Clinical Language Sciences, University of Reading, Earley Gate, Whiteknights Road, Reading, UK
- Research Institute, Kochi University of Technology, Kami, Kochi, Japan
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, Clifton, Bristol, UK.
- Department of Mathematics, University at Buffalo, State University of New York, Buffalo, New York, USA.
- Computational and Data-Enabled Science and Engineering Program, University at Buffalo, State University of New York, Buffalo, New York, USA.
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23
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Faqeeh A, Osat S, Radicchi F, Gleeson JP. Emergence of power laws in noncritical neuronal systems. Phys Rev E 2020; 100:010401. [PMID: 31499795 PMCID: PMC7217540 DOI: 10.1103/physreve.100.010401] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Indexed: 11/07/2022]
Abstract
Experimental and computational studies provide compelling evidence that neuronal systems are characterized by power-law distributions of neuronal avalanche sizes. This fact is interpreted as an indication that these systems are operating near criticality, and, in turn, typical properties of critical dynamical processes, such as optimal information transmission and stability, are attributed to neuronal systems. The purpose of this Rapid Communication is to show that the presence of power-law distributions for the size of neuronal avalanches is not a sufficient condition for the system to operate near criticality. Specifically, we consider a simplistic model of neuronal dynamics on networks and show that the degree distribution of the underlying neuronal network may trigger power-law distributions for neuronal avalanches even when the system is not in its critical regime. To certify and explain our findings we develop an analytical approach based on percolation theory and branching processes techniques.
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Affiliation(s)
- Ali Faqeeh
- Mathematics Consortium for Science and Industry, Department of Mathematics and Statistics, University of Limerick, Limerick V94 T9PX, Ireland.,Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA
| | - Saeed Osat
- Deep Quantum Labs, Skolkovo Institute of Science and Technology, Moscow 143026, Russia
| | - Filippo Radicchi
- Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA
| | - James P Gleeson
- Mathematics Consortium for Science and Industry, Department of Mathematics and Statistics, University of Limerick, Limerick V94 T9PX, Ireland
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24
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Peraza-Goicolea JA, Martínez-Montes E, Aubert E, Valdés-Hernández PA, Mulet R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Netw 2019; 123:52-69. [PMID: 31830607 DOI: 10.1016/j.neunet.2019.11.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 11/13/2019] [Accepted: 11/18/2019] [Indexed: 10/25/2022]
Abstract
In this work, we propose a natural model for information flow in the brain through a neural message-passing dynamics on a structural network of macroscopic regions, such as the human connectome (HC). In our model, each brain region is assumed to have a binary behavior (active or not), the strengths of interactions among them are encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by the Belief Propagation (BP) algorithm, working near the critical point of the network. We show that in the absence of direct external stimuli the BP algorithm converges to a spatial map of activations that is similar to the Default Mode Network (DMN) of the brain, which has been defined from the analysis of functional MRI data. Moreover, we use Susceptibility Propagation (SP) to compute the matrix of long-range correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting State Networks (RSN) determined experimentally. Both results suggest that the functional DMN and RSNs can be seen as simple consequences of the anatomical structure of the brain and a neural message-passing dynamics between macroscopic regions. With the new model, we explore predictions on how functional maps change when the anatomical brain network suffers structural alterations, like in Alzheimer's disease and in lesions of the Corpus Callosum. The implications and novel interpretations suggested by the model, as well as the role of criticality, are discussed.
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Affiliation(s)
- Julio A Peraza-Goicolea
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, University of Havana, Havana, Cuba; Department of Physics, Florida International University, Miami, FL, USA.
| | - Eduardo Martínez-Montes
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba; Advanced Center for Electrical and Electronic Engineering (AC3E), Universidad Técnica Federico Santa María, Valparaíso, Chile.
| | - Eduardo Aubert
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba.
| | | | - Roberto Mulet
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, University of Havana, Havana, Cuba.
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25
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Wang JWJL, Lombardi F, Zhang X, Anaclet C, Ivanov PC. Non-equilibrium critical dynamics of bursts in θ and δ rhythms as fundamental characteristic of sleep and wake micro-architecture. PLoS Comput Biol 2019; 15:e1007268. [PMID: 31725712 PMCID: PMC6855414 DOI: 10.1371/journal.pcbi.1007268] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 07/11/2019] [Indexed: 01/08/2023] Open
Abstract
Origin and functions of intermittent transitions among sleep stages, including short awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing the sleep-wake cycle results from an underlying non-equilibrium critical dynamics, bridging collective behaviors across spatio-temporal scales. We investigate θ and δ wave dynamics in control rats and in rats with lesions of sleep-promoting neurons in the parafacial zone. We demonstrate that intermittent bursts in θ and δ rhythms exhibit a complex temporal organization, with long-range power-law correlations and a robust duality of power law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, typical features of non-equilibrium systems self-organizing at criticality. Crucially, such temporal organization relates to anti-correlated coupling between θ- and δ-bursts, and is independent of the dominant physiologic state and lesions, a solid indication of a basic principle in sleep dynamics.
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Affiliation(s)
- Jilin W. J. L. Wang
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts, United States of America
| | - Fabrizio Lombardi
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts, United States of America
- Institute of Science and Technology Austria, A-3400 Klosterneuburg, Austria
| | - Xiyun Zhang
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts, United States of America
| | - Christelle Anaclet
- Department of Neurobiology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Neurology, Division of Sleep Medicine, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Plamen Ch. Ivanov
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts, United States of America
- Department of Neurology, Division of Sleep Medicine, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Harvard Medical School and Division of Sleep Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
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26
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Mallinson JB, Shirai S, Acharya SK, Bose SK, Galli E, Brown SA. Avalanches and criticality in self-organized nanoscale networks. SCIENCE ADVANCES 2019; 5:eaaw8438. [PMID: 31700999 PMCID: PMC6824861 DOI: 10.1126/sciadv.aaw8438] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 09/16/2019] [Indexed: 05/30/2023]
Abstract
Current efforts to achieve neuromorphic computation are focused on highly organized architectures, such as integrated circuits and regular arrays of memristors, which lack the complex interconnectivity of the brain and so are unable to exhibit brain-like dynamics. New architectures are required, both to emulate the complexity of the brain and to achieve critical dynamics and consequent maximal computational performance. We show here that electrical signals from self-organized networks of nanoparticles exhibit brain-like spatiotemporal correlations and criticality when fabricated at a percolating phase transition. Specifically, the sizes and durations of avalanches of switching events are power law distributed, and the power law exponents satisfy rigorous criteria for criticality. These signals are therefore qualitatively and quantitatively similar to those measured in the cortex. Our self-organized networks provide a low-cost platform for computational approaches that rely on spatiotemporal correlations, such as reservoir computing, and are an important step toward creating neuromorphic device architectures.
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27
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Bocaccio H, Pallavicini C, Castro MN, Sánchez SM, De Pino G, Laufs H, Villarreal MF, Tagliazucchi E. The avalanche-like behaviour of large-scale haemodynamic activity from wakefulness to deep sleep. J R Soc Interface 2019; 16:20190262. [PMID: 31506046 PMCID: PMC6769314 DOI: 10.1098/rsif.2019.0262] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 08/08/2019] [Indexed: 02/02/2023] Open
Abstract
Increasing evidence suggests that responsiveness is associated with critical or near-critical cortical dynamics, which exhibit scale-free cascades of spatio-temporal activity. These cascades, or 'avalanches', have been detected at multiple scales, from in vitro and in vivo microcircuits to voltage imaging and brain-wide functional magnetic resonance imaging (fMRI) recordings. Criticality endows the cortex with certain information-processing capacities postulated as necessary for conscious wakefulness, yet it remains unknown how unresponsiveness impacts on the avalanche-like behaviour of large-scale human haemodynamic activity. We observed a scale-free hierarchy of co-activated connected clusters by applying a point-process transformation to fMRI data recorded during wakefulness and non-rapid eye movement (NREM) sleep. Maximum-likelihood estimates revealed a significant effect of sleep stage on the scaling parameters of the cluster size power-law distributions. Post hoc statistical tests showed that differences were maximal between wakefulness and N2 sleep. These results were robust against spatial coarse graining, fitting alternative statistical models and different point-process thresholds, and disappeared upon phase shuffling the fMRI time series. Evoked neural bistabilities preventing arousals during N2 sleep do not suffice to explain these differences, which point towards changes in the intrinsic dynamics of the brain that could be necessary to consolidate a state of deep unresponsiveness.
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Affiliation(s)
- H. Bocaccio
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
| | - C. Pallavicini
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
| | - M. N. Castro
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Fisiología, Facultad de Medicina, UBA, Buenos Aires, Argentina
- Departamento Salud Mental, Unidad Docente FLENI, Facultad de Medicina, UBA, Buenos Aires, Argentina
| | - S. M. Sánchez
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
| | - G. De Pino
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- Laboratorio de Neuroimágenes, Departamento de Imágenes, FLENI, Buenos Aires, Argentina
- Escuela de Ciencia y Tecnología (ECyT), Universidad Nacional de San Martín, Argentina
| | - H. Laufs
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - M. F. Villarreal
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
| | - E. Tagliazucchi
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
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28
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Baumgarten L, Bornholdt S. Critical excitation-inhibition balance in dense neural networks. Phys Rev E 2019; 100:010301. [PMID: 31499927 DOI: 10.1103/physreve.100.010301] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Indexed: 11/07/2022]
Abstract
The "edge of chaos" phase transition in artificial neural networks is of renewed interest in light of recent evidence for criticality in brain dynamics. Statistical mechanics traditionally studied this transition with connectivity k as the control parameter and an exactly balanced excitation-inhibition ratio. While critical connectivity has been found to be low in these model systems, typically around k=2, which is unrealistic for natural neural systems, a recent study utilizing the excitation-inhibition ratio as the control parameter found a new, nearly degree independent, critical point when connectivity is large. However, the new phase transition is accompanied by an unnaturally high level of activity in the network. Here we study random neural networks with the additional properties of (i) a high clustering coefficient and (ii) neurons that are solely either excitatory or inhibitory, a prominent property of natural neurons. As a result, we observe an additional critical point for networks with large connectivity, regardless of degree distribution, which exhibits low activity levels that compare well with neuronal brain networks.
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Affiliation(s)
- Lorenz Baumgarten
- Institut für Theoretische Physik, Universität Bremen, 28759 Bremen, Germany
| | - Stefan Bornholdt
- Institut für Theoretische Physik, Universität Bremen, 28759 Bremen, Germany
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29
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Wink AM. Eigenvector Centrality Dynamics From Resting-State fMRI: Gender and Age Differences in Healthy Subjects. Front Neurosci 2019; 13:648. [PMID: 31316335 PMCID: PMC6609310 DOI: 10.3389/fnins.2019.00648] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 06/06/2019] [Indexed: 01/20/2023] Open
Abstract
With the increasing use of functional brain network properties as markers of brain disorders, efficient visualization and evaluation methods have become essential. Eigenvector centrality mapping (ECM) of functional MRI (fMRI) data enables the representation of per-node graph theoretical measures as brain maps. This paper studies the use of centrality dynamics for measuring group differences in imaging studies. Imaging data were used from a publicly available imaging study, which included resting fMRI data. After warping the images to a standard space and masking cortical regions, ECM were computed in a sliding window. The dual regression method was used to identify dynamic centrality differences inside well-known resting-state networks between gender and age groups. Gender-related differences were found in the medial and lateral visual, motor, default mode, and executive control RSN, where male subjects had more consistent centrality variations within the network. Age-related differences between the youngest and oldest subjects, based on a median split, were found in the medial visual, executive control and left frontoparietal networks, where younger subjects had more consistent centrality variations within the network. Our findings show that centrality dynamics can be used to identify between-group functional brain network centrality differences, and that age and gender distributions studies need to be taken into account in functional imaging studies.
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Affiliation(s)
- Alle Meije Wink
- Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands
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30
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Bavassi L, Forcato C, Fernández RS, De Pino G, Pedreira ME, Villarreal MF. Retrieval of retrained and reconsolidated memories are associated with a distinct neural network. Sci Rep 2019; 9:784. [PMID: 30692553 PMCID: PMC6349866 DOI: 10.1038/s41598-018-37089-2] [Citation(s) in RCA: 7] [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: 03/15/2018] [Accepted: 12/04/2018] [Indexed: 12/03/2022] Open
Abstract
Consolidated memories can persist from a single day to years, and persistence is improved by retraining or retrieval-mediated plasticity. One retrieval-based way to strengthen memory is the reconsolidation process. Strengthening occurs simply by the presentation of specific cues associated with the original learning. This enhancement function has a fundamental role in the maintenance of memory relevance in animals everyday life. In the present study, we made a step forward in the identification of brain correlates imprinted by the reconsolidation process studying the long-term neural consequences when the strengthened memory is stable again. To reach such a goal, we compared the retention of paired-associate memories that went through retraining process or were labilizated-reconsolidated. Using functional magnetic resonance imaging (fMRI), we studied the specific areas activated during retrieval and analyzed the functional connectivity of the whole brain associated with the event-related design. We used Graph Theory tools to analyze the global features of the network. We show that reconsolidated memories imprint a more locally efficient network that is better at exchanging information, compared with memories that were retrained or untreated. For the first time, we report a method to elucidate the neural footprints associated with a relevant function of memory reconsolidation.
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Affiliation(s)
- Luz Bavassi
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Ciudad de Buenos Aires, Argentina.
- CONICET-Universidad de Buenos Aires, Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), Ciudad de Buenos Aires, Argentina.
- Instituto de Fisiología, Biología Molecular y Neurociencias, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria (C1428EHA), Ciudad de Buenos Aires, Argentina.
| | - Cecilia Forcato
- Unidad Ejecutora de Estudios de Neurociencias y Sistemas Complejos, CONICET, Universidad Nacional Arturo Jauretche Hospital de Alta Complejidad en Red El Cruce "Néstor Kirchner",Av. Calchaqui 6200, (1888), Florencio Varela, Argentina
| | - Rodrigo S Fernández
- CONICET-Universidad de Buenos Aires, Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), Ciudad de Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Ciudad de Buenos Aires, Argentina
| | - Gabriela De Pino
- Laboratorio de Neuroimágenes, Departamento de Imágenes, FLENI, Montañeses 2325, Ciudad de Buenos Aires, C1428AQK, Argentina
- Centro Universitario de Imágenes Médicas (CEUNIM), Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina
- INAAC, FLENI, Montañeses 2325, C1428AQK, Ciudad de Buenos Aires, Argentina
| | - María E Pedreira
- CONICET-Universidad de Buenos Aires, Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), Ciudad de Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Ciudad de Buenos Aires, Argentina
| | - Mirta F Villarreal
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Ciudad de Buenos Aires, Argentina
- INAAC, FLENI, Montañeses 2325, C1428AQK, Ciudad de Buenos Aires, Argentina
- CONICET, Ciudad de Buenos Aires, Argentina
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31
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Dalege J, Borsboom D, van Harreveld F, van der Maas HLJ. The Attitudinal Entropy (AE) Framework as a General Theory of Individual Attitudes. PSYCHOLOGICAL INQUIRY 2019. [DOI: 10.1080/1047840x.2018.1537246] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Jonas Dalege
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Frenk van Harreveld
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
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32
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Rocha RP, Koçillari L, Suweis S, Corbetta M, Maritan A. Homeostatic plasticity and emergence of functional networks in a whole-brain model at criticality. Sci Rep 2018; 8:15682. [PMID: 30356174 PMCID: PMC6200722 DOI: 10.1038/s41598-018-33923-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 09/27/2018] [Indexed: 11/09/2022] Open
Abstract
Understanding the relationship between large-scale structural and functional brain networks remains a crucial issue in modern neuroscience. Recently, there has been growing interest in investigating the role of homeostatic plasticity mechanisms, across different spatiotemporal scales, in regulating network activity and brain functioning against a wide range of environmental conditions and brain states (e.g., during learning, development, ageing, neurological diseases). In the present study, we investigate how the inclusion of homeostatic plasticity in a stochastic whole-brain model, implemented as a normalization of the incoming node's excitatory input, affects the macroscopic activity during rest and the formation of functional networks. Importantly, we address the structure-function relationship both at the group and individual-based levels. In this work, we show that normalization of the node's excitatory input improves the correspondence between simulated neural patterns of the model and various brain functional data. Indeed, we find that the best match is achieved when the model control parameter is in its critical value and that normalization minimizes both the variability of the critical points and neuronal activity patterns among subjects. Therefore, our results suggest that the inclusion of homeostatic principles lead to more realistic brain activity consistent with the hallmarks of criticality. Our theoretical framework open new perspectives in personalized brain modeling with potential applications to investigate the deviation from criticality due to structural lesions (e.g. stroke) or brain disorders.
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Affiliation(s)
- Rodrigo P Rocha
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil. .,Dipartimento di Fisica e Astronomia, Università di Padova and INFN, via Marzolo 8, I-35131, Padova, Italy. .,Padova Neuroscience Center, Università di Padova, Padova, Italy.
| | - Loren Koçillari
- Dipartimento di Fisica e Astronomia, Università di Padova and INFN, via Marzolo 8, I-35131, Padova, Italy.,Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Samir Suweis
- Dipartimento di Fisica e Astronomia, Università di Padova and INFN, via Marzolo 8, I-35131, Padova, Italy.,Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, Università di Padova, Padova, Italy.,Dipartimento di Neuroscienze, Università di Padova, Padova, Italy.,Departments of Neurology, Radiology, Neuroscience, and Bioengineering, Washington University, School of Medicine, St. Louis, USA
| | - Amos Maritan
- Dipartimento di Fisica e Astronomia, Università di Padova and INFN, via Marzolo 8, I-35131, Padova, Italy.,Padova Neuroscience Center, Università di Padova, Padova, Italy
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33
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Daffertshofer A, Ton R, Kringelbach ML, Woolrich M, Deco G. Distinct criticality of phase and amplitude dynamics in the resting brain. Neuroimage 2018. [DOI: 10.1016/j.neuroimage.2018.03.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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34
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Bahrami M, Laurienti PJ, Simpson SL. A MATLAB toolbox for multivariate analysis of brain networks. Hum Brain Mapp 2018; 40:175-186. [PMID: 30256496 DOI: 10.1002/hbm.24363] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 07/23/2018] [Accepted: 08/07/2018] [Indexed: 11/10/2022] Open
Abstract
Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.
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Affiliation(s)
- Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Department of Biomedical Engineering, Virginia Tech - Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, North Carolina
| | - Paul J Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Sean L Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
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35
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Goodarzinick A, Niry MD, Valizadeh A, Perc M. Robustness of functional networks at criticality against structural defects. Phys Rev E 2018; 98:022312. [PMID: 30253621 DOI: 10.1103/physreve.98.022312] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Indexed: 06/08/2023]
Abstract
The robustness of dynamical properties of neuronal networks against structural damages is a central problem in computational and experimental neuroscience. Research has shown that the cortical network of a healthy brain works near a critical state and, moreover, that functional neuronal networks often have scale-free and small-world properties. In this work, we study how the robustness of simple functional networks at criticality is affected by structural defects. In particular, we consider a two-dimensional Ising model at the critical temperature and investigate how its functional network changes with the increasing degree of structural defects. We show that the scale-free and small-world properties of the functional network at criticality are robust against large degrees of structural lesions while the system remains below the percolation limit. Although the Ising model is only a conceptual description of a two-state neuron, our research reveals fundamental robustness properties of functional networks derived from classical statistical mechanics models.
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Affiliation(s)
- Abdorreza Goodarzinick
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
| | - Mohammad D Niry
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), P.O. Box 1954851167, Tehran, Iran
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia
- CAMTP-Center for Applied Mathematics and Theoretical Physics, University of Maribor, Mladinska 3, SI-2000 Maribor, Slovenia
- School of Electronic and Information Engineering, Beihang University, Beijing 100191, People's Republic of China
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36
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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] [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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37
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Goldhacker M, Tomé AM, Greenlee MW, Lang EW. Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States. Front Hum Neurosci 2018; 12:253. [PMID: 30013468 PMCID: PMC6036272 DOI: 10.3389/fnhum.2018.00253] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 06/01/2018] [Indexed: 11/29/2022] Open
Abstract
Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided.
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Affiliation(s)
- Markus Goldhacker
- CIML Lab, Department of Biophysics, University of Regensburg, Regensburg, Germany
- Department of Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Ana M. Tomé
- CIML Lab, Department of Biophysics, University of Regensburg, Regensburg, Germany
- Departamento de Eletrónica, Telecomunicações e Informática (DETI), Instituto de Engenharia Electrónica e Telemática de Aveiro (IEETA), Universidade de Aveiro, Aveiro, Portugal
| | - Mark W. Greenlee
- Department of Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Elmar W. Lang
- CIML Lab, Department of Biophysics, University of Regensburg, Regensburg, Germany
- Departamento de Eletrónica, Telecomunicações e Informática (DETI), Instituto de Engenharia Electrónica e Telemática de Aveiro (IEETA), Universidade de Aveiro, Aveiro, Portugal
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Hayton K, Moirogiannis D, Magnasco M. Adaptive scales of integration and response latencies in a critically-balanced model of the primary visual cortex. PLoS One 2018; 13:e0196566. [PMID: 29702661 PMCID: PMC5922535 DOI: 10.1371/journal.pone.0196566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 04/16/2018] [Indexed: 11/19/2022] Open
Abstract
The primary visual cortex (V1) integrates information over scales in visual space, which have been shown to vary, in an input-dependent manner, as a function of contrast and other visual parameters. Which algorithms the brain uses to achieve this feat are largely unknown and an open problem in visual neuroscience. We demonstrate that a simple dynamical mechanism can account for this contrast-dependent scale of integration in visuotopic space as well as connect this property to two other stimulus-dependent features of V1: extents of lateral integration on the cortical surface and response latencies.
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Affiliation(s)
- Keith Hayton
- Center for Studies in Physics and Biology, The Rockefeller University, New York, NY, United States of America
- * E-mail:
| | - Dimitrios Moirogiannis
- Center for Studies in Physics and Biology, The Rockefeller University, New York, NY, United States of America
| | - Marcelo Magnasco
- Center for Studies in Physics and Biology, The Rockefeller University, New York, NY, United States of America
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39
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Seshadri S, Klaus A, Winkowski DE, Kanold PO, Plenz D. Altered avalanche dynamics in a developmental NMDAR hypofunction model of cognitive impairment. Transl Psychiatry 2018; 8:3. [PMID: 29317600 PMCID: PMC5802445 DOI: 10.1038/s41398-017-0060-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 08/29/2017] [Accepted: 09/13/2017] [Indexed: 12/25/2022] Open
Abstract
Disturbed activity patterns in cortical networks contribute to the pathophysiology of schizophrenia (SZ). Several lines of evidence implicate NMDA receptor hypofunction in SZ, and blocking NMDA receptor signaling during early neurodevelopment produces cognitive deficits in rodent models that resemble those seen in schizophrenic patients. However, the altered network dynamics underlying these cognitive impairments largely remain to be characterized, especially at the cellular level. Here, we use in vivo two-photon calcium imaging to describe pathological dynamics, occurring in parallel with cognitive dysfunction, in a developmental NMDA receptor hypofunction model. We observed increased synchrony and specific alterations in spatiotemporal activity propagation, which could be causally linked to a previously unidentified persistent bursting phenotype. This phenotype was rescued by acute treatment with the NMDA receptor co-agonist D-serine or the GABAB receptor agonist baclofen, which similarly rescued working memory performance. It was not reproduced by optogenetic inhibition of fast-spiking interneurons. These results provide novel insight into network-level abnormalities mediating the cognitive impairment induced by NMDA receptor hypofunction.
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Affiliation(s)
- Saurav Seshadri
- 0000 0004 0464 0574grid.416868.5Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD USA
| | - Andreas Klaus
- 0000 0004 0464 0574grid.416868.5Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD USA
| | - Daniel E. Winkowski
- 0000 0001 0941 7177grid.164295.dDepartment of Biology, Univ. of Maryland, College Park, MD USA
| | - Patrick O. Kanold
- 0000 0001 0941 7177grid.164295.dDepartment of Biology, Univ. of Maryland, College Park, MD USA
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD, USA.
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Worrell JC, Rumschlag J, Betzel RF, Sporns O, Mišić B. Optimized connectome architecture for sensory-motor integration. Netw Neurosci 2017; 1:415-430. [PMID: 30090872 PMCID: PMC6063718 DOI: 10.1162/netn_a_00022] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 07/05/2017] [Indexed: 01/15/2023] Open
Abstract
The intricate connectivity patterns of neural circuits support a wide repertoire of communication processes and functional interactions. Here we systematically investigate how neural signaling is constrained by anatomical connectivity in the mesoscale Drosophila (fruit fly) brain network. We use a spreading model that describes how local perturbations, such as external stimuli, trigger global signaling cascades that spread through the network. Through a series of simple biological scenarios we demonstrate that anatomical embedding potentiates sensory-motor integration. We find that signal spreading is faster from nodes associated with sensory transduction (sensors) to nodes associated with motor output (effectors). Signal propagation was accelerated if sensor nodes were activated simultaneously, suggesting a topologically mediated synergy among sensors. In addition, the organization of the network increases the likelihood of convergence of multiple cascades towards effector nodes, thereby facilitating integration prior to motor output. Moreover, effector nodes tend to coactivate more frequently than other pairs of nodes, suggesting an anatomically enhanced coordination of motor output. Altogether, our results show that the organization of the mesoscale Drosophila connectome imparts privileged, behaviorally relevant communication patterns among sensors and effectors, shaping their capacity to collectively integrate information. The complex network spanned by neurons and their axonal projections promotes a diverse set of functions. In the present report, we study how the topological organization of the fruit fly brain supports sensory-motor integration. Using a simple communication model, we demonstrate that the topology of this network allows efficient coordination among sensory and motor neurons. Our results suggest that brain network organization may profoundly shape the functional repertoire of this simple organism.
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Affiliation(s)
- Jacob C Worrell
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
| | - Jeffrey Rumschlag
- Department of Cell Biology and Neuroscience, University of California Riverside, Riverside, CA, USA
| | - Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
| | - Bratislav Mišić
- Montréal Neurological Institute, McGill University, Montréal, Canada
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41
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Dommers S, Külske C, Schriever P. Continuous spin models on annealed generalized random graphs. Stoch Process Their Appl 2017. [DOI: 10.1016/j.spa.2017.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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42
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Goldhacker M, Keck P, Igel A, Lang EW, Tomé AM. A multi-variate blind source separation algorithm. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 151:91-99. [PMID: 28947009 DOI: 10.1016/j.cmpb.2017.08.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 07/06/2017] [Accepted: 08/21/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation. METHODS The proposed methodology follows an algebraic approach, which is common to space, temporal or spatiotemporal blind source separation algorithms. More specifically, the algebraic approach relies on singular value decomposition techniques, which avoids computationally costly and numerically instable matrix inversion. The method is equally applicable to correlation matrices determined from second order correlations or by considering fourth order correlations. RESULTS The resulting algorithms are applied to fMRI data sets either to extract the underlying fMRI components or to extract connectivity maps from resting state fMRI data collected for a dynamic functional connectivity analysis. Intriguingly, our algorithm shows increased spatial specificity compared to common approaches, while temporal precision stays similar. CONCLUSION The study presents a novel spatiotemporal blind source separation algorithm, which is both robust and avoids parameters that are difficult to fine tune. Applied on experimental data sets, the new method yields highly confined and focused areas with least spatial extent in the retinotopy case, and similar results in the dynamic functional connectivity analyses compared to other blind source separation algorithms. Therefore, we conclude that our novel algorithm is highly competitive and yields results, which are superior or at least similar to existing approaches.
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Affiliation(s)
- M Goldhacker
- CIML, Biophysics, University of Regensburg, 93040 Regensburg, Germany; Experimental Psychology, University of Regensburg, 93040 Regensburg, Germany.
| | - P Keck
- CIML, Biophysics, University of Regensburg, 93040 Regensburg, Germany
| | - A Igel
- CIML, Biophysics, University of Regensburg, 93040 Regensburg, Germany
| | - E W Lang
- CIML, Biophysics, University of Regensburg, 93040 Regensburg, Germany
| | - A M Tomé
- DETI- IEETA -Universidade Aveiro, 3810-193 Aveiro, Portugal
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Karimipanah Y, Ma Z, Wessel R. Criticality predicts maximum irregularity in recurrent networks of excitatory nodes. PLoS One 2017; 12:e0182501. [PMID: 28817580 PMCID: PMC5560579 DOI: 10.1371/journal.pone.0182501] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 07/19/2017] [Indexed: 12/15/2022] Open
Abstract
A rigorous understanding of brain dynamics and function requires a conceptual bridge between multiple levels of organization, including neural spiking and network-level population activity. Mounting evidence suggests that neural networks of cerebral cortex operate at a critical regime, which is defined as a transition point between two phases of short lasting and chaotic activity. However, despite the fact that criticality brings about certain functional advantages for information processing, its supporting evidence is still far from conclusive, as it has been mostly based on power law scaling of size and durations of cascades of activity. Moreover, to what degree such hypothesis could explain some fundamental features of neural activity is still largely unknown. One of the most prevalent features of cortical activity in vivo is known to be spike irregularity of spike trains, which is measured in terms of the coefficient of variation (CV) larger than one. Here, using a minimal computational model of excitatory nodes, we show that irregular spiking (CV > 1) naturally emerges in a recurrent network operating at criticality. More importantly, we show that even at the presence of other sources of spike irregularity, being at criticality maximizes the mean coefficient of variation of neurons, thereby maximizing their spike irregularity. Furthermore, we also show that such a maximized irregularity results in maximum correlation between neuronal firing rates and their corresponding spike irregularity (measured in terms of CV). On the one hand, using a model in the universality class of directed percolation, we propose new hallmarks of criticality at single-unit level, which could be applicable to any network of excitable nodes. On the other hand, given the controversy of the neural criticality hypothesis, we discuss the limitation of this approach to neural systems and to what degree they support the criticality hypothesis in real neural networks. Finally, we discuss the limitations of applying our results to real networks and to what degree they support the criticality hypothesis.
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Affiliation(s)
- Yahya Karimipanah
- Department of Physics, Washington University in St. Louis, St. Louis, MO, United States of America
| | - Zhengyu Ma
- Department of Physics, Washington University in St. Louis, St. Louis, MO, United States of America
| | - Ralf Wessel
- Department of Physics, Washington University in St. Louis, St. Louis, MO, United States of America
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Ezaki T, Watanabe T, Ohzeki M, Masuda N. Energy landscape analysis of neuroimaging data. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2017; 375:rsta.2016.0287. [PMID: 28507232 PMCID: PMC5434078 DOI: 10.1098/rsta.2016.0287] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/27/2017] [Indexed: 05/09/2023]
Abstract
Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analysed and the data length.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.
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Affiliation(s)
- Takahiro Ezaki
- National Institute of Informatics, Hitotsubashi, Chiyoda-ku, Tokyo, Japan
- Kawarabayashi Large Graph Project, ERATO, JST, c/o Global Research Center for Big Data Mathematics, NII, Chiyoda-ku, Tokyo, Japan
| | - Takamitsu Watanabe
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AZ, UK
| | - Masayuki Ohzeki
- Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK
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46
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The impacts of pesticide and nicotine exposures on functional brain networks in Latino immigrant workers. Neurotoxicology 2017; 62:138-150. [PMID: 28583619 DOI: 10.1016/j.neuro.2017.06.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 05/31/2017] [Accepted: 06/01/2017] [Indexed: 01/28/2023]
Abstract
Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and strength, brain networks in farmworkers had more clustered and modular structures when compared to non-farmworkers. Our findings suggest increased functional specificity and decreased functional integration in farmworkers when compared to non-farmworkers. Cholinesterase activity was associated with population differences in community structure and the strength of brain network functional connections. Urinary cotinine, a marker of nicotine exposure, was associated with the differences in network community structure. Brain network differences between farmworkers and non-farmworkers, as well as pesticide and nicotine exposure effects on brain functional connections in this study, may illuminate underlying mechanisms that cause neurological implications in later life.
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47
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Stramaglia S, Pellicoro M, Angelini L, Amico E, Aerts H, Cortés JM, Laureys S, Marinazzo D. Ising model with conserved magnetization on the human connectome: Implications on the relation structure-function in wakefulness and anesthesia. CHAOS (WOODBURY, N.Y.) 2017; 27:047407. [PMID: 28456159 DOI: 10.1063/1.4978999] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Dynamical models implemented on the large scale architecture of the human brain may shed light on how a function arises from the underlying structure. This is the case notably for simple abstract models, such as the Ising model. We compare the spin correlations of the Ising model and the empirical functional brain correlations, both at the single link level and at the modular level, and show that their match increases at the modular level in anesthesia, in line with recent results and theories. Moreover, we show that at the peak of the specific heat (the critical state), the spin correlations are minimally shaped by the underlying structural network, explaining how the best match between the structure and function is obtained at the onset of criticality, as previously observed. These findings confirm that brain dynamics under anesthesia shows a departure from criticality and could open the way to novel perspectives when the conserved magnetization is interpreted in terms of a homeostatic principle imposed to neural activity.
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Affiliation(s)
- S Stramaglia
- Dipartimento di Fisica, Università degli Studi di Bari, Bari, Italy
| | - M Pellicoro
- Dipartimento di Fisica, Università degli Studi di Bari, Bari, Italy
| | - L Angelini
- Dipartimento di Fisica, Università degli Studi di Bari, Bari, Italy
| | - E Amico
- Coma Science Group, University of Liège, Liège, Belgium
| | - H Aerts
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
| | - J M Cortés
- Ikerbasque, The Basque Foundation for Science, E-48011 Bilbao, Spain
| | - S Laureys
- Coma Science Group, University of Liège, Liège, Belgium
| | - D Marinazzo
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
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48
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Papo D, Goñi J, Buldú JM. Editorial: On the relation of dynamics and structure in brain networks. CHAOS (WOODBURY, N.Y.) 2017; 27:047201. [PMID: 28456177 DOI: 10.1063/1.4981391] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Affiliation(s)
- David Papo
- SCALab, CNRS, Université Lille 3, Villeneuve d'Ascq, France
| | - Joaquin Goñi
- School of Engineering, Purdue University, West-Lafayette, Indiana 47907-2023, USA
| | - Javier M Buldú
- Complex, Systems Group, Universidad Rey Juan Carlos, Madrid, Spain
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49
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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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50
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From phenotype to genotype in complex brain networks. Sci Rep 2016; 6:19790. [PMID: 26795752 PMCID: PMC4726251 DOI: 10.1038/srep19790] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Accepted: 11/30/2015] [Indexed: 01/29/2023] Open
Abstract
Generative models are a popular instrument for illuminating the relationships between the hidden variables driving the growth of a complex network and its final topological characteristics, a process known as the "genotype to phenotype problem". However, the definition of a complete methodology encompassing all stages of the analysis, and in particular the validation of the final model, is still an open problem. We here discuss a framework that allows to quantitatively optimise and validate each step of the model creation process. It is based on the execution of a classification task, and on estimating the additional precision provided by the modelled genotype. This encompasses the three main steps of the model creation, namely the selection of topological features, the optimisation of the parameters of the generative model, and the validation of the obtained results. We provide a minimum requirement for a generative model to be useful, prescribing the function mapping genotype to phenotype to be non-monotonic; and we further show how a previously published model does not fulfil such condition, casting doubts on its fitness for the study of neurological disorders. The generality of such framework guarantees its applicability beyond neuroscience, like the emergence of social or technological networks.
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